DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Remark(s)
Applicant's amendment filed July 21st, 2026 have been fully entered and considered. Applicant’s amendment to the claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed on April 24th, 2026. Regarding the prior art rejection, all new grounds of rejection set forth in the present action were necessitated by Applicants’ claim amendments. Accordingly, this action is made final.
Status of Claims
Claims 1-13 are pending, claims 1 and 11-13 have been amended. Claims 1-13 remains rejected.
Response to Argument(s)
In view of the Amendments to independent claims 1 and 11-13 the previously applied prior art rejections are withdrawn. Applicants’ arguments are rendered moot in view of the new grounds of rejection set forth below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang Mi et. al. (foreign patent document “CN 109887581 A” hereinafter as “Mi”) in view of Meng Fan (“US 2022/0222795 A1” hereinafter as “Fan”) and Henrik Marschner et. al. (“US 2018/0089863 A1” hereinafter as “Marschner”) (mapping is based on translation section of the foreign patent document).
Regarding claim 1, Mi teaches an image processing apparatus comprising (Page 1, “technical field,” discloses a medical device mode management system): a processor configured to (Page 2, 1st 3 paragraphs, discloses a processing device indicating the use of a processor): acquire user identification information identifying a user (Page 4, 5th paragraph, discloses performing patient identification to determine the visiting patient for further image processing); acquire a first imaging condition applied to an imaging apparatus (Page 1, “summary of the invention,” 1st 2 paragraphs, discloses using an imaging mode [analogous to the recited first image condition] applied to the medical device to image an image); acquire a first image generated by imaging with the imaging apparatus under the first imaging condition (Page 1, “summary of the invention,” 1st 2 paragraphs, discloses using an imaging mode [analogous to the recited first image condition] applied to the medical device [imaging apparatus] to image an image [analogous to the recited first image]); acquire a first signal-to-noise ratio by (Page 4, 3rd paragraph, discloses basing on a signal-to-noise ratio value to determine continue the filtering [indicating that the subsequential denoising is based on a signal-to-noise ratio value): identifying, for the user identified by the user identification information (Page 4, 5th paragraph, discloses “performing patient identification on the foreground region of the secondary filter image based on the facial features of the visiting patient”); set a first denoising strength based on the first signal-to-noise ratio (Page 4, 3rd paragraph, discloses basing on a signal-to-noise ratio value to determine continue the filtering [indicating that the subsequential denoising is based on a signal-to-noise ratio value, being analogous to the recited first signal-to-noise ratio] which is a filtering window size [denoising strength as claimed analogously] according to page 2, 5th par.); and execute denoising processing on the first image by applying the first denoising strength, to generate a first denoised image (Page 3, last paragraph, discloses performing the filtering according to the custom filtering, to obtain a filtered image [analogous to the recited first denoised image]).
However, Mi does not explicitly teach obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images; and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition, as the first signal-to-noise ratio.
Fan teaches obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images (Par. [0127] discloses denoising the first N frame images as history denoised images [plurality of second images generated by a plurality of times of past imaging], and determine a filtering strength for the second image based on the difference image obtained according to the first N frame images as history denoised images; wherein the filtering strength is based on the signal-to-noise ratio of the first restored image here being the history denoised image according to Par. [0125]; it analogous to the claimed limitation; the images here are analogous to the images of second and first images of Mi when the imaging modes are applied, here Fan teaches further that the past images can be used to determine the filtering strength for the current image based on their signal-to-noise ratio such as recited in the claimed limitation), and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition (the signal-to-noise ration of the past imaging according to Par. [0125] being used to determine the filtering strength for the current image according to Par. [0127] such as, discussed previously, wherein the claimed limitation recites “or” therefore, only one of the options is the instant scope, the examiner selects “identical…” which is taught in Fan’s Par. [0111], wherein the first and second images are obtained in the same manner of manner of Bayer, indicating that the conditions of imaging are identical), as a first signal-to-noise ratio (Par. [0125-0127] disclose the signal-to-noise ratio is used to determine the filtering strength [analogous to the recited first signal-to-noise ratio] as discussed previously).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mi of having an image processing apparatus comprising: a processor configured to: acquire user identification information identifying a user; acquire a first imaging condition applied to an imaging apparatus; acquire a first image generated by imaging with the imaging apparatus under the first imaging condition; acquire a first signal-to-noise ratio, with the teachings of Fan of having obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images; and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition, as the first signal-to-noise ratio.
Wherein having Mi’s apparatus of having obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images; and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition, as the first signal-to-noise ratio.
The motivation behind the modification would have been to improve image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, and further to improve image acquisition taking into account intensity of the visible light at different levels enabling improvement in flexibility of the image acquisition. Since both Mi and Fan perform image denoising based on signal-to-noise analysis. Wherein Mi’s system improves image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, (see Mi’s Page 2, 3rd to the last Par.) and Fan’s system improves image acquisition taking into account intensity of the visible light at different levels enabling improvement in flexibility of the image acquisition, (see Fan’s Par. [0104]).
However, Mi in view of Fan does not explicitly teach a plurality of second images generated in a plurality of past imaging instances performed separately from one another, each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions, wherein the plurality of second imaging conditions are different from one another.
Marschner teaches a plurality of second images generated in a plurality of past imaging instances performed separately from one another (Par. [0017] discloses “noise in the final image can be reduced through averaging, that is, the repetitive measurement of the data under identical conditions and subsequentially co-adding them”; the measurement data resulting in different images captured at different times in the past), each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions (Par. [0017] discloses “noise in the final image can be reduced through averaging, that is, the repetitive measurement of the data under identical conditions and subsequentially co-adding them” indicating the images are obtained under a plurality of imaging conditions), wherein the plurality of second imaging conditions are different from one another (Par. [0017] discloses “noise in the final image can be reduced through averaging, that is, the repetitive measurement of the data under identical conditions and subsequentially co-adding them…lead to final dataset with high SNR…this method requires an identical contrast for all images that are supposed to be averaged and is time consuming…in particular, while averaging of all data, e.g., from an image series acquired with variation of specific acquisition parameters…would lead to a final dataset with high SNR” indicating the current invention takes into account for a series of images acquired under variation of different imaging conditions such as disclosed in Par. [0086] of “selected within a reasonable range depending on the imaging conditions”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mi in view of Fan of having an image processing apparatus comprising: a processor configured to: acquire user identification information identifying a user; acquire a first imaging condition applied to an imaging apparatus; acquire a first image generated by imaging with the imaging apparatus under the first imaging condition; acquire a first signal-to-noise ratio, with the teachings of Marschner of having a plurality of second images generated in a plurality of past imaging instances performed separately from one another, each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions, wherein the plurality of second imaging conditions are different from one another.
Wherein having Mi’s apparatus of having a plurality of second images generated in a plurality of past imaging instances performed separately from one another, each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions, wherein the plurality of second imaging conditions are different from one another.
The motivation behind the modification would have been to improve image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, and further to improve image denoising for MR images with creased sensitivity, improved SNR and/or reduced loss of information contributions included in the MR images. Since both Mi and Marschner perform image denoising for medical imaging based on signal-to-noise analysis. Wherein Mi’s system improves image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, (see Mi’s Page 2, 3rd to the last Par.) and Marschner’s system improves image denoising for MR images with creased sensitivity, improved SNR and/or reduced loss of information contributions included in the MR images, (see Marschner’s Par. [0021]).
Regarding claim 2, Mi in view of Fan and Marschner teaches the image processing apparatus according to claim 1, Mi teaches wherein the processor is configured to acquire the first signal-to-noise ratio corresponding to the second imaging condition that is identical to the first imaging condition or the second imaging condition that is close to the first imaging condition (“or” indicates a selection, only one of the options is the instant scope, the examiner selects “identical…” which is taught in Mi’s page 1, 3rd to the last paragraph, discloses the CO2 laser includes a plurality of imaging units for imaging the scene to obtain the images as have been discussed, wherein the CO2 laser operate under a laser emission preparation mode, the imaging units of the same CO2 laser obtain the images under the identical mode [analogous to the recited the second imaging condition is identical to the first imaging condition]), with reference to a signal-to-noise ratio database in which the plurality of second imaging conditions are stored in association with the plurality of second signal-to-noise ratios (Page 1, 3rd to the last paragraph, discloses each imaging unit including a signal-to-noise ratio analyzer, indicates that each imaging unit has its respective signal-to-noise ratio value obtained from the signal-to-noise ratio analyzer being stored in the memory when its being analyzed, wherein each imaging unit is operated under a certain operation mode, such as discussed previously, moreover, each signal-to-noise is stored in association with its corresponding imaging unit [second imaging conditions as claimed] according to page 1, 3rd to the last par., last sentence).
Regarding claim 3, Mi in view of Fan and Marschner teaches the image processing apparatus according to claim 2, Mi teaches wherein the processor is configured to, in a case in which the first denoising strength is changed, update the signal-to-noise ratio database in accordance with a second denoising strength after the change (Page 1, 3rd to the last paragraph, discloses select the imaged image with the highest signal-to-noise ratio as the output image of the corresponding imaging unit, which indicates the determination can select a different signal-to-noise ratio to be stored, which, as discussed above being used to determine the filtering strength, indicating a change in filtering strength, then the storing of the imaged image being selected would be different indicating its corresponding signal-to-noise ratio and its corresponding imaging unit being stored being selected or updated in the storage [indicating a memory storing data which is analogous to the recited database]).
Regarding claim 4, Mi in view of Fan and Marschner teaches the image processing apparatus according to claim 1, Mi teaches wherein the processor is configured to: execute a plurality of times of the denoising processing with different denoising strengths on the first image, to generate a plurality of third denoised images (Page 3, from paragraph 6th, discloses a wide-view image is being denoised by using bandpass filter to obtain image segments of same size according to page 4, 1st par., wherein the filtering takes place using different frequency cutoff values according to page 4, 6th to last paragraphs, which indicates different filtering strengths; the plurality of image segments are analogous to the third denoised images); derive a third signal-to-noise ratio from each of the plurality of third denoised images (Page 4, 3rd paragraph, discloses signal-to-noise ratio mean value [analogous to the recited third-signal-to noise ratio] is obtained from the image segments; this process is repeated until the signal-to-noise ratio average satisfied the condition in page 4, 4th par., it can be understood as a third filtering process can happen and a plurality of third denoised images are obtained for this third filtering process to obtain the third time calculation of signal-to-noise mean value); and set a third denoising strength applied to the third denoised image in which the third signal-to-noise ratio having a smallest difference from the first signal-to-noise ratio is derived, as the first denoising strength (Page 4, 4th paragraph, discloses when the signal-to-noise ratio average is 1.2 times or more of the first signal-to-noise mean value, continue perform band pass filtering; when the filtering is terminated when the condition of page 4, 4th par., is met that the signal-to-noise ratio average is less than 1.2 times the first signal-to-noise ratio mean value [is analogous to being the smallest difference in of all of the iterations of filtering], as the first denoising strength according to page 4, 6th to the last paragraph, wherein the cutoff frequency is used based on the signal-to-noise ratio being used as the first filtering frequency for the iterative filtering processes as discussed [indicating “as the first denoising strength”]).
Regarding claim 11, Mi teaches an operation method of an image processing apparatus to which a computer provided with a processor is applied, the operation method comprising: (Page 1, “technical field,” discloses a medical device mode management system; page 2, 1st 3 paragraphs, discloses a processing device indicating the use of a processor): via the processor, acquiring user identification information identifying a user (Page 4, 5th paragraph, discloses performing patient identification to determine the visiting patient for further image processing); acquiring a first imaging condition applied to an imaging apparatus (page 1, “summary of the invention,” 1st 2 paragraphs, discloses using an imaging mode [analogous to the recited first image condition] applied to the medical device to image an image); acquiring a first image generated by imaging with the imaging apparatus under the first imaging condition is applied (Page 1, “summary of the invention,” 1st 2 paragraphs, discloses using an imaging mode [analogous to the recited first image condition] applied to the medical device [imaging apparatus] to image an image [analogous to the recited first image]) ; acquiring a first signal-to-noise ratio by (Page 4, 3rd paragraph, discloses basing on a signal-to-noise ratio value to determine continue the filtering [indicating that the subsequential denoising is based on a signal-to-noise ratio value): identifying, for the user identified by the user identification information (Page 4, 5th paragraph, discloses “performing patient identification on the foreground region of the secondary filter image based on the facial features of the visiting patient”) ; setting a first denoising strength based on the first signal-to-noise ratio (Page 4, 3rd paragraph, discloses basing on a signal-to-noise ratio value to determine continue the filtering [indicating that the subsequential denoising is based on a signal-to-noise ratio value, being analogous to the recited first signal-to-noise ratio] which is a filtering window size [denoising strength as claimed analogously] according to page 2, 5th par.); and executing denoising processing on the first image by applying the first denoising strength, to generate a first denoised image (Page 3, last paragraph, discloses performing the filtering according to the custom filtering, to obtain a filtered image [analogous to the recited first denoised image]).
However, Mi does not explicitly teach obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images; and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition, as the first signal-to-noise ratio.
Fan teaches obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images (Par. [0127] discloses denoising the first N frame images as history denoised images [plurality of second images generated by a plurality of times of past imaging], and determine a filtering strength for the second image based on the difference image obtained according to the first N frame images as history denoised images; wherein the filtering strength is based on the signal-to-noise ratio of the first restored image here being the history denoised image according to Par. [0125]; it analogous to the claimed limitation; the images here are analogous to the images of second and first images of Mi when the imaging modes are applied, here Fan teaches further that the past images can be used to determine the filtering strength for the current image based on their signal-to-noise ratio such as recited in the claimed limitation), and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition (the signal-to-noise ration of the past imaging according to Par. [0125] being used to determine the filtering strength for the current image according to Par. [0127] such as, discussed previously, wherein the claimed limitation recites “or” therefore, only one of the options is the instant scope, the examiner selects “identical…” which is taught in Fan’s Par. [0111], wherein the first and second images are obtained in the same manner of manner of Bayer, indicating that the conditions of imaging are identical), as a first signal-to-noise ratio (Pars. [0125-0127] disclose the signal-to-noise ratio is used to determine the filtering strength [analogous to the recited first signal-to-noise ratio] as discussed previously).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mi of having an image processing method comprising: acquiring user identification information identifying a user; acquiring a first imaging condition applied to an imaging apparatus; acquiring a first image generated by imaging with the imaging apparatus under the first imaging condition; acquiring a first signal-to-noise ratio, with the teachings of Fan of having obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images; and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition, as the first signal-to-noise ratio.
Wherein having Mi’s method of having obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images; and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition, as the first signal-to-noise ratio.
The motivation behind the modification would have been to improve image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, and further to improve image acquisition taking into account intensity of the visible light at different levels enabling improvement in flexibility of the image acquisition. Since both Mi and Fan perform image denoising based on signal-to-noise analysis. Wherein Mi’s system improves image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, (see Mi’s Page 2, 3rd to the last Par.) and Fan’s system improves image acquisition taking into account intensity of the visible light at different levels enabling improvement in flexibility of the image acquisition, (see Fan’s Par. [0104]).
However, Mi in view of Fan does not explicitly teach a plurality of second images generated in a plurality of past imaging instances performed separately from one another, each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions, wherein the plurality of second imaging conditions are different from one another.
Marschner teaches a plurality of second images generated in a plurality of past imaging instances performed separately from one another (Par. [0017] discloses “noise in the final image can be reduced through averaging, that is, the repetitive measurement of the data under identical conditions and subsequentially co-adding them”; the measurement data resulting in different images captured at different times in the past), each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions (Par. [0017] discloses “noise in the final image can be reduced through averaging, that is, the repetitive measurement of the data under identical conditions and subsequentially co-adding them” indicating the images are obtained under a plurality of imaging conditions), wherein the plurality of second imaging conditions are different from one another (Par. [0017] discloses “noise in the final image can be reduced through averaging, that is, the repetitive measurement of the data under identical conditions and subsequentially co-adding them…lead to final dataset with high SNR…this method requires an identical contrast for all images that are supposed to be averaged and is time consuming…in particular, while averaging of all data, e.g., from an image series acquired with variation of specific acquisition parameters…would lead to a final dataset with high SNR” indicating the current invention takes into account for a series of images acquired under variation of different imaging conditions such as disclosed in Par. [0086] of “selected within a reasonable range depending on the imaging conditions”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mi in view of Fan of having an image processing method comprising: acquiring user identification information identifying a user; acquiring a first imaging condition applied to an imaging apparatus; acquiring a first image generated by imaging with the imaging apparatus under the first imaging condition; acquiring a first signal-to-noise ratio, with the teachings of Marschner of having a plurality of second images generated in a plurality of past imaging instances performed separately from one another, each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions, wherein the plurality of second imaging conditions are different from one another.
Wherein having Mi’s method of having a plurality of second images generated in a plurality of past imaging instances performed separately from one another, each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions, wherein the plurality of second imaging conditions are different from one another.
The motivation behind the modification would have been to improve image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, and further to improve image denoising for MR images with creased sensitivity, improved SNR and/or reduced loss of information contributions included in the MR images. Since both Mi and Marschner perform image denoising for medical imaging based on signal-to-noise analysis. Wherein Mi’s system improves image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, (see Mi’s Page 2, 3rd to the last Par.) and Marschner’s system improves image denoising for MR images with creased sensitivity, improved SNR and/or reduced loss of information contributions included in the MR images, (see Marschner’s Par. [0021]).
Regarding claim 12, Mi discloses a non-transitory, computer- readable tangible recording medium which records thereon a program for causing a computer of a computer to perform: (Page 1, “technical field,” discloses a medical device mode management system; page 2, 1st 3 paragraphs, discloses a processing device indicating the use of a processor with the use of a non-transitory computer readable storage medium such as a ROM or a RAM): acquiring user identification information identifying a user (page 4, 5th paragraph, discloses performing patient identification to determine the visiting patient for further image processing); acquiring a first imaging condition applied to an imaging apparatus (page 1, “summary of the invention,” 1st 2 paragraphs, discloses using an imaging mode [analogous to the recited first image condition] applied to the medical device to image an image); acquiring a first image generated by imaging with the imaging apparatus under the first imaging condition (page 1, “summary of the invention,” 1st 2 paragraphs, discloses using an imaging mode [analogous to the recited first image condition] applied to the medical device [imaging apparatus] to image an image [analogous to the recited first image]); acquiring a first signal-to-noise ratio by (Page 4, 3rd paragraph, discloses basing on a signal-to-noise ratio value to determine continue the filtering [indicating that the subsequential denoising is based on a signal-to-noise ratio value): identifying, for the user identified by the user identification information (Page 4, 5th paragraph, discloses “performing patient identification on the foreground region of the secondary filter image based on the facial features of the visiting patient”); setting a first denoising strength based on the first signal-to-noise ratio (Page 4, 3rd paragraph, discloses basing on a signal-to-noise ratio value to determine continue the filtering [indicating that the subsequential denoising is based on a signal-to-noise ratio value, being analogous to the recited first signal-to-noise ratio] which is a filtering window size [denoising strength as claimed analogously] according to page 2, 5th par.); and executing denoising processing on the first image by applying the first denoising strength, to generate a first denoised image (Page 3, last paragraph, discloses performing the filtering according to the custom filtering, to obtain a filtered image [analogous to the recited first denoised image]).
However, Mi does not explicitly teach obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images; and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition, as the first signal-to-noise ratio.
Fan teaches obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images (Par. [0127] discloses denoising the first N frame images as history denoised images [plurality of second images generated by a plurality of times of past imaging], and determine a filtering strength for the second image based on the difference image obtained according to the first N frame images as history denoised images; wherein the filtering strength is based on the signal-to-noise ratio of the first restored image here being the history denoised image according to Par. [0125]; it analogous to the claimed limitation; the images here are analogous to the images of second and first images of Mi when the imaging modes are applied, here Fan teaches further that the past images can be used to determine the filtering strength for the current image based on their signal-to-noise ratio such as recited in the claimed limitation), and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition (The signal-to-noise ration of the past imaging according to Par. [0125] being used to determine the filtering strength for the current image according to Par. [0127] such as, discussed previously, wherein the claimed limitation recites “or” only one of the options is the instant scope, the examiner selects “identical…” which is taught in Fan’s Par. [0111], wherein the first and second images are obtained in the same manner of manner of Bayer, indicating that the conditions of imaging are identical), as a first signal-to-noise ratio (Pars. [0125-0127] disclose the signal-to-noise ratio is used to determine the filtering strength [analogous to the recited first signal-to-noise ratio] as discussed previously).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mi of having an image processing system comprising: acquiring user identification information identifying a user; acquiring a first imaging condition applied to an imaging apparatus; acquiring a first image generated by imaging with the imaging apparatus under the first imaging condition; acquiring a first signal-to-noise ratio, with the teachings of Fan of having obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images; and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition, as the first signal-to-noise ratio.
Wherein having Mi’s system of having obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images; and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition, as the first signal-to-noise ratio.
The motivation behind the modification would have been to improve image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, and further to improve image acquisition taking into account intensity of the visible light at different levels enabling improvement in flexibility of the image acquisition. Since both Mi and Fan perform image denoising based on signal-to-noise analysis. Wherein Mi’s system improves image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, (see Mi’s Page 2, 3rd to the last Par.) and Fan’s system improves image acquisition taking into account intensity of the visible light at different levels enabling improvement in flexibility of the image acquisition, (see Fan’s Par. [0104]).
However, Mi in view of Fan does not explicitly teach a plurality of second images generated in a plurality of past imaging instances performed separately from one another, each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions, wherein the plurality of second imaging conditions are different from one another.
Marschner teaches a plurality of second images generated in a plurality of past imaging instances performed separately from one another (Par. [0017] discloses “noise in the final image can be reduced through averaging, that is, the repetitive measurement of the data under identical conditions and subsequentially co-adding them”; the measurement data resulting in different images captured at different times in the past), each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions (Par. [0017] discloses “noise in the final image can be reduced through averaging, that is, the repetitive measurement of the data under identical conditions and subsequentially co-adding them” indicating the images are obtained under a plurality of imaging conditions), wherein the plurality of second imaging conditions are different from one another (Par. [0017] discloses “noise in the final image can be reduced through averaging, that is, the repetitive measurement of the data under identical conditions and subsequentially co-adding them…lead to final dataset with high SNR…this method requires an identical contrast for all images that are supposed to be averaged and is time consuming…in particular, while averaging of all data, e.g., from an image series acquired with variation of specific acquisition parameters…would lead to a final dataset with high SNR” indicating the current invention takes into account for a series of images acquired under variation of different imaging conditions such as disclosed in Par. [0086] of “selected within a reasonable range depending on the imaging conditions”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mi in view of Fan of having an image processing system comprising: acquiring user identification information identifying a user; acquiring a first imaging condition applied to an imaging apparatus; acquiring a first image generated by imaging with the imaging apparatus under the first imaging condition; acquiring a first signal-to-noise ratio, with the teachings of Marschner of having a plurality of second images generated in a plurality of past imaging instances performed separately from one another, each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions, wherein the plurality of second imaging conditions are different from one another.
Wherein having Mi’s system of having a plurality of second images generated in a plurality of past imaging instances performed separately from one another, each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions, wherein the plurality of second imaging conditions are different from one another.
The motivation behind the modification would have been to improve image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, and further to improve image denoising for MR images with creased sensitivity, improved SNR and/or reduced loss of information contributions included in the MR images. Since both Mi and Marschner perform image denoising for medical imaging based on signal-to-noise analysis. Wherein Mi’s system improves image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, (see Mi’s Page 2, 3rd to the last Par.) and Marschner’s system improves image denoising for MR images with creased sensitivity, improved SNR and/or reduced loss of information contributions included in the MR images, (see Marschner’s Par. [0021]).
Regarding claim 13, Mi discloses a medical image processing system comprising: (page 1, “technical field,” discloses a medical device mode management system): an image processing apparatus configured to execute defined image processing on a medical image generated by imaging a subject using an imaging apparatus, wherein the image processing apparatus includes a processor configured to: (page 2, 1st 3 paragraphs, discloses a processing device indicating the use of a processor): acquire user identification information identifying a user (page 4, 5th paragraph, discloses performing patient identification to determine the visiting patient for further image processing); acquire a first imaging condition applied to an imaging apparatus (page 1, “summary of the invention,” 1st 2 paragraphs, discloses using an imaging mode [analogous to the recited first image condition] applied to the medical device to image an image); acquire a first image generated by imaging with the imaging apparatus under the first imaging condition (page 1, “summary of the invention,” 1st 2 paragraphs, discloses using an imaging mode [analogous to the recited first image condition] applied to the medical device [imaging apparatus] to image an image [analogous to the recited first image]) ; acquire a first signal-to-noise ratio by (Page 4, 3rd paragraph, discloses basing on a signal-to-noise ratio value to determine continue the filtering [indicating that the subsequential denoising is based on a signal-to-noise ratio value): identifying, for the user identified by the user identification information (Page 4, 5th paragraph, discloses “performing patient identification on the foreground region of the secondary filter image based on the facial features of the visiting patient”); set a first denoising strength based on the first signal-to-noise ratio (Page 4, 3rd paragraph, discloses basing on a signal-to-noise ratio value to determine continue the filtering [indicating that the subsequential denoising is based on a signal-to-noise ratio value, being analogous to the recited first signal-to-noise ratio] which is a filtering window size [denoising strength as claimed analogously] according to page 2, 5th par.); and execute denoising processing on the first image by applying the first denoising strength, to generate a first denoised image (Page 3, last paragraph, discloses performing the filtering according to the custom filtering, to obtain a filtered image [analogous to the recited first denoised image]).
However, Mi does not explicitly teach obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images; and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition, as the first signal-to-noise ratio.
Fan teaches obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images (Par. [0127] discloses denoising the first N frame images as history denoised images [plurality of second images generated by a plurality of times of past imaging], and determine a filtering strength for the second image based on the difference image obtained according to the first N frame images as history denoised images; wherein the filtering strength is based on the signal-to-noise ratio of the first restored image here being the history denoised image according to Par. [0125]; it analogous to the claimed limitation; the images here are analogous to the images of second and first images of Mi when the imaging modes are applied, here Fan teaches further that the past images can be used to determine the filtering strength for the current image based on their signal-to-noise ratio such as recited in the claimed limitation), and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition (the signal-to-noise ration of the past imaging according to Par. [0125] being used to determine the filtering strength for the current image according to Par. [0127] such as, discussed previously, wherein the claimed limitation recites “or” therefore, only one of the options is the instant scope, the examiner selects “identical…” which is taught in Fan’s Par. [0111], wherein the first and second images are obtained in the same manner of manner of Bayer, indicating that the conditions of imaging are identical), as a first signal-to-noise ratio (Par. [0125-0127] disclose the signal-to-noise ratio is used to determine the filtering strength [analogous to the recited first signal-to-noise ratio] as discussed previously).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mi of having an image processing system to acquire user identification information identifying a user; acquire a first imaging condition applied to an imaging apparatus; acquire a first image generated by imaging with the imaging apparatus under the first imaging condition; acquire a first signal-to-noise ratio, with the teachings of Fan of having obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images; and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition, as the first signal-to-noise ratio.
Wherein having Mi’s system of having obtaining a plurality of second signal-to-noise ratios respectively derived from the plurality of second images; and selecting, from among the plurality of second signal-to-noise ratios, a second signal-to-noise ratio corresponding to one of the plurality of second imaging conditions that is identical to the first imaging condition or close to the first imaging condition, as the first signal-to-noise ratio.
The motivation behind the modification would have been to improve image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, and further to improve image acquisition taking into account intensity of the visible light at different levels enabling improvement in flexibility of the image acquisition. Since both Mi and Fan perform image denoising based on signal-to-noise analysis. Wherein Mi’s system improves image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, (see Mi’s Page 2, 3rd to the last Par.) and Fan’s system improves image acquisition taking into account intensity of the visible light at different levels enabling improvement in flexibility of the image acquisition, (see Fan’s Par. [0104]).
However, Mi in view of Fan does not explicitly teach a plurality of second images generated in a plurality of past imaging instances performed separately from one another, each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions, wherein the plurality of second imaging conditions are different from one another.
Marschner teaches a plurality of second images generated in a plurality of past imaging instances performed separately from one another (Par. [0017] discloses “noise in the final image can be reduced through averaging, that is, the repetitive measurement of the data under identical conditions and subsequentially co-adding them”; the measurement data resulting in different images captured at different times in the past), each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions (Par. [0017] discloses “noise in the final image can be reduced through averaging, that is, the repetitive measurement of the data under identical conditions and subsequentially co-adding them” indicating the images are obtained under a plurality of imaging conditions), wherein the plurality of second imaging conditions are different from one another (Par. [0017] discloses “noise in the final image can be reduced through averaging, that is, the repetitive measurement of the data under identical conditions and subsequentially co-adding them…lead to final dataset with high SNR…this method requires an identical contrast for all images that are supposed to be averaged and is time consuming…in particular, while averaging of all data, e.g., from an image series acquired with variation of specific acquisition parameters…would lead to a final dataset with high SNR” indicating the current invention takes into account for a series of images acquired under variation of different imaging conditions such as disclosed in Par. [0086] of “selected within a reasonable range depending on the imaging conditions”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mi in view of Fan of having an image processing system to: acquire user identification information identifying a user; acquire a first imaging condition applied to an imaging apparatus; acquire a first image generated by imaging with the imaging apparatus under the first imaging condition; acquire a first signal-to-noise ratio, with the teachings of Marschner of having a plurality of second images generated in a plurality of past imaging instances performed separately from one another, each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions, wherein the plurality of second imaging conditions are different from one another.
Wherein having Mi’s system of having a plurality of second images generated in a plurality of past imaging instances performed separately from one another, each of the plurality of second images being generated under a respective one of a plurality of second imaging conditions, wherein the plurality of second imaging conditions are different from one another.
The motivation behind the modification would have been to improve image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, and further to improve image denoising for MR images with creased sensitivity, improved SNR and/or reduced loss of information contributions included in the MR images. Since both Mi and Marschner perform image denoising for medical imaging based on signal-to-noise analysis. Wherein Mi’s system improves image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, (see Mi’s Page 2, 3rd to the last Par.) and Marschner’s system improves image denoising for MR images with creased sensitivity, improved SNR and/or reduced loss of information contributions included in the MR images, (see Marschner’s Par. [0021]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Jiang Mi et. al. (foreign patent document “CN 109887581 A” hereinafter as “Mi”) in view of Meng Fan (“US 2022/0222795 A1” hereinafter as “Fan”) further in view of Henrik Marschner et. al. (“US 2018/0089863 A1” hereinafter as “Marschner”) and Wan-Ju Tang et. al. (“US 2021/0004938 A1” hereinafter as “Tang”).
Regarding claim 5, Mi in view of Fan and Marschner teaches the image processing apparatus according to claim 1, Mi teaches wherein the processor is configured to: calculate a fourth signal-to-noise ratio from the first image (Page 1, 3rd to the last paragraph, discloses the imaging units, each is associated with a signal-to-noise ratio, hence, for N imaging units, there is a fourth signal-to-noise ratio being calculated for the image obtained [analogous to the recited first image since they are ]); and set a fourth denoising strength corresponding to the fourth signal-to-noise ratio (Page 4, 3rd paragraph, discloses basing on a signal-to-noise ratio value to determine continue the filtering [indicating that the subsequential denoising is based on a signal-to-noise ratio value, being analogous to the recited first signal-to-noise ratio] which is a filtering window size [denoising strength as claimed analogously] according to page 2, 5th par., for any imaging unit associated with the mapped fourth signal-to-noise ratio, its filtering is using the fourth denoising strength as claimed), as the first denoising strength (Page 4, 3rd par., discloses the signal-to-noise ratio mean value of all the signal-to-noise ratios in same band-pass filtering is being used as the second signal-to-noise ratio mean value for the secondary filtering hence, can be understood as to be used as a first denoising strength to perform the filtering of the corresponding, according to page 4, 4th par.).
However, Mi in view of Fan and Marschner does not explicitly teach set a fourth denoising strength corresponding to the fourth signal-to-noise ratio, as the first denoising strength, by using a function representing a relationship between a signal-to-noise ratio and a denoising strength.
Tang teaches set a fourth denoising strength corresponding to the fourth signal-to-noise ratio, as the first denoising strength, by using a function representing a relationship between a signal-to-noise ratio and a denoising strength (Par. [0011] discloses step of denoising using signal-to-noise ratio and a processing function [analogous to the recited function representing a relationship…] by using a relationship table between input coefficients and corresponding slops of the processing function using signal-to-noise ratios; moreover, the input coefficients being input to determine frequency domain [which as mapped in Mi to be analogous to the recited denoising strength, as discussed above in claim 1]; any denoising strength can be determined using Tang’s method of using the signal-to-noise ratios, including the recited fourth denoising strength and the fourth signal-to-noise ratio as the first denoising strength which, as mapped to Mi as discussed above).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mi in view of Fan and Marschner of having an image processing apparatus comprising: a processor configured to: acquire user identification information identifying a user; acquire a first imaging condition applied to an imaging apparatus; acquire a first image generated by imaging with the imaging apparatus under the first imaging condition; acquire a first signal-to-noise ratio, with the teachings of Tang of having wherein to set a fourth denoising strength corresponding to the fourth signal-to-noise ratio, as the first denoising strength, by using a function representing a relationship between a signal-to-noise ratio and a denoising strength.
Wherein having Mi’s apparatus of having wherein to set a fourth denoising strength corresponding to the fourth signal-to-noise ratio, as the first denoising strength, by using a function representing a relationship between a signal-to-noise ratio and a denoising strength.
The motivation behind the modification would have been to improve image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, and further to improve imaging denoising by overcoming indefinite number division enabling reduction of cost and computing load. Since both Mi and Tang perform image denoising for medical imaging based on signal-to-noise analysis. Wherein Mi’s system improves image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, (see Mi’s Page 2, 3rd to the last Par.) and Tang’s system improves imaging denoising by overcoming indefinite number division enabling reduction of cost and computing load s, (see Tang’s Pars. [0008-0009]).
Claims 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang Mi et. al. (foreign patent document “CN 109887581 A” hereinafter as “Mi”) in view of Meng Fan (“US 2022/0222795 A1” hereinafter as “Fan”) further in view of Henrik Marschner et. al. (“US 2018/0089863 A1” hereinafter as “Marschner”) and Lifeng Yu et. al. (“Radiation Dose Reduction in Pediatric Body CT Using Iterative Reconstruction and a Novel Image-Based Denoising Method, Oct. 2015, Medical Physics and Informatics, Vol. 205, Issue 5” hereinafter as “Yu”).
Regarding claim 6, Mi in view of Fan and Marschner teaches the image processing apparatus according to claim 1, Mi teaches wherein the processor is configured to: display, on a display device, the first denoised image (Par. [0180-0183] discloses displaying the denoised image on a display);
However, Mi in view of Fan and Marschner does not explicitly teach display, on the display device, options representing an evaluation of the first denoising strength applied to the denoising processing on the first denoised image; acquire a first evaluation signal representing the evaluation of the first denoising strength selected from among the options; and perform feedback of the evaluation of the first denoising strength represented by the first evaluation signal with respect to the setting of the first denoising strength.
Yu teaches display, on the display device, options representing an evaluation of the first denoising strength applied to the denoising processing on the first denoised image (Page 1028, 1st par., discloses selected denoising strength before a formal large-scale evaluation was started, moreover, page 1027, last paragraph, discloses the selecting of the denoising strength for denoising the image; therefore, indicating a selection has been done through options to be selected by the user on a display such as disclosed in page 1027, “Materials and Methods” section, 1st 3 paragraphs); acquire a first evaluation signal representing the evaluation of the first denoising strength selected from among the options (Page 1028, “Observer Study and Image Quality Evaluation” section, 1st 2 paragraphs, discloses evaluation of the results of the denoising using such selected denoising strength, hence, indicating evaluation of the denoising strength, the evaluation represented by noise acceptable scale of quantitative evaluation measurement according to “Observer Study and Image Quality Evaluation” section, last paragraph [analogous to the recited first evaluation signal as claimed]); and perform feedback of the evaluation of the first denoising strength represented by the first evaluation signal with respect to the setting of the first denoising strength (Page 1029, 1st paragraph, discloses calculating agreement based on the evaluation, indicating perform giving a feedback [analogous to calculating an agreement] based on the evaluation as discussed).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mi in view of Fan and Marschner of having an image processing apparatus comprising: a processor configured to: acquire user identification information identifying a user; acquire a first imaging condition applied to an imaging apparatus; acquire a first image generated by imaging with the imaging apparatus under the first imaging condition; acquire a first signal-to-noise ratio, with the teachings of Yu of having wherein to display, on the display device, options representing an evaluation of the first denoising strength applied to the denoising processing on the first denoised image; acquire a first evaluation signal representing the evaluation of the first denoising strength selected from among the options; and perform feedback of the evaluation of the first denoising strength represented by the first evaluation signal with respect to the setting of the first denoising strength.
Wherein having Mi’s apparatus of having wherein to display, on the display device, options representing an evaluation of the first denoising strength applied to the denoising processing on the first denoised image; acquire a first evaluation signal representing the evaluation of the first denoising strength selected from among the options; and perform feedback of the evaluation of the first denoising strength represented by the first evaluation signal with respect to the setting of the first denoising strength.
The motivation behind the modification would have been to improve image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, and further to improve image denoising through iterative reconstruction enabling reduction in noise and maintaining diagnostic quality. Since both Mi and Yu perform noise reduction in image processing in medical imaging. Wherein Mi’s system improves image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, (see Mi’s Page 2, 3rd to the last Par.) and Yu’s system improves image denoising through iterative reconstruction enabling reduction in noise and maintaining diagnostic quality, (see Yu’s Abstract).
Regarding claim 7, Mi in view of Fan and Marschner teaches the image processing apparatus according to claim 1, Mi teaches wherein the processor is configured to: display, on a display device, a fifth denoised image generated by executing the denoising processing by applying the first denoising strength to a past image generated (Page 4, 3rd paragraph, discloses basing on a signal-to-noise ratio value to determine continue the filtering [indicating that the subsequential denoising is based on a signal-to-noise ratio value, being analogous to the recited first signal-to-noise ratio] which is a filtering window size [denoising strength as claimed analogously] according to page 2, 5th par., for any imaging unit associated with the mapped fourth signal-to-noise ratio, its filtering is using the fourth denoising strength as claimed; hence, there can be a fifth denoised image performed by a fifth time performing of the denoising by applying the first denoising strength to an image taken [past image]) by being captured in the past by applying a fifth imaging condition that is identical to the first imaging condition or a fifth imaging condition that is close to the first imaging condition (“or” indicates a selection, only one of the options is the instant scope, the examiner selects “identical…” which is taught in Mi’s page 1, 3rd to the last paragraph, discloses the CO2 laser includes a plurality of imaging units for imaging the scene to obtain the images as have been discussed, wherein the CO2 laser operate under a laser emission preparation mode, the imaging units of the same CO2 laser obtain the images under the identical mode [analogous to the recited the second imaging condition is identical to the first imaging condition]).
However, Mi in view of Fan and Marschner does not explicitly teach acquire a fifth evaluation signal representing an evaluation of a fifth denoising strength applied to the denoising processing on the fifth denoised image; and determine whether to maintain or change the first denoising strength in accordance with the evaluation of the fifth denoising strength represented by the fifth evaluation signal.
Yu teaches acquire a fifth evaluation signal representing an evaluation of a fifth denoising strength applied to the denoising processing on the fifth denoised image (Page 1028, 1st par., discloses selected denoising strength before a formal large-scale evaluation was started, moreover, page 1027, last paragraph, discloses the selecting of the denoising strength for denoising the image, indicating a selection has been done through options to be selected by the user on a display such as disclosed in page 1027, “Materials and Methods” section, 1st 3 paragraphs; for the plurality of images being denoised and the plurality of options for selecting of denoising strengths, there can be a fifth evaluation signal representing an evaluation of the fifth denoising strength as claimed); and determine whether to maintain or change the first denoising strength in accordance with the evaluation of the fifth denoising strength represented by the fifth evaluation signal (“or” indicate a selection; the examiner selects, “change” for mapping which is disclosed in page 1027, “Materials and Methods” section, 1st 3 paragraphs, wherein the selection of the denoising strength can be changed for evaluation such as further explained in page 1028, 1st par.).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mi in view of Fan and Marschner of having an image processing apparatus comprising: a processor configured to: acquire user identification information identifying a user; acquire a first imaging condition applied to an imaging apparatus; acquire a first image generated by imaging with the imaging apparatus under the first imaging condition; acquire a first signal-to-noise ratio, with the teachings of Yu of having wherein to acquire a fifth evaluation signal representing an evaluation of a fifth denoising strength applied to the denoising processing on the fifth denoised image; and determine whether to maintain or change the first denoising strength in accordance with the evaluation of the fifth denoising strength represented by the fifth evaluation signal.
Wherein having Mi’s apparatus of having wherein to acquire a fifth evaluation signal representing an evaluation of a fifth denoising strength applied to the denoising processing on the fifth denoised image; and determine whether to maintain or change the first denoising strength in accordance with the evaluation of the fifth denoising strength represented by the fifth evaluation signal.
The motivation behind the modification would have been to improve image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, and further to improve image denoising through iterative reconstruction enabling reduction in noise and maintaining diagnostic quality. Since both Mi and Yu perform noise reduction in image processing in medical imaging. Wherein Mi’s system improves image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, (see Mi’s Page 2, 3rd to the last Par.) and Yu’s system improves image denoising through iterative reconstruction enabling reduction in noise and maintaining diagnostic quality, (see Yu’s Abstract).
Regarding claim 8, Mi in view of Fan further in view of Marschner and Yu teaches the image processing apparatus according to claim 7, Mi teaches wherein a normal image generated by imaging a normal imaging target in the past is applied to the past image (Page 3, 4th paragraph, discloses imaging of an imaging using the N image sensors [analogous to a normal image generated by imaging a target], wherein, any image can be understood to be a past image after its taken).
Regarding claim 9, Mi in view of Fan further in view of Marschner and Yu teaches the image processing apparatus according to claim 7, Mi teaches wherein an identical-imaging target image generated by imaging an identical imaging target in the past (Page 3, 4th paragraph, discloses imaging of an imaging using the N image sensors [analogous to an identical-imaging target generated by imaging a target], wherein, any image can be understood to be a past image after its taken; wherein the image being target is of identical target each time being imaged; any image taken can be understood to be in the past) by applying a sixth imaging condition that is identical to the first imaging condition or a sixth imaging condition that is close to the first imaging condition is applied to the past image (“or” indicates a selection, only one of the options is the instant scope, the examiner selects “identical…” which is taught in Mi’s page 1, 3rd to the last paragraph, discloses the CO2 laser includes a plurality of imaging units for imaging the scene to obtain the images as have been discussed, wherein the CO2 laser operate under a laser emission preparation mode, the imaging units of the same CO2 laser obtain the images under the identical mode [analogous to the recited the second imaging condition is identical to the first imaging condition]; any image already taken or imaged is analogous to the past image being applied the imaging condition; page 4, 3rd paragraph, discloses basing on a signal-to-noise ratio value to determine continue the filtering [indicating that the subsequential denoising is based on a signal-to-noise ratio value, being analogous to the recited first signal-to-noise ratio] which is a filtering window size [denoising strength as claimed analogously] according to page 2, 5th par.), and the processor is configured to display, on the display device, a sixth denoised image obtained by executing the denoising processing on the identical-imaging target image (Page 3, last par., discloses the filtered image being resulted, hence the sixth time performing of the image can be understood to be analogous to the recited sixth denoised image obtained by the execution of the denoising process on the already discussed and mapped identical-imaging target image), and a sixth denoising strength applied to the denoising processing executed on the identical-imaging target image (the filtered image of the sixth time performing of the denoising understood be based on a sixth denoising strength according to Page 3, last Par.).
Regarding claim 10, Mi in view of Fan further in view of Marschner and Yu teaches the image processing apparatus according to claim 9, Mi teaches wherein the processor is configured to.
However, Mi in view of Fan further in view of Marschner and Yu does not explicitly teach acquire a sixth evaluation signal representing an evaluation of the sixth denoising strength; and determine whether to maintain or change the first denoising strength in accordance with the evaluation of the sixth denoising strength represented by the sixth evaluation signal.
Yu teaches acquire a sixth evaluation signal representing an evaluation of the sixth denoising strength (Page 1028, 1st par., discloses selected denoising strength before a formal large-scale evaluation was started, moreover, page 1027, last paragraph, discloses the selecting of the denoising strength for denoising the image, indicating a selection has been done through options to be selected by the user on a display such as disclosed in page 1027, “Materials and Methods” section, 1st 3 paragraphs; for the plurality of images being denoised and the plurality of options for selecting of denoising strengths, there can be a sixth evaluation signal representing an evaluation of the sixth denoising strength as claimed); and determine whether to maintain or change the first denoising strength in accordance with the evaluation of the sixth denoising strength represented by the sixth evaluation signal (“or” indicate a selection; the examiner selects, “change” for mapping which is disclosed in page 1027, “Materials and Methods” section, 1st 3 paragraphs, wherein the selection of the denoising strength can be changed for evaluation such as further explained in page 1028, 1st par.).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mi in view of Fan and Marschner of having an image processing apparatus comprising: a processor configured to: acquire user identification information identifying a user; acquire a first imaging condition applied to an imaging apparatus; acquire a first image generated by imaging with the imaging apparatus under the first imaging condition; acquire a first signal-to-noise ratio, with the teachings of Yu of having wherein to acquire a sixth evaluation signal representing an evaluation of the sixth denoising strength; and determine whether to maintain or change the first denoising strength in accordance with the evaluation of the sixth denoising strength represented by the sixth evaluation signal.
Wherein having Mi’s apparatus of having wherein to acquire a sixth evaluation signal representing an evaluation of the sixth denoising strength; and determine whether to maintain or change the first denoising strength in accordance with the evaluation of the sixth denoising strength represented by the sixth evaluation signal.
The motivation behind the modification would have been to improve image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, and further to improve image denoising through iterative reconstruction enabling reduction in noise and maintaining diagnostic quality. Since both Mi and Yu perform noise reduction in image processing in medical imaging. Wherein Mi’s system improves image filtering by comparing signal-to-noise ratios through an analysis enabling improved image quality, (see Mi’s Page 2, 3rd to the last Par.) and Yu’s system improves image denoising through iterative reconstruction enabling reduction in noise and maintaining diagnostic quality, (see Yu’s Abstract).
Pertinent Prior Art(s)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Busse; Reed F., “US 20080319301 A1”, discloses a method for calculating a flip angle schedule for a train of refocusing radio frequency (RF) pulses with reduced flip angles allows control of RF power deposition and use of a longer echo train. A target signal is defined for each echo in the echo train, and flip angles are then calculated from the target signals. The target signal schedule includes two phases. In the first phase, the target signals drop asymptotically to efficiently establish a pseudo-steady state at a pre-defined minimum signal level, S.sub.min. In the second phase, the target signal is increased monotonically for the remainder of the train to a pre-defined maximum signal level, S.sub.max. By increasing the target signal, the effect of relaxation may be reduced, decreasing blurring and ringing artifacts. Flip angles are then calculated from the target signal schedule, using a simplified method that requires no information about the tissues' relaxation properties.
Gupta; Shalini, “US 8639050 B2”, discloses dynamic adjustment of noise filter strengths for use with dynamic range enhancement of images is performed to produce better quality images by adapting dynamically to the image noise profile. Global and local brightness and contrast enhancement (GLBCE) is performed on a digital image to form an enhanced image. The GLBCE applies local gain values to the digital image based on local intensity values. A GLBCE gain versus intensity curve is determined for the enhanced image. A set of noise filter thresholds is adjusted in response to the GLBCE gain versus intensity curve to form a set of dynamically adjusted noise filter thresholds. The enhanced image is noise filtered using the set of dynamically adjusted noise filter thresholds to form a noise filtered enhanced image.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PHUONG HAU CAI/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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