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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/13/2024 and 01/07/2026 has been considered by the examiner.
Claim Objections
Claims 1-2, 6-7, 9, 11, 24, 26, and 34 are objected to because of the following informalities:
Although the courts have found that the use of the term “and/or” would not be indefinite, (Employers Mut. Liability Ins. Co. v. Tollefsen, 219 Wis. 434 (1935)), the board did note that the preferred way of writing the claim is through use of “at least one of A and B" in the future. Therefore, the Examiner object to the terms "and/or" in claims 2, 6, 11, and 24 such that it is written in accordance with the courts preferred way.
In claims 1, 7, 26, 34, the examiner assumes “an image” and “the image” should be “the first speckle images” for clarity.
In claim 9, the examiner assumes “first said speckle image” should be “said first speckle images” for clarity.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 7, 19, 21, 26, 29, and 34 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sedai et al. (US 20200285880 A1, published September 10, 2020), hereinafter referred to as Sedai.
Regarding claim 1, and similarly for claims 26 and 34, Sedai teaches a method of motion-compensated laser speckle contrast imaging comprising:
exposing a target area to coherent first light of a first wavelength, the target area including living tissue (see para. 0029 – “For example, in an embodiment configured for enhancing OCT [optical coherence tomography] images of retinas [living tissue], the reference image is an image of a retina.” Where it is inherent and known in the art in OCT imaging to transmit coherent light at a wavelength);
capturing at least one sequence of images, the at least one sequence of images comprising first speckle images, the first speckle images being captured during the exposing step (Fig. 1, “set of images requiring improvement” as speckle images; Fig. 5; see para. 0089 - “Image 510 is a registered OCT image, including occluded area 502, that requires enhancement to fill in occluded area 502 and remove some speckle noise.”);
determining one or more registration parameters of an image registration algorithm for registering the first speckle images with each other based on the at least one sequence of images captured in said capturing step (Fig. 3; see para. 0075 – “Image registration module 310 uses a prior-art registration process to register the received images. The registration process adjusts each image to conform to the alignment and size [registration parameters] of a reference image of the known structure the embodiment is configured to process.”),
the determining of the one or more registration parameters comprising: determining transformed images by applying a transformation to the images in the at least one sequence of images captured in said capturing step and determining the one or more registration parameters based on a comparison of the transformed images (see para. 0075 – “If the received image was taken from a point of view different from that of the reference image, the registration process performs a perspective transformation on the received image to match that of the reference image.”); and
determining registered spatial speckle contrast images, the determining of the registered spatial speckle contrast images comprising: - determining registered first speckle images by registering the first speckle images based on the one or more registration parameters and the image registration algorithm, and determining the registered spatial speckle contrast images based on the registered first speckle images; or - determining first spatial speckle contrast images based on the first speckle images, and determining registered spatial speckle contrast images by registering the first spatial speckle contrast images based on the one or more registration parameters and the image registration algorithm (Fig. 3; see para. 0075 – “Image registration module 310 uses a prior-art registration process to register the received images. The registration process adjusts each image to conform to the alignment and size of a reference image of the known structure the embodiment is configured to process.”); and
determining a combined speckle contrast image, the determining of the combined speckle contrast image comprising computing a weighted average of the registered spatial speckle contrast images, wherein a weight of an image, of an image region or of pixels in the image is based on one or more of: the one or more registration parameters, or differences and changes in speckle contrast (Fig. 3; see para. 0079 – “Image composition module 340 combines at least two weighted images to create a composite image, by computing a weighted average of corresponding pixels in each weighted image.”).
Furthermore, regarding claim 2, Sedai further teaches wherein the determining of the one or more registration parameters is based on images selected from the first speckle images and/or from images associated with the first speckle images and/or from images derived from the first speckle images or from the images associated with the first speckle images (see para. 0075 – “If the received image was taken from a point of view different from that of the reference image, the registration process performs a perspective transformation on the received image to match that of the reference image.”).
Furthermore, regarding claim 3, Sedai further teaches wherein the registration parameters are based on a similarity measure of pixel values in one or more pixel groups in each of the at least one sequence of images (see para. 0075 – “If the received image was taken from a point of view different from that of the reference image, the registration process performs a perspective transformation on the received image to match [similarity] that of the reference image.”).
Regarding claim 7, Sedai teaches a method of motion-compensated laser speckle contrast imaging comprising:
exposing a target area to coherent first light of a first wavelength, the target area including living tissue (see para. 0029 – “For example, in an embodiment configured for enhancing OCT [optical coherence tomography] images of retinas [living tissue], the reference image is an image of a retina.” Where it is inherent and known in the art in OCT imaging to transmit coherent light at a wavelength);
capturing at least one sequence of images, the at least one sequence of images comprising first speckle images, the first speckle images being captured during the exposing step (Fig. 1, “set of images requiring improvement” as speckle images; Fig. 5; see para. 0089 - “Image 510 is a registered OCT image, including occluded area 502, that requires enhancement to fill in occluded area 502 and remove some speckle noise.”);
determining one or more registration parameters of an image registration algorithm for registering the first speckle images with each other based on the at least one sequence of images captured in said capturing step (Fig. 3; see para. 0075 – “Image registration module 310 uses a prior-art registration process to register the received images. The registration process adjusts each image to conform to the alignment and size [registration parameters] of a reference image of the known structure the embodiment is configured to process.”),
the determining of the one or more registration parameters comprising: - selecting, based on pixel coordinates, one or more first pixel groups in a first image of the at least one sequence of images, - selecting, based on pixel coordinates, one or more second pixel groups in a second image of the at least one sequence of images, the one or more first pixel groups having a different size than the one or more second pixel groups, and - determining the registration parameters based on a similarity measure of pixel values in the one or more first pixel groups and the one or more second pixel groups (see para. 0075 – “In particular, if the received image is larger or smaller than the reference image, the received image is rescaled to match the reference image…The registration process also performs additional adjustments to the received image to ensure image alignment and sizing consistent with the reference image.”); and
determining registered spatial speckle contrast images, the determining of the registered spatial speckle contrast images comprising: - determining registered first speckle images by registering the first speckle images based on the one or more registration parameters and the image registration algorithm, and determining the registered spatial speckle contrast images based on the registered first speckle images; or - determining first spatial speckle contrast images based on the first speckle images, and determining registered spatial speckle contrast images by registering the first spatial speckle contrast images based on the one or more registration parameters and the image registration algorithm (Fig. 3; see para. 0075 – “Image registration module 310 uses a prior-art registration process to register the received images. The registration process adjusts each image to conform to the alignment and size of a reference image of the known structure the embodiment is configured to process.”); and
determining a combined speckle contrast image, the determining of the combined speckle contrast image comprising computing a weighted average of the registered spatial speckle contrast images, wherein a weight of an image, of an image region or of pixels in the image is based on one or more of: the one or more registration parameters, or differences and changes in speckle contrast (Fig. 3; see para. 0079 – “Image composition module 340 combines at least two weighted images to create a composite image, by computing a weighted average of corresponding pixels in each weighted image.”).
Furthermore, regarding claim 19, Sedai further teaches wherein the one or more pixel groups represent predetermined features in the sequence of images (Fig. 7, original OCT images 702 and 704 with occluded area 714 and 724 as pixel groups representing predetermined features).
Furthermore, regarding claim 21, Sedai further teaches wherein a pixel group in a first image from the at least one sequence of images is smaller than a pixel group in a second image from the at least one sequence of images (Fig. 7, occluded area 714 in image 710 as pixel group smaller than occluded area 724 in image 720).
Furthermore, regarding claim 29, Sedai further teaches a hardware module for an imaging device, comprising:
a first light source for exposing a target area to coherent first light of a first wavelength, the target area including living tissue (see para. 0029 – “For example, in an embodiment configured for enhancing OCT [optical coherence tomography] images of retinas [living tissue], the reference image is an image of a retina.” Where it is inherent and known in the art in OCT imaging to transmit coherent light at a wavelength via a light source);
at least one image sensor system for capturing at least one sequence of images, the at least one sequence of images comprising first speckle images, the first speckle images being captured during exposure with the coherent first light (Fig. 1, “set of images requiring improvement” as speckle images; Fig. 5; see para. 0089 - “Image 510 is a registered OCT image, including occluded area 502, that requires enhancement to fill in occluded area 502 and remove some speckle noise.” Where it is inherent and known in the art in OCT imaging to have a sensor capturing OCT images); and
a computation module as claimed in claim 26 (see claim 26 above; Fig. 3; see para. 0074 – “Application 300 is an example of application 105 in FIG. 1 and executes in any of servers 104 and 106, clients 110, 112, and 114, and device 132 in FIG. 1.”).
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.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Sedai in view of Adie et al. (US 20240041327 A1, published February 8, 2024 with a priority date of January 28, 2021), hereinafter referred to as Adie.
Regarding claim 4, Sedai teaches all of the elements disclosed in claim 1 above.
Sedai teaches applying a transformation to the images, but does not explicitly teach where the transformation is one or more of: a Fourier transformation, a Mellin transformation, a Laplace transformation, a Radon transformation, and a log-polar coordinate transformation.
Whereas, Adie, in an analogous field of endeavor, teaches wherein the transformation comprises one or more of: a Fourier transformation, a Mellin transformation, a Laplace transformation, a Radon transformation, and a log-polar coordinate transformation (Fig. 1; see para. 0077 – “In some implementations, the computational bandwidth expansion operation may include: 2D Fourier Transform and depth averaging operation to generate an original magnitude spectrum…”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified applying a transformation to the images, as disclosed in Sedai, by having the transformation as one or more of: a Fourier transformation, a Mellin transformation, a Laplace transformation, a Radon transformation, and a log-polar coordinate transformation, as disclosed in Adie. One of ordinary skill in the art would have been motivated to make this modification in order to generate an original magnitude spectrum of the image, as taught in Adie (see para. 0077).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Sedai in view of Gao et al. (US 20190304105 A1, published October 3, 2019), hereinafter referred to as Gao.
Regarding claim 5, Sedai teaches all of the elements disclosed in claim 1 above.
Sedai teaches applying a transformation to the images, but does not explicitly teach determining a peak in the cross-correlation in the spatial domain.
Whereas, Gao, in an analogous field of endeavor, teaches wherein the transformation is a transformation to a frequency domain (see para. 0055 – “…a two-dimensional (2D) Fast Fourier Transform (FFT) can be used to find the Fourier representations of the two extracted ROI feature maps from the two co-located search windows.”), and wherein the comparison of the transformed images comprises:
determining a cross-correlation of the transformed images (see para. 0055 – “Next, cross-correlation between the frequency domain representations of the two extracted ROI feature maps can be computed (e.g., by computing element-wise product)…”);
determining a transformation of the cross-correlation to the spatial domain (see para. 0055 – “…and then an inverse FFT can be applied to the computed correlation maps back to the spatial domain.”); and
determining a peak in the cross-correlation in the spatial domain (see para. 0055 – “The peak value (e.g., if that value is above a predetermined threshold) in the correlation map in the spatial domain and its location in the correlation map...”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified applying a transformation to the images, as disclosed in Sedai, by also determining a peak in the cross-correlation in the spatial domain, as disclosed in Gao. One of ordinary skill in the art would have been motivated to make this modification in order to be used as the central location of an updated ROI of the target object in the current video frame, as taught in Gao (see para. 0055).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Sedai in view of Zhou et al. (US 20080063298 A1, published March 13, 2008), hereinafter referred to as Zhou.
Regarding claim 6, Sedai teaches all of the elements disclosed in claim 1 above.
Sedai teaches applying a transformation to the images, but does not explicitly teach where the transformation is a log-polar coordinate transformation.
Whereas, Zhou, in an analogous field of endeavor, teaches wherein the transformation is a log-polar coordinate transformation; the comparison of the transformed images comprises determining a shift on the transformed images relative to each other; and the determining of the registration parameters comprises determining a rotation and/or a scaling based on the determined shift (see para. 0052 – “In the description of the method for alignment of images shown in FIGS. 1 through 3, the reference image I1 and the sample image I2 are represented in Cartesian coordinates. Therefore, in order to obtain rotational shift and scaling factor, the Fourier transforms of the images I1 and I2 are transformed into log-polar coordinates, as described in steps S311 and S321 of FIG. 2B.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified applying a transformation to the images, as disclosed in Sedai, by having the transformation as a log-polar coordinate transformation, as disclosed in Zhou. One of ordinary skill in the art would have been motivated to make this modification in order to automatically align the video frames before quantitative processing is performed, as taught in Zhou (see para. 0007).
Claims 9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Sedai in view of Hara (US 20080031531 A1, published February 7, 2008), hereinafter referred to as Hara.
Regarding claim 9, Sedai teaches all of the elements disclosed in claim 1 above.
Sedai teaches generating speckle images, but does not explicitly teach generating masks of speckle images.
Whereas, Hara, in an analogous field of endeavor, teaches determining a plurality of masks for the first speckle images, each of the plurality of masks being associated with a respective first said speckle image (see para. 0091 – “Then, in step S3 of FIG. 3, the image binarizing device 24 shown in FIG. 2 converts the contrast image that is enhanced by the image enhancing device 23 into a binary image of white and black.”), and
each of the plurality of masks associating a reliability score with one or more pixels in the associated first speckle image (see para. 0099 – “Next, in step S6 of FIG. 3, the line noise reliability calculating device 26 analyzes each horizontal line of the binary image, calculates the number of consecutive black pixels and the edge feature quantity, and combines those to calculate the line noise reliability.”);
determining a plurality of registered masks by registering the plurality of masks, based on the one or more registration parameters and the image registration algorithm (see para. 0107 – “Then, in step S65 of FIG. 5, the line noise reliability calculated in this way is registered to the line noise plane.”); and
determining the combined speckle contrast image based on the plurality of registered masks (see para. 0111 – “Next, in step S66 of FIG. 5, it is judged whether or not the line noise reliability calculation processing is completed for all the lines. When judged that it is not completed, the next line is set and the procedure is returned to the step S62. When judged that it is completed for all the lines, the procedure is advanced to the step S7 of FIG. 3.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified generating speckle images, as disclosed in Sedai, by also generating masks of speckle images, as disclosed in Hara. One of ordinary skill in the art would have been motivated to make this modification in order to extract more accurate feature quantities, as disclosed in Hara (see para. 0164).
Furthermore, regarding claim 11, Hara further teaches wherein the determining of the plurality of masks comprises identifying deviating input pixel values, and/or deviating speckle contrast values, values above a predetermined absolute or relative upper threshold value or values below a predetermined absolute or relative lower threshold value; and/or the determining of the plurality of masks comprises identifying pixels not representing living tissue based on an image recognition algorithm (Fig. 8; see para. 0091 – “Then, in step S3 of FIG. 3, the image binarizing device 24 shown in FIG. 2 converts the contrast image that is enhanced by the image enhancing device 23 into a binary image of white and black. Among many kinds of proposed binarizing techniques, this example employed a simple binary processing that takes the intermediate value (127) as a threshold value, because the image is already being enhanced…This fingerprint image is expressed as B.”).
The motivation for claim 11 was shown previously in claim 9.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Sedai in view of Hara, as applied to claim 9 above, and in further view of Zhang et al. (US 20210369245 A1, published December 2, 2021), hereinafter referred to as Zhang.
Regarding claim 13, Sedai in view of Hara teaches all of the elements disclosed in claim 9 above.
Sedai in view of Hara teaches generating masks of speckle images, but does not explicitly teach where masked pixels have a weight based on the reliability score associated with the pixel.
Whereas, Zhang, in an analogous field of endeavor, teaches wherein masked pixels have a weight based on the reliability score associated with the pixel (see para. 0036-0037 – “In such an embodiment, the weighting mask may preferably have a value ranging between zero and one for each of those pixels in the current sparse color Doppler image frame for which observed color Doppler measurement exist, said value reflecting a confidence level on the observed color Doppler measurement at each respective pixel…In particular, the confidence level may be a function of the noise level at each respective pixel.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified generating masks of speckle images, as disclosed in Sedai in view of Hara, by having the masked pixels have a weight based on the reliability score associated with the pixel, as disclosed in Zhang. One of ordinary skill in the art would have been motivated to make this modification in order for the contribution of pixels for which the confidence level on the observed color Doppler measurement is low (e.g. due to noise) to the optimization framework to solve the flow vector field can be reduced or even suppressed (e.g., by making the value of the weighting mask equal to zero), while the contribution of pixels having high confidence level can be enhanced (e.g., by making the value of the weighting mask close to or even equal to one), as taught in Zhang (see para. 0038).
Claims 16, 24, 27, 30, and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Sedai in view of Tucker et al. (US 20190374140 A1, published December 12, 2019), hereinafter referred to as Tucker.
Regarding claims 16, 27, and 30, Sedai teaches all of the elements disclosed in claim 1, 26, and 29 above, respectively.
Sedai teaches exposing the target area to a light at a wavelength, but does not explicitly teach exposing the target area to a second light at a second wavelength.
Whereas, Tucker, in an analogous field of endeavor, teaches:
exposing the target area to second light of one or more second wavelengths; wherein the
the at least one sequence of images comprises a sequence of second images, the second images being captured during said exposing with the second light (Fig. 4; see para. 0063 – “…acquired visible images 460, acquired NIR images/data 461…”); and
the one or more registration parameters are based on the sequence of second images or from images derived from the second images, each of the second images or the images derived from the second images being associated with a first speckle image (see para. 0058 – “The NIR 283 and visible 285 images are directed to the camera 210 and a split image is created on one camera sensor or on separate camera sensors S1-SN (FIG. 11C) that have been synchronized and aligned.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified exposing the target area to a light at a wavelength, as disclosed in Sedai, by also exposing the target area to a second light at a second wavelength, as disclosed in Tucker. One of ordinary skill in the art would have been motivated to make this modification in order to capture and combine images of the anatomical structure and the measured physiological characteristics, as taught in Tucker (see para. 0045).
Furthermore, regarding claim 24, Tucker further teaches wherein the target area comprises a perfused organ, and/or comprises one or more blood vessels and/or lymphatic vessels (see para. 0074 – “Using LSI [Laser Speckle Imaging] as an example, using the Eqn. (1) above, the speckle contrast of coherent NIR laser light CNIR(i,j) is associated with v13(r), which is the velocity distribution of an object of interest (blood flow and perfusion) relative to detector (camera).”), the method further comprising:
computing a perfusion intensity based on the combined speckle image (see para. 0088 – “Referring now to FIG. 11B, a physiological layer is represented by image color: a processed image based on one or more raw image frames of near infra-red light to reflect 2D speed distribution of blood flow velocity and perfusion of the imaged tissue/organ using Laser Speckle or Laser Doppler Imaging technology. In a first step, an 8 bit indexed color image is generated with its numerical values mapped to a predefined color map. “).
One of ordinary skill in the art would have been motivated to make this modification in order to improve the visualization and increase accuracy of the quantification of the blood flow and perfusion imaging, as taught in Tucker (see para. 0105).
Furthermore, regarding claim 33, Sedai further teaches a medical imaging device comprising a hardware module as claimed in claims 29 (see claim 29 above), and
Tucker further teaches the medical imaging device being one of: an endoscope, a laparoscope, a surgical robot, a handheld laser speckle contrast imaging device or an open surgical laser speckle contrast imaging system (see para. 0049 – “These approaches may include, for example, direct contact or non-contact with tissues, exposure of the tissues during open surgical procedures, or via endoscopy to access tissues within closed anatomic structures or tissues in the alimentary tract or tracheobronchial tree without departing from the scope of the present inventive concept.”).
One of ordinary skill in the art would have been motivated to make this modification in order to provide visualization and quantification across multiple clinical and experimental settings, as taught in Tucker (see para. 0049).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Sedai in view of Kuriyama (US 20180103829 A1, published April 19, 2018), hereinafter referred to as Kuriyama.
Regarding claim 22, Sedai teaches all of the elements disclosed in claim 1 above.
Sedai teaches determining one or more registration parameters, but does not explicitly teach determining the one or more registration parameters based on the plurality of alignment vectors.
Whereas, Kuriyama, in an analogous field of endeavor, teaches wherein determining one or more registration parameters comprises:
determining a plurality of associated pixel groups based on the similarity measure, each said associated pixel group belonging to a different image from the at least one sequence of images (see para. 0099 – “…the correlation between a block area corresponding to the initial point of the motion vector in an image corresponding to the current frame and a block area corresponding to the terminal point of the motion vector in an image corresponding to a previous frame…The correlation between pixel values indicates similarity between the pixel values…”),
determining a plurality of alignment vectors based on positions of the associated pixel groups relative to respective ones of said images from the at least one sequence of images, the alignment vectors representing motion of the target area relative to an image sensor (see para. 0084 – “A low matching degree indicates a failure in local alignment (motion vector detection) [alignment vectors] between two images, and thus the determination results in “unreliable”…The motion information output from the motion information acquisition section 340 is a result of the local alignment, and thus whether or not the local alignment is reliable is determined based on the correlation between local areas in two images associated with each other by the motion information.”); and
determining the one or more registration parameters based on the plurality of alignment vectors (see para. 0099 – “For example, in the present embodiment, the reliability [registration parameter] is determined based on the correlation between a block area corresponding to the initial point of the motion vector [alignment vectors] in an image corresponding to the current frame and a block area corresponding to the terminal point of the motion vector in an image corresponding to a previous frame…The correlation between pixel values indicates similarity between the pixel values…”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified determining one or more registration parameters, as disclosed in Sedai, by determining the one or more registration parameters based on the plurality of alignment vectors, as disclosed in Kuriyama. One of ordinary skill in the art would have been motivated to make this modification in order for the focus operation to be performed for a change of scene requiring the AF process, such as movement of the imaging range of the tissue or movement by which the tissue or the treatment tool lies outside the depth of field, with influence of the motion information with low reliability reduced, as taught in Kuriyama (see para. 0091).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Sedai in view of Avinash et al. (US 20040022425 A1, published February 5, 2004), hereinafter referred to as Avinash.
Regarding claim 23, Sedai teaches all of the elements disclosed in claim 1 above.
Sedai teaches determining the one or more registration parameters, but does not explicitly teach determining registration parameters for each region.
Whereas, Avinash, in an analogous field of endeavor, teaches dividing each image of the first speckle images, respectively first speckle contrast images, and each image in the at least one sequence of images into a plurality of regions (see para 0027 – “… images are re-sampled at a given scale and subsequently divided into multiple regions.”); and
wherein determining the one or more registration parameters comprises determining registration parameters for each region (see para 0027 – “Separate shift vectors [registration parameters] are calculated for different regions.”); and
determining a sequence of the registered first speckle images, respectively first speckle contrast images comprises registering each region of the first speckle image, respectively the first speckle contrast image, based on the transformation based on a corresponding region in the second image (see para. 0027 – “Shift vectors are interpolated to produce a smooth shift transformation, which is applied to warp one of the images.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified determining the one or more registration parameters, as disclosed in Sedai, by determining registration parameters for each region, as disclosed in Avinash. One of ordinary skill in the art would have been motivated to make this modification in order to substantially increase accuracy of information acquired obtained from temporal processing, as taught in Avinash (see para. 0009).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Karino (US 20170301083 A1, published October 19, 2017) discloses sets divided regions by dividing each registration processing target of the plurality of image; determines, for each set of divided regions set at the same position of the respective registration processing targets, a pixel value conversion method for each divided region based on a pixel value of each divided region of the set; performs registration processing on the plurality of images subjected to pixel value conversion.
Aoyama (US 20180289240 A1, published October 11, 2018) discloses an endoscope system includes an image acquiring unit that acquires a first image and a second image, the first image being obtained by using first illumination light, the second image being obtained by using second illumination light at a different timing from the first image.
Leung (US 20130230228 A1, published September 5, 2013) discloses each image may be defined by a measurement noise added to the image transformation function operating on the registration vector with respect to a reference image. The registration vector may be a function of a breathing motion of a prior registration vector added to a transition noise value. The technologies may estimate motion parameters based on the registration vector.
Murukeshan et al. (US 20200219273 A1, published July 9, 2020) discloses capturing a first image of the component at a first location at a first wavelength, and then capturing a second image of the component at the first location at a second wavelength; determining the Speckle Statistical Correlation (SSC) coefficient of the first and second images; plotting the SSC coefficient for the combined first and second images.
Bergen et al. (US 20060109903 A1, published May 25, 2006) discloses a minimum function is applied to the at least one frame to produce a plurality of minimum values, and a mask is generated in accordance with the plurality of minimum values. The mask is applied to reduce the noise in the at least one frame.
Abe (US 20090129635 A1, published May 21, 2009) discloses when it is judged at the judgment unit that the mask prepared at the mask preparation unit suitably cuts out the image of the object, the registration unit uses this mask to mask the captured image, extracts the information of the blood vessel patterns from the masked image, and stores this in the storage unit as the template.
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