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 .
Notice to Applicant
This office action is in response to application filed on 01/18/2025.
Limitations appearing inside of {} are intended to indicate the limitations not taught by said prior art(s)/combinations.
Claims 1-18 are pending in the application.
Information Disclosure Statement
Information Disclosure Statement(s) (IDS) filed on 07/22/2025 have been considered.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, and 16-18 are rejected under 35 U.S.C. 102(a)(1)as being anticipated by “Yu” (Yu et al., US2021391056A1, the US Patent Application Publication corresponding to CN110875092A, cited in the IDS).
Yu teaches an image calibration method, comprising:
determining, based on a target fundus image, a papilla region of the target fundus image (judging whether the fundus image includes the optic disc (i.e., papilla); Yu, ¶[0033]);
determining, based on the target fundus image, an effective imaging region of the target fundus image, wherein the effective imaging region comprises a region where a fundus structure is visualized (if the parabolic vertex falls in the original fundus image, the parabolic vertex is defined as the center of the optic disc; Yu, ¶[0038]; a region is defined as a region of interest 1 with the optic disc as the circle center and ranging 1.5 times the diameter of the optic disc, and a region of interest 2 is defined with the macula fovea as the circle center and ranging 1 times the diameter of the optic disc; Yu, ¶[0041]); and
determining, based on the effective imaging region and the papilla region, a calibration result of the target fundus image, wherein the calibration result comprises
a calibration result of a pixel unit dimension of the target fundus image (Step S103: if the information to be analyzed is qualified, the characteristic data are extracted from the information to be analyzed, and a structured quantitative index is formed, specifically, by calculating quantitative parameters of a temporal side of the optic disc and a macula fovea according to the calibrated optic disc and macula. The temporal coordinates (ODX, ODY) of the optic disc are calculated according to the coordinates of the center of the optic disc and the radius of the optic disc; Yu, ¶[0043]; each pixel in the main blood vessel is mapped to have coordinates in the coordinate system; Yu, ¶[0036]. One of ordinary skill in the art would be able to utilize the coordinate values to infer the pixels unit dimension as is done for the main blood vessel as described in ¶[0036]) and
a calibration result of a fundus characteristic dimension of the target fundus image (Structural elements are such configured that the diameter of the optic disc is ⅛-⅕ of the width of the image ;Yu, ¶[0036]).
2. Yu teaches the image calibration method according to claim 1. Yu further teaches wherein the determining, based on the effective imaging region and the papilla region, the calibration result of the target fundus image comprises: determining, based on at least one of
a ratio of a diameter of the effective imaging region to a diameter of the papilla region (Structural elements are such configured (i.e., calibration) that the diameter of the optic disc is ⅛-⅕ of the width of the image; Yu, ¶[0036]),
a distance between a center position of a macula in the effective imaging region and a center position of the papilla region, and
a ratio of an area of the effective imaging region to an area of the papilla region,
the calibration result of the target fundus image.
16. Yu teaches an image processing method, comprising:
calibrating a plurality of fundus images (fundus images; Yu, ¶[0057]) to be calibrated by the image calibration method according to claim 1 to generate calibration results respectively corresponding to the plurality of fundus images to be calibrated;
determining, based on the calibration results respectively corresponding to the plurality of fundus images to be calibrated, a plurality of dimensional calibration results for a same fundus characteristic (we can analyze quantitatively, gather statistics and compare the retinopathy characteristics or vascular changes of fundus images collected from different people, or from the same person but at different times or with different devices, so as to form structured health data; Yu, ¶[0049]); and
comparing the plurality of dimensional calibration results for the same fundus characteristic to obtain a comparison result of the plurality of dimensional calibration results (above steps can produce the quantitative index and characteristic data with uniform comparability for the final fundus images such processed; form meaningful and comparable quantitative indexes, so that all the fundus images can be generally comparable; Yu, ¶[0051]).
17. Yu teaches an electronic device, comprising:
a processor (computer devices; Yu, ¶[0025]); and
a memory (RAM, ROM, mobile hard disk, network server storage, and network cloud storage; Yu, ¶[0025]), configured to store executable instructions of the processor, wherein the processor is configured to implement the image calibration method according to claim 1 (performed by programming to instruct relevant hardware, the program may be stored in a storage medium readable by a computer device, for performing some or all of the steps of the embodiments described below; Yu, ¶[0025]).
18. Yu teaches a non-transitory computer-readable storage medium, on which computer executable instructions are stored, wherein when the executable instructions are executed by a processor, the image calibration method according to claim 1 is implemented (performed by programming to instruct relevant hardware, the program may be stored in a storage medium readable by a computer device, for performing some or all of the steps of the embodiments described below; Yu, ¶[0025]).
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Yu in view of “Yin” (F. Yin et al., "Model-based optic nerve head segmentation on retinal fundus images," 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 2011, pp. 2626-2629, doi: 10.1109/IEMBS.2011.6090724.).
3. Yu teaches the image calibration method according to claim 2.
Yu further teaches wherein before the determining, based on at least one of
a ratio of a diameter of the effective imaging region to a diameter of the papilla region,
a distance between a center position of a macula in the effective imaging region and a center position of the papilla region, and
a ratio of an area of the effective imaging region to an area of the papilla region, the calibration result of the target fundus image, the image calibration method further comprises:
determining a minimum bounding {graphic} of a papilla corresponding to the papilla region (a region is defined as a region of interest 1 with the optic disc as the circle center and ranging 1.5 times the diameter of the optic disc; Yu, ¶[0041]); and
{determining, based on the minimum bounding graphic of the papilla, the diameter of the papilla region} (the macula fovea is generally about 3 mm away from the temporal side edge of the optic disc, so a standard d for subsequent quantitative analysis is obtained according to the given absolute distance from the temporal side of the optic disc to the macula fovea and the diameter of the optic disc according to the following formula: d=DMD−ODD Formula 3; In this embodiment, the data obtained is converted from an absolute representation to a relative representation on the scale of d, and normalized to form meaningful and comparable data; Yu, ¶¶ [0045]-[0046]).
Yu does not explicitly disclose a determining a graphic, and
does not explicitly disclose determining the diameter of the papilla region.
However, Yin, a similar field of endeavor of Automatic localization and segmentation of the optic disc, teaches a minimum bounding graphic of a papilla, (
I
e
m
is the edge map of the optimal image; Yin, [§III.C. ¶2]); and
determining, based on the minimum bounding graphic of the papilla, the diameter of the papilla region (The optic disc can be approximated by a circle in the fundus image. The knowledge-based Circular Hough Transform is used to detect a circle that can best estimate the optic disc with appropriate disc diameter range. A circle can be represented in the parametric form as(x−a)2+(y−b)2=r2 (Eq8), where (a,b) is the center of the circle and r is the radius; Yin, [§II. C., ¶2]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a bounding graphic for determining the papilla diameter as taught by Yin to the invention of Yu. The motivation to do so would be to obtain a good initialization to locate the disc center and estimate the disc size for each image, avoiding the problem of local maxima/minima and reduce the computing time.
4. The combination of Yu and You teaches the image calibration method according to claim 3. Yu further teaches wherein the minimum bounding {graphic} of the papilla comprises at least one of
a minimum bounding circle of the papilla (a region is defined as a region of interest 1 with the optic disc as the circle center and ranging 1.5 times the diameter of the optic disc; Yu, ¶[0041]),
a minimum bounding ellipse of the papilla, and
a minimum bounding rectangle of the papilla.
5. The combination of Yu and Yin teaches the image calibration method according to claim 4. Yin further teaches wherein
when the minimum bounding graphic of papilla comprises the minimum bounding circle of the papilla, the determining, based on the minimum bounding graphic of the papilla, the diameter of the papilla region comprises: determining, based on a diameter of the minimum bounding circle of the papilla, the diameter of the papilla region (a circle that can best estimate the optic disc with appropriate disc diameter range; Yin [§II. C, ¶2]);
when the minimum bounding graphic of papilla comprises the minimum bounding ellipse of the papilla, the determining, based on the minimum bounding graphic of the papilla, the diameter of the papilla region comprises: determining, based on a long axis of the minimum bounding ellipse of the papilla, the diameter of the papilla region; and
when the minimum bounding graphic of papilla comprises the minimum bounding rectangle of the papilla, the determining, based on the minimum bounding graphic of the papilla, the diameter of the papilla region comprises: determining, based on a long axis of the minimum bounding rectangle of the papilla, the diameter of the papilla region.
Claim 6 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Yu in view of “Lee” (Lee et al., KR20210000021A), and further in view of “Aquino” (A. Aquino, et al., "Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques," in IEEE Transactions on Medical Imaging, vol. 29, no. 11, pp. 1860-1869, Nov. 2010, doi: 10.1109/TMI.2010.2053042).
6. Yu teaches the image calibration method according to claim 1. Yu further teaches, wherein the determining, based on the target fundus image, the papilla region of the target fundus image comprises:
processing the target fundus image {through a deep learning network model} to obtain position data of the papilla region of the target fundus image in a rectangular coordinate system(The temporal coordinates (ODX, ODY) of the optic disc are calculated according to the coordinates of the center of the optic disc and the radius of the optic disc; Yu, ¶[0043]).
{performing polar coordinate transformation to the position data in the rectangular coordinate system, and
determining papilla boundary coordinates of the papilla region in a polar coordinate; and
determining, based on the papilla boundary coordinates, the papilla region of the target fundus image}.
Yu does not explicitly disclose deep learning network model to obtain position data of the papilla region; performing polar coordinate transformation to the position data in the rectangular coordinate system, and determining papilla boundary coordinates of the papilla region in a polar coordinate; and determining, based on the papilla boundary coordinates, the papilla region of the target fundus image
However, Lee teaches performing polar coordinate transformation to the position data in the rectangular coordinate system (the optic nerve papillary detection module 204 performs polar coordinate transformation on the fundus image 602 based on a straight line (indicated by a dotted line in the drawing) connected from the center of the fundus image 602 to the edge.; Lee ¶0060], and Fig 3, shown below, exhibits image with location of papilla in cartesian coordinates 308, transformed to polar coordinates 310; Lee)
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The combination of Yu and Lee does not explicitly disclose determining papilla boundary coordinates of the papilla region in a polar coordinate; and
determining, based on the papilla boundary coordinates, the papilla region of the target fundus image.
However, Aquino, a similar field of endeavor of automatic localization and segmentation of the optic disc, teaches determining papilla boundary coordinates of the papilla region in a polar coordinate (the circular shapes present in IBM (i.e., boundary coordinates of the papilla ration) can be obtained by performing the Circular Hough Transform on this image. It can be defined as (Pc,r)=CHT(IBM,rmin,rmax)(Equation 8), where Pc=(ic,jc) and r are respectively the center position and the radius (i.e., polar coordinates) that define the circular shape; Aquino, ¶[§III. B. 3, ¶1]);
and determining, based on the papilla boundary coordinates, the papilla region of the target fundus image (This score quantifies the point by point matching degree between the estimated circumference and the fitted shape in IBM. Therefore, higher scores generally involve better OD border extraction; Aquino, [§III. B. 3, ¶2]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include converting from cartesian to polar coordinates as taught by Lee to the invention of Yu. The motivation to do so would be to efficiently detect circular anatomy of the papilla.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include determining a boundary and region of the papilla as taught by Aquino to the combined invention of Yu and Lee. The motivation to do so would be for efficient optical disk (i.e., papilla) segmentation.
Claims 7, and 8 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Yu in view of “Gao” (Gao J, Jiang Y, Zhang H, Wang F (2020) Joint disc and cup segmentation based on recurrent fully convolutional network. PLoS ONE 15(9): e0238983. https://doi.org/10.1371/journal.pone.0238983).
7. The image calibration method according to claim 1, wherein the determining, based on the target fundus image, the papilla region of the target fundus image comprises:
processing the target fundus image through computer vision technology to obtain the papilla region of the target fundus image (See Fig 3 exhibits a deep learning network for segmentation tasks which detects optical disk, See Fig 6F; Gao).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a computer vision as taught by Aquino to the invention of Yu. The motivation to do so would be to fully understand the global information, and accurately identify some large, segmented fundus regions.
8. The image calibration method according to claim 1, wherein the determining, based on the target fundus image, the papilla region of the target fundus image comprises:
processing the target fundus image through deep learning segmentation network to obtain the papilla region of the target fundus image (See Fig 3 exhibits a deep learning network for segmentation tasks which detects optical disk, See Fig 6F; Gao) .
Claims 9-13 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Yu in view of Aquino.
9. Yu teaches the image calibration method according to claim 1. Yu does not explicitly disclose wherein the determining, based on the target fundus image, the effective imaging region of the target fundus image comprises:
determining, based on the target fundus image, an effective imaging region edge of the target fundus image; and
determining, based on the effective imaging region edge, the effective imaging region of the target fundus image by fitting bounding shape.
However, Aquino teaches wherein the determining, based on the target fundus image, the effective imaging region of the target fundus image comprises: determining, based on the target fundus image, an effective imaging region edge of the target fundus image (a 400×400 RGB sub-image is extracted centered on an OD pixel provided by the OD location methodology previously presented, or by any other if desired; Aquino, [§III. B., ¶1]); and
determining, based on the effective imaging region edge, the effective imaging region of the target fundus image by fitting bounding shape (the Prewitt edge detector [28] is used to obtain a gradient magnitude image (hereafter IGM). This operator estimates image edge and orientation … in general the most significant edges in the gradient image correspond to the OD boundary (i.e., papilla boundary); Aquino, [§III. B. 2., ¶1])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a extracting a sub-image and detecting the optic disk boundary as taught by Aquino to the invention of Yu. The motivation to do so would be to first localize then define the optical disk boundary for subsequent automated diagnosis of various serious ophthalmic pathologies.
10. The combination of Yu and Aquino teaches the image calibration method according to claim 9. Aquino further teaches wherein the determining, based on the effective imaging region edge, the effective imaging region of the target fundus image by fitting bounding shape comprises:
performing circular Hough transform to the effective imaging region edge of the target fundus image; and
determining a circle with the most votes as the effective imaging region of the target fundus image.
([The Circular Hough Transform] is used to transform a set of feature points in the image space into a set of accumulated votes in a parameter space. Then, for each feature point, votes are accumulated in an accumulator array for all parameter combinations. The array elements that contain the highest number of votes indicate the presence of the shape; Aquino, [§III. B. 3., ¶1]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a circular Hough transform as taught by Aquino to the invention of Yu. The motivation to do so would be to efficiently detect edges of circular objects.
11. The combination of Yu and Aquino teaches the image calibration method according to claim 9. Aquino further teaches , wherein the determining, based on the target fundus image, the effective imaging region edge of the target fundus image comprises: determining, based on the target fundus image, an effective imaging region edge of the target fundus image through a gradient threshold or an edge detection operator (the OD boundary can be detected by measuring the gradient magnitude of gray-level changes in small neighborhoods of the image. Firstly, a mean filter is applied to eliminate pixel values unrepresentative of their environment. Then, the Prewitt edge detector [28] is used to obtain a gradient magnitude image (hereafter IGM); Aquino, [§III.B. 2, ¶1]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a gradient threshold as taught by Aquino to the invention of Yu. The motivation to do so would be because the OD boundary is characterized by a sudden variation in gray levels between the OD and background, with these values higher within the OD than in its surroundings.
12. The combination of Yu and Aquino teaches the image calibration method according to claim 9. Aquino further teaches wherein the determining, based on the target fundus image, the effective imaging region edge of the target fundus image comprises: performing channel separation to the target fundus image to obtain a grayscale image corresponding to the target fundus image (gray-level image I from the red or green field of the sub-image containing the OD; Aquino, [§III.B.1., ¶1]);
binarizing the grayscale image to obtain a binary image(a binary mask of OD boundary candidates can be produced by thresholding the image IGM; Aquino, [§III. B. 1, ¶1] ); and
determining, based on the binary image, the effective imaging region edge of the target fundus image (The one with the higher score in the Circular Hough Transform algorithm is then selected as the definitive circular OD boundary approximation (Fig. 3, images R-5 and G-5); Aquino, [§III. B. 2, ¶2]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include separate the color channels, binarize the gray-level image extract the imaging region as taught by Aquino to the invention of Yu. The motivation to do so would be because the different color channels may mask the blood vessels which disrupt OD detection, and utilize a mask for segmentation.
13. The combination of Yu and Aquino teaches the image calibration method according to claim 12. Yu further teaches wherein the performing channel separation to the target fundus image to obtain the grayscale image corresponding to the target fundus image comprises: selecting one or a combination of red, green, and blue color channels as an extraction channel, and performing channel separation to the target fundus image to obtain the grayscale image corresponding to the target fundus image (green channel selection, median filtering, limited contrast enhancement and gray normalization on the fundus image to be inspected; Yu, ¶[0034]).
Claims 14-15 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Yu in view of Aquino and further in view of “Bressler” (Bressler et al., US 20110242306 A1).
14. The combination of Yu and Aquino teaches the image calibration method according to claim 12. The combination does not explicitly disclose wherein the performing channel separation to the target fundus image to obtain the grayscale image corresponding to the target fundus image comprises:
selecting one or a combination of hue, saturation, and brightness attributes as an extraction channel, and
performing channel separation to the target fundus image to obtain the grayscale image corresponding to the target fundus image.
However, Bressler, a similar field of endeavor of automated detection in retinal scans, teaches wherein the performing channel separation to the target fundus image to obtain the grayscale image corresponding to the target fundus image comprises:
selecting one or a combination of hue, saturation, and brightness attributes as an extraction channel, and
performing channel separation to the target fundus image to obtain the grayscale image corresponding to the target fundus image(Analyzing of the retinal data and/or a retinal image may also include using the full RGB data or extracting the green, red and/or blue channels, linear filtering, spectral filtering, nonlinear filtering, low pass filtering, median filtering, performing intensity remapping, performing histogram equalization, performing contrast stretching, performing adaptive histogram equalization, normalizing the data, whitening the data, performing image enhancement, performing thresholding, performing image registration and matching, performing remapping of the original red, green, and blue (RGB) color space into another color space (such as, but not limited to, Hue/Saturation/Value or CYMK), performing stochastic classification of the pixels into different classes (classes may include vessels, background, drusen, etc.), performing band selection on the retinal data, performing dimensionality reduction on the retinal data; Bressler et al., ¶[0081]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a combination of hue, saturation and brightness as taught by Bressler to the combined invention of Yu and Aquino. The motivation to do so would be to enhance detectability of anatomical features.
15. The combination of Yu and Aquino teaches the image calibration method according to claim 12. The combination does not explicitly disclose wherein the performing channel separation to the target fundus image to obtain the grayscale image corresponding to the target fundus image comprises:
selecting a combination of two or more of red, green, and blue color channels, and hue, saturation, and brightness attributes as an extraction channel, and
performing channel separation to the target fundus image to obtain the grayscale image corresponding to the target fundus image.
However, Bressler teaches wherein the performing channel separation to the target fundus image to obtain the grayscale image corresponding to the target fundus image comprises:
selecting a combination of two or more of red, green, and blue color channels, and hue, saturation, and brightness attributes as an extraction channel, and
performing channel separation to the target fundus image to obtain the grayscale image corresponding to the target fundus image (Analyzing of the retinal data and/or a retinal image may also include using the full RGB data or extracting the green, red and/or blue channels, linear filtering, spectral filtering, nonlinear filtering, low pass filtering, median filtering, performing intensity remapping, performing histogram equalization, performing contrast stretching, performing adaptive histogram equalization, normalizing the data, whitening the data, performing image enhancement, performing thresholding, performing image registration and matching, performing remapping of the original red, green, and blue (RGB) color space into another color space (such as, but not limited to, Hue/Saturation/Value or CYMK), performing stochastic classification of the pixels into different classes (classes may include vessels, background, drusen, etc.), performing band selection on the retinal data, performing dimensionality reduction on the retinal data; Bressler et al., ¶[0081]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 Notice of References Cited for full citations.
Liu et al., US 20220130052 A1, teaches image transformation on the color fundus image, and would have been relied upon for teaching the limitations of claim 14 and 15.
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/CHANDHANA PEDAPATI/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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