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 .
Claim Objections
Claim 16 is objected to because of the following informalities: the claim limitation “An apparatus (10)” should recite “An apparatus” without the reference number. 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.
Claim 10-11 and 14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by non-patent literature "Localized Energy-Based Normalization of Medical Images: Application to Chest Radiography"; IEEE Transactions on Medical Imaging, vol. 34, no. 9, pp. 1965-1975, Sept. 2015, doi: 10.1109/TMI.2015.2418031 (Philipsen et al.) (hereinafter Philipsen).
Regarding claim 10, Philipsen teaches a computer-implemented method for training a medical image analysis algorithm, the method comprising: (Philipsen, abstract: “Automated quantitative analysis systems for medical images often lack the capability to successfully process images from multiple sources. Normalization of such images prior to further analysis is a possible solution to this limitation. This work presents a general method to normalize medical images and thoroughly investigates its effectiveness for chest radiography (CXR) … We conclude that the normalization can be successfully applied in chest radiography and makes supervised systems more generally applicable to data from different sources”)
receiving a medical image of a body part of a patient (Philipsen, page 1967, FIG. 1 and FIG. 1:
PNG
media_image1.png
350
1020
media_image1.png
Greyscale
;
“Fig. 1. Two images from each of the six datasets are displayed. The top row contains normal CXRs and the bottom row contains abnormal CXRs. From left to right the images come from: odelcadr, atomed, philips, siemens, odelcadr-kv and JSRT. Except for the JSRT images, all images are shown using original DICOM window center/width settings”)
generating a modified medical image (Philipsen, abstract; page 1968, Section IV. Evaluation Design, para. 2; FIG. 3: “The method starts with an energy decomposition of the image in different bands. Next, each band's localized energy is scaled to a reference value and the image is reconstructed.”; “A set of R=50 images from the same scanner as dataset B were selected as reference images as this was the main source of data. All images in this set were normal images. An initial set of pilot experiments, with varying B in the range of 3 to 9, was conducted to determine the optimal number of frequency bands and we chose B=6 as the optimal number of bands. Fig. 3 shows an example of the frequency decomposition”;
PNG
media_image2.png
248
1024
media_image2.png
Greyscale
; and
training a medical image analysis machine learning algorithm utilizing the modified medical image (Philipsen, page 1969, Section 3) Results; FIG. 6; page 1968, para. 1; FIG. 2: “For each method and for each training set, the overlap results of all images are shown in Fig. 6. The figure is divided in six columns with four boxplots: one column for each source of training. The blue, red, magenta and green boxes represent the results of the baseline method, histogram equalization, normalization with one iteration and the iterated normalization, respectively. Table II shows all values of Pk(di,dj) and it's variation. Less brightly colored cells in the columns implies that the segmentation performance is less dependent on the utilized training set. From the table and boxplots it is seen that the segmentation performance has improved compared to the baseline and histogram equalization when the proposed normalization method is used.”;
PNG
media_image3.png
98
508
media_image3.png
Greyscale
;
PNG
media_image4.png
508
506
media_image4.png
Greyscale
;
“After this first stage a rough lung segmentation can be obtained from a supervised method [2], and
the resulting lung region was set as the region of interest in the second stage to give optimal lung standardization. The process is summarized in the flowchart in Fig. 2.”;
PNG
media_image5.png
148
498
media_image5.png
Greyscale
).
wherein the generating comprises a modification of two or more spatial frequency bands associated with the medical image (Philipsen, abstract; page 1968, Section IV. Evaluation Design, para. 2; FIG. 3; see rejection above in the step of generating a modified medical image).
Regarding claim 14, Philipsen teaches a computer-implemented medical image analysis method, comprising: (Philipsen, abstract: “Automated quantitative analysis systems for medical images often lack the capability to successfully process images from multiple sources. Normalization of such images prior to further analysis is a possible solution to this limitation. This work presents a general method to normalize medical images and thoroughly investigates its effectiveness for chest radiography (CXR) … We conclude that the normalization can be successfully applied in chest radiography and makes supervised systems more generally applicable to data from different sources”)
receiving an examination medical image of a body part of a patient (Philipsen, page 1967, FIG. 1:
PNG
media_image1.png
350
1020
media_image1.png
Greyscale
;
“Fig. 1. Two images from each of the six datasets are displayed.”);
analyzing the body part of the patient, wherein the analyzing comprises interrogating the examination medical image with a trained machine learning algorithm (Philipsen page 1968, para. 1; FIG. 2: “After this first stage a rough lung segmentation can be obtained from a supervised method [2], and the resulting lung region was set as the region of interest in the second stage to give optimal lung standardization. The process is summarized in the flowchart in Fig. 2.”;
PNG
media_image5.png
148
498
media_image5.png
Greyscale
);
wherein the machine learning algorithm was trained by: receiving the medical image of a body part of a patient; generating a modified medical image (Philipsen, abstract; page 1968, Section IV. Evaluation Design, para. 2; FIG. 3: “The method starts with an energy decomposition of the image in different bands. Next, each band's localized energy is scaled to a reference value and the image is reconstructed.”; “A set of R=50 images from the same scanner as dataset B were selected as reference images as this was the main source of data. All images in this set were normal images. An initial set of pilot experiments, with varying B in the range of 3 to 9, was conducted to determine the optimal number of frequency bands and we chose B=6 as the optimal number of bands. Fig. 3 shows an example of the frequency decomposition”;
PNG
media_image2.png
248
1024
media_image2.png
Greyscale
); and
training the machine learning algorithm utilizing the modified medical image, wherein the generating comprises a modification of two or more spatial frequency bands associated with the medical image (Philipsen, abstract; page 1968, Section IV. Evaluation Design, para. 2; FIG. 3; see above discussion different spatial frequency band modification; page 1969, Section 3) Results; FIG. 6; page 1968, para. 1; FIG. 2: “For each method and for each training set, the overlap results of all images are shown in Fig. 6. The figure is divided in six columns with four boxplots: one column for each source of training. The blue, red, magenta and green boxes represent the results of the baseline method, histogram equalization, normalization with one iteration and the iterated normalization, respectively. Table II shows all values of Pk(di,dj) and it's variation. Less brightly colored cells in the columns implies that the segmentation performance is less dependent on the utilized training set. From the table and boxplots it is seen that the segmentation performance has improved compared to the baseline and histogram equalization when the proposed normalization method is used.”;
PNG
media_image3.png
98
508
media_image3.png
Greyscale
;
PNG
media_image4.png
508
506
media_image4.png
Greyscale
;
“After this first stage a rough lung segmentation can be obtained from a supervised method [2], and
the resulting lung region was set as the region of interest in the second stage to give optimal lung standardization. The process is summarized in the flowchart in Fig. 2.”;
PNG
media_image5.png
148
498
media_image5.png
Greyscale
).
Regarding claim 11, Philipsen teaches the method according to claim 10, further comprising:
generating a scaled image set comprising a plurality of scaled images, wherein the generating comprises utilizing the medical image, wherein each scaled image comprises a representation of a spatial frequency in the medical image, and wherein the representation of the spatial frequency in each of the plurality of scaled images is different; generating a modified scaled image set from the scaled image set, wherein the generating comprises modifying two or more scaled images of the plurality of scaled images; and generating the modified medical image comprising utilizing the modified scaled image set (Philipsen, abstract; page 1968, Section IV. Evaluation Design, para. 2; FIG. 3; see rejection of claim 10 above showing the different modified images with different frequency bands that are all scaled; page 1971, Section VI. Discussion, para. 2; page 1974, Conclusion: “Two key elements in the proposed method are the applied energy band scaling and the addition of a region of interest … The decomposition into frequency bands provides a separation of structures of different sizes, which is a useful property, as it allows for applying specific scaling factors to each band, which can enhance or suppress specific structures … By taking all λi(Ω) values equal to a reference value, the image's frequency information is standardized among images, which gives them similar appearance and intensity characteristics.”; “The method uses an energy decomposition of the image, after which the energy of each band is scaled separately to a reference energy to acquire a normalized image.”).
Allowable Subject Matter
Claim 16 is allowed under the condition Applicant addresses the claim objection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL ADAM SHARIFF whose telephone number is 571-272-9741. The examiner can normally be reached M-F 8:30-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached on 571-272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHAEL ADAM SHARIFF/
Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
Prosecution Insights