CA2797238A1 - Microcalcification detection and classification in radiographic images - Google Patents
Microcalcification detection and classification in radiographic images Download PDFInfo
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- CA2797238A1 CA2797238A1 CA2797238A CA2797238A CA2797238A1 CA 2797238 A1 CA2797238 A1 CA 2797238A1 CA 2797238 A CA2797238 A CA 2797238A CA 2797238 A CA2797238 A CA 2797238A CA 2797238 A1 CA2797238 A1 CA 2797238A1
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- 208000004434 Calcinosis Diseases 0.000 title 1
- 238000001514 detection method Methods 0.000 title 1
- 238000004590 computer program Methods 0.000 claims 18
- 238000000034 method Methods 0.000 claims 8
- 238000001356 surgical procedure Methods 0.000 claims 5
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/10—Image enhancement or restoration by non-spatial domain filtering
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/143—Segmentation; Edge detection involving probabilistic approaches, e.g. Markov random field [MRF] modelling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
- G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
- G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
- G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
- G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10116—X-ray image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30068—Mammography; Breast
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30096—Tumor; Lesion
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- Apparatus For Radiation Diagnosis (AREA)
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Abstract
An analysis of a digitized image is provided. The digitized image is repeatedly convolved to form first convolved images, which first convolved images are convolved a second time to form second convolved images. Each first convolved image and the respective second convolved image representing a stage, and each stage represents a different scale or size of anomaly. As an example, the first convolution may utilize a Gaussian convolver, and the second convolution may utilize a Laplacian convolver, but other convolvers may be used. The second convolved image from a current stage and the first convolved image from a previous stage are used with a neighborhood median determined from the second convolved image from the current stage by a peak detector to detect peaks, or possible anomalies for that particular scale.
Claims (20)
1. A method for detecting an anomaly in an image, the method comprising:
convolving a digital image to create a plurality of first convolved images at differing scales;
convolving each of the plurality of first convolved images, thereby creating a plurality of second convolved images, each of the plurality of first convolved images and a corresponding one of the plurality of second convolved images corresponding to respective ones of a plurality of stages;
creating a plurality of surgery masks, each surgery mask being based at least in part on one of the plurality of the second convolved images of a current stage and one of the plurality of second convolved images from a previous stage;
determining a neighborhood median for each pixel location of the plurality of surgery masks; and identifying one or more peaks based at least upon the second convolved image from the current stage, the first convolved image from the previous stage, and the neighborhood medians for the current stage.
convolving a digital image to create a plurality of first convolved images at differing scales;
convolving each of the plurality of first convolved images, thereby creating a plurality of second convolved images, each of the plurality of first convolved images and a corresponding one of the plurality of second convolved images corresponding to respective ones of a plurality of stages;
creating a plurality of surgery masks, each surgery mask being based at least in part on one of the plurality of the second convolved images of a current stage and one of the plurality of second convolved images from a previous stage;
determining a neighborhood median for each pixel location of the plurality of surgery masks; and identifying one or more peaks based at least upon the second convolved image from the current stage, the first convolved image from the previous stage, and the neighborhood medians for the current stage.
2. The method of claim 1, wherein the creating the plurality of surgery masks based at least in part on one of the plurality of second convolved images from the previous stage is performed at least in part by using the neighborhood median from the previous stage.
3. The method of claim 2, wherein the creating the plurality of surgery masks based at least in part on one of the plurality of second convolved images from the previous stage is performed at least in part by comparing, for each pixel of respective ones of the plurality of second convolved images, a pixel relative contrast to a threshold.
4. The method of claim 3, wherein the pixel relative contrast is determined at least in part by dividing the pixel value of the respective second convolved images of the current stage by a neighborhood median of a same pixel location of a previous stage.
5. The method of claim 1, further comprising excluding a peak detected at a smaller scale from a larger scale.
6. The method of claim 1, wherein the digitized image is a scaled image.
7. The method of claim 1, wherein a standard deviation doubles from one scale to a next scale.
8. A system for identifying anomalies in a digitized image, the system comprising:
a first convolver convolving a digitized image, creating a first convolved image;
a second convolver convolving the first convolved image, creating a second convolved image; and a peak detector receiving the digitized image and the second convolved image, the peak detector identifying pixels having peak values.
a first convolver convolving a digitized image, creating a first convolved image;
a second convolver convolving the first convolved image, creating a second convolved image; and a peak detector receiving the digitized image and the second convolved image, the peak detector identifying pixels having peak values.
9. The system of claim 8, wherein the digitized image is a convolved image convolved by the first convolver in a previous stage.
10. The system of claim 8, further comprising a neighborhood median calculator coupled to the peak detector for providing neighborhood medians for each pixel.
11. The system of claim 10, further comprising an extractor coupled to the second convolver and the neighborhood median calculator, the extractor removing pixels having a pixel relative contrast greater than a threshold from the second convolved image, the neighborhood median calculator using at least in part the second convolved image after the extractor removes the pixels.
12. The system of claim 11, wherein the pixel relative contrast is determined at least in part by dividing the pixel value of the respective second convolved image of the current stage by a neighborhood median of a same pixel location based upon a previous stage second convolved image.
13. The system of claim 8, further comprising a thresholder coupled to the peak detector for receiving the pixels and corresponding peak values, the thresholder comparing the corresponding peak values to a threshold.
14. The system of claim 13, wherein the threshold is a multiple of a global median absolute deviation.
15. A computer program product for identifying anomalies, the computer program product having a non-transitory computer-readable medium with a computer program embodied thereon, the computer program comprising:
computer program code for convolving a digitized image creating a plurality of first convolved images, each of the convolved images corresponding to a different scale;
computer program code for convolving each of the plurality of first convolved images, creating a plurality of second convolved images;
computer program code for determining a neighborhood median for each pixel of each of the plurality of second convolved images; and computer program code for identifying peak regions based upon one of the plurality of first convolved images and one of the second convolved images, the one of the second convolved images corresponding to the one of the plurality of first convolved images after being convolved by the convolving a digitized image and the convolving each of the plurality of first convolved images.
computer program code for convolving a digitized image creating a plurality of first convolved images, each of the convolved images corresponding to a different scale;
computer program code for convolving each of the plurality of first convolved images, creating a plurality of second convolved images;
computer program code for determining a neighborhood median for each pixel of each of the plurality of second convolved images; and computer program code for identifying peak regions based upon one of the plurality of first convolved images and one of the second convolved images, the one of the second convolved images corresponding to the one of the plurality of first convolved images after being convolved by the convolving a digitized image and the convolving each of the plurality of first convolved images.
16. The computer program product of claim 15, wherein the plurality of first convolved images represent Gaussian-blurred images.
17. The computer program product of claim 15, wherein the plurality of second convolved images represent Laplacian curvature images.
18. The computer program product of claim 15, wherein the computer program code for determining the neighborhood median includes computer program code for excluding peak regions identified in a previous scale.
19. The computer program product of claim 15, further comprising computer program code for excluding pixels from the plurality of second convolved images used for determining the neighborhood median.
20. The computer program product of claim 19, wherein the computer program code for excluding pixels includes computer program code for comparing a pixel relative contrast to the neighborhood median.
Applications Claiming Priority (17)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US34355710P | 2010-04-30 | 2010-04-30 | |
| US61/343,557 | 2010-04-30 | ||
| US34360910P | 2010-05-02 | 2010-05-02 | |
| US34360810P | 2010-05-02 | 2010-05-02 | |
| US34355210P | 2010-05-02 | 2010-05-02 | |
| US61/343,552 | 2010-05-02 | ||
| US61/343,608 | 2010-05-02 | ||
| US61/343,609 | 2010-05-02 | ||
| US39502910P | 2010-05-06 | 2010-05-06 | |
| US61/395,029 | 2010-05-06 | ||
| US39857110P | 2010-06-25 | 2010-06-25 | |
| US61/398,571 | 2010-06-25 | ||
| US39909410P | 2010-07-07 | 2010-07-07 | |
| US61/399,094 | 2010-07-07 | ||
| US40057310P | 2010-07-28 | 2010-07-28 | |
| US61/400,573 | 2010-07-28 | ||
| PCT/US2011/034696 WO2011137407A1 (en) | 2010-04-30 | 2011-04-29 | Microcalcification detection and classification in radiographic images |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2797238A1 true CA2797238A1 (en) | 2011-11-03 |
Family
ID=44861947
Family Applications (4)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2797266A Abandoned CA2797266A1 (en) | 2010-04-30 | 2011-04-29 | Spiculated malignant mass detection and classification in radiographic image |
| CA2797267A Abandoned CA2797267A1 (en) | 2010-04-30 | 2011-04-29 | Probability density function estimator |
| CA2797240A Abandoned CA2797240A1 (en) | 2010-04-30 | 2011-04-29 | Malignant mass detection and classification in radiographic images |
| CA2797238A Abandoned CA2797238A1 (en) | 2010-04-30 | 2011-04-29 | Microcalcification detection and classification in radiographic images |
Family Applications Before (3)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2797266A Abandoned CA2797266A1 (en) | 2010-04-30 | 2011-04-29 | Spiculated malignant mass detection and classification in radiographic image |
| CA2797267A Abandoned CA2797267A1 (en) | 2010-04-30 | 2011-04-29 | Probability density function estimator |
| CA2797240A Abandoned CA2797240A1 (en) | 2010-04-30 | 2011-04-29 | Malignant mass detection and classification in radiographic images |
Country Status (6)
| Country | Link |
|---|---|
| US (7) | US8923594B2 (en) |
| EP (4) | EP2564353A1 (en) |
| JP (4) | JP5864542B2 (en) |
| CN (4) | CN102934128A (en) |
| CA (4) | CA2797266A1 (en) |
| WO (4) | WO2011137410A1 (en) |
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