CA2797266A1 - Spiculated malignant mass detection and classification in radiographic image - Google Patents
Spiculated malignant mass detection and classification in radiographic image Download PDFInfo
- Publication number
- CA2797266A1 CA2797266A1 CA2797266A CA2797266A CA2797266A1 CA 2797266 A1 CA2797266 A1 CA 2797266A1 CA 2797266 A CA2797266 A CA 2797266A CA 2797266 A CA2797266 A CA 2797266A CA 2797266 A1 CA2797266 A1 CA 2797266A1
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- ridge
- computer program
- wedge
- ridges
- convergence
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- 238000000105 evaporative light scattering detection Methods 0.000 title 1
- 230000003211 malignant effect Effects 0.000 title 1
- 239000013598 vector Substances 0.000 claims abstract 6
- 238000004590 computer program Methods 0.000 claims 28
- 238000000034 method Methods 0.000 claims 12
- 238000005259 measurement Methods 0.000 claims 4
- 210000002445 nipple Anatomy 0.000 claims 3
- 238000010191 image analysis Methods 0.000 abstract 1
Classifications
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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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- 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
-
- G06T5/70—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- 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
-
- 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]
-
- 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
-
- 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
-
- 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
-
- 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
Abstract
An image analysis embodiment comprises generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies, detecting ridges in the digital image to generate a detected ridges map, projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction projection maps, determining wedge features for the potential convergence hubs from the set of ridge direction projection maps, selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features, extracting classification features for each of the selected ridge convergence hubs, and classifying the selected ridge convergence hubs based on the extracted classification features.
Claims (20)
1. A method for identifying spiculated anomalies in an image comprising pixels, the method comprising:
generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies;
detecting ridges in the digital image to generate a detected ridges map;
projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction projection maps;
determining wedge features for the potential convergence hubs from the set of ridge direction projection maps;
selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features;
extracting classification features for each of the selected ridge convergence hubs; and classifying the selected ridge convergence hubs based on the extracted classification features.
generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies;
detecting ridges in the digital image to generate a detected ridges map;
projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction projection maps;
determining wedge features for the potential convergence hubs from the set of ridge direction projection maps;
selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features;
extracting classification features for each of the selected ridge convergence hubs; and classifying the selected ridge convergence hubs based on the extracted classification features.
2. The method of claim 1, wherein the image is a mammogram, the bulges are potentially-malignant masses, the spiculated anomalies are potentially-malignant spiculated masses, and the one or more classification features are selected from the group consisting of:
wedge width, radius, signal-to-noise ratio (SNR), minimum hub contrast, nipple distance and y position, and combinations thereof.
wedge width, radius, signal-to-noise ratio (SNR), minimum hub contrast, nipple distance and y position, and combinations thereof.
3. The method of claim 1, wherein detecting the ridges is performed for multiple ridge widths, multiple ridge orientations, and multiple ridge aspect ratios.
4. The method of claim 3, wherein detecting the ridges comprises:
determining an image noise map for each ridge width;
calculating second derivative measurements at each ridge width, orientation and aspect ratio;
determining an average contrast and contrast standard deviation from the second derivative measurements;
determining the SNR from the average contrast and the noise map; and selecting the ridges to include in the detected ridges map based on relative values of at least one of SNR, contrast density, and contrast standard deviation.
determining an image noise map for each ridge width;
calculating second derivative measurements at each ridge width, orientation and aspect ratio;
determining an average contrast and contrast standard deviation from the second derivative measurements;
determining the SNR from the average contrast and the noise map; and selecting the ridges to include in the detected ridges map based on relative values of at least one of SNR, contrast density, and contrast standard deviation.
5. The method of claim 4, further comprising thinning the selected ridges.
6. The method of claim 1, wherein projecting the detected ridges map onto the set of direction maps comprises:
separating the detected ridges map into a line mask image, a row component image, and a column component image; and determining a dot product of the line mask image, the row component image, and the column component image with the directional vectors.
separating the detected ridges map into a line mask image, a row component image, and a column component image; and determining a dot product of the line mask image, the row component image, and the column component image with the directional vectors.
7. The method of claim 1, wherein determining the wedge features comprises:
measuring the wedge features for multiple wedge widths, multiple wedge offsets, and multiple wedge radii; and generating a set of radius/wedge width maps comprising a highest SNR and respective offset for each potential convergence hub.
measuring the wedge features for multiple wedge widths, multiple wedge offsets, and multiple wedge radii; and generating a set of radius/wedge width maps comprising a highest SNR and respective offset for each potential convergence hub.
8. The method of claim 7, wherein selecting ridge convergence hubs comprises:
thresholding each radius/wedge width map to a minimum significant SNR; and thinning contiguous groups of ridge convergence hubs to one ridge convergence hub having maximum relative SNR.
thresholding each radius/wedge width map to a minimum significant SNR; and thinning contiguous groups of ridge convergence hubs to one ridge convergence hub having maximum relative SNR.
9. The method of claim 1, further comprising, before generating the bulge mask, removing bright areas from and flattening an intensity of the digital image.
10. The method of claim 1, further comprising, after classifying the selected ridge convergence hubs, marking on an output image ones of the selected ridge convergence hubs having extracted classification features exceeding a threshold.
11. A system for identifying spiculated anomalies in an image comprising pixels, the method comprising:
a bulge mask generator generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies;
a ridge detector detecting ridges in the digital image to generate a detected ridges map;
a convergence projector projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction projection maps;
a wedge feature calculator determining wedge features for the potential convergence hubs from the set of ridge direction projection maps;
a convergence hub selector selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features;
a feature extractor extracting classification features for each of the selected ridge convergence hubs; and a classifier classifying the selected ridge convergence hubs based on the extracted classification features.
a bulge mask generator generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies;
a ridge detector detecting ridges in the digital image to generate a detected ridges map;
a convergence projector projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction projection maps;
a wedge feature calculator determining wedge features for the potential convergence hubs from the set of ridge direction projection maps;
a convergence hub selector selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features;
a feature extractor extracting classification features for each of the selected ridge convergence hubs; and a classifier classifying the selected ridge convergence hubs based on the extracted classification features.
12. The system of claim 11, wherein the image is a mammogram, the bulges are potentially-malignant masses, the spiculated anomalies are potentially-malignant spiculated masses, and the one or more classification features are selected from the group consisting of:
wedge width, radius, signal-to-noise ratio (SNR), minimum hub contrast, nipple distance and y position, and combinations thereof.
wedge width, radius, signal-to-noise ratio (SNR), minimum hub contrast, nipple distance and y position, and combinations thereof.
13. The system of claim 11, wherein the ridge detector detects ridges for multiple ridge widths, multiple ridge orientations, and multiple ridge aspect ratios.
14. The system of claim 11, wherein the convergence projector separates the detected ridges map into a line mask image, a row component image, and a column component image, and determines a dot product of the line mask image, the row component image, and the column component image with the directional vectors.
15. The system of claim 11, wherein the wedge feature calculator measures the wedge features for multiple wedge widths, multiple wedge offsets, and multiple wedge radii, and generates a set of radius/wedge width maps comprising a highest SNR and respective offset for each potential convergence hub.
16. A computer program product for identifying spiculated anomalies in an image comprising pixels, 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 generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies;
computer program code for detecting ridges in the digital image to generate a detected ridges map;
computer program code for projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction projection maps;
computer program code for determining wedge features for the potential convergence hubs from the set of ridge direction projection maps;
computer program code for selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features;
computer program code for extracting classification features for each of the selected ridge convergence hubs; and computer program code for classifying the selected ridge convergence hubs based on the extracted classification features.
computer program code for generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies;
computer program code for detecting ridges in the digital image to generate a detected ridges map;
computer program code for projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction projection maps;
computer program code for determining wedge features for the potential convergence hubs from the set of ridge direction projection maps;
computer program code for selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features;
computer program code for extracting classification features for each of the selected ridge convergence hubs; and computer program code for classifying the selected ridge convergence hubs based on the extracted classification features.
17. The computer program product of claim 16, wherein the image is a mammogram, the bulges are potentially-malignant masses, the spiculated anomalies are potentially-malignant spiculated masses, and the one or more classification features are selected from the group consisting of: wedge width, radius, signal-to-noise ratio (SNR), minimum hub contrast, nipple distance and y position, and combinations thereof.
18. The computer program product of claim 16, wherein the computer program code for detecting the ridges comprises:
computer program code for detecting the ridges at multiple ridge widths, multiple ridge orientations, and multiple ridge aspect ratios;
computer program code for determining an image noise map for each ridge width;
computer program code for calculating second derivative measurements at each ridge width, orientation and aspect ratio;
computer program code for determining an average contrast and contrast standard deviation from the second derivative measurements;
computer program code for determining the SNR from the average contrast and the noise map; and computer program code for selecting the ridges to include in the detected ridges map based on relative values of at least one of SNR, contrast density, and contrast standard deviation.
computer program code for detecting the ridges at multiple ridge widths, multiple ridge orientations, and multiple ridge aspect ratios;
computer program code for determining an image noise map for each ridge width;
computer program code for calculating second derivative measurements at each ridge width, orientation and aspect ratio;
computer program code for determining an average contrast and contrast standard deviation from the second derivative measurements;
computer program code for determining the SNR from the average contrast and the noise map; and computer program code for selecting the ridges to include in the detected ridges map based on relative values of at least one of SNR, contrast density, and contrast standard deviation.
19. The computer program product of claim 16, wherein the computer program code for determining the wedge features comprises:
computer program code for measuring the wedge features for multiple wedge widths, multiple wedge offsets, and multiple wedge radii; and computer program code for generating a set of radius/wedge width maps comprising a highest SNR and respective offset for each potential convergence hub.
computer program code for measuring the wedge features for multiple wedge widths, multiple wedge offsets, and multiple wedge radii; and computer program code for generating a set of radius/wedge width maps comprising a highest SNR and respective offset for each potential convergence hub.
20. The computer program product of claim 19, wherein the computer program code for selecting ridge convergence hubs comprises:
computer program code for thresholding each radius/wedge width map to a minimum significant SNR; and computer program code for thinning contiguous groups of ridge convergence hubs to one ridge convergence hub having maximum relative SNR.
computer program code for thresholding each radius/wedge width map to a minimum significant SNR; and computer program code for thinning contiguous groups of ridge convergence hubs to one ridge convergence hub having maximum relative SNR.
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 | |
| US34355210P | 2010-05-02 | 2010-05-02 | |
| US34360810P | 2010-05-02 | 2010-05-02 | |
| US61/343,609 | 2010-05-02 | ||
| US61/343,608 | 2010-05-02 | ||
| US61/343,552 | 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/US2011/034699 WO2011137410A1 (en) | 2010-04-30 | 2011-04-29 | Spiculated malignant mass detection and classification in radiographic image |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2797266A1 true CA2797266A1 (en) | 2011-11-03 |
Family
ID=44861947
Family Applications (4)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2797240A Abandoned CA2797240A1 (en) | 2010-04-30 | 2011-04-29 | Malignant mass detection and classification in radiographic images |
| 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 |
| CA2797238A Abandoned CA2797238A1 (en) | 2010-04-30 | 2011-04-29 | Microcalcification detection and classification in radiographic images |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2797240A Abandoned CA2797240A1 (en) | 2010-04-30 | 2011-04-29 | Malignant mass detection and classification in radiographic images |
Family Applications After (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2797267A Abandoned CA2797267A1 (en) | 2010-04-30 | 2011-04-29 | Probability density function estimator |
| CA2797238A Abandoned CA2797238A1 (en) | 2010-04-30 | 2011-04-29 | Microcalcification detection and classification in radiographic images |
Country Status (6)
| Country | Link |
|---|---|
| US (7) | US9262822B2 (en) |
| EP (4) | EP2564355A1 (en) |
| JP (4) | JP5927180B2 (en) |
| CN (4) | CN102947841A (en) |
| CA (4) | CA2797240A1 (en) |
| WO (4) | WO2011137410A1 (en) |
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