Breast Cancer Diagnosis System Based on Semantic Analysis and Choquet Integral Feature Selection for High Risk Subjects
Abstract
:1. Introduction
2. Material and Methods
2.1. Mass Segmentation
2.2. Feature Extraction
- Compactness is used to quantify the connection between portions of a region. A highly non-convex lesion (malignant) have a high compactness index, whereas benign lesions have a low compactness value index.
- Roundness is a measure of the similarity of an object shape to a circle. Shape with a roundness index closer to 1 indicates that the mass is approximately round so it is rather benign.
- Eccentricity is the measure of aspect ratio of a region. It is defined by the ratio of the major axis to the minor axis. A shape with an eccentricity index too close to 1 is almost a circle.
- Zernike moments are used as an object descriptor in several pattern recognition systems, edge detection and image retrieval applications with significant results. It allows us to represent image properties without redundancy and overlap of information between the moments thanks to complex kernel functions based on Zernike polynomials orthogonal to each other. The discrete form of the Zernike moments of an image size N × N is defined as follows:
- GFD extracted from spectral domain by applying 2D Fourier transform on polar raster sampled shape image. It allows multi-resolution feature analysis in both radial and angular directions. The GFD, based on the polar Fourier (PF), is defined as:
2.3. Feature Selection
2.3.1. Choquet Integral Selection
2.3.2. Learning Step
2.3.3. Extraction Step
2.4. Bag of Words Modeling
2.5. Image Annotation
3. Results and Discussion
3.1. Database
3.2. Feature Selection
3.3. A Comparison between Choquet Intagral for Feature Selection and Other Selection Method
3.3.1. Structural Selection
3.3.2. Inter-Modal Selection
3.4. Labeling Results
3.4.1. Performance Metrics
3.4.2. Texture Annotation
3.4.3. Shape Annotation
3.5. Decision Making and Discussion
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
MRI | Magnetic resonance imaging |
DECEDM | Dual energy contrast enhanced digital mammography |
CAD | Computer aided diagnosis |
BoW | Bag of words |
KBS | Knowledge-based systems |
FCM | Fuzzy C-means clustering |
DRLSE | Distance regularized level set evolution |
LSF | Level set function |
GLCM | Gray level co-occurrence matrix |
GFD | Generic Fourier descriptor |
ZM | Zernike moments |
OWA | Ordered weighted average |
SWA | Simple weighted average |
QAM | Quasi arithmetic means |
CEA | Contrast enhancement agent |
BIRADS | Breast imaging reporting and data system |
ACR | American college of radiology |
DT | Decision tree |
KNN | K-nearest neighbors |
SVM | Support vector machine |
NB | Naive Bayes |
SMO | Sequential minimal optimization |
SFFS | Sequential floating forward selection |
SFS | Sequential forward selection |
SBS | Sequential backward selection |
SFBS | Sequential floating backward selection |
CCR | Correct classification rate |
ROC | Receiver operating characteristics |
AUC | Area under the curve |
TPR | True positive rate |
TNR | True negative rate |
PPV | Positive predictive value |
NPV | Negative predictive value |
ANN | Artificial neural network |
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Lesion | Class | ||
---|---|---|---|
L1 | 0.76 | 0.61 | B |
L2 | 0.61 | 0.82 | M |
L3 | 0.74 | 0.72 | B |
L4 | 0.62 | 0.62 | M |
L5 | 0.58 | 0.63 | M |
L6 | 0.79 | 0.67 | B |
L7 | 0.49 | 0.79 | M |
L8 | 0.54 | 0.82 | M |
L9 | 0.63 | 0.76 | M |
L10 | 0.66 | 0.82 | M |
L11 | 0.54 | 0.80 | M |
L12 | 0.54 | 0.81 | M |
L13 | 0.57 | 0.81 | M |
Shapley Values | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Cr | Ep | MP | Ds | Hg | Eg | CS | Vr | CP | Ac | HOG | LBP | Ct | 13 features |
0.069 | 0.072 | 0.072 | 0.076 | 0.076 | 0.076 | 0.076 | 0.077 | 0.077 | 0.079 | 0.079 | 0.080 | 0.090 | 0.077 |
Interaction Values | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Ct | Cr | Eg | Hg | Ep | Ac | CP | CS | Ds | MP | Vr | |
Ct | - | 7.56× 10 | −1.42× 10 | −2.19× 10 | −7.37× 10 | 1.24× 10 | −2.96× 10 | −1.19× 10 | 3.03× 10 | 7.60× 10 | 1.91× 10 |
Cr | 7.56× 10 | - | 1.49× 10 | 4.69× 10 | 1.93× 10 | −5.29× 10 | −2.17× 10 | −1.12× 10 | 1.67× 10 | 1.25× 10 | 7.51× 10 |
Eg | −1.42× 10 | 1.49× 10 | - | −2.80× 10 | −1.26× 10 | −5.78× 10 | −2.14× 10 | −8.45× 10 | 7.90× 10 | −2.55× 10 | 3.30× 10 |
Hg | −2.19× 10 | 4.69× 10 | −2.80× 10 | - | 3.88× 10 | 1.16× 10 | −1.42× 10 | −1.48× 10 | 1.15× 10 | 7.51× 10 | 1.81× 10 |
Ep | −7.37× 10 | 1.93× 10 | −1.26× 10 | 3.88× 10 | - | 2.09× 10 | 4.43× 10 | 4.06× 10 | −1.54× 10 | −1.89× 10 | −2.43× 10 |
Ac | 1.24× 10 | −5.29× 10 | −5.78× 10 | 1.16× 10 | 2.09× 10 | - | −1.97× 10 | −2.24× 10 | −1.52× 10 | 2.08× 10 | −1.94× 10 |
CP | −2.96× 10 | −2.17× 10 | −2.14× 10 | −1.42× 10 | 4.43× 10 | −1.97× 10 | - | 5.93× 10 | 2.27× 10 | 1.32× 10 | 5.83× 10 |
CS | −1.19× 10 | −1.12× 10 | −8.45× 10 | −1.48× 10 | 4.06× 10 | −2.24× 10 | 5.93× 10 | - | 4.50× 10 | 2.95× 10 | 2.90× 10 |
Ds | 3.03× 10 | 1.67× 10 | 7.90× 10 | 1.15× 10 | −1.54× 10 | −1.52× 10 | 2.27× 10 | 4.50× 10 | - | −2.11× 10 | −1.55× 10 |
MP | 7.60× 10 | 1.25× 10 | −2.55× 10 | 7.51× 10 | −1.89× 10 | 2.08× 10 | 1.32× 10 | 2.95× 10 | −2.11× 10 | - | −1.89× 10 |
Vr | 1.91× 10 | 7.51× 10 | 3.30× 10 | 1.81× 10 | −2.43× 10 | −1.94× 10 | 5.83× 10 | 2.90× 10 | −1.55× 10 | −1.89× 10 | - |
LBP | 8.31× 10 | −5.73× 10 | 2.82× 10 | 1.38× 10 | 5.76× 10 | 1.16× 10 | 7.44× 10 | −3.78× 10 | −1.57× 10 | −1.82× 10 | −1.18× 10 |
HOG | −5.02× 10 | −1.50× 10 | 2.59× 10 | 5.43× 10 | 1.06× 10 | 1.18× 10 | 7.69× 10 | −4.35× 10 | −1.35× 10 | 7.12× 10 | −3.71× 10 |
LBP | HOG | ||||||||||
Ct | 8.31× 10 | −5.02× 10 | |||||||||
Cr | −5.73× 10 | −1.50× 10 | |||||||||
Eg | 2.82× 10 | 2.59× 10 | |||||||||
Hg | 1.38× 10 | 5.43× 10 | |||||||||
Ep | 5.76× 10 | 1.06× 10 | |||||||||
Ac | 1.16× 10 | 1.18× 10 | |||||||||
CP | 7.44× 10 | 7.69× 10 | |||||||||
CS | −3.78× 10 | −4.35× 10 | |||||||||
Ds | −1.57× 10 | −1.35× 10 | |||||||||
MP | −1.82× 10 | 7.12× 10 | |||||||||
Vr | −1.18× 10 | −3.71× 10 | |||||||||
LBP | - | 2.17× 10 | |||||||||
HOG | 2.17× 10 | - |
Selection Method | CCR |
---|---|
SFS | 0.92 |
SFFS | 0.93 |
SBS | 0.89 |
SFBS | 0.86 |
Choquet | 0.99 |
Ite | N | F | CCR | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ct | Cr | Eg | Hg | Ep | Ac | CP | CS | Ds | MP | Vr | LBP | HOG | |||
1 | 13 | 0.85 | |||||||||||||
1 | 12 | X | 0.85 | ||||||||||||
2 | 11 | X | X | 0.85 | |||||||||||
3 | 10 | X | X | X | 0.85 | ||||||||||
4 | 9 | X | X | X | X | 0.85 | |||||||||
5 | 8 | X | X | X | X | X | 0.85 | ||||||||
6 | 7 | X | X | X | X | X | X | 0.92 | |||||||
7 | 6 | X | X | X | X | X | X | X | 0.92 | ||||||
8 | 5 | X | X | X | X | X | X | X | X | 0.92 | |||||
9 | 4 | X | X | X | X | X | X | X | X | X | 0.92 |
Ite | N | F | CCR | ||||
---|---|---|---|---|---|---|---|
Rd | Cp | Ec | ZM | GFD | |||
1 | 5 | 0.92 | |||||
2 | 4 | X | 0.92 | ||||
3 | 2 | X | X | X | 0.92 |
Ite | N | F | CCR | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ct | Cr | Eg | Hg | Ep | Ac | CP | CS | Ds | MP | Vr | LBP | HOG | |||
1 | 13 | 0.85 | |||||||||||||
1 | 12 | X | 0.85 | ||||||||||||
2 | 11 | X | X | 0.85 | |||||||||||
3 | 10 | X | X | X | 0.85 | ||||||||||
4 | 9 | X | X | X | X | 0.85 | |||||||||
5 | 8 | X | X | X | X | X | 0.85 | ||||||||
6 | 7 | X | X | X | X | X | X | 0.92 | |||||||
7 | 6 | X | X | X | X | X | X | X | 0.92 | ||||||
8 | 5 | X | X | X | X | X | X | X | X | 0.92 | |||||
9 | 4 | X | X | X | X | X | X | X | X | X | 0.92 |
Ite | N | F | CCR | ||||
---|---|---|---|---|---|---|---|
Rd | Cp | Ec | ZM | GFD | |||
1 | 5 | 0.92 | |||||
2 | 4 | X | 0.92 | ||||
3 | 2 | X | X | X | X | 0.92 |
Ite | N | F | CCR | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Ct1 | Hg1 | LBP1 | HOG1 | Ct2 | Ac2 | LBP2 | HOG2 | |||
1 | 8 | 0.92 | ||||||||
2 | 6 | X | X | 0.92 | ||||||
3 | 5 | X | X | X | 0.92 |
Shapley Values | |||||||||
---|---|---|---|---|---|---|---|---|---|
Ite | Ct1 | Hg1 | LBP1 | HOG1 | Ct2 | Ac2 | LBP2 | HOG2 | 1/N |
1 | 0.121 | 0.127 | 0.130 | 0.129 | 0.121 | 0.122 | 0.125 | 0.125 | 0.125 |
2 | - | 0.165 | 0.171 | 0.171 | - | 0.160 | 0.166 | 0.167 | 0.167 |
3 | - | 0.200 | 0.200 | 0.200 | - | - | 0.200 | 0.200 | 0.200 |
KNN | ANN | SVM | DT | ||
---|---|---|---|---|---|
BoW | CCR | 93% | 97% | 99% | 99% |
p-value | 0.02 | - |
KNN | ANN | SVM | DT | |
---|---|---|---|---|
Texture and shape without selection | 93% | 90% | 93% | 87% |
Texture and shape selected | 93% | 96.7% | 99% | 93% |
BoW | 93% | 97% | 99% | 99% |
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Trabelsi Ben Ameur, S.; Sellami, D.; Wendling, L.; Cloppet, F. Breast Cancer Diagnosis System Based on Semantic Analysis and Choquet Integral Feature Selection for High Risk Subjects. Big Data Cogn. Comput. 2019, 3, 41. https://doi.org/10.3390/bdcc3030041
Trabelsi Ben Ameur S, Sellami D, Wendling L, Cloppet F. Breast Cancer Diagnosis System Based on Semantic Analysis and Choquet Integral Feature Selection for High Risk Subjects. Big Data and Cognitive Computing. 2019; 3(3):41. https://doi.org/10.3390/bdcc3030041
Chicago/Turabian StyleTrabelsi Ben Ameur, Soumaya, Dorra Sellami, Laurent Wendling, and Florence Cloppet. 2019. "Breast Cancer Diagnosis System Based on Semantic Analysis and Choquet Integral Feature Selection for High Risk Subjects" Big Data and Cognitive Computing 3, no. 3: 41. https://doi.org/10.3390/bdcc3030041