TY - JOUR
T1 - Asymmetry analysis of breast thermograms using automated segmentation and texture features
AU - Sathish, Dayakshini
AU - Kamath, Surekha
AU - Prasad, Keerthana
AU - Kadavigere, Rajagopal
AU - Martis, Roshan J.
PY - 2017/5/1
Y1 - 2017/5/1
N2 - In this article, we present a new approach for breast thermogram image analysis by developing a fully automatic segmentation of right and left breast for asymmetry analysis, using shape features of the breast and Polynomial curve fitting. Segmentation results are validated with their respective Ground Truths. Histogram and grey level co-occurrence matrix-based texture features are extracted from the segmented images. Statistical test shows that features are highly significant in detection of breast cancer. We have obtained an accuracy of 90%, sensitivity of 87.5% and specificity of 92.5% for a set of eighty images with forty normal and forty abnormal using SVM RBF classifier.
AB - In this article, we present a new approach for breast thermogram image analysis by developing a fully automatic segmentation of right and left breast for asymmetry analysis, using shape features of the breast and Polynomial curve fitting. Segmentation results are validated with their respective Ground Truths. Histogram and grey level co-occurrence matrix-based texture features are extracted from the segmented images. Statistical test shows that features are highly significant in detection of breast cancer. We have obtained an accuracy of 90%, sensitivity of 87.5% and specificity of 92.5% for a set of eighty images with forty normal and forty abnormal using SVM RBF classifier.
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U2 - 10.1007/s11760-016-1018-y
DO - 10.1007/s11760-016-1018-y
M3 - Article
AN - SCOPUS:84996939021
SN - 1863-1703
VL - 11
SP - 745
EP - 752
JO - Signal, Image and Video Processing
JF - Signal, Image and Video Processing
IS - 4
ER -
