FEATURES EXTRACTION FOR MASSES IN DIGITAL MAMMOGRAMS USING STATISTICAL AND TEXTURAL MEASURES
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Abstract
Breast Cancer is reported to be one of the major causes of mortality among the women across the world. Digital mammogram is
considered to be a reliable breast screening tool due to its low-dose of energy conservation. This paper aims at the detection and segmentation of
breast masses using watershed segmentation. Further, this research work explores the statistical and texture features of the segmented masses.
These features form the foundation for the classification and detection of breast cancer. The proposed method named as Feature Extraction using
Statistical and Texture Measure (FESTM) is experimented on the Mammographic Image Analysis Society (MIAS) test datasets, for validation
and verification. It is confirmed that the segmentation results align with the ground truth data of test images.
Keywords: Digital Mammogram, Threshold, Watershed Segmentation, Feature Extraction , Cancer Detection, Statistical and Textural
features
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