Feature Extraction for Characterization of Breast Lesions in Ultrasound Echography and Elastography
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Copyright: © 2020 Shirley Selvan, M. Kavitha, S. Shenbagadevi and S. Suresh. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: Elastography is developed as a quantitative approach to imaging linear elastic properties of tissues to detect suspicious tumors. We propose an automatic feature extraction method in ultrasound elastography and echography for characterization of breast lesions. Approach: The proposed algorithm was tested on 40 pairs of biopsy proven ultrasound elastography and echography images of which 11 are cystic, 16 benign and 13 malignant lesions. Ultrasound elastography and echography images of breast tissue are acquired using Siemens (Acuston Antares) ultrasound scanner with a 7.3 MHz linear array transducer. The images were preprocessed and subjected to automatic threshold, resulting in binary images. The contours of a breast tumor from both echographic and elastographic images were segmented using level set method. Initially, six texture features of segmented lesions are computed from the two image types followed by computing three strain and two shape features using parameters from segmented lesions of both elastographic and echographic images. Results: These features were computed to assess their effectiveness at distinguishing benign, malignant and cystic lesions. It was found that the texture features extracted from benign and cystic lesions of an elastogram are more distinct than that of an ultrasound image .The strain and shape features of malignant lesions are distinct from that of benign lesions, but these features do not show much variation between benign and cystic lesions. Conclusion: As strain, shape and texture features are distinct for benign, malignant and cystic lesions, classification of breast lesions using these features is under implementation.
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