Aiding the Digital Mammogram for Detecting the Breast Cancer Using Shearlet Transform and Neural Network

Abstract

Objective: Breast Cancer is the most invasive disease and fatal disease next to lung cancer in human. Early detection<br />of breast cancer is accomplished by X-ray mammography. Mammography is the most effective and efficient technique<br />used for detection of breast cancer in women and also to improve the breast cancer prognosis. The numbers of images<br />need to be examined by the radiologists, the resulting may be misdiagnosis due to human errors by visual Fatigue.<br />In order to avoid human errors, Computer Aided Diagnosis is implemented. In Computer Aided Diagnosis system,<br />number of processing and analysis of an image is done by the suitable algorithm. Methods: This paper proposed a<br />technique to aid radiologist to diagnosis breast cancer using Shearlet transform image enhancement method. Similar to<br />wavelet filter, Shearlet coefficients are more directional sensitive than wavelet filters which helps detecting the cancer<br />cells particularly for small contours. After enhancement of an image, segmentation algorithm is applied to identify the<br />suspicious region. Result: Many features are extracted and utilized to classify the mammographic images into harmful<br />or harmless tissues using neural network classifier. Conclusions: Multi-scale Shearlet transform because more details on<br />data phase, directionality and shift invariance than wavelet based transforms. The proposed Shearlet transform gives multi resolution result and generate malign and benign classification more accurate up to 93.45% utilizing DDSM database.