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

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