Identification of Micro calcifications in Mammogram Using Artificial Neural Networks

Abstract

Mammogram is one of the methods in understanding the presence of Microcalcification (MC) in the breast of women. MC is an initial form of breast cancer. With the help of mammogram image, a doctor can understand an approximate amount of MC present in the breast. Many analytical methods have been developed to quantify the amount of MC from the mammogram image. Such analytical methods can help in Computer Aided Detection (CAD) of MC. Commercial software s have been developed to encircle the MC locations. However, there is no guarantee that the information highlighted by the software as MC is real MCs. Many researchers have worked on the implementation of computer aided diagnosis (CAD) of MC. In this research work, the mammogram image is decomposed using wavelet filters. Daubechi wavelet has been used to decompose the given mammogram image into 5 levels. In each level of decomposition, the approximation is further decomposed. newline