Analysis of Breast Cancer Using Image Processing Techniques
Ranjeet Singh Tomar, Tripty Singh, Sulochana Wadhwani, Sarita Singh Bhadoria · 2009
Mammograms can depict most of the significant changes of breast disease. The primary radiographic signs of cancer are masses (its density, site, shape, borders), spicular lesions and calcification content.These features may be extracted using various detection system .The common are Neural network, wavelet, fuzzy logic, evolutionary approach and finally hybrid system ,which employs integration of above techniques.. This work is to focus mainly on Image Processing Technique on MATLAB platform.The basic idea is to convert the mammogram image and convert into 3-D matrix. Obtained matrix is used to convert the mammogram into binary image. Several techniques like detecting cell, filling gaps, dilating gaps, removing border, smoothing the objects ,finding structures & extracting large objects have been used. Finally finding the Granulometry of tissues in an Image without explicitly segmenting (detecting) each object. Compared to existing multiscale enhancement approaches, images processed with this method appear more familiar to radiologists and naturally close to the original mammogram.