Gray level clustering and contrast enhancement (GLC–CE) of mammographic breast cancer images
Bhagwati Charan Patel, Ganesh Ram Sinha · CSI Transactions on ICT · 2015
Computer-aided diagnosis (CAD) system for detection of cancer or other abnormalities in medical images includes image enhancement and clustering techniques as most important stages of automated medical image analysis and diagnosis system. The contrast of mammograms is always required to be good so that further investigation of breast cancer images is accurate. Size, shape, area, cancer stage identification are assessed in any CAD based segmentation. Signs of breast cancer on mammography are indicated by mass tissues, micro-calcifications, skin thickening and architectural distortions of breast tissue. The proposed work combines gray level clustering and contrast enhancement algorithm which aims at improving contrast features and the suppression of noise. This technique is very helpful to visualize breast tumors of breasts of higher density that further helps in detection of breast cancer. Firstly, same gray level intensity values are grouped and clusters are formed accordingly then contrast enhancement method is applied over to it. The quantitative analysis using contrast improvement index, signal to noise ratio and root mean square error, was made to investigate the characteristic of the breast cancer images. The low contrast features are enhanced without introducing any artifacts.