Detected Breast Cancer on Mammographic Image Classification Using Fuzzy C-Means Algorithm
S. Julian Savari Antony · 2014
Breast cancer is one of the most common tumour in women. It is a foremost cause of death in the world. A proper screening procedure can help an early diagnosis of the tumor so that reducing the death risk. A suitable computer aided detection system can help the radiologist to sense many subtle signs, normally missed during the showing phase, submitting to the radiologist's attention those areas that could comprehend an irregularity. The proposed method has following work plans: at first the quality of image is increased using histogram equalization method which normalizes the image. At second stage, the intensity features are computed from the image and then shape features, region features which are extracted to compute volumetric values. Computed feature set is used to classify the image using fuzzy c means clustering which reduces false positive result arise in mammogram classification and overcome missing features while using single feature mammogram classification. Experimental results show that, when compared to several other methods fuzzy c means shows 98.1% microcalcification detection in mammograms