Application of dwt for medical image processing and detection Abnormality in mammographic image

Research Article
Snehali D. Sable and Alpana Deshmukh
Breast Cancer, Mammograms, Clustering Technique, Fuzzy C means (FCM), Fuzzy K Means (FKM).

Mammographic mass detection is an important task for the early diagnosis of breast cancer. However, it is difficult to distinguish masses from normal regions because of their abundant morphological characteristics and ambiguous margins. To improve the mass detection performance, it is essential to effectively pre-process mammogram to preserve both the intensity distribution and morphological characteristics of regions. Breast cancer is the second most common cause of cancer death in women. Early detection is the only way to reduce the mortality. Mammography is the best available technique used for earlier detection.Mammography is a special case of CT scan who adopts X-ray method & uses the high resolution film so that it can detect well the tumors in the breast. Low radiation is the strength of this method. Mammogram breast cancer images have the ability to assist physicians in detecting disease caused by cells abnormal growth. Developing algorithms and software to analyze these images may also assist physicians in their daily work. The real-time implementation of this paper can be implemented using data acquisition hardware and software interface with the mammography systems. In the proposed work breast tumor detection by using fuzzy K-means & fuzzy C-means clustering technique is proposed. K Means algorithm is Centroid Based and Fuzzy C Means is Representative Object Based. These two algorithms are to be implemented and their performance is to be analyzed based on their clustering result quality.