Novel Fuzzy Technique for Cancer Detection in Noisy Breast Ultrasound Images

Problem statement: Detecting tumor areas in breast Ultrasound (US) images is a challenging task. The occurrence of benign areas in breast may result in false identification of malignant areas which may have serious outcome. Approach: The CAD system could act as a major function in the early detection of breast cancer and could decrease the death rate among women with breast cancer. This challenge was especially daunting in non homogenous noisy US Images where benign and malignant images were difficult to identify. The US images possess speckle noise which was its inherent property. This study was an attempt to reduce false alarm in Breast cancer detection using computationally efficient fuzzy based image clustering. Results: The proposed system was tested using images which was obtained from the famous American Cancer database for conducting experiments. We had compared the Noise Induced images with that of the De-speckled images and found that the de-speckled images yeild a better image for diagnosis based. Later the image was clustered based on Fuzzy C-Means based clustering technique to identify the cancerous cells. Conclusion: An efficient method is suggested in this study which assist in diagnosing the cancer cells. The Fuzzy C-Means clustering system identifies various important artifacts, such as cyst, tumor and micro calcifications. The challenge in this system is the speckle noise. It can be extended to FCM class 2 non-homogeneous images.