A Novel Clustering Algorithm Introducing New Denoising Technique

Abstract

Breast cancer is one of common cancers in the developing countries. Detection at an early stage is very crucial for better chance of treatment. The techniques used to detect breast cancer are complex and time consuming. Computerized extraction of tumor areas from mammogram images is challenging due to shape and density of breast tumors which can sometimes surrounded by mucous (mucin). One of the challenges is to detect boundaries which can be blurred under noise factor. In this paper, we are introducing a clustering technique combined with specific structural features operations. A new noise elimination algorithm eases the noise problem and enhance the segmentation process using discrete cosine transform. Followed is the segmentation phase where classifying breast tumor from normal tumor are performed using a combined DCT and fuzzy c means algorithm. The contributions of the research are utilizing new filtering technique for noise removal. We also use Fuzzy C mean clustering algorithm using DCT information to determine the initial number of clusters. The tumor extracted segments are then transferred to the frequency domain using DCT and is used to for classifications. A test algorithm is implemented to classify new mammograms. Experimental results for all the proposed algorithms are extensively performed. The noise removal algorithm are proven robust. The experimental results of the search algorithm depicted different match and mismatch cases. 93% of the cases were a match case and predicted correctly. 5% were light cases and could not be detected from the images.