MAMMOGRAM IMAGE SEGMENTATION USING ROUGH CLUSTERING

R. Subash Chandra Boss, K. Thangavel, D. Arul Pon Daniel

Abstract: The mammography is the most effective procedure to diagnosis the breast cancer at an early stage. This paper proposes mammogram image segmentation using Rough K-Means (RKM) clustering algorithm. The median filter is used for pre-processing of image and it is normally used to reduce noise in an image. The 14 Haralick features are extracted from mammogram image using Gray Level Cooccurrence Matrix (GLCM) for different angles. The features are clustered by K-Means, Fuzzy C-Means (FCM) and Rough K-Means algorithms to segment the region of interests for classification. The result of the segmentation algorithms compared and analyzed using Mean Square Error (MSE) and Root Means Square Error (RMSE). It is observed that the proposed method produces better results that the existing methods.

Keywords: Mammogram, Data mining, Image Processing, Feature Extraction, Rough K- Means and Image Segmentation

DOI: https://doi.org/10.15623/ijret.2013.0210009