Combination of Novel Enhancement Technique and Fuzzy C Means ClusteringTechnique in Breast Cancer Detection.

Computer aided detection (CAD) is the main aid used by radiologists in detecting microcalcification in digital mammogram for the early detection of breast cancer. In this paper we have improved the preprocessing method involves in CAD by modifying the local range modification (LRM) as modified LRM (MLRM) for the noise removal and enhancement. And we have combined this method with the fuzzy C means clustering (FCMC) method and tested for over 30 mammogram images and found the microcalcification detection accuracy of 98.1 % which is better than the other existing methods.

[1]  Salah Bourennane,et al.  Filtering noise on mammographic phantom images using local contrast modification functions , 2008, Image Vis. Comput..

[2]  Heng-Da Cheng,et al.  A novel fuzzy logic approach to mammogram contrast enhancement , 2002, Inf. Sci..

[3]  Frédéric Precioso,et al.  Robust real-time segmentation of images and videos using a smooth-spline snake-based algorithm , 2005, IEEE Transactions on Image Processing.

[4]  Rangaraj M. Rangayyan,et al.  Region-based contrast enhancement of mammograms , 1992, IEEE Trans. Medical Imaging.

[5]  Asoke K. Nandi,et al.  Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection , 2008, Comput. Medical Imaging Graph..

[6]  David A. Clausi,et al.  Unsupervised segmentation of synthetic aperture Radar sea ice imagery using a novel Markov random field model , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Jared Padayachee,et al.  Identification of the breast edge using areas enclosed by iso-intensity contours , 2007, Comput. Medical Imaging Graph..

[8]  Wei Qian,et al.  Tree-structured nonlinear filters in digital mammography , 1994, IEEE Trans. Medical Imaging.

[9]  Heng-Da Cheng,et al.  Approaches for automated detection and classification of masses in mammograms , 2006, Pattern Recognit..

[10]  Jacob Scharcanski,et al.  Denoising and enhancing digital mammographic images for visual screening , 2006, Comput. Medical Imaging Graph..

[11]  Dimitrios I. Fotiadis,et al.  Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques , 2008, Comput. Biol. Medicine.

[12]  Rongchun Zhao,et al.  Image segmentation by clustering of spatial patterns , 2007, Pattern Recognit. Lett..

[13]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[14]  L. Zhang,et al.  Advances in micro-calcification clusters detection in mammography , 2002, Comput. Biol. Medicine.