Paper Details: Downloads: 825
Serial Number: P1160949925
Title: Performance Evaluation of KFCG and LBG Algorithms in Tumor Demarcation of Mammograms
Authors: Dr. H. B. Kekre and Tanuja K. Sarode and Saylee M. Gharge
Abstract: X-ray mammography is the most effective and economical breast imaging modality. Segmenting a mammographic images into homogeneous texture regions representing disparate tissue types is often a useful preprocessing step in the computer-assisted detection of breast cancer. That is why we proposed new algorithm to detect cancer in mammogram breast cancer images. In this paper we proposed segmentation using vector quantization technique. Here we used Kekre’s Fast Codebook Generation algorithm (KFCG) for segmentation of mammographic images. Initially a codebook of size 128 was generated for mammographic images. These code vectors were further clustered in 8 clusters using same KFCG algorithm. These 8 images were displayed as a result. This approach does not leads to over segmentation or under segmentation. For the comparison purpose we displayed results of Equalized Entropy using Gray Level Co-occurrence Matrix, watershed segmentation and Linde Buzo Gray(LBG) algorithm along with this method.
Keywords: Mammography, segmentation, tumor detection, LBG,KFCG
Journal/Conference: International Journal of Bioinformatics and Medical Engineering
Volume: 10
Issue: 1
Submission Date: 12/2/2009 12:00:00 AM
Review Date: 4/4/2010 12:00:00 AM
Publishing Date: 4/27/2010 12:00:00 AM
Article Downloads: 825
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