Paper Details: Downloads: 584
Serial Number: P1120542001
Title: Automatic Detection of Asymmetries in MammogramsUsing Genetic Algorithm
Authors: K.Thangavel and M.Karnan
Abstract: Microcalcification on x-ray mammogram is a significant mark for early detection of breast cancer. In this paper, Genetic Algorithm (GA) is proposed to automatically detect the suspicious regions on digital mammograms based on asymmetries between left and right breast image. The basic idea of the asymmetry approach is corresponding left and right images are subtracted to extract the suspicious region. One of the major problem in this approach is due to the recording procedure the size and shape of the corresponding mammograms do not match. The proposed system consists of two steps: First, the mammogram images are enhanced using median filter, pectoral muscle region is removed and the border of the mammogram is detected for both left and right images from the binary image. Further GA is applied to enhance the detected border. The figure of merit is calculated to evaluate whether the detected border is exact or not. And the nipple position is identified using GA and second derivative method hybrid with GA. The performance is compared with the existing methods. Second, using the border points and nipple position as the reference the mammogram images are aligned and subtracted to extract the suspicious region. The algorithms are tested on 322 digitized mammograms from MIAS database..
Keywords: Mammogram, Markov Random Field, Ant Colony Optimization, Genetic Algorithm, Backpropagation Neural Network.
Journal/Conference: International Journal of Artificial Intelligence and Machine Learning
Volume: 5
Issue: 3
Submission Date: 6/1/2005 12:00:00 AM
Review Date: 7/1/2005 12:00:00 AM
Publishing Date: 9/1/2005 12:00:00 AM
Article Downloads: 584
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