| Paper Details: | Downloads: 591 |
| Serial Number: | P1120524001
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| Title: | Ant Colony System for Segmentation and Classification of Microcalcification in Mammograms
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| Authors: | K.Thangavel and M.Karnan and R.Sivakumar and A. Kaja Mohideen
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| Abstract: | Detection of microcalcification based on textural image segmentation and classification is the most effective method for the early-diagnosis of breast cancer. In this paper, a proposed technique, Markov Random Field method hybrid with Ant Colony System, Genetic Algorithm and Backpropagation Network (MRF–ACSGA-BPN) is implemented for the detection of microcalcification in digital mammogram. Identification of microcalcification is performed in two steps: segmentation, feature extraction and classification. First, the mammogram image is segmented using MRF-ACSGA method to extract the suspicious region. Second, the conventional textural analysis method and the Spatial Gray Level Dependence Method (SGLDM) are used to extract the features from the segmented image and their performance is studied and a three-layer backpropagation neural network classifier, trained by jack knife method and round robin method, is used to classify the extracted features into benign or malignant. The results of the neural network for the texture-analysis methods are evaluated by using a Receiver Operating-Characteristics (ROC) analysis. The proposed algorithm and the techniques are tested on 161 pairs of digitized mammograms from MIAS database.
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| Keywords: | Mammogram, Markov Random Field, Ant Colony Optimization, Genetic Algorithm, Backpropagation Neural Network.
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| Journal/Conference: | International Journal of Artificial Intelligence and Machine Learning
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| Volume: | 5
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| Issue: | 3
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| Submission Date: | 6/1/2005 12:00:00 AM
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| Review Date: | 7/1/2005 12:00:00 AM
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| Publishing Date: | 9/1/2005 12:00:00 AM
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| Article Downloads: | 591
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