Computer Aided Diagnosis in Digital Mammograms: Detection of Microcalcifications by Meta Heuristic Algorithms

 

K.Thangavel  (1),  M.Karnan (2)
1: Department of Mathematics, Gandhigram Rural Institute-Deemed University,
2: Department of computer science, Gandhigram Rural Institute-Deemed University,
Gandhigram-624302, Tamil Nadu, India. Fax:91-4551-227229

Abstract:

This research applies the meta-heuristic methods such as Ant Colony Optimization (ACO) and Genetic Algorithm (GA) for identification of suspicious region in mammograms. The proposed method uses the asymmetry principle (bilateral subtraction): Strong structural asymmetries between corresponding regions in the left and right breast are taken as evidence for the possible presence of microcalcification in that region. Bilateral subtraction is achieved in 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. The enhancement technique is evaluated by signal to noise ratios. Further GA is applied to enhance the detected border. The figure of merit is calculated to identify whether the detected border is exact or not. And the nipple position is identified for both left and right images using GA and ACO, and their performance is studied. Second, using the border points and nipple position as the reference the mammogram images are aligned and subtracted to extract the suspicious region. Results obtained with a set of mammograms indicate that this method can improve the sensitivity and reliability of the systems for automated detection of breast tumors i.e. microcalcification. The algorithms are tested on 161 pairs of digitized mammograms from Mammographic Image Analysis Society (MIAS) database. A Free-Response Receiver Operating Characteristic (FROC) curve is generated for the mean value of the detection rate for all the 161 pairs of mammograms in MIAS database, to evaluate the performance of the proposed method.

 

Keywords: Breast boarder, nipple identification, genetic algorithm, ant colony optimization, bilateral subtraction. FROC.

 

	THANGAVEL KUTTIANNAN, received the Master of Science from Department of Mathematics, Bharathidasan University in 1986, and Master of Computer Applications Degree from Madurai Kamaraj University, India in 2001. He got his Ph. D. Degree from Mathematics department, Gandhigram Rural Institute-Deemed University in 1999. Currently he is working as Reader in Mathematics Department, Gandhigram Rural Institute-Deemed University, and his experience started from 1988; His area of interests includes medical image processing, artificial intelligence, neural network, and fuzzy logic.
	KARNAN MARCUS, received the Master of Computer Science and Engineering Degree from Computer Science and Engineering Department, from Government College of Engineering, Manonmaniam Sundaranar University, Tamil Nadu, India, in 2000. Currently he is working as Assistant Professor, Department of Computer Science & Engineering Department, RVS College of Engineering & Technology, Tamil Nadu, India. And doing part-time research in the Department of computer Science and Applications, Gandhigram Rural Institute-Deemed University, Tamil Nadu, India. His area of interests includes medical image processing, artificial intelligence, neural network, genetic algorithm, pattern recognition and fuzzy logic.
	The following author designated as corresponding author: Karnan Marcus, E-mail id: karnanme@yahoo.com, rsk_siva@mailcity.com, ktvel@rediffmail.com Postal Address: 153, Palayam, Palani, Dindigul Dt, Tamil Nadu, India – 624601. Fax.No:91-4551-227229,91-4551-227230, Telephone.No:91-4551-227229,91-4551-2272.

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