Quantitative analysis of morphological techniques for automatic classification of micro-calcifications in digitized mammograms |
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Affiliation: | 1. Research and Higher Studies Center, National Polytechnic Institute, A.P. 14-740, 07000 Mexico City, Mexico;2. Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Batiz w/n and Miguel Othon de Mendizabal, P.O. 07738, Mexico City, Mexico;1. Faculty of Engineering and Computer Science, Concordia University, Canada;2. Faculty of Computers and Information, Menofia University, Egypt;3. Department of Automatic Control and Systems Engineering, Sheffield University, UK;1. Grupo de Pesquisa em Inteligência de Negócio – GPIN, Faculdade de Informática, PUCRS, Av. Ipiranga, 6681-Prédio 32, Sala 628, 90619-900 Porto Alegre, RS, Brazil;2. Laboratório de Bioinformática, Modelagem e Simulação de Biossistemas – LABIO, Faculdade de Informática, PUCRS, Av. Ipiranga, 6681-Prédio 32, Sala 602, 90619-900 Porto Alegre, RS, Brazil |
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Abstract: | In this paper we present an evaluation of four different algorithms based on Mathematical Morphology, to detect the occurrence of individual micro-calcifications in digitized mammogram images from the mini-MIAS database. A morphological algorithm based on contrast enhancement operator followed by extended maxima thresholding retrieved most of micro-calcifications. In order to reduce the number of false positives produced in that stage, a set of features in the spatial, texture and spectral domains was extracted and used as input in a support vector machine (SVM). Results provided by TMVA (Toolkit for Multivariate Analysis) produced the ranking of features that allowed discrimination between real micro-calcifications and normal tissue. An additional parameter, that we called Signal Efficiency*Purity (denoted SE*P), is proposed as a measure of the number of micro-calcifications with the lowest quantity of noise. The SVM with Gaussian kernel was the most suitable for detecting micro-calcifications. Sensitivity was obtained for the three types of breast. For glandular, it detected 137 of 163 (84.0%); for dense tissue, it detected 74 of 85 (87.1%) and for fatty breast, it detected 63 of 71 (88.7%). The overall sensitivity was 85.9%. The system also was tested in normal images, producing an average of false positives per image of 13 in glandular tissue, 11 in dense tissue and 15 in fatty tissue. |
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Keywords: | Mammogram analysis Morphological reconstruction Digital mammography Micro-calcification detection Mathematical Morphology |
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