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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/7660
Title: PCA-PNN and PCA-SVM based cad systems for breast density classification
Authors: Kriti
Virmani J.
Dey N.
Kumar V.
Published in: Intelligent Systems Reference Library
Abstract: Early prediction of breast density is clinically significant as there is an association between the risk of breast cancer development and breast density. In the present work, the performance of two computer aided diagnostic (CAD) systems has been compared for classification of breast tissue density. The work has been carried out on MIAS dataset with 322 mammographic images consisting of 106 fatty and 216 dense images. The ROIs have been selected from densest region (i.e., the center of each image, ignoring the pectoral muscle) of each mammogram. The total dataset consisted of 322 ROIs (106 fatty ROIs and 216 dense ROIs). Five statistical texture features namely, mean, standard deviation, entropy, kurtosis and skewness are evaluated from Laws' texture energy images resulting from Laws' masks of length 5, 7 and 9. The texture feature vectors computed from Laws' masks of different lengths are then subjected to principal component analysis (PCA) for reduction in feature space dimensionality. The SVM and PNN classifiers are used for the classification task. It is observed that the highest classification accuracy of 92.5 % is achieved with first four principal components derived from texture features computed with Laws' masks of length 7 by using PNN classifier and the highest classification accuracy of 94.4 % is achieved with first four principal components derived from texture features computed with Laws' masks of length 5 by using SVM classifier. It can be concluded that the first four principal components derived from Laws' texture energy images resulting from Laws' masks of length 5 are sufficient to account for textural changes exhibited by fatty and dense mammograms. The promising results obtained by the proposed CAD design indicate its usefulness to assist radiologists for breast density classification. © Springer International Publishing Switzerland 2016.
Citation: Intelligent Systems Reference Library (2016), 96(): 159-180
URI: https://doi.org/10.1007/978-3-319-21212-8_7
http://repository.iitr.ac.in/handle/123456789/7660
Issue Date: 2016
Publisher: Springer Science and Business Media Deutschland GmbH
Keywords: Breast density classification
Laws' texture features
Mammograms
Principal component analysis (PCA)
Probabilistic neural network (PNN) classifier
Support vector machine (SVM) classifier
ISSN: 18684394
Author Scopus IDs: 56728815200
54897388000
55356190900
25646515800
Author Affiliations: Kriti, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India
Virmani, J., Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India
Dey, N., Bengal College of Engineering and Technology, Durgapur, India
Kumar, V., Indian Institute of Technology, Roorkee, India
Corresponding Author: Virmani, J.; Jaypee University of Information TechnologyIndia
Appears in Collections:Journal Publications [EE]

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