Title: Breast density classification using Laws' mask texture features

Authors: Kriti; Jitendra Virmani

Addresses: Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Waknaghat 173215, Solan, Himachal Pradesh, India ' Department of Electrical and Instrumentation Engineering, Thapar University, Patiala 147004, Punjab, India

Abstract: Characterisation of tissue density is clinically significant as high density is associated with the risk of developing breast cancer and also masks lesions. Accordingly, in the present work, PCA-kNN and PCA-NN based computer aided diagnostic (CAD) systems for breast tissue density classification have been proposed. The work has been carried out on the MIAS dataset. Five statistical texture features mean, standard deviation, entropy, kurtosis and skewness are evaluated from Laws' texture energy images resulting from Laws' masks of lengths 3, 5, 7 and 9. Principal component analysis is then applied to these texture feature vectors for feature space dimensionality reduction. The kNN classifier and the NN classifier are used for the classification task. The highest classification accuracy of 95.6% is achieved by using the first 8 principal components computed from texture features derived from Law's mask of length 5 for k = 8 using the kNN classifier.

Keywords: breast density classification; mammography; Laws' texture descriptors; PCA; principal component analysis; kNN; k-nearest neighbour; neural networks; classifiers; mammograms; breast cancer; lesions; texture features; mean deviation; standard deviation; entropy; kurtosis; skewness.

DOI: 10.1504/IJBET.2015.072999

International Journal of Biomedical Engineering and Technology, 2015 Vol.19 No.3, pp.279 - 302

Received: 02 Mar 2015
Accepted: 04 Jun 2015
Published online: 11 Nov 2015 *

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