Title: Contour and region characterisation of breast tumour masses with fractal and statistical attributes

Authors: Kamila Khemis; Sihem Amel Lazzouni; Mahammed Messadi; Abdelhafid Bessaid

Addresses: Biomedical Engineering Laboratory, Tlemcen University, Tlemcen 13000, Algeria ' Biomedical Engineering Laboratory, Tlemcen University, Tlemcen 13000, Algeria ' Biomedical Engineering Laboratory, Tlemcen University, Tlemcen 13000, Algeria ' Biomedical Engineering Laboratory, Tlemcen University, Tlemcen 13000, Algeria

Abstract: Breast cancer continues to rank at the forefront of public health problems. Characterisation of breast tissue is a step in computer-aided diagnosis, so we focus on it considering in particular texture and contour analysis of tumour masses with fractal and statistical approaches. Fist we extracted the mammographic mass with the mathematical morphology segmentation tool Watershed Line algorithm. Then we calculated fractal dimension of the mass contour using box counting algorithm. In addition to that we measured textural attributes from the grey-level co-occurrence matrix of the segmented image (region). Finally, we used Support Vector Machine classifier evaluated in K-fold cross-validation mode with OneVsOne strategy considering multiclass classification: Benin masses/Malignant masses. As a result we obtained a classification rate of 98%.

Keywords: mammography; fractal; texture; grey-level co-occurrence matrix; contour; watershed line; characterisation; classification; SVM.

DOI: 10.1504/IJBET.2018.089311

International Journal of Biomedical Engineering and Technology, 2018 Vol.26 No.2, pp.186 - 196

Received: 21 Jan 2016
Accepted: 26 Aug 2016

Published online: 06 Jan 2018 *

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