Title: An efficient framework for segmentation and identification of tumours in brain MR images

Authors: D. Sai Parameshwari; P. Aparna

Addresses: Department of Electronics and Communication, National Institute of Technology Karnataka, Surathkal 575025, Karnataka, India ' Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal 575025, Karnataka, India

Abstract: In this research work, two efficient textural feature extraction (TFE) algorithms (TFEA-I and TFEA-II) are proposed for a class of brain magnetic resonance imaging (MRI) applications. TFEA-I employs higher order statistical cumulant, namely, Kurtosis in order to generate a feature set based on the probability density function (PDF) of generalised Gaussian model that represents the wavelet coefficient energies of the sub-bands of decomposed image. TFEA-II derives a feature set employing cooccurrence matrix model for second order statistical characterisation of wavelet coefficients. In conjunction with TFEA-I and TFEA-II, we propose segmentation framework to compute coarse and smooth segmented boundaries for the tumour. When compared with the conventional TFEA methods reported in the literature, the use of proposed TFEA-I and TFEA-II results in two important advantages; considerable reduction in the feature set size and elimination of the need for using specialised feature selection/reduction algorithms thereby making them highly attractive for a class of brain MR imaging application.

Keywords: textural analysis; feature extraction; image segmentation; discrete wavelet transform; DWT; Kurtosis; cooccurrence matrix; active contour; magnetic resonance imaging; MRI; tumour detection; ROI; region of interest; tumour identification; brain tumours; brain images; brain scans; probability density function; generalised Gaussian model.

DOI: 10.1504/IJAMC.2016.080970

International Journal of Advanced Media and Communication, 2016 Vol.6 No.2/3/4, pp.211 - 234

Received: 31 Dec 2015
Accepted: 04 Jul 2016

Published online: 13 Dec 2016 *

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