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 the...
['D. Sai Parameshwari', 'P. Aparna']
An efficient framework for segmentation and identification of tumours in brain MR images