Parameshwari, D.S.Aparna., P.2026-02-052016International Journal of Advanced Media and Communication, 2016, 6, 46114, pp. 211-23414624613https://doi.org/10.1504/IJAMC.2016.080970https://idr.nitk.ac.in/handle/123456789/26101In 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 thewavelet 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. © © 2016 Inderscience Enterprises Ltd.Discrete wavelet transformsExtractionFeature extractionHigher order statisticsImage segmentationIodineProbability density functionTumorsActive contoursCo-occurrence-matrixKurtosisRegion of interestTextural analysisTumour detectionMagnetic resonance imaging