An efficient algorithm for textural feature extraction and detection of tumors for a class of brain MR imaging applications

In this paper, we propose an efficient textural feature extraction algorithm (TFEA) based on higher order statistical cumulant namely Kurtosis for a class of brain MR imaging applications. Using a model that represents the wavelet coefficient energies of the sub-bands of multi-level decomposition of the image as a basis, a feature set involving three parameters for each band corresponding to probability density function (PDF) of generalized Gaussian type is derived. The logical correctness and working of the proposed TFEA are first verified based on MATLAB ver.2010a tool. The algorithm is applied in conjunction with one of the popularly used canny edge detection algorithm for segmenting a class of real and synthetic magnetic resonance (MR) images to detect the region of a tumor if present. The use of the proposed approach results in reduced feature set size thus obviating the need for employing specialized feature selection/reduction algorithms. A detailed look at the experimental results clearly show an improvement in the segmentation quality compared with conventional method.