2D-Sigmoid Enhancement Prior to Segment MRI Glioma Tumour

Abstract

Tumour identification has always been a topic that interested researchers around the world. The most challenging phase in tumour identification based on brain MR image is the segmentation of the tumour contour which may contain many unwanted details. Intensity inhomogeneities often occur in real world images and may cause the difficulties in image segmentation. In order to overcome the difficulties caused by intensity inhomogeneity, the study presented pre-processing prior to a region based active contour model with modification of Region Scalable Fitting (MRF) method for image segmentation. Region based active contour model that draw upon intensity information in local regions. The pre-processing is a kind of image enhancement which applies the 2D-sigmoid function at tumour boundary. 2D-sigmoid function enhances the contrast in the brain MRI image for pre-processing steps. Enhanced pixel value, F(x, y), is the ‘S’ shape function of intensity I (x, y) of the image at the point (x, y) , width of the gradient magnitude around brain image (α) and gradient magnitude around brain image (β). Experimental results show desirable of MRF method in terms of computation efficiency.