load or time-consuming manual delineation. This paper introduced WHASA (White matter Hyperintensities Automated Segmentation Algorithm), a new method for automatically segmenting WMH from FLAIR and T1 images in multicentre studies. Contrary to previous approaches that were based on intensities

The segmentation of MRI scans of patients with white matter lesions (WML) is difficult because the MRI characteristics of white matter lesions are similar to those of grey matter. Intensity based statistical classification techniques misclassify some WML as grey matter and some grey matter as WML

# The Author(s) 2015. This article is published with open access at Springerlink.com Abstract White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular dis-orders. A truly reliable and fully automated method

Abstract: We propose a new semi-automatic segmentation based Active Contour Model and statistic prior knowledge of Multiple Sclerosis (MS) Lesions in Regions Of Interest (RIO) within brain Magnetic Resonance Images(MRI). Reliable segmentation of MS lesion is important for at least three types

This paper describes an intensity-based method for the segmentation of multiple sclerosis lesions in dual-echo PD and T2-weighted magnetic resonance brain images. The method consists of two stages: feature extraction and image analysis. For feature extraction, we use a ratio filter transformation

categories. Index Terms—Magnetic resonance imaging (MRI), multiple sclerosis (MS), seed points, segmentation, white matter lesions (WMLs). I.

and minimum user bias. We proposed to develop and evaluate an automated lesion segmentation method based on Active Contours (AC) model incorporating tissue knowledge issued from T1-weighted and tissues distribution on Attenuated Inversion recovery (FLAIR) image. The Gray Matter (GM) and White Matter (WM

This paper presents a method for automatic segmentation and labeling of computed tomography (CT) head images of stroke lesions. The method is composed of three steps. The first step is automatic determination of head symmetry axis, with possibility of manual improvement of result, if necessary

facilitated the task of understanding complex sys-tems at cellular and molecular levels in recent years. Segmentation, an important yet dif-ficult problem, is often the first processing step following acquisition. Our team previously demonstrated that a stochastic active contour based algorithm together

that is benign, malignant or normal. The segmentation results will be used as a base for a Computer Aided Diagnosis (CAD) system for early detection of Brain Tumor which will improves the chances of survival for the patient. This paper describes an efficient method for automatic brain tumor segmentation