The application of image processing techniques has rapidly increased in recent years. Medical images almost are stored and represented digitally [26]. Medical image segmentation has very important rule in many computer aided diagnostic tools. These tools could save clinicians time by simplifying the time consuming process [27]. The brain images segmentation is a complicated and challenging task...

This paper presents a novel application of compositional data analysis methods in the context of color image processing. A vector decomposition method is proposed to reveal compositional components of any vector with positive components followed by compositional data analysis to demonstrate the relation between color space concepts such as hue and saturation to their compositional counterparts....

In this paper, we present a method that combines region growing and edge detection for magnetic resonance (MR) brain image segmentation. Starting with a simple region growing algorithm which produces an over segmented image, we apply a sophisticated region merging method which is capable of handling complex image structures. Edge information is then integrated to verify and, where necessary, to...

In this paper, we present a method that combines region growing and edge detection for magnetic resonance (MR) brain image segmentation. Starting with a simple region growing algorithm which produces an over segmented image, we apply a sophisticated region merging method which is capable of handling complex image structures. Edge information is then integrated to verify and, where necessary, to...

Segmentation means segregating area of interest from the image. The aim of image segmentation is to cluster the pixels into salient image regions i.e. regions corresponding to individual surfaces, objects, or natural parts of objects. Automatic Brain tumour segmentation is a sensitive step in medical field. A significant medical informatics task is to perform the indexing of the patient databas...

Automated quality control (QC) procedures are critical for efficiently obtaining precise quantitative brain imaging-based metrics of in vivo brain pathology. This is especially important for multi-centre clinical trials of therapeutics for neurological diseases, in which brain imaging-based metrics may be used to quantify therapeutic efficacy. While there are many different types of brain imagi...

A technique for magnetic resonance brain image classification using perceptual texture features, fuzzy weighting and support vector machine is proposed. In contrast to existing literature which generally classifies the magnetic resonance brain images into normal and abnormal classes, classification with in the abnormal brain which is relatively hard and challenging problem is addressed here. Te...

Background: Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), ...

Skull stripping is a process of segmenting brain portion in MR brain images. It is an important image processing step in many neuroimage studies. In this paper, we propose a new skull stripping method for magnetic resonance image (MRI) of human head scans based on 2D region growing. This is a fully automatic method for segmenting the brain portion from T1, T2 and PD weighted MR images. The prop...