This paper presents a brain tumor automatic segmentation approach applied to magnetic resonance (MR) images. The authors’ approach addresses all types of brain tumors. The proposed method involves therefore: image pre-processing, feature extraction via wavelet transform-spatial gray level dependence matrix (WTSGLDM), dimensionality reduction using genetic algorithm (GA), parameters optimization...

The Gegenbauer image reconstruction method, previously shown to improve the quality of magnetic resonance images, is utilized in this study as a segmentation preprocessing step. It is demonstrated that, for all simulated and real magnetic resonance images used in this study, the Gegenbauer reconstruction method improves the accuracy of segmentation. Although it is more desirable to use the k-sp...

Brain magnetic resonance (MR) segmentation algorithms are critical to analyze tissues and diagnose edema and tumor in a quantitative way. The primary aim of brain image segmentation is to partition a given brain image into different regions representing anatomical structures. In this paper, we present a new effective segmentation algorithm that segments brain MR images into tumor, edema, white ...

Image Segmentation is an important and challenging factor in the field of medical image processing. In the present days, for the human body anatomical study and for the treatment planning medical science very much depend on the medical imaging technology and medical images. Specifically for the human brain, MRI (Magnetic Resonance Imaging) widely prefers and using for the imaging. But by nature...

In brain magnetic resonance (MR) images, image segmentation and 3D visualization are very useful tools for the diagnosis of abnormalities. Segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is the basic process for 3D visualization of brain MR images. Of the many algorithms, the fuzzy c-means (FCM) technique has been widely used for segmentation of brain MR image...

This paper looks at the difficulties that can confound published T1-weighted Magnetic Resonance Imaging (MRI) brain segmentation methods, and compares their strengths and weaknesses. Using data from the Internet Brain Segmentation Repository (IBSR) as a "gold standard", we ran three different segmentation methods with and without correcting for intensity inhomogeneity. We then calculated the si...

Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images...

Medical Image segmentation is to partition the image into a set of regions that are visually obvious and consistent with respect to some properties such as gray level, texture or color. Brain tumor classification is an imperative and difficult task in cancer radiotherapy. The objective of this research is to examine the use of pattern classification methods for distinguishing different types of...

Automated tissue segmentation of brain magnetic resonance (MR) images has attracted extensive research attention. Many segmentation algorithms have been proposed for this issue. However, due to the existence of noise and intensity inhomogeneity in brain MR images, the accuracy of the segmentation results is usually unsatisfactory. In this paper, a high-accuracy brain MR image segmentation algor...

Brain MR image segmentation is an important research topic in medical image analysis area. In this paper, we propose an active contour model for brain MR image segmentation, based on a generalized level set formulation of the Mumford-Shah functional. The model embeds explicitly gradient information into the Mumford-Shah functional, and incorporates in a generic framework both regional and gradi...