Effective, precise and consistent brain cortical tissue segmentation from magnetic resonance (MR) images is one of the most prominent issues in many applications of medical image processing. These applications include surgical planning (Kikinis et al., 1996), surgery navigation (Grimson et al., 1997), multimodality image registration (Saeed, 1998), abnormality detection (Rusinek et al., 1991), ...

The brain tumor segmentation on MRI images is a very difficult and important task which is used in surgical and medical planning and assessments. If experts do the segmentation manually with their own medical knowledge, it will be time-consuming. Therefore, researchers propose methods and systems which can do the segmentation automatically and without any interference. In this article, an unsup...

Recent advances in computing power and additive manufacturing (3D printing) have now made possible the efficient simulation, optimization, and replication of patient-specific procedures and prosthetics for neurosurgery applications. Two very promising applications are in finite element modeling for brain injury simulation and detection and applying additive manufacturing towards brain analogues...

The brain tumor segmentation on MRI images is a very difficult and important task which is used in surgical and medical planning and assessments. If experts do the segmentation manually with their own medical knowledge, it will be time-consuming. Therefore, researchers propose methods and systems which can do the segmentation automatically and without any interference. In this article, an unsup...

In this paper, a modified fuzzy c-means (FCM) clustering for medical image segmentation is presented. A conventional FCM algorithm does not fully utilize the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborh...

Sonography, Maternal Serum Screening, amniocentesis, and sampling are among the techniques utilized to examine a developing fetus and diagnose fetal abnormalities in the uterus. Despite the fact that Sonography is the main technique used for imaging and monitoring, the use of Magnetic Resonance Imaging (MRI) to evaluate the fetus is growing. Moreover, MRI is used for further examinations in cas...

Image segmentation on surgical images plays a vital role in diagnosing and analyzing the anatomy of human body. The area of image segmentation has made an extensive ideology for classifying biomedical images. One such application for segmenting and classifying MRI brain images using fuzzy based control theory is proposed in this project. A special technique called FIS is used in brain image seg...

A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on...

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...

In computer vision, image segmentation is an important problem and plays vital role in medical imaging. Analysis and diagnosis of tumor in MRI brain image involves segmentation as very essential steep. It separates the region of interest objects from the background and the other objects. Several approaches are used for MRI brain tumor segmentation. Fuzzy C Means (FCM) is most widely used fuzzy ...