Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical
for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic …

Clinical diagnosis and treatment decisions rely upon the integration of patient-specific data
with clinical reasoning. Cancer presents a unique context that influences treatment …

In this paper, a new image segmentation method is proposed by combining the FCM
clustering algorithm with a rough set theory. First, the attribute value table is constructed …

Image segmentation plays a fundamental role in image processing. Active contour models
have been widely used since they handle topological change easily and provide smooth …

Segmentation of the brain into gray matter, white matter, and cerebrospinal fluid (CSF) using
magnetic resonance (MR) imaging plays a fundamental role in neuroimaging research and …

Segregation of tumor region in brain MR image is a prominent task that instantly provides
easier tumor diagnosis, which leads to effective radiotherapy planning. For decades …

Image segmentation is still an open problem due to the existing of intensity inhomogeneity
and noise. To accurately segment images with these biases, a local inhomogeneous …

In this paper, we propose a novel image segmentation algorithm that is based on the
probability distributions of the object and background. It uses the variational level sets …

Brain tissue segmentation from Magnetic resonance (MR) image is significant for assessing
both neurologic conditions and brain disease. Manual brain tissue segmentation is time …

Moving human localization is the first pre-requisite step of human activity analysis in video
surveillance. Identifying human targets accurately and efficiently is always of high demands …