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...

Low-rank image decomposition has the potential to address a broad range of challenges that routinely occur in clinical practice. Its novelty and utility in the context of atlas-based analysis stems from its ability to handle images containing large pathologies and large deformations. Potential applications include atlas-based tissue segmentation and unbiased atlas building from data containing ...

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...

A wide number of magnetic resonance imaging (MRI) analysis techniques rely on brain tissue segmentation. Automated and reliable tissue classification is a challenging task as the intensity of the data typically does not allow a clear delimitation of the different tissue types because of partial volume effects, image noise and intensity non-uniformities caused by magnetic field inhomogeneities. ...

Brain MRI segmentation remains a challenging problem in spite of numerous existing techniques. To overcome the inherent difficulties associated with this segmentation problem, we present a new method of information integration in a graph based framework. In addition to image intensity, tissue priors and local boundary information are integrated into the edge weight metrics in the graph. Further...

White matter lesions (WML) are the main brain imaging surrogate of cerebral small-vessel disease. A new MRI tissue segmentation method, based on a discriminative clustering approach without explicit model-based added prior, detects partial WML volumes, likely representing very early-stage changes in normal-appearing brain tissue. This study investigated how the different stages of WML, from a "...

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...

We present a method for automated brain tissue segmentation based on diffusion MRI. This provides information that is complementary to structural MRI and facilitates fusion of information between the two imaging modalities. Unlike existing segmentation approaches that are based on diffusion tensor imaging (DTI), our method explicitly models the coexistence of various diffusion compartments with...