Automatic segmentation of medical images is an important task for many clinical applications. In practice, a wide range of anatomical structures are visualised using different imaging modalities. In this paper, we investigate whether a single convolutional neural network (CNN) can be trained to perform different segmentation tasks. A single CNN is trained to segment six tissues in MR brain imag...

Computational applications are gaining significant importance in the routine life. Specially, the tradition of the computer aided systems for computational biomedical applications has been explored to a higher extent. Detection of brain tumor is the most common fatality in the current scenario of health care civilization. Automated brain disorder analysis with MR images is one of the specific m...

To cope with the difficulty of MRI brain scans automatic segmentation, we need to constrain and control the selection and the adjustment of processing tools depending on the local image characteristics. To extract domain and control knowledge from the image, we propose to use situated cooperative agents whose dedicated behavior, i.e. segmentation of one type of tissue, is dynamically adapted wi...

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining ...

Brain tumor analysis from Magnetic Resonance Images (MRI) is one of the mainly challenging tasks. Brain MRI provides details of soft tissues. The image segmentation is done to simplify and to change the representation of an image into meaningful image for better analysis. The image segmentation is a very difficult job in the image processing and challenging task for clinical diagnostic tools. A...

The diagnosis of brain neoplasms has been facilitated by the emerging of high-quality imaging techniques, such as Magnetic Resonance Imaging (MRI), while the combination of several sequences from conventional and advanced protocols has increased the diagnostic information. Treatment planning and therapy follow-up require the detection of neoplastic and edematous tissue boundaries, a very time c...

background: multiple sclerosis (ms) is a degenerative disease of central nervous system. ms patients have some dead tissues in their brains called ms lesions. mri is an imaging technique sensitive to soft tissues such as brain that shows ms lesions as hyper-intense or hypo-intense signals. since manual segmentation of these lesions is a laborious and time consuming task, automatic segmentation ...

materials and methods the segmentation process is evaluated using four different clustering methods with different number of clusters where some dti scalar indices for 10 human brains are processed. results the aim was to produce results with less segmentation error and a lower computational cost while attempting to minimizing boundary overlapping and minimizing the effect of artifacts due to m...

An efficient brain segmentation technique of magnetic resonance image (MRI) is proposed for the sake of atrophy detection. Early detection of brain atrophy indicates many neurodiseases. The paper also proposes a new simple assessment for brain atrophy as an atrophy ratio measure. Experiments on cognitive normal and atrophied MRI Brain images demonstrate the promising segmentation results of the...

The ability to study changes in brain morphometry in longitudinal studies majorly depends on the accuracy and reproducibility of the brain tissue quantification. We evaluate the accuracy and reproducibility of four previously proposed automatic brain tissue segmentation methods: FAST, SPM5, an automatically trained k-nearest neighbor (kNN) classifier, and a conventional kNN classifier based on ...