| Quantitative analysis of head images obtained by computed tomography (CT) requires accurate segment-ation. A new method for automatic segmentation of human spontaneous intracerebral brain hemorrhage (ICH) from digitized CT lms is presented in the paper. The proposed segmentation method has a two-level hierarchical structure. The segmentation at both levels is based on the unsuper-vised fuzzy ...

We present a novel 3-D deformable model-based approach for accurate, robust, and automated tissue segmentation of brain MRI data of single as well as multiple magnetic resonance sequences. The main contribution of this study is that we employ an edge-based geodesic active contour for the segmentation task by integrating both image edge geometry and voxel statistical homogeneity into a novel hyb...

-The most important task in digital image processing is image segmentation. This paper put forward an unique image segmentation algorithm that make use of a Markov Random Field (MRF) hybrid with biologically inspired technique Bacteria Foraging Optimization Algorithm (BFOA) for Brain Magnetic Resonance Images The proposed new algorithm works on the image pixel data and a region/neighborhood map...

The accurate segmentation of the brain from three-dimensional medical imagery is important as the basis for visualization, morphometry, surgical planning and intraoperative navigation. The complex and variable nature of brain anatomy makes recognition of the brain boundaries a difficult problem and frustrates segmentation schemes based solely on local image features. We have developed a deforma...

Quantitative analysis of magnetic resonance (MR) brain images are facilitated by the development of automated segmentation algorithms. A single image voxel may contain of several types of tissues due to the finite spatial resolution of the imaging device. This phenomenon, termed partial volume effect (PVE), complicates the segmentation process, and, due to the complexity of human brain anatomy,...

Accurate segmentation of neonatal brain MR images remains challenging mainly due to poor spatial resolution, low tissue contrast, high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although parametric or geometric deformable models have been successfully applied to adult brain segmentation, to the best of our knowledge, they are n...

1.1 Statement of the problem Segmentation in volumetric images is a tool allowing a diagnostics automation and as well will assist experts in quantitative and qualitative analysis. It’s an important step in various applications such as visualization, morphometrics and image-guided surgery. In the context of neuro-imaging, brain tumor segmentation from Magnetic Resonance Images (MRI) is extremel...

Based on the brain tumor statistics provided by American Brain Tumor Association, the five-year survival rate for an adult who is diagnosed with malignant brain tumor is about 30 percent. It is estimated that new cases and deaths of brain tumor will reach 22020 and 13140, respectively in 2010. Early detection and diagnosis had been proven in increasing the chance of survival. Also, the medical ...

Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this st...