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Glioma is one of the most common brain tumors with high mortality and its histological grading and typing is important both in therapeutic decision and prognosis evaluation. This paper aims at using the high-throughput image feature analysis method to estimate the histological grade and type of a patient by using Magnetic Resonance Imaging (MRI) instead of histological examination. The proposed method...
Brain segmentation is important in the field of neuropsychiatric disorders. With Computed Tomography (CT) scan being the gold standard in brain scan, brain segmentation in CT images is also very important in the detection of many pathology related to the brain. Fuzzy c-Means (FCM) is a popular method in data clustering and also in image segmentation due to it being robust. Graph cut is a segmentation...
This paper proposes a method for segmentation of brain MRI medical images based on the type of knowledge provided preconceptions as Anatomical Atlas. Our approach proceeds by registration of an atlas on a patient image picture of seeking repeatedly to maximize the correspondence between the features of Atlas model and segmented image. The procedure of our approach is essentially divided into two steps:...
This paper proposes an approach to automatically segment MS lesions in MR images using fuzzy c-means (FCM) and a support vector machines (SVM) based on the sequential minimal optimization (SMO) in learning step. A postprocessing based on morphological operations was applied to refine the obtained results. The proposed approach was tested on 3D MR images and the obtained results are encouraging.
Deformable models and graph cuts are two standard image segmentation techniques. Combining some of their benefits, we introduce a new segmentation system for (semi-) automatic delineation of epicardium and endocardium of Left Ventricle of the heart in Magnetic Resonance Images (MRI). Specifically, a temporal information among consecutive phases is exploited via a coupling between deformable models...
Spinal cord analysis is an important problem in the study of various neurological diseases. Current segmentation and analysis methods in clinical use are slow and labor-intensive, especially for pathological data. ``Spinal Crawlers'' are a recently developed technique based on an artificial life framework for medical image analysis that complements classical deformable models (snakes and deformable...
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