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Object boundary extraction is an important task in brain image analysis. Acquiring detailed 3D representations of the brain structures could improve the detection rate of diseases at earlier stages. Deformable model based segmentation methods have been widely used with considerable success. Recently, 3D Active Volume Model (AVM) was proposed, which incorporates both gradient and region information...
The study of in utero fetal MR images is essential for the diagnosis of abnormal brain development and the study of the maturation of the brain structures. Because of the particular properties of these images, only a few automated segmentation methods have been developed so far compared to the numerous ones existing for the adult brain anatomy. In this paper, we propose a two-step cortex segmentation...
We propose a technique for fusing the output of multiple Magnetic Resonance (MR) sequences to robustly and accurately segment brain lesions. It is based on a Bayesian multi-sequence Markov model that includes weight parameters to account for the relative importance and control the impact of each sequence. The Bayesian framework has the advantage of allowing 1) the incorporation of expert knowledge...
Automated methods for image segmentation, image registration, clustering of images and probabilistic atlas construction are of great interest in medical image analysis. In this work, we propose a model where these different aspects are combined in one comprehensive probabilistic framework. The framework is formulated as an EM optimization algorithm. Validation is performed on simulated and real images...
In recent years, statistical shape models, of which Active Appearance Models (AAMs) are a subset have been increasingly applied to the automatic segmentation of medical images. AAMs are a local search technique requiring good initialisation. In 3D automatic initialisation can be achieved by multiple initialisations, registration, template matching or by application dependent heuristics. The first...
Image segmentation is a focused issue in image processing. Especially, brain segmentation is a key problem in neuroscience. In this study, our aim is to segment the real MR image into gray matter, white matter and cerebrospinal fluid. Several methods were compared. However, traditional methods such as fuzzy c-means, mixture Gaussian model can't achieve a satisfied result successfully. Markov random...
Multi-atlas segmentation has been proved to perform well in segmenting sub-cortical structures from images. In this work, we study different components of multi-atlas segmentation and propose new techniques to improve the segmentation accuracy. We found that the use of gradient information in addition to standard normalised mutual information increases the registration accuracy. We also studied different...
In this work, we propose a kind of supervised classification - support vector machine (SVM) to segment magnetic resonance image (MRI). As a classifier, SVM can employ structural risk minimization principle and perform better in classification task. Based on those excellent capabilities of SVM, we conduct many detailed experiments on some standard simulated data and real data. According to the experiments...
A validation framework for MR image segmentation is proposed in this paper. It includes three stages: intensity inhomogeneity (IIH) correction, noise suppression without blurring structures and tissue classification. Based on MR brain images, in the first stage, an improved process is used to implement IIH correction. Subsequently, a new enhancement method on moments for noise removal and edge sharpening...
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