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Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting...
Moving shadow detection and removal are key steps for motion detection algorithm. But by far most of the traditional methods, relying solely on single information, can not eliminate shadow effectively. This paper describes a mixed approach to deal with the shadow of the foreground objects from video surveillance. Firstly, a new description of local texture operator—LMTO (Local Match Texture Operator)...
Longitudinal magnetic resonance (MR) images of the same subject often vary significantly in their overall contrast. Intensity standardization aims to minimize the inter-scan intensity variations by transforming the intensities into a standard gray scale, but true anatomical changes over time are often masked out. We propose an intensity standardization method based on four dimensional Fuzzy C-means...
Medical image segmentation has become an essential technique in clinical and research-oriented applications. Because manual segmentation methods are tedious, and fully automatic segmentation lacks the flexibility of human intervention or correction, semi-automatic methods have become the preferred type of medical image segmentation. We present a hybrid, semi-automatic segmentation method in 3D that...
Brain imaging studies of the corpus callosum (CC) in autism have yielded inconsistent results. In this paper, we explore the three-dimensional profile of CC abnormalities in autism. The CC is segmented from mid-sagittal MRI and four adjacent slices on both sides, using our newly developed semiautomatic method. A subsequent contour stitching is performed to create the 3D surface of the CC, and the...
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