Current Issue : January - March Volume : 2015 Issue Number : 1 Articles : 5 Articles
White matter hyperintensities (WMH) seen on T2WI are a hallmark of multiple sclerosis (MS) as it indicates inflammation\nassociated with the disease. Automatic detection of the WMH can be valuable in diagnosing and monitoring of treatment\neffectiveness. T2 fluid attenuated inversion recovery (FLAIR) MR images provided good contrast between the lesions and other\ntissue; however the signal intensity of gray matter tissue was close to the lesions in FLAIR images that may cause more false positives\nin the segment result. We developed and evaluated a tool for automated WMH detection only using high resolution 3D T2 fluid\nattenuated inversion recovery (FLAIR) MR images.We use a high spatial frequency suppression method to reduce the gray matter\narea signal intensity.We evaluate our method in 26MS patients and 26 age matched health controls. The data from the automated\nalgorithm showed good agreement with that from the manual segmentation.The linear correlation between these two approaches\nin comparing WMH volumes was found to be Y = 1.04X + 1.74 (R2 = 0.96). The automated algorithm estimates the number,\nvolume, and category of WMH....
Context. MRI of the spinal cord provides a variety of biomarkers sensitive to white matter integrity and neuronal function. Current\nprocessing methods are based on manual labeling of vertebral levels, which is time consuming and prone to user bias. Although\nseveral methods for automatic labeling have been published; they are not robust towards image contrast or towards susceptibility related\nartifacts. Methods. Intervertebral disks are detected from the 3D analysis of the intensity profile along the spine. The\nrobustness of the disk detection is improved by using a template of vertebral distance, which was generated froma training data set.\nThe developed method has been validated using T1- and T2-weighted contrasts in ten healthy subjects and one patient with spinal\ncord injury. Results. Accuracy of vertebral labeling was 100%. Mean absolute error was 2.1 �± 1.7mm for T2-weighted images and\n2.3 �± 1.6mm for T1-weighted images. The vertebrae of the spinal cord injured patient were correctly labeled, despite the presence\nof artifacts caused by metallic implants. Discussion. We proposed a template-based method for robust labeling of vertebral levels\nalong the whole spinal cord for T1- and T2-weighted contrasts. The method is freely available as part of the spinal cord toolbox....
Medical imaging systems often require image enhancement, such as improving the image contrast, to provide medical professionals\nwith the best visual image quality. This helps in anomaly detection and diagnosis. Most enhancement algorithms are iterative\nprocesses that require many parameters be selected. Poor or nonoptimal parameter selection can have a negative effect on the\nenhancement process. In this paper, a quantitative metric for measuring the image quality is used to select the optimal operating\nparameters for the enhancement algorithms.Avariety ofmeasures evaluating the quality of an image enhancementwill be presented\nalong with each measure�s basis for analysis, namely, on image content and image attributes. We also provide guidelines for\nsystematically choosing the proper measure of image quality for medical images...
Line integral convolution (LIC) is used as a texture-based technique in computer graphics for flow field visualization. In diffusion\ntensor imaging (DTI), LIC bridges the gap between local approaches, for example directionally encoded fractional anisotropy\nmapping and techniques analyzing global relationships between brain regions, such as streamline tracking. In this paper an\nadvancement of a previously published multi kernel LIC approach for high angular resolution diffusion imaging visualization is\nproposed: a novel sampling scheme is developed to generate anisotropic glyph samples that can be used as an input pattern to\nthe LIC algorithm. Multicylindrical glyph samples, derived from fiber orientation distribution (FOD) functions, are used, which\nprovide a method for anisotropic packing along integrated fiber lines controlled by a uniform random algorithm. This allows two and\nthree-dimensional LIC maps to be generated, depicting fiber structures with excellent contrast, even in regions of crossing and\nbranching fibers. Furthermore, a color-codingmodel for the fused visualization of slices fromT1 datasets together with directionally\nencoded LICmaps is proposed. The methodology is evaluated by a simulation study with a synthetic dataset, representing crossing\nand bending fibers. In addition, results from in vivo studies with a healthy volunteer and a brain tumor patient are presented to\ndemonstrate the method�s practicality....
Edge detection is a key step in medical image processing. It is widely used to extract features, perform segmentation, and further\nassist in diagnosis. A poor quality edge map can result in false alarms and misses in cancer detection algorithms. Therefore, it is\nnecessary to have a reliable edgemeasure to assist in selecting the optimal edgemap. Existing reference based edgemeasures require\na ground truth edge map to evaluate the similarity between the generated edge map and the ground truth. However, the ground\ntruth images are not available for medical images. Therefore, a nonreference edge measure is ideal for medical image processing\napplications. In this paper, a non reference reconstruction based edge map evaluation (NREM) is proposed. The theoretical basis\nis that a good edge map keeps the structure and details of the original image thus would yield a good reconstructed image. The\nNREM is based on comparing the similarity between the reconstructed image with the original image using this concept. The edge\nmeasure is used for selecting the optimal edge detection algorithm and optimal parameters for the algorithm. Experimental results\nshow that the quantitative evaluations given by the edge measure have good correlations with human visual analysis....
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