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Segmenting Brain Structures

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In the diagnosis and treatment of various diseases, often segmenting the brain structures from MRI data is the key step. Since there are larger variations in the anatomical structures of brain, segmentation becomes a crucial process. Using only the intensity information is not enough to segment structures since two or more structures may share the same tissues. Recently use of multiple pre-labeled images called atlases or templates are used in the process of segmentation of image data. Both single atlas and multiple atlases can be used. However, using multiple templates proves a dominant method in segmenting brain structures even for challenging structure variations and overlapping structures. In this paper, we propose two multi atlas segmentation …show more content…
The MRI scan gives clear and exhaustive images of soft tissue. Bones can’t be visualized using MRI because bone tissue doesn’t compose of much water. Due to this, MRI and fMRI are widely used in tissue and structure segmentation. Tissue segmentation can be done using intensity thresholding. But segmenting brain structures are complicated as a structure may be comprised of more than one tissue type. Because of this, segmenting brain structures is a very crucial step. Though a lot of research had been done in this area, still it is a challenging field. Using prior knowledge about the spatial relationships among structures, called as atlases, we can segment the structures with larger dissimilarities. Among other proposed methods, atlas based methods gives best result [1]. Multiple atlases [2,3] proves a best one when compared to single atlas, especially when there are larger dissimilarities in the structures. If a single atlas is used during segmentation, then the result will be biased. To remove such bias and also segment structures with larger dissimilarities, multiple atlases are used. When multiple atlases are used the accuracy of the segmentation is not that much affected by the errors produced during registration. This method has two steps viz, registration and fusion. The registration is done for each atlas image with the test image to align both …show more content…
An atlas, Ai, is an intensity and label pair denoted by AIi and ALi respectively. The intensity inhomogeneity is caused mainly due to the RF coil imperfections [13][14]. This artifact, results in the variations of intensities in the same tissues. These variations affect the segmentation results. First intensity inhomogeneity correction should be done on all images. Then both test and atlas images are aligned rigidly to a common template for further processing. The brain tissues should be extracted from non-brain area called skull stripping for good results and to increase the speed of the algorithm. Then the test image IT should be non-rigidly aligned with each atlases Ai which yields a transformation field for each registration. Using this transformation field the labels from corresponding atlas label image ALi is propagated to produce an equivalent intermediate segmentation Si. This registration process yields ‘n’ intermediate segmentations, Si, where i=1,2,…n, one for each atlas. Our objective is to combine all these intermediate segmentations and produce final true segmentation SFinal using some combination rule. The segmentation accuracy is computed by comparing SFinal with the golden standard using any similarity measures. Our problem is to formulate a fusion approach that produces SFinal with greater accuracy. Our algorithm should

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