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3 March 2007 Automatic brain cropping and atlas slice matching using a PCNN and a generalized invariant Hough transform
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Abstract
Medical research is dominated by animal models, especially rats and mice. Within a species most laboratory subjects exhibit little variation in brain anatomy. This uniformity of features is used to crop regions of interest based upon a known, cropped brain atlas. For any study involving N subjects, image registration or alignment to an atlas is required to construct a composite result. A highly resolved stack of T2 weighted MRI anatomy images of a Sprague-Dawley rat was registered and cropped to a known segmented atlas. This registered MRI volume was used as the reference atlas. A Pulse Coupled Neural Network (PCNN) was used to separate brain tissue from surrounding structures, such as cranium and muscle. Each iteration of the PCNN produces binary images of increasing area as the intensity spectrum is increased. A rapid filtering algorithm is applied that breaks narrow passages connecting larger segmented areas. A Generalized Invariant Hough Transform is applied subsequently to each PCNN segmented area to identify which segmented reference slice it matches. This process is repeated for multiple slices within each subject. Since we have apriori knowledge of the image ordering and fields of view this information provides initial estimates for subsequent registration codes. This process of subject slice extraction to PCNN mask creations and GIHT matching with known atlas locations is fully automatic.
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M. M. Swathanthira Kumar and John M. Sullivan Jr. "Automatic brain cropping and atlas slice matching using a PCNN and a generalized invariant Hough transform", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65120P (3 March 2007); https://doi.org/10.1117/12.709062
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