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10.1016/j.neuroimage.2014.08.021 http://scihub22266oqcxt.onion/10.1016/j.neuroimage.2014.08.021 C4252913!4252913 !25134977     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\25134977 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Neuroimage 2014 ; 102 Pt 2 (0 2 ): 596-607 Nephropedia Template TP {{tp|p=25134977 |t=2014. Automated tract extraction via atlas based Adaptive Clustering |pdf=|usr=}}{{25134977 }} gab.com Text Automated tract extraction via atlas based Adaptive Clustering JO: Neuroimage http://www.kidney.de/pget.php?&tw=1&pmid=25134977 #FOAvip Advancements in imaging protocols such as the high angular resolution diffusion-weighted imaging (HARDI) and in tractography techniques are expected to cause an increase in the tract-based analyses. Statistical analyses over white matter tracts can contribute greatly towards understanding structural mechanisms of the brain since tracts are representative of connectivity pathways. The main challenge with tract-based studies is the extraction of the tracts of interest in a consistent and comparable manner over a large group of individuals without drawing the inclusion and exclusion regions of interest. In this work, we design a framework for automated extraction of white matter tracts. The framework introduces three main components, namely a connectivity based fiber representation, a fiber bundle atlas, and a clustering approach called Adaptive Clustering. The fiber representation relies on the connectivity signatures of fibers to establish an easy correspondence between different subjects. A group-wise clustering of these fibers that are represented by the connectivity signatures is then used to generate a fiber bundle atlas. Finally, Adaptive Clustering incorporates the previously generated clustering atlas as a prior, to cluster the fibers of a new subject automatically. Experiments on the HARDI scans of healthy individuals acquired repeatedly, demonstrate the applicability, reliability and the repeatability of our approach in extracting white matter tracts. By alleviating the seed region selection and the inclusion/exclusion ROI drawing requirements that are usually handled by trained radiologists, the proposed framework expands the range of possible clinical applications and establishes the ability to perform tract-based analyses with large samples. Twit Text FOAVip Automated tract extraction via atlas based Adaptive Clustering ????Neuroimage http://www.kidney.de/pget.php?&tw=1&pmid=25134977 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25134977 #FOAvip English Wikipedia <ref>{{Cite pmid|25134977 }}</ref> Automated tract extraction via atlas based Adaptive Clustering #MMPMID25134977 Tunç B ; Parker WA ; Ingalhalikar M ; Verma R Neuroimage 2014[Nov]; 102 Pt 2 (0 2 ): 596-607 PMID25134977 show ga Advancements in imaging protocols such as the high angular resolution diffusion-weighted imaging (HARDI) and in tractography techniques are expected to cause an increase in the tract-based analyses. Statistical analyses over white matter tracts can contribute greatly towards understanding structural mechanisms of the brain since tracts are representative of connectivity pathways. The main challenge with tract-based studies is the extraction of the tracts of interest in a consistent and comparable manner over a large group of individuals without drawing the inclusion and exclusion regions of interest. In this work, we design a framework for automated extraction of white matter tracts. The framework introduces three main components, namely a connectivity based fiber representation, a fiber bundle atlas, and a clustering approach called Adaptive Clustering. The fiber representation relies on the connectivity signatures of fibers to establish an easy correspondence between different subjects. A group-wise clustering of these fibers that are represented by the connectivity signatures is then used to generate a fiber bundle atlas. Finally, Adaptive Clustering incorporates the previously generated clustering atlas as a prior, to cluster the fibers of a new subject automatically. Experiments on the HARDI scans of healthy individuals acquired repeatedly, demonstrate the applicability, reliability and the repeatability of our approach in extracting white matter tracts. By alleviating the seed region selection and the inclusion/exclusion ROI drawing requirements that are usually handled by trained radiologists, the proposed framework expands the range of possible clinical applications and establishes the ability to perform tract-based analyses with large samples. |Adult [MESH] |Algorithms [MESH] |Brain/*anatomy & histology [MESH] |Cluster Analysis [MESH] |Diffusion Magnetic Resonance Imaging/*methods [MESH] |Humans [MESH] |Image Processing, Computer-Assisted/*methods [MESH] |Male [MESH] |Reproducibility of Results [MESH]Automated tract extraction via atlas based Adaptive Clustering (epmc).pdf DeepDyve Pubget Overpricing