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10.3233/ACP-2011-0043 http://scihub22266oqcxt.onion/10.3233/ACP-2011-0043 C4605758!4605758!22063730     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Anal+Cell+Pathol+(Amst) 2012 ; 35 (2): 117-22 Nephropedia Template TP {{tp|p=22063730|t=2012. Histopathologic Patterns of Nervous System Tumors Based on Computer Vision Methods and Whole Slide Imaging (WSI) |pdf=|usr=}}{{22063730}} gab.com Text Histopathologic Patterns of Nervous System Tumors Based on Computer Vision Methods and Whole Slide Imaging (WSI) JO: Anal Cell Pathol (Amst) http://www.kidney.de/pget.php?&tw=1&pmid=22063730 #FOAvip Background: Making an automatic diagnosis based on virtual slides and whole slide imaging or even determining whether a case belongs to a single class, representing a specific disease, is a big challenge. In this work we focus on WHO Classification of Tumours of the Central Nervous System. We try to design a method which allows to automatically distinguish virtual slides which contain histopathologic patterns characteristic of glioblastoma ? pseudopalisading necrosis and discriminate cases with neurinoma (schwannoma), which contain similar structures ? palisading (Verocay bodies).Methods: Our method is based on computer vision approaches like structural analysis and shape descriptors. We start with image segmentation in a virtual slide, find specific patterns and use a set of features which can describe pseudopalisading necrosis and distinguish it from palisades. Type of structures found in a slide decides about its classification.Results: Described method is tested on a set of 49 virtual slides, captured using robotic microscope. Results show that 82% of glioblastoma cases and 90% of neurinoma cases were correctly identified by the proposed algorithm.Conclusion: Our method is a promising approach to automatic detection of nervous system tumors using virtual slides. Twit Text FOAVip Histopathologic Patterns of Nervous System Tumors Based on Computer Vision Methods and Whole Slide Imaging (WSI) ????Anal Cell Pathol (Amst) http://www.kidney.de/pget.php?&tw=1&pmid=22063730 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=22063730 #FOAvip English Wikipedia <ref>{{Cite pmid|22063730}}</ref> Histopathologic Patterns of Nervous System Tumors Based on Computer Vision Methods and Whole Slide Imaging (WSI) #MMPMID22063730 Walkowski S; Szymas J Anal Cell Pathol (Amst) 2012[]; 35 (2): 117-22 PMID22063730show ga Background: Making an automatic diagnosis based on virtual slides and whole slide imaging or even determining whether a case belongs to a single class, representing a specific disease, is a big challenge. In this work we focus on WHO Classification of Tumours of the Central Nervous System. We try to design a method which allows to automatically distinguish virtual slides which contain histopathologic patterns characteristic of glioblastoma ? pseudopalisading necrosis and discriminate cases with neurinoma (schwannoma), which contain similar structures ? palisading (Verocay bodies).Methods: Our method is based on computer vision approaches like structural analysis and shape descriptors. We start with image segmentation in a virtual slide, find specific patterns and use a set of features which can describe pseudopalisading necrosis and distinguish it from palisades. Type of structures found in a slide decides about its classification.Results: Described method is tested on a set of 49 virtual slides, captured using robotic microscope. Results show that 82% of glioblastoma cases and 90% of neurinoma cases were correctly identified by the proposed algorithm.Conclusion: Our method is a promising approach to automatic detection of nervous system tumors using virtual slides. äHistopathologic Patterns of Nervous System Tumors Based on Computer Vi(epmc).pdf DeepDyve Pubget Overpricing