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10.1117/1.JBO.19.9.096007 http://scihub22266oqcxt.onion/10.1117/1.JBO.19.9.096007 C4161736!4161736!26296156     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Biomed+Opt 2014 ; 19 (9): ä Nephropedia Template TP {{tp|p=26296156|t=2014. Texture analysis applied to second harmonic generation image data for ovarian cancer classification |pdf=|usr=}}{{26296156}} gab.com Text Texture analysis applied to second harmonic generation image data for ovarian cancer classification JO: J Biomed Opt http://www.kidney.de/pget.php?&tw=1&pmid=26296156 #FOAvip Remodeling of the extracellular matrix has been implicated in ovarian cancer. To quantitate the remodeling, we implement a form of texture analysis to delineate the collagen fibrillar morphology observed in second harmonic generation microscopy images of human normal and high grade malignant ovarian tissues. In the learning stage, a dictionary of ?textons??frequently occurring texture features that are identified by measuring the image response to a filter bank of various shapes, sizes, and orientations?is created. By calculating a representative model based on the texton distribution for each tissue type using a training set of respective second harmonic generation images, we then perform classification between images of normal and high grade malignant ovarian tissues. By optimizing the number of textons and nearest neighbors, we achieved classification accuracy up to 97% based on the area under receiver operating characteristic curves (true positives versus false positives). The local analysis algorithm is a more general method to probe rapidly changing fibrillar morphologies than global analyses such as FFT. It is also more versatile than other texture approaches as the filter bank can be highly tailored to specific applications (e.g., different disease states) by creating customized libraries based on common image features. Twit Text FOAVip Texture analysis applied to second harmonic generation image data for ovarian cancer classification ????J Biomed Opt http://www.kidney.de/pget.php?&tw=1&pmid=26296156 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26296156 #FOAvip English Wikipedia <ref>{{Cite pmid|26296156}}</ref> Texture analysis applied to second harmonic generation image data for ovarian cancer classification #MMPMID26296156 Wen BL; Brewer MA; Nadiarnykh O; Hocker J; Singh V; Mackie TR; Campagnola PJ J Biomed Opt 2014[Sep]; 19 (9): ä PMID26296156show ga Remodeling of the extracellular matrix has been implicated in ovarian cancer. To quantitate the remodeling, we implement a form of texture analysis to delineate the collagen fibrillar morphology observed in second harmonic generation microscopy images of human normal and high grade malignant ovarian tissues. In the learning stage, a dictionary of ?textons??frequently occurring texture features that are identified by measuring the image response to a filter bank of various shapes, sizes, and orientations?is created. By calculating a representative model based on the texton distribution for each tissue type using a training set of respective second harmonic generation images, we then perform classification between images of normal and high grade malignant ovarian tissues. By optimizing the number of textons and nearest neighbors, we achieved classification accuracy up to 97% based on the area under receiver operating characteristic curves (true positives versus false positives). The local analysis algorithm is a more general method to probe rapidly changing fibrillar morphologies than global analyses such as FFT. It is also more versatile than other texture approaches as the filter bank can be highly tailored to specific applications (e.g., different disease states) by creating customized libraries based on common image features. äTexture analysis applied to second harmonic generation image data for (epmc).pdf DeepDyve Pubget Overpricing