Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1155/2018/4015613 http://scihub22266oqcxt.onion/10.1155/2018/4015613 C5954872!5954872!29854359     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       J+Healthc+Eng 2018 ; 2018 (ä): ä Nephropedia Template TP {{tp|p=29854359|t=2018. Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology |pdf=|usr=}}{{29854359}} gab.com Text Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology JO: J Healthc Eng http://www.kidney.de/pget.php?&tw=1&pmid=29854359 #FOAvip Inspired by gestalt psychology, we combine human cognitive characteristics with knowledge of radiologists in medical image analysis. In this paper, a novel framework is proposed to detect breast masses in digitized mammograms. It can be divided into three modules: sensation integration, semantic integration, and verification. After analyzing the progress of radiologist's mammography screening, a series of visual rules based on the morphological characteristics of breast masses are presented and quantified by mathematical methods. The framework can be seen as an effective trade-off between bottom-up sensation and top-down recognition methods. This is a new exploratory method for the automatic detection of lesions. The experiments are performed on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) data sets. The sensitivity reached to 92% at 1.94 false positive per image (FPI) on MIAS and 93.84% at 2.21?FPI on DDSM. Our framework has achieved a better performance compared with other algorithms. Twit Text FOAVip Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology ????J Healthc Eng http://www.kidney.de/pget.php?&tw=1&pmid=29854359 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29854359 #FOAvip English Wikipedia <ref>{{Cite pmid|29854359}}</ref> Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology #MMPMID29854359 Wang H; Feng J; Bu Q; Liu F; Zhang M; Ren Y; Lv Y J Healthc Eng 2018[]; 2018 (ä): ä PMID29854359show ga Inspired by gestalt psychology, we combine human cognitive characteristics with knowledge of radiologists in medical image analysis. In this paper, a novel framework is proposed to detect breast masses in digitized mammograms. It can be divided into three modules: sensation integration, semantic integration, and verification. After analyzing the progress of radiologist's mammography screening, a series of visual rules based on the morphological characteristics of breast masses are presented and quantified by mathematical methods. The framework can be seen as an effective trade-off between bottom-up sensation and top-down recognition methods. This is a new exploratory method for the automatic detection of lesions. The experiments are performed on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) data sets. The sensitivity reached to 92% at 1.94 false positive per image (FPI) on MIAS and 93.84% at 2.21?FPI on DDSM. Our framework has achieved a better performance compared with other algorithms. äBreast Mass Detection in Digital Mammogram Based on Gestalt Psychology(epmc).pdf DeepDyve Pubget Overpricing