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10.1117/1.JBO.21.7.076015 http://scihub22266oqcxt.onion/10.1117/1.JBO.21.7.076015 C4963530!4963530 !27533243     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27533243 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       J+Biomed+Opt 2016 ; 21 (7 ): 76015 Nephropedia Template TP {{tp|p=27533243 |t=2016. Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images |pdf=|usr=}}{{27533243 }} gab.com Text Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images JO: J Biomed Opt http://www.kidney.de/pget.php?&tw=1&pmid=27533243 #FOAvip We introduce a metric in graph search and demonstrate its application for segmenting retinal optical coherence tomography (OCT) images of macular pathology. Our proposed ?adjusted mean arc length? (AMAL) metric is an adaptation of the lowest mean arc length search technique for automated OCT segmentation. We compare this method to Dijkstra?s shortest path algorithm, which we utilized previously in our popular graph theory and dynamic programming segmentation technique. As an illustrative example, we show that AMAL-based length-adaptive segmentation outperforms the shortest path in delineating the retina/vitreous boundary of patients with full-thickness macular holes when compared with expert manual grading. Twit Text FOAVip Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images ????J Biomed Opt http://www.kidney.de/pget.php?&tw=1&pmid=27533243 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27533243 #FOAvip English Wikipedia <ref>{{Cite pmid|27533243 }}</ref> Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images #MMPMID27533243 Keller B ; Cunefare D ; Grewal DS ; Mahmoud TH ; Izatt JA ; Farsiu S J Biomed Opt 2016[Jul]; 21 (7 ): 76015 PMID27533243 show ga We introduce a metric in graph search and demonstrate its application for segmenting retinal optical coherence tomography (OCT) images of macular pathology. Our proposed ?adjusted mean arc length? (AMAL) metric is an adaptation of the lowest mean arc length search technique for automated OCT segmentation. We compare this method to Dijkstra?s shortest path algorithm, which we utilized previously in our popular graph theory and dynamic programming segmentation technique. As an illustrative example, we show that AMAL-based length-adaptive segmentation outperforms the shortest path in delineating the retina/vitreous boundary of patients with full-thickness macular holes when compared with expert manual grading. |*Algorithms [MESH] |*Tomography, Optical Coherence [MESH] |Humans [MESH]Length-adaptive graph search for automatic segmentation of pathologica(epmc).pdf DeepDyve Pubget Overpricing