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10.1155/2016/6814791 http://scihub22266oqcxt.onion/10.1155/2016/6814791 C4826680!4826680 !27110272     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27110272 &cmd=llinks): Failed to open stream: HTTP request failed! 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Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques |pdf=|usr=}}{{27110272 }} gab.com Text Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques JO: Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=27110272 #FOAvip With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC. Twit Text FOAVip Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques ????Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=27110272 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27110272 #FOAvip English Wikipedia <ref>{{Cite pmid|27110272 }}</ref> Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques #MMPMID27110272 Akyol K ; ?en B ; Bay?r ? Comput Math Methods Med 2016[]; 2016 (ä): 6814791 PMID27110272 show ga With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC. |*Pattern Recognition, Automated [MESH] |Algorithms [MESH] |Computer-Aided Design [MESH] |Databases, Factual [MESH] |Diabetic Retinopathy/*diagnostic imaging/pathology [MESH] |Diagnostic Techniques, Ophthalmological [MESH] |Fundus Oculi [MESH] |Humans [MESH] |Image Interpretation, Computer-Assisted/methods [MESH] |Image Processing, Computer-Assisted/methods [MESH] |Machine Learning [MESH] |Models, Statistical [MESH] |Neovascularization, Pathologic [MESH] |Optic Disk/diagnostic imaging/pathology [MESH] |Reproducibility of Results [MESH] |Retina/diagnostic imaging/pathology/physiology [MESH] |Retinal Vessels/diagnostic imaging/pathology [MESH]Automatic Detection of Optic Disc in Retinal Image by Using Keypoint D(epmc).pdf DeepDyve Pubget Overpricing