000			02526 a2200325 4500
001			1138114499
005			20250317100352.0
008			250312042017xx 59 eng
020			_a9781138114494
037			_bTaylor & Francis _cGBP 81.99 _fBB
040			_a01
041			_aeng
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072	7		_aMED009000 _2bisac
072	7		_aMED085000 _2bisac
072	7		_aTEC059000 _2bisac
072	7		_a617.7350754 _2bisac
100	1		_aHerbert Jelinek
245	1	0	_aAutomated Image Detection of Retinal Pathology
250			_a1
260			_bCRC Press _c20170912
300			_a394 p
520			_bDiscusses the Effect of Automated Assessment Programs on Health Care Provision Diabetes is approaching pandemic numbers, and as an associated complication, diabetic retinopathy is also on the rise. Much about the computer-based diagnosis of this intricate illness has been discovered and proven effective in research labs. But, unfortunately, many of these advances have subsequently failed during transition from the lab to the clinic. So what is the best way to diagnose and treat retinopathy? Automated Image Detection of Retinal Pathology discusses the epidemiology of the disease, proper screening protocols, algorithm development, image processing, and feature analysis applied to the retina. Conveys the Need for Widely Implemented Risk-Reduction Programs Offering an array of informative examples, this book analyzes the use of automated computer techniques, such as pattern recognition, in analyzing retinal images and detecting diabetic retinopathy and its progression as well as other retinal-based diseases. It also addresses the benefits and challenges of automated health care in the field of ophthalmology. The book then details the increasing practice of telemedicine screening and other advanced applications including arteriolar-venous ratio, which has been shown to be an early indicator of cardiovascular, diabetes, and cerebrovascular risk. Although tremendous advances have been made in this complex field, there are still many questions that remain unanswered. This book is a valuable resource for researchers looking to take retinal pathology to that next level of discovery as well as for clinicians and primary health care professionals that aim to utilize automated diagnostics as part of their health care program.
700	1		_aMichael J. Cree _4A01
999			_c213 _d213