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Retinal vasculature segmentation in smartphone ophthalmoscope images

Publication Type : Conference Paper

Publisher : IFMBE Proceedings

Source : IFMBE Proceedings, Springer Verlag, Volume 52, p.64-67 (2015)

Url :

ISBN : 9783319194516

Keywords : Blood vessels, Bottom hat transformation, Diagnosis, Essential features, Fluorescein angiography, Image processing, Image segmentation, K-means, Level-set techniques, Morphological changes, Numerical methods, Ophthalmology, Retinal vasculature, Signal encoding, Smartphones, Total variation

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Mechanical Engineering

Year : 2015

Abstract : Retinal imaging system assists ophthalmologists to diagnose the diseases and to monitor the treatment processes. Conventionally, fundus retinal images are obtained from expensive systems like fluorescein angiography and fundus photography but these systems are large tabletop units and can only be handled by trained technicians. Hence, this study reports a low cost, compact and user friendly smartphone ophthalmoscope to perform indirect ophthalmoscopy. By using this system, initial and periodic screening of retina (both center and periphery regions) becomes easier. Traditionally, retinal diseases are diagnosed by manual observations of fundus images and it is a time consuming process. So, automatic retinal disease diagnosing systems are introduced by extracting the essential features of the fundus retinal images. One of the most essential features of the retina is the blood vessels as its morphological changes helps in diagnosing the retinal diseases. Hence, in this study blood vessels are extracted from smartphone ophthalmoscope (SO) images using level set method to develop an automatic retinal disease diagnosing systems for ophthalmologists. The performance of the retinal vasculature segmentation algorithm is compared and analyzed on DRIVE database of retinal images and on smartphone ophthalmoscope images using the measures like sensitivity, specificity and accuracy level. © Springer International Publishing Switzerland 2015.

Cite this Research Publication : SaSaranya Devi, Dr. K. I. Ramachandran, and Sharma, Ab, “Retinal vasculature segmentation in smartphone ophthalmoscope images”, in IFMBE Proceedings, 2015, vol. 52, pp. 64-67.

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