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A Mobile Computer Aided System for Optic Nerve Head Detection
A Mobile Computer Aided System for Optic Nerve Head Detection
Abstract : Background and objective - The detection of optic nerve head (ONH) in retinal fundus images plays a key role in identifying Diabetic Retinopathy (DR) as well as other abnormal conditions in eye examinations. This paper presents a method and its associated software towards the development of an Android smartphone app based on a previously developed ONH detection algorithm. The development of this app and the use of the d-Eye lens which can be snapped onto a smartphone provide a mobile and cost-effective computer-aided diagnosis (CAD) system in ophthalmology. In particular, this CAD system would allow eye examination to be conducted in remote locations with limited access to clinical facilities. Methods - A pre-processing step is first carried out to enable the ONH detection on the smartphone platform. Then, the optimization steps taken to run the algorithm in a computationally and memory efficient manner on the smartphone platform is discussed. Results - The smartphone code of the ONH detection algorithm was applied to the STARE and DRIVE databases resulting in about 96% and 100% detection rates, respectively, with an average execution time of about 2 s and 1.3 s. In addition, two other databases captured by the d-Eye and iExaminer snap-on lenses for smartphones were considered resulting in about 93% and 91% detection rates, respectively, with an average execution time of about 2.7 s and 2.2 s, respectively.
Cited literature [52 references]
https://hal-upec-upem.archives-ouvertes.fr/hal-01796762
Contributor : Yaroub Elloumi Connect in order to contact the contributor
Submitted on : Tuesday, May 22, 2018 - 1:54:20 AM
Last modification on : Tuesday, October 19, 2021 - 11:26:22 AM
Long-term archiving on: : Tuesday, September 25, 2018 - 12:04:08 PM
Contributor : Yaroub Elloumi Connect in order to contact the contributor
Submitted on : Tuesday, May 22, 2018 - 1:54:20 AM
Last modification on : Tuesday, October 19, 2021 - 11:26:22 AM
Long-term archiving on: : Tuesday, September 25, 2018 - 12:04:08 PM
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