A mobile computer aided system for optic nerve head detection. (August 2018)

• Record Type: Journal Article
• Authors: Elloumi, Yaroub; Akil, Mohamed; Kehtarnavaz, Nasser
• Is Part Of: Computer methods and programs in biomedicine. Volume 162(2018)
• Page Start: 139
• Page End: 148
• Publication Date: 2018-08
• Subjects: Optic nerve head detection -- Smartphone-based CAD in ophthalmology -- Fundus image processing -- Radon Transform; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.05.004
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Propose a method of a computational efficient Optic Head Nerve (ONH) detection algorithm. Provide a processing for fundus location in smartphone-captured retinal image. Develop a software performing the fundus location and the ONH detection algorithms on Android smartphone as an app. Propose a mobile and cost-effective computer-aided diagnosis (CAD) system in ophthalmology. 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 thed -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 thed -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.