A mobile computer aided system for optic nerve head detection

Yaroub Elloumi; Mohamed Akil; Nasser Kehtarnavaz

doi:10.1016/j.cmpb.2018.05.004

A mobile computer aided system for optic nerve head detection

Comput Methods Programs Biomed. 2018 Aug:162:139-148. doi: 10.1016/j.cmpb.2018.05.004. Epub 2018 May 4.

Authors

Yaroub Elloumi¹, Mohamed Akil², Nasser Kehtarnavaz³

Affiliations

¹ Gaspard Monge Computer Science Laboratory, ESIEE-Paris, University Paris-Est Marne-la-Vallée, France; Medical Technology and Image Processing Laboratory, Faculty of medicine, University of Monastir, Tunisia. Electronic address: yaroub.elloumi@esiee.fr.
² Gaspard Monge Computer Science Laboratory, ESIEE-Paris, University Paris-Est Marne-la-Vallée, France.
³ Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA.

PMID: 29903480
DOI: 10.1016/j.cmpb.2018.05.004

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.

Keywords: Fundus image processing; Optic nerve head detection; Radon Transform; Smartphone-based CAD in ophthalmology.

MeSH terms

Algorithms
Computer Systems
Cost-Benefit Analysis
Diabetic Retinopathy / diagnostic imaging*
Diagnosis, Computer-Assisted*
Fundus Oculi
Humans
Image Interpretation, Computer-Assisted*
Image Processing, Computer-Assisted*
Mobile Applications*
Models, Statistical
Ophthalmology / instrumentation
Optic Disk
Smartphone