Automated detection of glaucoma using optical coherence tomography angiogram images

Yam Meng Chan; E Y K Ng; V Jahmunah; Joel En Wei Koh; Oh Shu Lih; Leonard Yip Wei Leon; U Rajendra Acharya

doi:10.1016/j.compbiomed.2019.103483

Automated detection of glaucoma using optical coherence tomography angiogram images

Comput Biol Med. 2019 Dec:115:103483. doi: 10.1016/j.compbiomed.2019.103483. Epub 2019 Oct 10.

Authors

Yam Meng Chan¹, E Y K Ng², V Jahmunah³, Joel En Wei Koh³, Oh Shu Lih³, Leonard Yip Wei Leon⁴, U Rajendra Acharya⁵

Affiliations

¹ Department of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
² Department of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore. Electronic address: MYKNG@ntu.edu.sg.
³ Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
⁴ Department of Ophthalmology, Tan Tock Seng Hospital, National Healthcare Group, Singapore.
⁵ Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore.

PMID: 31698235
DOI: 10.1016/j.compbiomed.2019.103483

Abstract

Glaucoma is a malady that occurs due to the buildup of fluid pressure in the inner eye. Detection of glaucoma at an early stage is crucial as by 2040, 111.8 million people are expected to be afflicted with glaucoma globally. Feature extraction methods prove to be promising in the diagnosis of glaucoma. In this study, we have used optical coherence tomography angiogram (OCTA) images for automated glaucoma detection. Ocular sinister (OS) from the left eye while ocular dexter (OD) were obtained from right eye of subjects. We have used OS macular, OS disc, OD macular and OD disc images. In this work, local phase quantization (LPQ) technique was applied to extract the features. Information fusion and principal component analysis (PCA) are used to combine and reduce the features. Our method achieved the highest accuracy of 94.3% using LPQ coupled with PCA for right eye optic disc images with AdaBoost classifier. The proposed technique can aid clinicians in glaucoma detection at an early stage. The developed model is ready to be tested with more images before deploying for clinical application.

Keywords: 10-fold cross validation; Glaucoma; Image analysis; Information fusion; Local phase quantization; Principal component analysis.

MeSH terms

Angiography*
Databases, Factual*
Female
Glaucoma / diagnostic imaging*
Humans
Image Processing, Computer-Assisted*
Male
Optic Disk / diagnostic imaging*
Tomography, Optical Coherence*