Visualising ganglion cell layer based on image entropy optimisation for adaptive contrast enhancement. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Han, J.‐H.; Cha, J.
• Is Part Of: Electronics letters. Volume 56:Issue 1(2020)
• Page Start: 25
• Page End: 27
• Publication Date: 2020-01-01
• Subjects: diseases -- biomedical optical imaging -- image segmentation -- medical image processing -- entropy -- eye -- optical tomography -- image enhancement; ganglion cell layer -- image entropy optimisation -- adaptive contrast enhancement -- optical coherence tomography -- visual identification -- retinal diseases -- extremely low image contrast -- limit‐clipping optimisation method -- targeted layers -- image morphology -- manual segmentation results -- important retinal layers; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2019.3006
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Optical coherence tomography cannot easily be used for visual identification of the ganglion cell layer (GCL) for diagnosing retinal diseases owing to the extremely low image contrast between adjacent layers. To solve this problem, the authors used a limit‐clipping optimisation method along with the image entropy to enhance the image contrast of targeted layers. As a result, the GCL was successfully extracted using an intelligent tracking system without impacting the segmentation of other retinal layers and image morphology. The segmentation results were evaluated through comparisons with manual segmentation results provided by clinical experts. The results of this study should help realise simple and efficient discrimination of important retinal layers for the early diagnosis of glaucoma.