Visualising ganglion cell layer based on image entropy optimisation for adaptive contrast enhancement

• Published in: Electronics letters Vol. 56; no. 1; pp. 25 - 27
• Main Authors: Han, J.-H, Cha, J
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 09.01.2020
• Subjects: adaptive contrast enhancement; biomedical optical imaging; diseases; entropy; extremely low image contrast; eye; ganglion cell layer; Image and vision processing and display technology; image enhancement; image entropy optimisation; image morphology; image segmentation; important retinal layers; limit‐clipping optimisation method; manual segmentation results; medical image processing; optical coherence tomography; optical tomography; retinal diseases; targeted layers; visual identification; image entropy optimisation; retinal diseases; ganglion cell layer; optical coherence tomography; visual identification; image morphology; diseases; optical tomography; limit-clipping optimisation method; important retinal layers; eye; entropy; image enhancement; targeted layers; image segmentation; adaptive contrast enhancement; extremely low image contrast; manual segmentation results; medical image processing; biomedical optical imaging
• ISSN: 0013-5194; 1350-911X; 1350-911X
• DOI: 10.1049/el.2019.3006

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.