An efficient intelligent analysis system for confocal corneal endothelium images

Abstract

A confocal microscope provides a sequence of images of the corneal layers and structures at different depths from which medical clinicians can extract clinical information on the state of health of the patient's cornea. Hybrid model based on snake and particle swarm optimisation (S-PSO) is proposed in this paper to analyse the confocal endothelium images. The proposed system is able to pre-process (quality enhancement, noise reduction), detect the cells, measure the cell density and identify abnormalities in the analysed data sets. Three normal corneal data sets acquired using confocal microscope, and two abnormal endothelium images associated with diseases have been investigated in the proposed system. Promising results are achieved and the performance of this system are compared with the performance of two morphological based approaches. The developed system can be deployed as clinical tool to underpin the expertise of ophthalmologists in analysing confocal corneal images.

A fully automated cell segmentation and morphometric parameter system for quantifying corneal endothelial cell morphology

“…The performance of the two algorithms was evaluated on a set of 52 corneal endothelial images captured from 23 patients using a white-light slit-scanning confocal microscope and compared with the NAVIS software. Sharif et al [3] developed a hybrid model for analyzing confocal endothelial images based on a combination of Active Contour version of the Snake (S) model and a Particle Swarm Optimization (PSO) approach (S-PSO). Firstly, a pre-processing procedure was employed using DFT combined with a Band-pass Butterworth filter to enhance the quality and reduce the noise level of the input image.…”

“…As shown in Fig.1, the cornea is the anterior transparent part of the eye, which transfers and focuses light onto the retina. It is composed of five different layers, covering crucial internal structures of the human eye, including the iris, pupil, lens, and anterior chamber [3] [4]. Corneal transparency is primarily dependent on corneal stromal hydration, which is maintained by an active transport mechanism in the corneal endothelium [5].…”

Improved Accuracy and Robustness of a Corneal Endothelial Cell Segmentation Method Based on Merging Superpixels

“…In recent years, several cell segmentation techniques for in vivo corneal endothelium images were proposed. More studies were focused on segmenting confocal images [10,11] rather than specular [9], probably because of its better image quality and signal-to-noise ratio (SNR). Hence, there is a need to develop algorithms that can accurately segment specular microscopy endothelial images.…”

A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images

“…CCM has also been used in the assessment of peripheral neuropathies [10,11,12,13,14,15,16,17,18,19]. The development of automated imaging algorithms for the processing of CCM images [20,21,22,23] is a necessary accompaniment to such work. Diabetes is the main cause of neuropathy complications and in the present work patients with a known history of cancer, chemotherapy, alcoholism, celiac disease or a deficiency of vitamin B12 or folate, abnormality in ANA or immunoglobulins were excluded to remove other causes of peripheral neuropathy from the input data.…”