Dual-Model Automatic Detection of Nerve-Fibres in Corneal Confocal Microscopy Images

• DOI: 10.1007/978-3-642-15705-9_37
• Publication Date: 2010-09-20T02:04:39Z
• Authors: M. A. Dabbah; J. Graham; I. Petropoulos; M. Tavakoli; R. A. Malik
• Keywords: Diabetic Retinopathy; Microscopy, Confocal; Reproducibility of Results; Image Enhancement; Models, Biological; Sensitivity and Specificity; Pattern Recognition, Automated; Cornea; Ophthalmoscopy; 03 medical and health sciences; Nerve Fibers; 0302 clinical medicine; Artificial Intelligence; Image Interpretation, Computer-Assisted; Humans; Algorithms

Corneal Confocal Microscopy (CCM) imaging is a non-invasive surrogate of detecting, quantifying and monitoring diabetic peripheral neuropathy. This paper presents an automated method for detecting nerve-fibres from CCM images using a dual-model detection algorithm and compares the performance to well-established texture and feature detection methods. The algorithm comprises two separate models, one for the background and another for the foreground (nerve-fibres), which work interactively. Our evaluation shows significant improvement (p approximately 0) in both error rate and signal-to-noise ratio of this model over the competitor methods. The automatic method is also evaluated in comparison with manual ground truth analysis in assessing diabetic neuropathy on the basis of nerve-fibre length, and shows a strong correlation (r = 0.92). Both analyses significantly separate diabetic patients from control subjects (p approximately 0).