Centerline extraction by neighborhood-statistics thinning for quantitative analysis of corneal nerve fibers

Abstract

Objective. Corneal nerve fiber (CNF) has been found to exhibit morphological changes associated with various diseases, which can therefore be utilized to aid in the early diagnosis of those diseases. CNF is usually visualized under corneal confocal microscopy (CCM) in clinic. To obtain the diagnostic biomarkers from CNF image produced from CCM, image processing and quantitative analysis are needed. Usually, CNF is segmented first and then CNF’s centerline is extracted, allowing for measuring geometrical and topological biomarkers of CNF, such as density, tortuosity, and length. Consequently, the accuracy of the segmentation and centerline extraction can make a big impact on the biomarker measurement. Thus, this study is aimed to improve the accuracy and universality of centerline extraction. Approach. We developed a new thinning algorithm based on neighborhood statistics, called neighborhood-statistics thinning (NST), to extract the centerline of CNF. Compared with traditional thinning and skeletonization techniques, NST exhibits a better capability to preserve the fine structure of CNF which can effectively benefit the biomarkers measurement above. Moreover, NST incorporates a fitting process, which can make centerline extraction be less influenced by image segmentation. Main results. This new method is evaluated on three datasets which are segmented with five different deep learning networks. The results show that NST is superior to thinning and skeletonization on all the CNF-segmented datasets with a precision rate above 0.82. Last, NST is attempted to be applied for the diagnosis of keratitis with the quantitative biomarkers measured from the extracted centerlines. Longer length and higher density but lower tortuosity were found on the CNF of keratitis patients as compared to healthy patients. Significance. This demonstrates that NST has a good potential to aid in the diagnostics of eye diseases in clinic.

A Retinex-based variational model for noise suppression and nonuniform illumination correction in corneal confocal microscopy images

“…In recent years, Corneal confocal microscopy (CCM) (Vagenas et al 2012, Chen et al 2022 has provided a turnaround for DPN detection, a rapid non-invasive in vivo clinical technique for acquiring CCM images of small nerve-fibers from patients. Nevertheless, the major limitation preventing the technique from being extended to wider clinical practice is that CCM images often require manual segmentation by the ophthalmologists in order to calculate the tortuosity level of the nerve fibers to define regeneration of the nerve fibers or early degeneration (Grisan et al 2008).…”