Automated Tortuosity Analysis of Nerve Fibers in Corneal Confocal Microscopy

Abstract

Precise characterization and analysis of corneal nerve fiber tortuosity are of great importance in facilitating examination and diagnosis of many eye-related diseases. In this paper we propose a fully automated method for image-level tortuosity estimation, comprising image enhancement, exponential curvature estimation, and tortuosity level classification. The image enhancement component is based on an extended Retinex model, which not only corrects imbalanced illumination and improves image contrast in an image, but also models noise explicitly to aid removal of imaging noise. Afterwards, we take advantage of exponential curvature estimation in the 3D space of positions and orientations to directly measure curvature based on the enhanced images, rather than relying on the explicit segmentation and skeletonization steps in a conventional pipeline usually with accumulated preprocessing errors. The proposed method has been applied over two corneal nerve microscopy datasets for the estimation of a tortuosity level for each image. The experimental results show that it performs better than several selected state-of-the-art methods. Furthermore, we have performed manual gradings at tortuosity level of four hundred and three corneal nerve microscopic images, and this dataset has been released for public access to facilitate other researchers in the community in carrying out further research on the same and related topics.

Corneal nerve tortuosity grading via ordered weighted averaging‐based feature extraction

“…15,16 A number of measures have been defined with respect to different criteria, such as the length-based, [17][18][19] the angle-based, 12,13,15,20 and the curvature-based measures. 14,21 It is worth noting that most of these existing measures are designed for quantifying specific anatomical structures (such as retinal vessel, 17 intracerebral vasculature, 18 and corneal nerve 22 ). As such, there is no universal agreement as to which standard or measure to apply for when quantifying the tortuosity of nerve fibers.…”

“…The definition and measurement of tortuosity has been extensively studied on medical 13,14 and other forms of images in the literature 15,16 . A number of measures have been defined with respect to different criteria, such as the length‐based, 17–19 the angle‐based, 12,13,15,20 and the curvature‐based measures 14,21 . It is worth noting that most of these existing measures are designed for quantifying specific anatomical structures (such as retinal vessel, 17 intracerebral vasculature, 18 and corneal nerve 22 ).…”

Automatic Tortuosity Estimation of Nerve Fibers and Retinal Vessels in Ophthalmic Images

“…In order to make the tortuosity measurement more robust, the method is also invariant to scaling, rotation, and translation [6] in the clinical scence. Our previous work [28] evaluated the tortuosity assessment on the Retinex-based nerve fiber enhancement image. Therefore, an automatic and accurate measurement method is needed to evaluate the curvature of the curve.…”

“…To tackle these problems, this paper introduces a new reliable framework for tortuosity assessment, which can conquer the human subjective error and metric variations. In addition, compared with the previous work [28], we evaluate the tortuosity assessment both on the nerve fiber and retinal vessel image based on the local phase tensor enhancement in order to verify the robustness of the method. Most of the tortuosity metrics are evaluated quantitatively on the dataset.…”

Corrections to “Automated Tortuosity Analysis of Nerve Fibers in Corneal Confocal Microscopy”

“…I N THE above article [1], there were two errors in the printed article that the authors want to correct. 2) In Section II-D, "Implementation Details" (p. 2728), the second sentence "The whole process is repeated until the difference between R k and R k+1 is smaller than 0.001."…”