Abstract
The tortuosity changes of curvilinear anatomical organs such as nerve fibers or vessels have a close relationship with a number of diseases. Therefore, the automatic estimation and representation of the tortuosity is desired in medical image for such organs. In this paper, an automated framework for tortuosity estimation is proposed for corneal nerve and retinal vessel images. First, the weighted local phase tensor-based enhancement method is employed and the curvilinear structure is extracted from raw image. For each curvilinear structure with a different position and orientation, the curvature is measured by the exponential curvature estimation in the 3D space. Then, the tortuosity of an image is calculated as the weighted average of all the curvilinear structures. Our proposed framework has been evaluated on two corneal nerve fiber datasets and one retinal vessel dataset. Experiments on three curvilinear organ datasets demonstrate that our proposed tortuosity estimation method achieves a promising performance compared with other state-of-the-art methods in terms of accuracy and generality. In our nerve fiber dataset, the method achieved overall accuray of 0.820, and 0.734, 0.881 for sensitivity and specificity, respectively. The proposed method also achieved Spearman correlation scores 0.945 and 0.868 correlated with tortuosity grading ground truth for arteries and veins in the retinal vessel dataset. Furthermore, the manual labeled 403 corneal nerve fiber images with different levels of tortuosity, and all of them are also released for public access for further research.
Highlights
As one of the most significant biomarkers, tortuosity of anatomical curvilinear organ has a close relationship with human diseases in medical images
The imbalanced intensity dims the appearance of curvilinear structures, such as nerve fibers in confocal microscopy (CCM) imagery, and creates difficulty in distinguishing the nerve fibers and estimating their tortuosity from the background [29]
Our proposed curvilinear structure enhancement method is evaluated by tortuosity grading and fiber segmentation
Summary
As one of the most significant biomarkers, tortuosity of anatomical curvilinear organ has a close relationship with human diseases in medical images. Various automatic tortuosity measurement approaches [1,2,7] fall into five steps: first, original image usually is enhanced by filters in the vessel segmentation problems. The performance of conventional tortuosity measurements sometimes will be affected by data differences, image pre-processing, and segmentation of curve structures. 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. 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. We have made all the tortuosity datasets available online
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