تقسیم بندی عروق شبکیه چشم بر اساس آشکارساز خطی
Abstract
I. Introduction
II. Gabor Wavelet and Line Detector
III. Material and Methods
IV. Results and Discussion
V. Conclusion
Authors
Figures
References
Abstract
Eye and systemic diseases are known to manifest themselves in retinal vasculature. Segmentation of retinal vessel is one of the important steps in retinal image analysis. A simple unsupervised method based on Gabor wavelet and Multiscale Line Detector is proposed for retinal vessel segmentation. Vessels are enhanced by linear superposition of first scale Gabor wavelet image and complemented Green channel. Multiscale Line Detector is used to segment the blood vessels. Finally, a simple post processing scheme based on median filtering is deployed to remove false positives. The proposed scheme was evaluated with publicly available datasets called DRIVE, STARE and HRF, obtaining an accuracy of 0.9470, 0.9472, and 0.9559, and a sensitivity of 0.7421, 0.8004, and 0.7207, respectively. These results are comparable to the state-of-the-art methods, albeit with a simpler approach.
Introduction
One of the important tasks in diagnosing different medical conditions such as diabetic retinopathy, cardiovascular diseases, and stroke is the segmentation of blood vessels in color medical images. To this end, different strategies have been devised. The strategies can be roughly grouped into i) multiscale, ii) matched filtering, iii) mathematical morphology, iv) hierarchical, v) model and vi) deep learning approach [1]. Furthermore, they can also be categorized into supervised and unsupervised algorithm. The prominent strategies based on multiscale are [2], [3]. Soares et al. [2] used Gabor wavelet transform with four scales (2,3,4,5) to account for different width sizes of blood vessel, and supervised classification. Nyugen et al. [4] proposed blood vessel segmentation using a multi-scale line detection based technique. The approach is an extension of the scheme based on single scale line detector and support vector machine [5]. Examples of filter based approach are [6]–[8] and mathematical morphology based are [9]–[11]. Retinal vessel segmentations based on hierarchical detections are [12], [13], model based approaches [14]–[16], and deep learning based schemes [13], [17]–[21]. One major challenge in retinal image analysis especially for accurate vessel detection is low and varying contrast. A method based on Gabor wavelet and multi-scale line detector is being proposed here. The Gabor wavelet transform presents high frequency precision in low frequencies and high spatial precision in high frequencies. In other words, the transform is suitable for detecting edges and other singularities in the image [22], [23].