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Fast, accurate and robust retinal vessel segmentation system

100%

Jiang Z. , Yepez J. , An S. , Ko S.

2017

tom Vol. 37, no. 3

412--421

The accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic ophthalmological and cardiovascular diagnosis systems. Aside from accuracy, robustness and processing speed are also considered crucial for medical purposes. In order to meet those requirements, this work presents a novel approach to extract blood vessels from the retinal fundus, by using morphology-based global thresholding to draw the retinal venule structure and centerline detection method for capillaries. The proposed system is tested on DRIVE and STARE databases and has an average accuracy of 95.88% for single-database test and 95.27% for the cross-database test. Meanwhile, the system is designed to minimize the computing complexity and processes multiple independent procedures in parallel, thus having an execution time of 1.677 s per image on CPU platform.

A hybrid method for blood vessel segmentation in images

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Díaz P. , Rodríguez A. , Cuevas E. , Valdivia A. , Chavolla E. , Pérez-Cisneros M. , Zaldívar D.

tom Vol. 39, no. 3

814--824

In the last years, image processing has been an important tool for health care. The analysis of retinal vessel images has become crucial to achieving a better diagnosis and treatment for several cardiovascular and ophthalmological deceases. Therefore, an automatic and accurate procedure for retinal vessel and optic disc segmentation is essential for illness detection. This task is extremely hard and time-consuming, often requiring the assistance of human experts with a high degree of professional skills. Several retinal vessel segmentation methods have been developed with satisfactory results. Nevertheless, most of such techniques present a poor performance mainly due to the complex structure of vessels in retinal images. In this paper, an accurate methodology for retinal vessel and optic disc segmentation is presented. The proposed scheme combines two different techniques: the Lateral Inhibition (LI) and the Differential Evolution (DE). The LI scheme produces a new image with enhanced contrast between the background and retinal vessels. Then, the DE algorithm is used to obtain the appropriate threshold values through the minimization of the cross-entropy function from the enhanced image. To evaluate the performance of the proposed approach, several experiments over images extracted from STARE, DRIVE, and DRISHTI-GS databases have been conducted. Simulation results demonstrate a high performance of the proposed scheme in comparison with similar methods reported in the literature.

A dark and bright channel prior guided deep network for retinal image quality assessment

100%

Xu Z. , Zou B. , Liu Q.

Biocybernetics and Biomedical Engineering

2022

tom Vol. 42, no. 3

772--783

Background and objective: Retinal image quality assessment is an essential task for the diagnosis of retinal diseases. Recently, there are emerging deep models to grade quality of retinal images. However, current models either directly transfer classification networks originally designed for natural images to quality classification of retinal images or introduce extra image quality priors via multiple CNN branches or independent CNNs. The purpose of this work is to address retinal image quality assessment by a simple deep model. Methods: We propose a dark and bright channel prior guided deep network for retinal image quality assessment named GuidedNet. It introduces dark and bright channel priors into deep network without extra parameters increasing and allows for training end-to-end. In detail, the dark and bright channel priors are embedded into the start layer of a deep network to improve the discriminate ability of deep features. Moreover, we re-annotate a new retinal image quality dataset called RIQA-RFMiD for further validation. Results: The proposed method is evaluated on a public retinal image quality dataset Eye-Quality and our re-annotated dataset RIQA-RFMiD. We obtain the average F-score of 88.03% on Eye-Quality and 66.13% on RIQA-RFMiD, respectively. Conclusions: We investigate the utility of the dark and bright channel priors for retinal image quality assessment. And we propose a GuidedNet by embedding the dark and bright channel priors into CNNs without much model burden. Moreover, to valid the GuidedNet, we re-create a new dataset RIQA-RFMiD. With the GuidedNet, we achieves state-of-the-art performances on a public dataset Eye-Quality and our re-annotated dataset RIQA-RFMiD.

Automatic parameters selection of Gabor filters with the imperialism competitive algorithm with application to retinal vessel segmentation

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Farokhian F. , Yang C. , Demirel H. , Wu S. , Beheshti I.

tom Vol. 37, no. 1

246--254

Retinal images play an important role in the early diagnosis of diseases such as diabetes. In the present study, an automatic image processing technique is proposed to segment retinal blood vessels in fundus images. The technique includes the design of a bank of 180 Gabor filters with varying scale and elongation parameters. Furthermore, an optimization method, namely, the imperialism competitive algorithm (ICA), is adopted for automatic parameter selection of the Gabor filter. In addition, a systematic method is proposed to determine the threshold value for reliable performance. Finally, the performance of the proposed approach is analyzed and compared with that of other approaches on the basis of the publicly available DRIVE database. The proposed method achieves an area under the receiver operating characteristic curve of 0.953 and an average accuracy of up to 0.9392. Thus, the results show that the proposed method is well comparable with alternative methods in the literature.