Although retinal vessel segmentation has been extensively researched, a robust and time efficient segmentation method is highly needed. This paper presents a local adaptive thresholding technique based on gray level cooccurrence matrix- (GLCM-) energy information for retinal vessel segmentation. Different thresholds were computed using GLCM-energy information. An experimental evaluation on DRIVE database using the grayscale intensity and Green Channel of the retinal image demonstrates the high performance of the proposed local adaptive thresholding technique. The maximum average accuracy rates of 0.9511 and 0.9510 with maximum average sensitivity rates of 0.7650 and 0.7641 were achieved on DRIVE and STARE databases, respectively. When compared to the widely previously used techniques on the databases, the proposed adaptive thresholding technique is time efficient with a higher average sensitivity and average accuracy rates in the same range of very good specificity.

As the use of robotic-assisted surgery systems continue to increase, highly accurate and timely efficient automatic vasculature detection techniques for large and thin vessels in the retinal images are needed. Vascular segmentation has however been challenging due to uneven illumination in retinal images. The use of efficient pre-processing techniques as well as good segmentation techniques are highly needed to produce good vessel segmentation results. This paper presents an investigatory study on the combination of phase congruence with fuzzy c-means and the combination of phase congruence with gray level co-occurrence (GLCM) matrix sum entropy for the segmentation of retinal vessels. Fuzzy C-Means combined with phase congruence yields a higher accuracy rate but a longer running time while compared to GLCM sum entropy combined with phase congruence. While compared with the widely previously used techniques on DRIVE and STARE databases, the techniques investigated yield high average accuracy rates.

Due to noise from uneven contrast and illumination during acquisition process of retinal fundus images, the use of efficient preprocessing techniques is highly desirable to produce good retinal vessel segmentation results. This paper develops and compares the performance of different vessel segmentation techniques based on global thresholding using phase congruency and contrast limited adaptive histogram equalization (CLAHE) for the preprocessing of the retinal images. The results obtained show that the combination of preprocessing technique, global thresholding, and postprocessing techniques must be carefully chosen to achieve a good segmentation performance.

As retinopathies continue to be major causes of visual loss and blindness worldwide, early detection and management of these diseases will help achieve significant reduction of blindness cases. However, an efficient automatic retinal vessel segmentation approach remains a challenge. Since efficient vessel network detection is a very important step needed in ophthalmology for reliable retinal vessel characterization, this paper presents the study on the combination of difference image and k-means clustering for the segmentation of retinal vessels. Stationary points in the vessel center-lines are used to model the detection of twists in the vessel segments. The combination of arc-chord ratio with stationary points is used to compute tortuosity index. Experimental results show that the proposed k-means combined with difference image achieved a robust segmentation of retinal vessels. A maximum average accuracy of 0.9556 and a maximum average sensitivity of 0.7581 were achieved on DRIVE database while a maximum average accuracy of 0.9509 and a maximum average sensitivity of 0.7666 were achieved on STARE database. When compared with the previously proposed techniques on DRIVE and STARE databases, the proposed technique yields higher mean sensitivity and mean accuracy rates in the same range of very good specificity. In a related development, a non-normalized tortuosity index that combined distance metric and the vessel twist frequency proposed in this paper also achieved a strong correlation of 0.80 with the expert ground truth.

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Segmentation of vessels in retinal images has become challenging due to the presence of non-homogeneous illumination across retinal images. This paper develops a novel adaptive thresholding technique based on local homogeneity information for Retinal vessel segmentation. Different types of local homogeneity information were investigated. An experimental evaluation on DRIVE database demonstrates the high performance of all types of homogeneity considered. An average accuracy of 0.9469 and average sensitivity of 0.7477 were achieved. While compared with widely previously used techniques on DRIVE database, the proposed adaptive thresholding technique is superior, with a higher average sensitivity and average accuracy rates in the same range of very good specificity.

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