A new robust method for blood vessel segmentation in retinal fundus images based on weighted line detector and hidden...

Automatic vessel segmentation is a crucial preliminary processing step to facilitate ophthalmologist diagnosis in some diseases. But, due to the complexity of retinal fundus image, there are some problems on accurate segmentation of retinal vessel. In this paper, a new method for retinal vessel segmentation is proposed to handle two main problems: thin vessel missing and false detection in difficult regions.

First, an improved line detector is proposed and used to fast extract the major structures of vessels. Then, Hidden Markov model (HMM) is applied to effectively detect vessel centerlines that include thin vessels. Finally, a denoising approach is presented to remove noises and two types of vessels are unified to obtain the complete segmentation results.

Our method is tested on two public databases (DRIVE and STARE databases), and five measures namely accuracy (Acc), sensitivity (Se), specificity (Sp), Dice coefficient (Dc), structural similarity index (SSIM) and feature similarity index (FSIM) are used to evaluate our segmentation performance. The respective values of the performance measures are 0.9475, 0.7262, 0.9803, 0.7781, 0.9992 and 0.9793 for DRIVE dataset and 0.9535, 0.7865, 0.9730, 0.7764, 0.9987 and 0.9742 for STARE dataset.

The experiment results show that our method outperforms most published state-of-the-art methods and is better the result of a human observer. Moreover, in term of specificity, our proposed algorithm can obtain the best score among the unsupervised methods. Meanwhile, there are excellent structure and feature similarities between our result and the ground truth according to achieved SSIM and FSIM. Visual inspection on the segmentation results shows that the proposed method produces more accurate segmentations on some difficult regions such as optic disc and central light reflex while detecting thin vessels effectively compared with the other methods.