Analysis of Vessel Segmentation Based on Various Enhancement Techniques for Improvement of Vessel Intensity Profile

Table 1

Summary of advantages and disadvantages of vessel segmentation methods.

Methods and References	Data set	Advantages	Disadvantages
Extended median filter [23]	DRIVE	Simple to implement	Less accuracy
Blood vessels segmentation based on simplified PCNN and fast 2D-otsu algorithm [24]	DRIVE	Performed better in small vessels with high accuracy	Cannot remove pathological regions with low specificity
Clifford matched filter [25]	DRIVE	High accuracy	Low sensitivity
Using MF/ant (matched filter/ant colony) [26]	DRIVE	High accuracy	Cannot remove pathological regions with low specificity
Improved matched filter [29]	DRIVE	High accuracy	Low specificity
Genetic algorithm matched filter optimization [30]	DRIVE	High sensitivity	Low accuracy
Improved multi-scale matched filter using PSO algorithm [31]	DRIVE	High sensitivity	Low specificity and accuracy
Segmentation of retinal vessels by the use of Gabor wavelet and linear mean-squared-error classifier [32]	DRIVE	—	Very low accuracy
Gabor filters with the imperialism competitive algorithm [37]	DRIVE	High sensitivity and specificity	Low accuracy
Hybrid segmentation approach [38]	DRIVE	High accuracy	Very low sensitivity
A hybrid method to enhance thick and thin vessels [39]	DRIVE	High sensitivity and specificity	Low accuracy
A hybrid method for blood vessel segmentation [41]	DRIVE	—	Very high accuracy, sensitivity, and specificity
Frangi filter coupled with the probabilistic patch-based denoiser [44]	DRIVE	High sensitivity	Low accuracy
A context spatial U-Net for accurate blood vessel segmentation [45]	DRIVE	High sensitivity and specificity	Low accuracy