# Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation
## 3 Materials and Methods
### Human Visual System Based Binarization  
To binarize the enhanced image, shown in Figure 1, last row middle image, the OFF-center surround method is used. This method is inspired from human visual system (HVS) [19; 20] and was originally tested for document binarization and produced better results for constraints like shadows, varying illumination, noise, smear, along with strain degradations in the image. The ability to percept brightness from darkness in HVS is promoted based on two distinct cell populations for antagonistic responses, which are ON and OFF [19; 20] center ganglion cells. Stimulations of the OFF cells is done based upon light decrements (by using dark stimuli across bright background) as is the case of blood vessel segmentation. The OFF center surround cells have two different sizes in order to respond to small and large stimuli. At least, the surround size has to be wide like the width of the thickest vessel in our case. First, the image is preprocessed pixel by pixel as described by the following equation so as to make the pixel value 0-255:  
\[O^{\prime}_{i,j}=\frac{O_{i,j}-O_{min}}{O_{max}-O_{min}}\times 255 \tag{9}\]  
where \(O_{min}\) represents the minimum for the original image \(O\), \(O_{max}  
\[\text{Spe}=1-\text{TN}/(\text{TN}+\text{FP}) \tag{18}\] \[\text{Acc}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+ \text{FN}) \tag{19}\]  
The strength and weaknesses of proposed algorithm is found using images of the both above mentioned datasets. For the DRIVE dataset, separate images for training and testing are provided which are a set of 20 images each while in the case of the STARE dataset only 20 images are there. So there is no subdivision of dataset between train and test. Hence, similar to [21], we have used the first five images to estimate parameters of the proposed algorithm. In testing, all the 20 images from STARE dataset are used as described in Table 3. The image-by-image results of the proposed algorithm are presented in Table 3, where the first three columns are for the DRIVE dataset and the last three columns are for the STARE dataset. On DRIVE dataset, the mean values for sensitivity (Sen), specificity (Spe) and accuracy (Acc) are 0.7272, 0.9798, and 0.9573 respectively. The maximum values obtained were 0.8303, 0.9874, and 0.9624 for Sen, Sep, and Acc, respectively. Similarly, For the STARE dataset, the mean values obtained were 0.7164, 0.9760, and 0.9560 for Sen, Spe, and Acc, respectively. The maximum values obtained were 0.8483, 0.9888, and 0.9751 for Sen, Spe, and Acc, respectively. Shown in Figure 2 are the best-case accuracies obtained on the DRIVE (first row) and STARE benchmark datasets (second row). The third column in Figure 2 illustrates the segmented images using the proposed algorithm. White pixels are the true vessel pixels, while the red are false positives and green represent missed vessel pixels. It can be noticed that the proposed method is good at segmenting the thick vessel very accurately, having very few false positives along the thick vessel edges. Figure 3, describes the worst-case accuracies obtained on DRIVE (first row) and STARE benchmark (second row) datasets. By observing Figure 3, it can be noted that the suggested technique has missed many fine vessel pixels. The thick vessels are still detected more precisely and clearly with very few false positives detected along their edges. In addition, there are negligible numbers of false positive pixels due to the region of interest (ROI) boundary and optic disc region, as can be observed both in the best-case and the worst-case accuracies, shown in Figures 2 and 3, respectively. By careful observation of Figures 2 and 3, it can be noticed that the suggested method is not able to segment many fine blood vessels. Also in case of fine vessels, there are false positives near their edges. Thus limitation of the suggested approach is the accurate detection for the fine vessel.