## 4 Discussion  
The proposed vein extraction module represents an integration among several image processing tools to extract the correct vein from the acquired images. The first stage applies the necessary steps to focus only on the area that contains veins by extracting the region of interest (ROI). Next, image enhancement is a convenient step to enhance the veins' sharpness through deploying CLAHE. Furthermore, this stage tackles removing several types of noises. The benefit of combining both adaptive thresholding with morphological operations results in extracting potential areas that contain veins. Selecting the best candidate vein for injection is the most critical stage as it identifies the vein using two approaches. The first approach, Model A, selects the vein,ocated within theragest perimeter contour. So, all contours are extracted first where the Hough transform is applied to the one with theragest perimeter. Therefore, Model A quickly finds theonegestine and marks it as a selected vein, as shown in Figure 8(a). This is aligned with searching for a visible vein that normally has aarge thickness compared to the other edges found in the acquired image. Although  
The results presented in the previous section provide a set of observations related to the accuracy of the vein extraction module. These observations are:  
* Identifying veins using both Model A and Model B: Vein selection was marked successfully for 56.5% of all test cases, using both models proposed in the vein selection stage. * Images with no vein selection: The proposed module did not identify the veins for about 8.7% of test cases. It could identify the veins in these cases when changingighting source intensity in such a way that makes the veins appear more visible. * Identifying veins using single model Model A, or Model B: The former model achieves higher accuracy in selecting the veins compared to Model B. Since the former model has the homogenate without applying any validation, it outputs directly theongestine detected from theargest perimeter contour. Model B achievesess accuracy performance, 61%. It omits the vein selection when there are noises coincident. The test images where its veins were successfully selected by using Model A are already identified by deploying Model B. In these images, Model B is providing ateast two vein selections. However, these selections do not provideines coincidentally. This explains theo accuracy when deploying Model B in the vein selection stage. * Running time: The proposed vein extraction module using Model A offers a quick solution for finding the vein, in comparison to Model B. This has a relationship to the amount of image processed by the Hough transform. Model A processes a region that is a subset of the entire image, while Model B applies the Hough transform at most three times using 100%, 25%, and 6.3% from images' size. From the results section, the vein extraction module using Model B takes twice the execution time when compared to the corresponding time taken by applying Model A. Although the proposed vein extraction takes a few seconds to find the suggested vein for injection, it does not target real-time applications. This is due to the robotic device, which will be strapped around the patient's arm. * Gender effect: The ratio among male and female participants is 6.7:1. It cannot obtain a clear statement for the given test images since the majority of images belong to male participants. * Skin condition: it could be plain or not (covered by hair). Skin covered by hair increases the difficulty of detecting veins as it decreases the veins' visibility. Test cases have about 47.8% of all test images where their skin is covered by hair. Both Models A and B for vein selection successfully detected the veins (about 63.6% from all images covered by hair)
- Light intensity: Changing the LED source intensity has a direct effect on the veins' visibility. - ROI: Defining a wrong region of interest for the acquired image could increase the difficulty of identifying the correct veins.