A Trio-Method for Retinal Vessel Segmentation using Image Processing
This work addresses efficient preprocessing for retinal vessel segmentation, which could aid in medical image analysis, but it appears incremental with no clear SOTA claims.
The paper tackles retinal vessel segmentation by proposing a triple preprocessing approach (Gabor filtering, Gaussian blur, edge detection with Sobel and pruning) and two U-Net architectures, achieving varied results on the DRIVE database.
Inner Retinal neurons are a most essential part of the retina and they are supplied with blood via retinal vessels. This paper primarily focuses on the segmentation of retinal vessels using a triple preprocessing approach. DRIVE database was taken into consideration and preprocessed by Gabor Filtering, Gaussian Blur, and Edge Detection by Sobel and Pruning. Segmentation was driven out by 2 proposed U-Net architectures. Both the architectures were compared in terms of all the standard performance metrics. Preprocessing generated varied interesting results which impacted the results shown by the UNet architectures for segmentation. This real-time deployment can help in the efficient pre-processing of images with better segmentation and detection.