Blood vessel segmentation from digital images is one of the valuable processes for medical diagnosis. Many researchers have proposed blood vessel segmentation algorithms, which can segment the blood vessels automatically or with minimum human interventions. One of the popular blood vessel segmentation branches is edge-based segmentation. In this approach, only the edges are detected by the algorithm. While developing edge segmentation algorithms, researchers must evaluate their proposed methods’ performance. If full-reference-based quality measures are utilized, the ground truth, which shows the targetted segmentation output, is needed. This ground truth is commonly generated manually, where human experts identify and draw the edges. However, the manually segmented edges may differ depending on the experts due to several factors, including individual preference. The work in this paper aims to give some insight into how to combine these images. This paper suggests that the edges be classified as useful edges, weak edges, and unintentional edges.
Ground Truth from Multiple Manually Marked Images to Evaluate Blood Vessel Segmentation
Lect. Notes Electrical Eng.
International Conference on Robotics, Vision, Signal Processing and Power Applications ; 2021 April 05, 2021 - April 06, 2021
Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications ; Chapter : 67 ; 531-536
2024-03-31
6 pages
Article/Chapter (Book)
Electronic Resource
English
Automatic tuning for the segmentation of infrared images considering uncertain ground truth
British Library Online Contents | 2009
|British Library Conference Proceedings | 2013
|