A Comparative Study of Different Blood Vessel Detection on Retinal Images

  • Wan Azani Mustafa Universiti Malaysia Perlis
  • Ahmad Syauqi Mahmud Universiti Malaysia Perlis
  • Muhammad Zaid Aihsan Universiti Malaysia Perlis
  • M. Saifizi Universiti Malaysia Perlis
  • C. Charin Universiti Malaysia Perlis
Keywords: Blood Vessel, Retinal, Review, Detection

Abstract

Detection of blood vessel plays an important stage in different medical areas, such as ophthalmology, oncology, neurosurgery, and laryngology. The significance of the vessel analysis was helped by the continuous overview in clinical studies of new medical technologies intended for improving the visualization of vessels. In this paper, several local segmentation techniques which include such as Vascular Tree Extraction, Tyler L. Coye and Line tracking, Kirsch’s Template and Fuzzy C Mean methods were studied. The main objective is to determine the best approaches in order to detect the blood vessel on the degraded retinal input image (DRIVE dataset). A few Image Quality Assessment (IQA) was obtained to prove the effectiveness of each detection methods. Overall, the result of sensitivity highest came from Kirsch Templates (96.928), while specificity from Fuzzy C means (77.573). However, in term of accuracy average, the Line Tracking method is more successful compared to the other methods.

Downloads

Download data is not yet available.
Published
05-09-2019
How to Cite
Mustafa, W. A., Mahmud, A. S., Aihsan, M. Z., Saifizi, M., & Charin, C. (2019). A Comparative Study of Different Blood Vessel Detection on Retinal Images. International Journal of Integrated Engineering, 11(4). Retrieved from https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/4718