Detection of Glaucoma using ORB (Oriented FAST and Rotated BRIEF) Feature Extraction


  • Kazi Safayet Md. Shabbir Department of Mechatronics and Industrial Engineering, Chittagong University of Engineering and Technology, Chittagong-4349, Bangladesh
  • Md. Imteaz Ahmed Department of Mechanical Engineering, Chittagong University of Engineering and Technology, Chittagong-4349, Bangladesh
  • Marzan Alam Department of Mechatronics and Industrial Engineering, Chittagong University of Engineering and Technology, Chittagong-4349, Bangladesh



ORB, Glaucoma, Fundus, SIFT, Python


This research was utilized to identify glaucoma, a type of eye illness. This endeavor necessitates the use of pictures from the fundus camera for image processing. This study reflects the effort done to detect glaucoma-affected eyes utilizing image feature extraction using Oriented FAST and Rotated BRIEF (ORB). ORB is a binary descriptor approach that is based on BRIEF and is highly fast. This technique is insensitive to picture noise and is invariant to any rotation. ORB is two orders of magnitude faster than SURF and performs similarly to SIFT. It is more efficient than other texture analysis methods. It is less computationally difficult than other approaches in the literature. This technique extracts features and detects texture by inspecting each pixel of the retina picture. It was trained on 160 fundus pictures of normal and glaucoma-affected retinas. After that, any healthy or glaucoma-affected eye may be easily recognized by obtaining an accurate eye picture. The results reveal that this technique has a precision and accuracy of more than 90%.


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How to Cite

Md. Shabbir, K. S., Ahmed, M. I., & Marzan Alam. (2021). Detection of Glaucoma using ORB (Oriented FAST and Rotated BRIEF) Feature Extraction. Journal of Engineering Advancements, 2(03), 153–158.



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