Authors:
Amit Agarwal
1
;
Jayanthi Sivaswamy
1
;
Alka Rani
2
and
Taraprasad Das
3
Affiliations:
1
CVIT, International Institute of Information Technology, India
;
2
Aravind Eye Institute, India
;
3
LV Prasad Eye Institute, India
Keyword(s):
Capillary Non-Perfusion, Retina, Extrema Pyramid, Disease detection.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
Abstract:
Capillary Non-Perfusion (CNP) is a condition in diabetic retinopathy where blood ceases to flow to certain parts of the retina, potentially leading to blindness. This paper presents a solution for automatically detecting and segmenting CNP regions from fundus fluorescein angiograms (FFAs). CNPs are modelled as valleys, and a novel multiresolution technique for trough-based valley detection is presented. The proposed algorithm has been tested on 40 images and validated against expert-marked ground truth. Obtained results are presented as a receiver operating characteristic (ROC) curve. The area under this curve is 0.842 and the distance of ROC from the ideal point (0, 1) is 0.31.