Authors:
Elizabeth M. Massey
1
;
James A. Lowell
1
;
Andrew Hunter
2
and
David Steel
2
Affiliations:
1
University of Lincoln; Foster Findlay Associates Limited, United Kingdom
;
2
University of Lincoln; Sunderland Eye Infirmary, United Kingdom
Keyword(s):
Computer vision, Retinal lesion segmentation, Segmentation, Level set methods.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Segmentation and Grouping
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We implement the level set using a fast upwind scheme and compare the proposed method against five other segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician
marked-up boundaries as ground truth.