Image classification for age-related macular degeneration screening using hierarchical image decompositions and graph mining

Authors:
Mohd Hanafi Ahmad Hijazi;Chuntao Jiang;Frans Coenen;Yalin Zheng
Affiliations:
Department of Computer Science, University of Liverpool, Liverpool, UK and School of Engineering and Information Technology, Universiti Malaysia Sabah, Sabah, Malaysia;Department of Computer Science, University of Liverpool, Liverpool, UK;Department of Computer Science, University of Liverpool, Liverpool, UK;Department of Eye and Vision Science, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
Venue:
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Year:
2011

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Abstract

Age-related Macular Degeneration (AMD) is the most common cause of adult blindness in the developed world. This paper describes a new image mining technique to perform automated detection of AMD from colour fundus photographs. The technique comprises a novel hierarchical image decomposition mechanism founded on a circular and angular partitioning. The resulting decomposition is then stored in a tree structure to which a weighted frequent sub-tree mining algorithm is applied. The identified sub-graphs are then incorporated into a feature vector representation (one vector per image) to which classification techniques can be applied. The results show that the proposed approach performs both efficiently and accurately.