Multi-space clustering for segmentation of exudates in retinal color photographs

Exudates are a class of lipid retinal lesions visible through optical fundus imaging, and indicative of diabetic retinopathy. We propose a clustering-based method to segment exudates, using multi-space clustering, and colorspace features. The method was evaluated on a set of 89 images from a publicl...

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Bibliographic Details
Published in2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2009; pp. 1437 - 1440
Main Authors Ram, K., Sivaswamy, J.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.01.2009
Subjects
Algorithms
Biomedical Engineering
Cluster Analysis
Cost function
Databases, Factual
Diabetes
Diabetic Retinopathy - diagnosis
Diagnostic Techniques, Ophthalmological - statistics & numerical data
Extraterrestrial measurements
Exudates and Transudates
Humans
Image Processing, Computer-Assisted - statistics & numerical data
Image segmentation
Lesions
Lipidomics
Optical imaging
Photography - methods
Photography - statistics & numerical data
Pixel
Retina
Retina - pathology
Space technology
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Summary:Exudates are a class of lipid retinal lesions visible through optical fundus imaging, and indicative of diabetic retinopathy. We propose a clustering-based method to segment exudates, using multi-space clustering, and colorspace features. The method was evaluated on a set of 89 images from a publicly available dataset, and achieves an accuracy of 89.7% and positive predictive value of 87%.
ISSN:1094-687X
1557-170X
1558-4615
DOI:10.1109/IEMBS.2009.5332911