A probabilistic framework for content-based diagnosis of retinal disease.

Kenneth W. Tobin; Mohamed Abdelrahman; Edward Chaum; V. Govindasamy; Thomas P. Karnowski

A probabilistic framework for content-based diagnosis of retinal disease.

Kenneth W. Tobin, Mohamed Abdelrahman, Edward Chaum, V. Govindasamy, Thomas P. Karnowski

Cyber Resilience and Intelligence Divisi

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

Diabetic retinopathy is the leading cause of blindness in the working age population in the industrialized world. Computer assisted analysis has the potential to assist in the early detection of diabetes by regular screening of large populations. The widespread availability of digital fundus cameras today is leading to the accumulation of large image archives of diagnosed patient data that captures historical knowledge of retinal pathology. Through this research we are developing a content-based image retrieval method to verify our hypothesis that retinal pathology can be identified and quantified from visually similar retinal images in an image archive. We will present diagnostic results for specificity and sensitivity on a population of 395 fundus images representing the normal fundus and 14 stratified disease states.

Original language	English
Pages (from-to)	6744-6747
Number of pages	4
Journal	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
State	Published - 2007

Cite this

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A probabilistic framework for content-based diagnosis of retinal disease. / Tobin, Kenneth W.; Abdelrahman, Mohamed; Chaum, Edward et al.
In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2007, p. 6744-6747.

Research output: Contribution to journal › Article › peer-review

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AU - Chaum, Edward

AU - Govindasamy, V.

AU - Karnowski, Thomas P.

PY - 2007

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AB - Diabetic retinopathy is the leading cause of blindness in the working age population in the industrialized world. Computer assisted analysis has the potential to assist in the early detection of diabetes by regular screening of large populations. The widespread availability of digital fundus cameras today is leading to the accumulation of large image archives of diagnosed patient data that captures historical knowledge of retinal pathology. Through this research we are developing a content-based image retrieval method to verify our hypothesis that retinal pathology can be identified and quantified from visually similar retinal images in an image archive. We will present diagnostic results for specificity and sensitivity on a population of 395 fundus images representing the normal fundus and 14 stratified disease states.

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JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

ER -

A probabilistic framework for content-based diagnosis of retinal disease.

Abstract

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