Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images

Clara I. Sánchez; Roberto Hornero; Agustín Mayo; María García

doi:10.1117/12.812088

3 March 2009 Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images

Clara I. Sánchez, Roberto Hornero, Agustín Mayo, María García

Author Affiliations +

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72601M (2009) https://doi.org/10.1117/12.812088
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Diabetic Retinopathy is one of the leading causes of blindness and vision defects in developed countries. An early detection and diagnosis is crucial to avoid visual complication. Microaneurysms are the first ocular signs of the presence of this ocular disease. Their detection is of paramount importance for the development of a computer-aided diagnosis technique which permits a prompt diagnosis of the disease. However, the detection of microaneurysms in retinal images is a difficult task due to the wide variability that these images usually present in screening programs. We propose a statistical approach based on mixture model-based clustering and logistic regression which is robust to the changes in the appearance of retinal fundus images. The method is evaluated on the public database proposed by the Retinal Online Challenge in order to obtain an objective performance measure and to allow a comparative study with other proposed algorithms.

Citation Download Citation

Clara I. Sánchez, Roberto Hornero, Agustín Mayo, and María García "Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72601M (3 March 2009); https://doi.org/10.1117/12.812088

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