Edge-directed inference for microaneurysms detection in digital fundus images

Ke Huang; Michelle Yan; Selin Aviyente

doi:10.1117/12.708631

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5 March 2007 Edge-directed inference for microaneurysms detection in digital fundus images

Ke Huang, Michelle Yan, Selin Aviyente

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Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 651237 (2007) https://doi.org/10.1117/12.708631
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

Microaneurysms (MAs) detection is a critical step in diabetic retinopathy screening, since MAs are the earliest visible warning of potential future problems. A variety of algorithms have been proposed for MAs detection in mass screening. Different methods have been proposed for MAs detection. The core technology for most of existing methods is based on a directional mathematical morphological operation called "Top-Hat" filter that requires multiple filtering operations at each pixel. Background structure, uneven illumination and noise often cause confusion between MAs and some non-MA structures and limits the applicability of the filter. In this paper, a novel detection framework based on edge directed inference is proposed for MAs detection. The candidate MA regions are first delineated from the edge map of a fundus image. Features measuring shape, brightness and contrast are extracted for each candidate MA region to better exclude false detection from true MAs. Algorithmic analysis and empirical evaluation reveal that the proposed edge directed inference outperforms the "Top-Hat" based algorithm in both detection accuracy and computational speed.

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Ke Huang, Michelle Yan, and Selin Aviyente "Edge-directed inference for microaneurysms detection in digital fundus images", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 651237 (5 March 2007); https://doi.org/10.1117/12.708631

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