A multiscale optimization approach to detect exudates in the macula

Carla Agurto, Victor Murray, Honggang Yu, Jeffrey Wigdahl, Marios Pattichis, Sheila Nemeth, E. Simon Barriga, Peter Soliz

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

72 Citas (Scopus)


Pathologies that occur on or near the fovea, such as clinically significant macular edema (CSME), represent high risk for vision loss. The presence of exudates, lipid residues of serous leakage from damaged capillaries, has been associated with CSME, in particular if they are located one optic disc-diameter away from the fovea. In this paper, we present an automatic system to detect exudates in the macula. Our approach uses optimal thresholding of instantaneous amplitude (IA) components that are extracted from multiple frequency scales to generate candidate exudate regions. For each candidate region, we extract color, shape, and texture features that are used for classification. Classification is performed using partial least squares (PLS). We tested the performance of the system on two different databases of 652 and 400 images. The system achieved an area under the receiver operator characteristic curve (AUC) of 0.96 for the combination of both databases and an AUC of 0.97 for each of them when they were evaluated independently.

Idioma originalInglés
Número de artículo6693707
Páginas (desde-hasta)1328-1336
Número de páginas9
PublicaciónIEEE Journal of Biomedical and Health Informatics
EstadoPublicada - jul. 2014
Publicado de forma externa


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