TY - JOUR
T1 - A multiscale optimization approach to detect exudates in the macula
AU - Agurto, Carla
AU - Murray, Victor
AU - Yu, Honggang
AU - Wigdahl, Jeffrey
AU - Pattichis, Marios
AU - Nemeth, Sheila
AU - Barriga, E. Simon
AU - Soliz, Peter
PY - 2014/7
Y1 - 2014/7
N2 - 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.
AB - 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.
KW - Amplitude-modulation frequency-modulation
KW - clinically significant macular edema (CSME)
KW - diabetic retinopathy
KW - partial least squares (PLS)
UR - http://www.scopus.com/inward/record.url?scp=84904277436&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2013.2296399
DO - 10.1109/JBHI.2013.2296399
M3 - Article
C2 - 25014937
AN - SCOPUS:84904277436
SN - 2168-2194
VL - 18
SP - 1328
EP - 1336
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 4
M1 - 6693707
ER -