TY  - JOUR
PB  - Elsevier Ireland Ltd
SN  - 01692607
EP  - 117
AV  - none
N1  - cited By 48
TI  - An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images
SP  - 109
Y1  - 2017///
JF  - Computer Methods and Programs in Biomedicine
A1  - Sidibé, D.
A1  - Sankar, S.
A1  - Lemaître, G.
A1  - Rastgoo, M.
A1  - Massich, J.
A1  - Cheung, C.Y.
A1  - Tan, G.S.W.
A1  - Milea, D.
A1  - Lamoureux, E.
A1  - Wong, T.Y.
A1  - Mériaudeau, F.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995655027&doi=10.1016%2fj.cmpb.2016.11.001&partnerID=40&md5=730b22c8872bda4e7130883365c3c564
VL  - 139
N2  - This paper proposes a method for automatic classification of spectral domain OCT data for the identification of patients with retinal diseases such as Diabetic Macular Edema (DME). We address this issue as an anomaly detection problem and propose a method that not only allows the classification of the OCT volume, but also allows the identification of the individual diseased B-scans inside the volume. Our approach is based on modeling the appearance of normal OCT images with a Gaussian Mixture Model (GMM) and detecting abnormal OCT images as outliers. The classification of an OCT volume is based on the number of detected outliers. Experimental results with two different datasets show that the proposed method achieves a sensitivity and a specificity of 80 and 93 on the first dataset, and 100 and 80 on the second one. Moreover, the experiments show that the proposed method achieves better classification performance than other recently published works. © 2016 Elsevier Ireland Ltd
ID  - scholars8893
KW  - Classification (of information); Coherent light; Eye protection; Gaussian distribution; Signal detection; Statistics; Strain measurement
KW  -  Anomaly detection; Automatic classification; Classification performance; Diabetic retinopathy; Gaussian Mixture Model; Macular edema; SD-OCT; Spectral domain optical coherence tomographies
KW  -  Optical tomography
KW  -  Article; B scan; classification algorithm; clinical article; controlled study; diabetic macular edema; diagnostic test accuracy study; gaussian mixture model; human; image analysis; image processing; optical coherence tomography device; principal component analysis; sensitivity and specificity; spectral domain optical coherence tomography; statistical model; volume; complication; diabetes mellitus; diagnostic imaging; female; macular edema; male; optical coherence tomography
KW  -  Diabetes Complications; Female; Humans; Macular Edema; Male; Tomography
KW  -  Optical Coherence
ER  -