eprintid: 8893 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/00/88/93 datestamp: 2023-11-09 16:20:49 lastmod: 2023-11-09 16:20:49 status_changed: 2023-11-09 16:13:47 type: article metadata_visibility: show creators_name: Sidibé, D. creators_name: Sankar, S. creators_name: Lemaître, G. creators_name: Rastgoo, M. creators_name: Massich, J. creators_name: Cheung, C.Y. creators_name: Tan, G.S.W. creators_name: Milea, D. creators_name: Lamoureux, E. creators_name: Wong, T.Y. creators_name: Mériaudeau, F. title: An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images ispublished: pub keywords: Classification (of information); Coherent light; Eye protection; Gaussian distribution; Signal detection; Statistics; Strain measurement, Anomaly detection; Automatic classification; Classification performance; Diabetic retinopathy; Gaussian Mixture Model; Macular edema; SD-OCT; Spectral domain optical coherence tomographies, Optical tomography, 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, Diabetes Complications; Female; Humans; Macular Edema; Male; Tomography, Optical Coherence note: cited By 48 abstract: 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 date: 2017 publisher: Elsevier Ireland Ltd official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995655027&doi=10.1016%2fj.cmpb.2016.11.001&partnerID=40&md5=730b22c8872bda4e7130883365c3c564 id_number: 10.1016/j.cmpb.2016.11.001 full_text_status: none publication: Computer Methods and Programs in Biomedicine volume: 139 pagerange: 109-117 refereed: TRUE issn: 01692607 citation: Sidibé, D. and Sankar, S. and Lemaître, G. and Rastgoo, M. and Massich, J. and Cheung, C.Y. and Tan, G.S.W. and Milea, D. and Lamoureux, E. and Wong, T.Y. and Mériaudeau, F. (2017) An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images. Computer Methods and Programs in Biomedicine, 139. pp. 109-117. ISSN 01692607