TY - CONF A1 - Alsaih, K. A1 - Lemaître, G. A1 - Vall, J.M. A1 - Rastgoo, M. A1 - Sidibé, D. A1 - Wong, T.Y. A1 - Lamoureux, E. A1 - Milea, D. A1 - Cheung, C.Y. A1 - Mériaudeau, F. UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009112298&doi=10.1109%2fEMBC.2016.7590956&partnerID=40&md5=a523600f69dfb58f27271b2f30436aa1 N1 - cited By 23; Conference of 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 ; Conference Date: 16 August 2016 Through 20 August 2016; Conference Code:124354 ID - scholars6783 EP - 1347 N2 - This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier. Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset. © 2016 IEEE. SN - 1557170X Y1 - 2016/// KW - diabetic retinopathy; diagnostic imaging; factual database; human; image processing; macular edema; optical coherence tomography; procedures; sensitivity and specificity; support vector machine KW - Databases KW - Factual; Diabetic Retinopathy; Humans; Image Processing KW - Computer-Assisted; Macular Edema; Sensitivity and Specificity; Support Vector Machine; Tomography KW - Optical Coherence TI - Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: Application to DME detections VL - 2016-O SP - 1344 PB - Institute of Electrical and Electronics Engineers Inc. AV - none ER -