TY  - CONF
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.
PB  - Institute of Electrical and Electronics Engineers Inc.
VL  - 2016-O
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009112298&doi=10.1109%2fEMBC.2016.7590956&partnerID=40&md5=a523600f69dfb58f27271b2f30436aa1
AV  - none
ID  - scholars6783
SN  - 1557170X
TI  - Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: Application to DME detections
Y1  - 2016///
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.
EP  - 1347
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
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
SP  - 1344
ER  -