Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: Application to DME detections

Dimensions

Alsaih, K. and Lemaître, G. and Vall, J.M. and Rastgoo, M. and Sidibé, D. and Wong, T.Y. and Lamoureux, E. and Milea, D. and Cheung, C.Y. and Mériaudeau, F. (2016) Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: Application to DME detections. In: UNSPECIFIED.

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: 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
Uncontrolled Keywords: diabetic retinopathy; diagnostic imaging; factual database; human; image processing; macular edema; optical coherence tomography; procedures; sensitivity and specificity; support vector machine, Databases, Factual; Diabetic Retinopathy; Humans; Image Processing, Computer-Assisted; Macular Edema; Sensitivity and Specificity; Support Vector Machine; Tomography, Optical Coherence
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 16:18
Last Modified: 09 Nov 2023 16:18
URI: https://khub.utp.edu.my/scholars/id/eprint/6783

Actions (login required)

View Item
View Item
Altmetric