Title: Detection of retinal area from scanning laser ophthalmoscope images (SLO) using deep neural network
Authors: V. Ashok; G. Murugesan
Addresses: Department of Electronics and Communication Engineering, Kongu Engineering College, Perundurai, Erode, India ' Department of Electronics and Communication Engineering, Kongu Engineering College, Perundurai, Erode, India
Abstract: Earlier detection and treatment of the retinal disease are crucial to avoid preventable vision loss. Scanning laser ophthalmoscopes (SLOs) can be used for early detection of retinal diseases and the SLO can image a large part of the retinal image to diagnose it in a better way. Eyelashes and eyelids are also imaged with the retinal image during the image process used to exclude the artefacts of the retinal image. The proposed SLO approach automatically extracts both the true retinal area and artefacts of the image based on image processing and machine learning approach. Superpixel and Deep Neural Network (DNN) are used to reduce the complexity of image processing tasks, the result is being provided with a primitive image pattern. The framework performs the calculations of textural and structural-based information of features and this approach results in effective analysis of retinal area and the artefacts.
Keywords: DNN; deep neural networks; superpixels; feature generation; SLOs; scanned laser ophthalmoscopes; retinal disease detection; early detection; vision loss; retinal images; medical images; feature extraction; image processing; machine learning.
International Journal of Biomedical Engineering and Technology, 2017 Vol.23 No.2/3/4, pp.303 - 314
Received: 20 Jun 2016
Accepted: 26 Aug 2016
Published online: 25 Feb 2017 *