Simple jQuery Dropdowns
Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/14679
Title: Automated detection of malarial retinopathy-associated retinal hemorrhages
Authors: Vinayak S. Joshi
Richard J. Maude
Joseph M. Reinhardt
Li Tang
Mona K. Garvin
Abdullah Abu Sayeed
Aniruddha Ghose
Mahtab Uddin Hassan
Michael D. Abràmoff
University of Iowa
University of Iowa Hospitals & Clinics
Chittagong Medical College Hospital
Mahidol University
Nuffield Department of Clinical Medicine
University of Edinburgh, College of Medicine and Veterinary Medicine
Iowa City VA Health Care System
Keywords: Medicine;Neuroscience
Issue Date: 1-Sep-2012
Citation: Investigative Ophthalmology and Visual Science. Vol.53, No.10 (2012), 6582-6588
Abstract: Purpose. To develop an automated method for the detection of retinal hemorrhages on color fundus images to characterize malarial retinopathy, which may help in the assessment of patients with cerebral malaria. Methods. A fundus image dataset from 14 patients (200 fundus images, with an average of 14 images per patient) previously diagnosed with malarial retinopathy was examined. We developed a pattern recognition-based algorithm, which extracted features from image watershed regions called splats (tobogganing). A reference standard was obtained by manual segmentation of hemorrhages, which assigned a label to each splat. The splat features with the associated splat label were used to train a linear k-nearest neighbor classifier that learnt the color properties of hemorrhages and identified the splats belonging to hemorrhages in a test dataset. In a crossover design experiment, data from 12 patients were used for training and data from two patients were used for testing, with 14 different permutations; and the derived sensitivity and specificity values were averaged. Results. The experiment resulted in hemorrhage detection sensitivities in terms of splats as 80.83%, and in terms of lesions as 84.84%. The splat-based specificity was 96.67%, whereas for the lesion-based analysis, an average of three false positives was obtained per image. The area under the receiver operating characteristic curve was reported as 0.9148 for splat-based, and as 0.9030 for lesion-based analysis. Conclusions. The method provides an automated means of detecting retinal hemorrhages associated with malarial retinopathy. The results matched well with the reference standard. With further development, this technique may provide automated assistance for screening and quantification of malarial retinopathy. © 2012 The Association for Research in Vision and Ophthalmology, Inc.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84871875855&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/14679
ISSN: 15525783
01460404
Appears in Collections:Scopus 2011-2015

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.