NDLI: Deep neural network and random forest hybrid architecture for learning to detect retinal vessels in fundus images

Content Provider	IEEE Xplore Digital Library
Author	Maji, D. Santara, A. Ghosh, S. Sheet, D. Mitra, P.
Copyright Year	2015
Description	Author affiliation: Dept. of Ophthalmology, North Bengal Med. Coll. & Hosp., Darjeeling, India (Ghosh, S.) \|\| Indian Inst. of Technol. Kharagpur, Kharagpur, India (Maji, D.; Santara, A.; Sheet, D.; Mitra, P.)
Abstract	Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large-scale screening is the inability to exhaustively detect fine blood vessels crucial to disease diagnosis. In this work we present a computational imaging framework using deep and ensemble learning based hybrid architecture for reliable detection of blood vessels in fundus color images. A deep neural network (DNN) is used for unsupervised learning of vesselness dictionaries using sparse trained denoising auto-encoders (DAE), followed by supervised learning of the DNN response using a random forest for detecting vessels in color fundus images. In experimental evaluation with the DRIVE database, we achieve the objective of vessel detection with max. avg. accuracy of 0.9327 and area under ROC curve of 0.9195.
Starting Page	3029
Ending Page	3032
File Size	5964629
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319030
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biomedical imaging Vegetation Retinal vessels Image analysis Radio frequency vessel detection Computational imaging deep learning denoising auto-encoder random forests
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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