Boosting Sensitivity of a Retinal Vessel Segmentation Algorithm with Convolutional Neural Network

Toufique A. Soomro, Ahmed J. Afifi , Junbin Gao, Olaf Hellwich, Mohammad A. U. Khan , Manoranjan Paul, Lihong Zheng

Research output: Book chapter/Published conference paperConference paper

14 Citations (Scopus)

Abstract

Accurate vessel segmentation is a tough task for various medical images applications especially the segmentation of retinal images vessels. A computerised algorithm is required for analysing the progress of eye diseases. A variety of computerised retinal segmentation methods have been proposed but almost all methods to date show low sensitivity for narrowly low contrast vessels. We propose a new retinal vessel segmentation algorithm to address the issue of low sensitivity. The proposed method introduces a deep learning model along with pre-processing and post-processing. The pre-processing is used to handle the issue of uneven illuminations. We design a fully Convolutional Neural Network (CNN) and train it to get fine vessels observation. The post-processing step is used to remove the background noise pixels to achieve well-segmented vessels. The proposed segmentation method gives good segmented images especially for detecting tiny vessels. We evaluate our method on the commonly used publicly available databases: DRIVE and STARE databases. The higher sensitivity of 75% leads to proper detection of tiny vessels with an accuracy of 94.7%.
Original languageEnglish
Title of host publicationProceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-8
Number of pages8
DOIs
Publication statusPublished - 21 Dec 2017
Event2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Novotel Sydney Manly Pacific, Sydney, Australia
Duration: 29 Nov 201701 Dec 2017
http://dicta2017.dictaconference.org/index.html (Conference website)
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8226656 (Conference proceedings)

Conference

Conference2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
CountryAustralia
CitySydney
Period29/11/1701/12/17
OtherThe International Conference on Digital Image Computing: Techniques and Applications (DICTA) is the main Australian Conference on computer vision, image processing, pattern recognition, and related areas. DICTA was established in 1991 as the premier conference of the Australian Pattern Recognition Society (APRS).
Internet address

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    Soomro, T. A., Afifi , A. J., Gao, J., Hellwich, O., Khan , M. A. U., Paul, M., & Zheng, L. (2017). Boosting Sensitivity of a Retinal Vessel Segmentation Algorithm with Convolutional Neural Network. In Proceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DICTA.2017.8227413