Improving vessel segmentation in ultra-wide field-of-view retinal fluorescein angiograms

A. Perez-Rovira, K. Zutis, J. P. Hubschman, E. Trucco

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    15 Citations (Scopus)

    Abstract

    Vessel segmentation on ultra-wide field-of-view fluorescein angiogram sequences of the retina is a challenging problem. Vessel appearance undergoes severe changes, as different portions of the vascular structure become perfused in different frames. This paper presents a method for segmenting vessels in such sequences using steerable filters and automatic thresholding. We introduce a penalization stage on regions with high vessel response in the filtered image, improving the detection of peripheral vessels and reducing false positives around the optic disc and in regions of choroidal vessels and lesions. Quantitative results are provided, in which the penalization stage improves the segmentation precision segmentation by 11.84%, the recall by 12.98% and the accuracy by 0.40%. To facilitate further evaluation, usage, and algorithm comparison, the algorithm, the data set used, the ground truth, and the results are made available on the internet.

    Original languageEnglish
    Title of host publicationProceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC
    Place of PublicationNew York
    PublisherIEEE Computer Society
    Pages2614-2617
    Number of pages4
    ISBN (Electronic)9781424441228
    ISBN (Print)9781424441211
    DOIs
    Publication statusPublished - 2011
    Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Boston, United States
    Duration: 30 Aug 20113 Sep 2011

    Conference

    Conference33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
    Abbreviated titleEMBC 2011
    CountryUnited States
    CityBoston
    Period30/08/113/09/11

    Keywords

    • BLOOD-VESSELS

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