Geodesic Tracking of Retinal Vascular Trees with Optical and TV-Flow Enhancement in SE(2)

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Abstract

Retinal images are often used to examine the vascular system in a non-invasive way. Studying the behavior of the vasculature on the retina allows for noninvasive diagnosis of several diseases as these vessels and their behavior are representative of the behavior of vessels throughout the human body. For early diagnosis and analysis of diseases, it is important to compare and analyze the complex vasculature in retinal images automatically. In previous work, PDE-based geometric tracking and PDE-based enhancements in the homogeneous space of positions and orientations have been studied and turned out to be useful when dealing with complex structures (crossing of blood vessels in particular). In this article, we propose a single new, more effective, Finsler function that integrates the strength of these two PDE-based approaches and additionally accounts for a number of optical effects (dehazing and illumination in particular). The results greatly improve both the previous left-invariant models and a recent data-driven model, when applied to real clinical and highly challenging images. Moreover, we show clear advantages of each module in our new single Finsler geometrical method.

Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision - 9th International Conference, SSVM 2023, Proceedings
EditorsLuca Calatroni, Marco Donatelli, Serena Morigi, Marco Prato, Matteo Santacesaria
Place of PublicationCham
PublisherSpringer Nature
Pages525-537
Number of pages13
ISBN (Electronic)978-3-031-31975-4
ISBN (Print)978-3-031-31974-7
DOIs
Publication statusPublished - 2023

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Cham
Volume14009
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Geodesic Tracking
  • Optical Image Enhancement
  • TV-Flow Enhancement
  • Vascular Tree Tracking
  • Finsler Geometry

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