Robust Vessel Segmentation in Fundus Images

By Attila Budai, Rüdiger Bock, Andreas K. Maier, Joachim Hornegger, Georg Michelson. International Journal of Biomedical Imaging, 2013

668 citations recorded by the source corpus.

One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a387ee98a1cf5a2e6d2a5019a9b2e46aacd00f1f

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In Ophthalmology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification (2015, methodology): “The gradient-based features have been analyzed in [23] and [22] for multilevel vessel pixel identification.”
CNNs for automatic glaucoma assessment using fundus images: an extensive validation (2019, methodology): “Apart from the ACRIMA database, other four public databases were used in this work: HRF database [25], which contains 45 images; Drishti-GS1 database [4], which consists of 101 images; RIM-ONE database [26], which is composed of 455 images; and sjchoi86-HRF [27] database which is composed of 401…”
Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation (2018, methodology): “Four public datasets DRIVE [23], STARE [38], CHASE_ DB1 [39] and HRF [40] are used to evaluate the proposed”
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images (2017, methodology): “To the best of our knowledge, only unsupervised methods were previously tested on HRF [24], [55].”