Retinal Blood Vessels Segmentation: Improving State-of-the-Art Deep Methods - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

Retinal Blood Vessels Segmentation: Improving State-of-the-Art Deep Methods

Abstract

Retinal blood vessels segmentation is an important step for computer-aided early diagnosis of several retinal vascular diseases, in particular diabetic retinopathy. This segmentation is necessary to evaluate the state of the vascular network and to detect abnormalities (aneurysms, hemorrhages, etc). Many image processing and machine learning methods have been developed in recent years in order to achieve this segmentation. These methods are difficult to compare with one another since the evaluation conditions vary greatly. Moreover, public databases often provide multiple ground truths. In this paper, we implement a competitive state-of-the art method and evaluate it on the DRIVE (Digital Retinal Images for Vessel Extraction) public database. Based on this method, we test and present several improvements which are evaluated using a dedicated performance evaluation protocol. This protocol uses five criteria and three different evaluations in order to assess the robustness of the methods’ performances.
No file

Dates and versions

hal-02281099 , version 1 (07-09-2019)

Identifiers

Cite

Valentine Wargnier-Dauchelle, Camille Simon-Chane, Aymeric Histace. Retinal Blood Vessels Segmentation: Improving State-of-the-Art Deep Methods. Visual Computing and Machine Learning for Biomedical Applications (co-located with CAIP 2019 conference), Sep 2019, Salerno, Italy. pp.5-16, ⟨10.1007/978-3-030-29930-9_1⟩. ⟨hal-02281099⟩
89 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More