A New Approach to Segment Both Main and Peripheral Retinal Vessels Based on Gray-Voting and Gaussian Mixture Model
Autor: | Hanwei Sheng, Peishan Dai, Kenji Suzuki, Jing Wu, Yali Zhao, Hanyuan Luo, Ling Li, Yuqian Zhao |
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Jazyk: | angličtina |
Rok vydání: | 2015 |
Předmět: |
Databases
Factual Computer science Science Image processing Fundus (eye) Models Biological chemistry.chemical_compound Image Processing Computer-Assisted medicine Humans Sensitivity (control systems) Multidisciplinary business.industry Gabor wavelet Retinal Vessels Wavelet transform Retinal Pattern recognition Filter (signal processing) Gold standard (test) Diabetic retinopathy Mixture model medicine.disease chemistry cardiovascular system Medicine Artificial intelligence business Algorithms Research Article |
Zdroj: | PLoS ONE, Vol 10, Iss 6, p e0127748 (2015) PLoS ONE |
ISSN: | 1932-6203 |
Popis: | Vessel segmentation in retinal fundus images is a preliminary step to clinical diagnosis for some systemic diseases and some eye diseases. The performances of existing methods for segmenting small vessels which are usually of more importance than the main vessels in a clinical diagnosis are not satisfactory in clinical use. In this paper, we present a method for both main and peripheral vessel segmentation. A local gray-level change enhancement algorithm called gray-voting is used to enhance the small vessels, while a two-dimensional Gabor wavelet is used to extract the main vessels. We fuse the gray-voting results with the 2D-Gabor filter results as pre-processing outcome. A Gaussian mixture model is then used to extract vessel clusters from the pre-processing outcome, while small vessels fragments are obtained using another gray-voting process, which complements the vessel cluster extraction already performed. At the last step, we eliminate the fragments that do not belong to the vessels based on the shape of the fragments. We evaluated the approach with two publicly available DRIVE (Staal et al., 2004) and STARE (Hoover et at., 2000) datasets with manually segmented results. For the STARE dataset, when using the second manually segmented results which include much more small vessels than the first manually segmented results as the "gold standard," this approach achieved an average sensitivity, accuracy and specificity of 65.0%, 92.1% and 97.0%, respectively. The sensitivities of this approach were much higher than those of the other existing methods, with comparable specificities; these results thus demonstrated that this approach was sensitive to detection of small vessels. |
Databáze: | OpenAIRE |
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