Unsupervised sorting of retinal vessels using locally consistent Gaussian mixtures. (February 2021)

• Record Type: Journal Article
• Authors: Relan, D.; Relan, R.
• Is Part Of: Computer methods and programs in biomedicine. Volume 199(2021)
• Publication Date: 2021-02
• Subjects: Retinal imaging -- Blood vessels -- Classification -- Homomorphic filtering -- Multiscale line operator -- Locally consistent Gaussian mixture model; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105894
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A four-step novel unsupervised methodology for retinal vessels clustering for fundus camera images. Homomorphic filtering (HF) to pre-process the input image for non-uniform illumination and denoising. Unsupervised multi-scale line operator segmentation technique. Use of only three discriminant features. Locally consistent Gaussian mixture model (LCGMM) for unsupervised classification of retinal vessels. Abstract: Background and Objectives: Retinal blood vessels classification into arterioles and venules is a major task for biomarker identification. Especially, clustering of retinal blood vessels is a challenging task due to factors affecting the images such as contrast variability, non-uniform illumination etc. Hence, a high performance automatic retinal vessel classification system is highly prized. In this paper, we propose a novel unsupervised methodology to classify retinal vessels extracted from fundus camera images into arterioles and venules. Methods: The proposed method utilises the homomorphic filtering (HF) to preprocess the input image for non-uniform illumination and denoising. In the next step, an unsupervised multiscale line operator segmentation technique is used to segment the retinal vasculature before extracting the discriminating features. Finally, the Locally Consistent Gaussian Mixture Model (LCGMM) is utilised for unsupervised sorting of retinal vessels. Results: The performance of the proposed unsupervised method was assessed using three publicly accessible databases: INSPIRE-AVR, VICAVR, and MESSIDOR. The proposed framework achieved 90.14 %, 90.3 % and 93.8 % classification rate in zone B for the three datasets respectively. Conclusions: The proposed clustering framework provided high classification rate as compared to conventional Gaussian mixture model using Expectation-Maximisation (GMM-EM) approach, thus have a great capability to enhance computer assisted diagnosis and research in field of biomarker discovery.