Vascular tree tracking and bifurcation points detection in retinal images using a hierarchical probabilistic model. (November 2017)

• Record Type: Journal Article
• Authors: Kalaie, Soodeh; Gooya, Ali
• Is Part Of: Computer methods and programs in biomedicine. Volume 151(2017)
• Page Start: 139
• Page End: 149
• Publication Date: 2017-11
• Subjects: Bifurcation -- Classification -- Machine learning -- Probabilistic graphical model -- Retinal blood vessel tracking; 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.2017.08.018
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A new machine learning algorithm for joint classification and tracking of retinal blood vessels is presented. Proposed method is based on a hierarchical probabilistic framework, where the local vessel intensity profiles are classified as either junction or vessel points. The model hyperparameters are estimated using a Maximum Likelihood (ML) solution based on Laplace approximation. Abstract: Background and Objective: Retinal vascular tree extraction plays an important role in computer-aided diagnosis and surgical operations. Junction point detection and classification provide useful information about the structure of the vascular network, facilitating objective analysis of retinal diseases. Methods: In this study, we present a new machine learning algorithm for joint classification and tracking of retinal blood vessels. Our method is based on a hierarchical probabilistic framework, where the local intensity cross sections are classified as either junction or vessel points. Gaussian basis functions are used for intensity interpolation, and the corresponding linear coefficients are assumed to be samples from class-specific Gamma distributions. Hence, a directed Probabilistic Graphical Model (PGM) is proposed and the hyperparameters are estimated using a Maximum Likelihood (ML) solution based on Laplace approximation. Results: The performance of proposed method is evaluated using precision and recall rates on the REVIEW database. Our experiments show the proposed approach reaches promising results in bifurcation point detection and classification, achieving 88.67% precision and 88.67% recall rates. Conclusions: This technique results in a classifier with high precision and recall when comparing it with Xu's method.