A recursive Bayesian approach to describe retinal vasculature geometry. (March 2019)

• Record Type: Journal Article
• Authors: Uslu, Fatmatülzehra; Bharath, Anil Anthony
• Is Part Of: Pattern recognition. Volume 87(2019:Mar.)
• Page Start: 157
• Page End: 169
• Publication Date: 2019-03
• Subjects: Particle filtering -- Deep neural network -- Deep Belief Net -- Fundus image -- Width estimation -- Tracking; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.10.017
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A Deep Belief Net (DBN) is trained to detect vessel interior, centreline and edges. Particle filtering is used to quantify vasculature by using the output of the DBN. A vessel profile is represented with probability profiles of centreline and edges. The appearance of vessels in fundus images is considered in a vessel geometry model. The lack of labelled data for segmentation was tackled using a probabilistic approach. Graphical abstract: Abstract: Deep networks have recently seen significant application to the analysis of medical image data, particularly for segmentation and disease classification. However, there are many situations in which the purpose of analysing a medical image is to perform parameter estimation, assess connectivity or determine geometric relationships. Some of these tasks are well served by probabilistic trackers, including Kalman and particle filters. In this work, we explore how the probabilistic outputs of a single-architecture deep network may be coupled to a probabilistic tracker, taking the form of a particle filter. The tracker provides information not easily available with current deep networks, such as a unique ordering of points along vessel centrelines and edges, whilst the construction of observation models for the tracker is simplified by the use of a deep network. We use the analysis of retinal images in several datasets as the problem domain, and compare estimates of vessel width in a standard dataset (REVIEW) with manually determined measurements.