Use this URL to cite or link to this record in EThOS:
Title: Segmentation and analysis of vascular networks
Author: Allen, K. E.
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2010
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Restricted access.
Access from Institution:
From a clinical perspective retinal vascular segmentation and analysis are important tasks in aiding quantification of vascular disease progression for such prevalent pathologies as diabetic retinopathy, arteriolosclerosis and hypertension. Combined with the emergence of inexpensive digital imaging, retinal fundus images are becoming increasingly available through public databases fuelling interest in retinal vessel research. Vessel segmentation is a challenging task which needs to fulfil many requirements: the accurate segmentation of both normal and pathological vessels; the extraction of vessels of different sizes from large high contrast to small low contrast; minimal user interaction; low computational requirements; and the potential for application among different imaging modalities. We demonstrate a novel and significant improvement on an emerging stochastic vessel segmentation technique, particle filtering, in terms of improved performance at vascular bifurcations and extensibility. An alternative deterministic approach is also presented in the form of a framework utilising morphological Tramline filtering and non-parametric windows pdf estimation. Results of the deterministic algorithm on retinal images match those of state-of-art unsupervised methods in terms of pixel accuracy. In analysing retinal vascular networks, an important initial step is to distinguish between arteries and veins in order to proceed with pathological metrics such as branching angle, diameter, length and arteriole to venule diameter ratio. Practical difficulties include the lack of intensity and textural differences between arteries and veins in all but the largest vessels and the obstruction of vessels and connectivity by low contrast or other vessels. To this end, an innovative Markov Chain Monte Carlo Metropolis-Hastings framework is formulated for the separation of vessel trees. It is subsequently applied to both synthetic and retinal image data with promising results.
Supervisor: Noble, J. A. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Applications and algorithms ; Biomedical engineering ; Vascular research ; Stochastic processes ; vascular segmentation ; image segmentation ; vascular analysis ; particle filter ; Markov Chain Monte Carlo