Title

Precise Cerebrovascular Segmentation

Source of Publication

Proceedings - International Conference on Image Processing, ICIP

Abstract

© 2020 IEEE. Analyzing cerebrovascular changes using Time-of-Flight Magnetic Resonance Angiography (ToF-MRA) images can detect the presence of serious diseases and track their progress, e.g., hypertension. Such analysis requires accurate segmentation of the vasculature from the surroundings, which motivated us to propose a fully automated cerebral vasculature segmentation approach based on extracting both prior and current appearance features that capture the appearance of macro and micro-vessels. The appearance prior is modeled with a novel translation and rotation invariant Markov-Gibbs Random Field (MGRF) of voxel intensities with pairwise interaction analytically identified from a set of training data sets, while the current appearance is represented with a marginal probability distribution of voxel intensities by using a Linear Combination of Discrete Gaussians (LCDG) whose parameters are estimated by a modified Expectation-Maximization (EM) algorithm. The proposed approach was validated on 190 data sets using three metrics, which revealed high accuracy compared to existing approaches.

Document Type

Conference Proceeding

ISBN

9781728163956

Publisher

IEEE

First Page

394

Last Page

397

Publication Date

10-1-2020

DOI

10.1109/icip40778.2020.9191077

Scopus ID

85098646805

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