NDLI: A Parallel Markov Cerebrovascular Segmentation Algorithm Based on Statistical Model

Content Provider	Springer Nature Link
Author	Cao, Rong Fei Wang, Xing Ce Wu, Zhong Ke Zhou, Ming Quan Liu, Xin Yu
Copyright Year	2016
Abstract	For segmenting cerebral blood vessels from the time-of-flight magnetic resonance angiography (TOF-MRA) images accurately, we propose a parallel segmentation algorithm based on statistical model with Markov random field (MRF). Firstly, we improve traditional non-local means filter with patch-based Fourier transformation to preprocess the TOF-MRA images. In this step, we mainly utilize the sparseness and self-similarity of the MRA brain images sequence. Secondly, we add the MRF information to the finite mixture mode (FMM) to fit the intensity distribution of medical images. We make use of the MRF in image sequence to estimate the proportion of cerebral tissues. Finally, we choose the particle swarm optimization (PSO) algorithm to parallelize the parameter estimation of FMM. A large number of experiments verify the high accuracy and robustness of our approach especially for narrow vessels. The work will offer significant assistance for physicians on the prevention and diagnosis of cerebrovascular diseases.
Starting Page	400
Ending Page	416
Page Count	17
File Format	PDF
ISSN	10009000
Journal	Journal of Computer Science and Technology
Volume Number	31
Issue Number	2
e-ISSN	18604749
Language	English
Publisher	Springer US
Publisher Date	2016-03-07
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	cerebrovascular segmentation non-local means filtering Markov random field particle swarm optimization algorithm Computer Science Software Engineering Theory of Computation Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Information Systems Applications (incl. Internet)
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computational Theory and Mathematics Computer Science Applications Software Hardware and Architecture

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