Result: A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization
Title:
A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization
Authors:
Source:
Neurocomputing (Amsterdam). 148:569-577
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 30 ref
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Sciences biologiques et medicales, Biological and medical sciences, Sciences medicales, Medical sciences, Techniques d'exploration et de diagnostic (generalites), Investigative techniques, diagnostic techniques (general aspects), Radiodiagnostic. Imagerie rmn. Spectrométrie rmn, Radiodiagnosis. Nmr imagery. Nmr spectrometry, Coeur et vaisseaux, Cardiovascular system, Analyse donnée, Data analysis, Análisis datos, Analyse statistique, Statistical analysis, Análisis estadístico, Angiographie, Angiography, Angiografía, Appareil circulatoire, Circulatory system, Aparato circulatorio, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Estimation paramètre, Parameter estimation, Estimación parámetro, Fonction répartition, Distribution function, Función distribución, Histogramme, Histogram, Histograma, Imagerie RMN, Nuclear magnetic resonance imaging, Imaginería RMN, Intelligence en essaim, Swarm intelligence, Inteligencia de enjambre, Loi Rayleigh, Rayleigh distribution, Ley Rayleigh, Loi normale, Gaussian distribution, Curva Gauss, Mise à jour, Updating, Actualización, Modélisation, Modeling, Modelización, Mélange loi probabilité, Mixed distribution, Mezcla ley probabilidad, Méthode temps vol, Time of flight method, Método tiempo vuelo, Optimisation PSO, Particle swarm optimization, Optimización PSO, Système nerveux central, Central nervous system, Sistema nervioso central, Séquence image, Image sequence, Secuencia imagen, Tissu, Tissue, Tejido, Topologie, Topology, Topología, Traitement image, Image processing, Procesamiento imagen, Segmentation image, Image segmentation, Segmentación de imágenes, Cerebrovascular segmentation, Finite mixture model, Intensity histogram
Document Type:
Academic journal
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Information Science and Technology, Beijing Normal University, Beijing, China
Software College, Tianjin University, Tianjin, China
Software College, Tianjin University, Tianjin, China
ISSN:
0925-2312
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Scanning and diagnostic techniques (generalities)
Scanning and diagnostic techniques (generalities)
Accession Number:
edscal.28844570
Database:
PASCAL Archive
Further Information
We present an automatic statistical intensity-based approach to extract the 3D cerebrovascular structure from time-of flight (TOF) magnetic resonance angiography (MRA) data. We use the finite mixture model (FMM) to fit the intensity histogram of the brain image sequence, where the cerebral vascular structure is modeled by a Gaussian distribution function and the other low intensity tissues are modeled by Gaussian and Rayleigh distribution functions. To estimate the parameters of the FMM, we propose an improved particle swarm optimization (PSO) algorithm, which has a disturbing term in speeding updating the formula of PSO to ensure its convergence. We also use the ring shape topology of the particles neighborhood to improve the performance of the algorithm. Computational results on 34 test data show that the proposed method provides accurate segmentation, especially for those blood vessels of small sizes.