Geometry-driven-diffusion filtering of magnetic resonance images using model-based conductance

Autor:	Ivan Bajla, Igor Holländer
Rok vydání:	2001
Předmět:	Pixel business.industry Brain atlas Image segmentation Imaging phantom Computer Science Applications Hardware and Architecture Histogram Computer vision Computer Vision and Pattern Recognition Tomography Artificial intelligence Diffusion (business) business Software Smoothing Mathematics
Zdroj:	Machine Vision and Applications. 12:223-237
ISSN:	1432-1769 0932-8092
DOI:	10.1007/s001380050142
Popis:	The paper deals with the problems of staircase artifacts and low-contrast boundary smoothing in filtering (magnetic resonance MR) brain tomograms that is based on geometry-driven diffusion (GDD). A novel method of the model-based GDD filtering of MR brain tomograms is proposed to tackle these problems. It is based on a local adaptation of the conductance that is defined for each diffusion iteration within the variable limits. The local adaptation uses a neighborhood inhomogeneity measure, pixel dissimilarity, while gradient histograms of MR brain template regions are used as the variable limits for the conductance. A methodology is developed for implementing the template image selected from an MR brain atlas to the model-based GDD filtering. The proposed method is tested on an MR brain phantom. The methodology developed is exemplified on the real MR brain tomogram with the corresponding template selected from the Brainweb. The performance of the developed algorithms is evaluated quantitatively and visually.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::260f553f133588098738feb962f89403 https://doi.org/10.1007/s001380050142 Zobrazit plný text záznamu Full text from SpringerLink