SCHWARZ, Daniel and Tomáš KAŠPÁREK. Brain Tissue Classification with Automated Generation of Training Data Improved by Deformable Registration. W.G.Kropatsch, M. Kampel, A. Hanbury (Eds.). In LECTURE NOTES IN COMPUTER SCIENCE. Berlin, Heidelberg: Springer-Verlag, 2007. p. 301-308. ISBN 978-3-540-74271-5.
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Basic information
Original name Brain Tissue Classification with Automated Generation of Training Data Improved by Deformable Registration
Name in Czech Klasifikace mozkových tkání s automatickým generováním trénovacích dat vylepšeným pružnou registrací
Authors SCHWARZ, Daniel (203 Czech Republic, guarantor) and Tomáš KAŠPÁREK (203 Czech Republic).
W.G.Kropatsch, M. Kampel, A. Hanbury (Eds.).
Edition Berlin, Heidelberg, LECTURE NOTES IN COMPUTER SCIENCE, p. 301-308, 8 pp. 2007.
Publisher Springer-Verlag
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 20200 2.2 Electrical engineering, Electronic engineering, Information engineering
Country of publisher Austria
Confidentiality degree is not subject to a state or trade secret
Impact factor Impact factor: 0.402 in 2005
RIV identification code RIV/00216224:14110/07:00020428
Organization unit Faculty of Medicine
ISBN 978-3-540-74271-5
ISSN 0302-9743
UT WoS 000249585600038
Keywords in English image analysis;image registration;MRI;computational neuroanatomy;brain tissue classification;atlas-based segmentation
Tags atlas-based segmentation, brain tissue classification, computational neuroanatomy, Image analysis, image registration, MRI
Tags International impact, Reviewed
Changed by Changed by: doc. Ing. Daniel Schwarz, Ph.D., učo 195581. Changed: 29/6/2009 13:55.
Abstract
Methods of tissue classification in MRI brain images play a significant role in computational neuroanatomy, particularly in automated ROI-based volumetry. A well-known and very simple k-NN classifier is used here without the need for user input during the training process. The classifier is trained with the use of tissue probability maps which are available in selected digital atlases of brain. The influence of misalignement between images and the tissue probability maps on the classifier's efficiency is studied in this paper. Deformable registration is used here to align the images and maps. The classifier's efficiency is tested in an experiment with data obtained from standard Simulated Brain Database.
Abstract (in Czech)
Metody klasifikace tkání hrají důležitou roli ve výpočetní neuroanatomii, zvláště pak automatické volumetrii na základě oblastí zájmu. Dobře známý a velmi jednoduchý klasifikátro k-NN je zde použit bez nutnosti uživatelského vstupu ve fázi trénování. Klasifikátor je natrénováns využitím tkáňových pravděpodobnostních map. Studován je vliv rozlícování mezi obrazy a mapami na efektivitu klasifikátoru. Pro slícování je využita pružná registrace. Efektivita je vyhodnocena na simulových datech ze Simulated Brain Database.
Links
GP102/07/P263, research and development projectName: Nelineární multimodální registrace pro automatickou morfometrii obrazů mozku z MRI založenou na anatomicky omezených prostorových deformacích
Investor: Czech Science Foundation, Postdoctoral projects
MSM0021622404, plan (intention)Name: Vnitřní organizace a neurobiologické mechanismy funkčních systémů CNS
Investor: Ministry of Education, Youth and Sports of the CR, Research Intents
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