Principles and Applications of Medical Image Processing

Reconstruction of Cortical Surface in Magnetic Resonance Images
59,90 €
(inkl. MwSt.)
Versandkostenfrei in DE
Wird besorgt

Artikelbeschreibung

This book presents a novel system for the automated Reconstruction of cortical surfaces from T1-weighted magnetic resonance images. At the core of system is a unified Reeb analysis framework for the detection and removal of geometric and topological outliers on tissue boundaries. Using intrinsic Reeb analysis, the system can pin point the location of spurious branches and topological outliers, and rectify them with localized filtering using information from both image intensity distributions and geometric regularity. This system develops an enhanced tissue classification with Hessian features for improved robustness to image Inhomogeneity, and adaptive interpolation to achieve sub-voxel accuracy in reconstructed surfaces. By integrating these novel developments, a new system was designed. The system can automatically reconstruct cortical surfaces with improved quality and dramatically reduced computational cost as compared with the popular Free Surfer software. Incomparisons with Free Surfer, the presented work shows that the system is able to generate surfaces that better represent cortical anatomy and produce thickness features with higher statistical power in population studies.

Produktsicherheit

Hersteller: Scholars Press
Anschrift: Brivibas gatve 197
LV-1039 Riga
Kontakt: customerservice@vdm-vsg.de

Personeninformation

Udayakumar, E.
Dr.S.Balamurugan is the Director - Research and Development,Mindnotix Technologies, India. He has to his credit 175 papers, 22Books and 34 International Awards for Excellence in Research.Mr.E.Udayakumar is working as Assistant Professor at KIT-Kalaignar Karunanidhi Institute of Technology, Coimbatore, India. He has to hiscredit 20 papers.
Mehr von Udayakumar, E.; Balamurugan, S.

Bewertungen

Die Bewertungen werden vor ihrer Veröffentlichung nicht auf ihre Echtheit überprüft. Sie können daher auch von Verbrauchern stammen, die die bewerteten Produkte tatsächlich gar nicht erworben/genutzt haben.