Chandler, A.G.* ; Netsch, T.* ; Cocosco, C.A.* ; Schnabel, J.A.* ; Hawkes, D.J.*
    
    
        
Slice-to-volume registration using mutual information between probabilistic image classifications.
    
    
        
    
    
        
        In:. SPIE, 2004. 1120-1129 (Proc. SPIE ; 5370 II)
    
    
    
      
      
	
	    Intensity based registration algorithms have proved to be accurate and robust for 3D-3D registration tasks. However, these methods utilise the information content within an image, and therefore their performance is hindered for image data that is sparse. This is the case for the registration of a single image slice to a 3D image volume. There are some important applications that could benefit from improved slice-to-volume registration, for example, the planning of magnetic resonance (MR) scans or cardiac MR imaging, where images are acquired as stacks of single slices. We have developed and validated an information based slice-to-volume registration algorithm that uses vector valued probabilistic images of tissue classification that have been derived from the original intensity images. We believe that using such methods inherently incorporates into the registration framework more information about the images, especially in images containing severe partial volume artifacts. Initial experimental results indicate that the suggested method can achieve a more robust registration compared to standard intensity based methods for the rigid registration of a single thick brain MR slice, containing severe partial volume artifacts in the through-plane direction, to a complete 3D MR brain volume.
	
	
	    
	
       
      
	
	    
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        Publication type
        Article: Conference contribution
    
 
    
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        Keywords
        C-means ; Fuzzy Classification ; Normalised Mutual Information ; Slice-to-volume Registration
    
 
    
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        Language
        english
    
 
    
        Publication Year
        2004
    
 
    
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        HGF-reported in Year
        2004
    
 
    
    
        ISSN (print) / ISBN
        0277-786X
    
 
    
        e-ISSN
        1996-756X
    
 
    
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	    Volume: 5370 II,  
	    Issue: ,  
	    Pages: 1120-1129 
	    Article Number: ,  
	    Supplement: ,  
	
    
 
    
        
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            SPIE
        
 
        
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        Reviewing status
        Peer reviewed
    
 
    
        Institute(s)
        Institute for Machine Learning in Biomed Imaging (IML)
    
 
    
        POF-Topic(s)
        30205 - Bioengineering and Digital Health
    
 
    
        Research field(s)
        Enabling and Novel Technologies
    
 
    
        PSP Element(s)
        G-507100-001
    
 
    
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        Erfassungsdatum
        2022-05-25