DocumentCode :
3414046
Title :
Weighted likelihood function of multiple statistical parameters to retrieve 2D TRUS-MR slice correspondence for prostate biopsy
Author :
Mitra, Joydeep ; Ghose, Sarbani ; Sidibe, Desire ; Oliver, Arnau ; Marti, Robert ; Llado, Xavier ; Vilanova, J.C. ; Comet, J. ; Meriaudeau, Fabrice
Author_Institution :
Le2i, Univ. de Bourgogne, Le Creusot, France
fYear :
2012
fDate :
Sept. 30 2012-Oct. 3 2012
Firstpage :
2949
Lastpage :
2952
Abstract :
This paper presents a novel method to identify the 2D axial Magnetic Resonance (MR) slice from a pre-acquired MR prostate volume that closely corresponds to the 2D axial Transrectal Ultrasound (TRUS) slice obtained during prostate biopsy. The shape-context representations of the segmented prostate contours in both the imaging modalities are used to establish point correspondences using Bhattacharyya distance. Thereafter, Chi-square distance is used to find the prostate shape similarities between the MR slices and the TRUS slice. Normalized mutual information and correlation coefficient between the TRUS and MR slices are computed to find the information theoretic similarities between the TRUS-MR slices. The maximum of the weighted likelihood function of the afore-mentioned statistical similarity measures finally yields the MR slice that closely resembles the TRUS slice acquired during the biopsy procedure. The method is evaluated for 20 patient datasets and close matches with the ground truth are obtained for 16 cases.
Keywords :
biomedical MRI; image matching; image representation; image retrieval; image segmentation; medical image processing; shape recognition; statistical analysis; ultrasonic imaging; 2D TRUS-MR slice retrieval; 2D axial MR slice; 2D axial magnetic resonance slice; 2D axial transrectal ultrasound slice; Bhattacharyya distance; MR prostate volume; chi-square distance; imaging modalities; information theoretic similarities; multiple statistical parameters; patient datasets; prostate biopsy; prostate contour segmentation; shape similarities; shape-context representation; statistical similarity measurement; weighted likelihood function; Accuracy; Biopsy; Histograms; Magnetic resonance imaging; Probes; Prostate cancer; Shape; 2D TRUS/3D MR correspondence; Prostate biopsy; image similarity; shape similarity; weighted likelihood function;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
ISSN :
1522-4880
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2012.6467518
Filename :
6467518
Link To Document :
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