DocumentCode :
2630505
Title :
Population analysis of knee cartilage thickness maps using model based correspondence
Author :
Williams, Tomos G. ; Taylor, Christopher J. ; Waterton, John C. ; Holmes, Andrew
Author_Institution :
Imaging Sci. & Biomedical Eng., Manchester Univ., UK
fYear :
2004
fDate :
15-18 April 2004
Firstpage :
193
Abstract :
We present a method for aggregating and comparing articular cartilage thickness in a population of patients using the underlying bone as the frame of reference. Femur, patella and tibia knee bone surfaces are constructed from segmentations of MR scans for 19 healthy female patients. Dense correspondences are defined on each example by building a minimum description length (MDL) statistical shape model of the bone shapes. Using repeat scans of the same subjects, these correspondences are shown to identify anatomically equivalent locations. Cartilage thickness is measured at corresponding points and aggregated for all patients to establish a normal range in healthy females. Linear discriminant analysis and permutation testing are used to visualise class differences and evaluate their significance. We illustrate this using a study of inter-segmentor differences.
Keywords :
biomedical MRI; bone; image segmentation; medical image processing; statistical analysis; MR image segmentation; articular cartilage thickness; bone; dense correspondence; femur; healthy female patients; inter-segmentor differences; knee cartilage thickness maps; linear discriminant analysis; minimum description length statistical shape model; patella; permutation testing; population analysis; tibia knee bone surfaces; Biomedical imaging; Biomedical measurements; Bones; Image segmentation; Knee; Linear discriminant analysis; Shape; Testing; Thickness measurement; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on
Print_ISBN :
0-7803-8388-5
Type :
conf
DOI :
10.1109/ISBI.2004.1398507
Filename :
1398507
Link To Document :
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