Articular Cartilage Damage | Electrostimulation of Joint | Arthritic Knee Treatment
Combined Image Processing Techniques for Characterization of MRI Cartilage of the Knee
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Transcript of Combined Image Processing Techniques for Characterization of MRI Cartilage of the Knee
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Author :J. Carballido-Gamio J.S. Bauer Keh-YangLee
S. Krause S. MajumdarSource :27th Annual International Conference of the IEEE-EMBS 2005; On page(s): 3043-3046
Speaker : Ren-Li ShenAdvisor : Ku-Yaw Chang
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OutlineIntroductionMethodologyResultsDiscussionConclusion
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IntroductionArticular cartilage
Common manifestation of osteoarthritis (OA) Morphological degeneration
Magnetic resonance imaging (MRI)Visualize and analyze
Purpose Development new image processing techniques
For quantitative analysis
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IntroductionImage process consists
MRI acquisitionCartilage segmentation
Automatic Semi-automatic
InteractiveInterpolation
Morphing techniqueRegistration
3D shape-contextsQuantification
Minimum 3D Euclidean distances 3D shape-contexts
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OutlineIntroductionMethodologyResultsDiscussionConclusion
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Methodology17 porcine kneesSagittal 3D SPGR MR images ( Intra-subject)Knee of 6 subjects ( resolution : 0.234 mm x
0.234 mm, slice thickness : 2mm,obtained at 1.5T)
Sagittal fast spin-echo(FSE) images ( Inter-subject)Both knees of 6 different subjects ( resolution :
0.3125 mm x 0.3125mm, slice thickness : 1.5 mm,obtained at 1.5T)
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Methodology
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Methodology-Cartilage segmentationSemi-automatic segmentation technique
Based on edge detection and Bezier splinesControl points
Placed inside the cartilage Following its shape to create Bezier spline
Smoothing techniquesAnisotropic diffusionMedian filteringMultiplication
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Methodology-Thickness and volumeCompute 3D cartilage thickness
Labeling bone-cartilage and articular Automatically
For each point on the articular surface Can find point on the bone-cartilage interface
Corresponding distance was assigned Thickness value
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Methodology-Image registrationBones to be registered
Using 3D shape-contexts Robust contour matching
Manual and automaticUsing minimum Euclidean
distancesAvoid false matching
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OutlineIntroductionMethodologyResultsDiscussionConclusion
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ResultsUsing Bland-Altman method
Check for any deviationAll methods
Good agreement on all data samples
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OutlineIntroductionMethodologyResultsDiscussionConclusion
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DiscussionProper quantification of thickness and
volumeImportant to OA of the knee
In follow-up studiesCompare common regions
Have presented an validatedAutomatic registration technique
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OutlineIntroductionMethodologyResultsDiscussionConclusion
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ConclusionIt’s important to assess the quantification
Knee cartilage morphologyMonitor the progression of joint diseases
Have presented and validatedAccurate image processing tools
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