X-ray Image Segmentation using Active Shape Models
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X-ray Image Segmentation using Active Shape Models
Mayuresh Kulkarni(KLKMAY001)
1Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
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Presentation Overview
• Introduction• Problem Description• Basic Segmentation Techniques• Active Shape Models (ASMs)• Performance Evaluation of ASMs• Conclusions
2Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
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Introduction
• Medical Imaging– Using Digital Imaging for applications in medicine– MRI scans, CT scans, digital X-rays etc.
• In this thesis– Digital X-rays of the tibia– Image Segmentation
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The Problem
• The Big Picture– Detecting bone fractures automatically• X-ray Image segmentation• Feature Extraction• Pattern Recognition
• The first step– X-ray image segmentation– Extracting the bone from the image
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Basic Segmentation Techniques
• Image Segmentation methods– Edge detection: Sobel, Prewitt, Roberts, Canny– Texture Analysis: Range and Std filtering
• Limitations of basic techniques– Detect all edges– Detects the skin and the bone edge– Difficult to separate the bone from the X-ray
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Filtering and Thresholding
6Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
• Assumes that X-rays are ideal– The brightness is uniform– Bone boundary is brighter than the skin boundary– 2 Levels of thresholding– Multiplying mask with original image
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Filtering and Thresholding
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Active Shape Models
• Training images are landmarked• Learning the shape from training images• Creating profile models at landmark points• Recording the shape• Searching the shape in the test image
8Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
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ASM: Sub-models
• Profile Model– Analyzes the landmark points– Stores the image behaviour around landmarks– Builds a profile model for each landmark
• Shape Model– Defines the permissible shapes and landmarks– Introduces a constraint on the search shape– Calculates the mean shape
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Training Images
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The Mean Shapes
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Creating profile
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Searching the shape
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Defining the Error
• Hand annotating the X-ray images• Distance transform• Comparing the ASM output to hand
annotated images
• Visual Check– Does the ASM track the bone?– Is it effective?
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Performance Evaluation
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Performance Evaluation
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Performance Evaluation
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Performance Evaluation
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Conclusions
• Basic segmentation techniques– Work for certain images– Separate the bone– But are susceptible to noise
• Active Shape Models– Extract the bone effectively– Perform well with different bone orientations
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THANK YOU
21Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
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References1. M. Donnelley. Computer aided Long-bone Segmentation and Fracture Detection.
PhD thesis, Flinders University of South Australia, January 2008.2. C. Ying. Model-Based Approach for Extracting Femur Contours in X-ray Images.
Master’s thesis, National University of Singapore, 2005.3. T. F. Cootes and C. J. Taylor. Technical Report: Statistical Models of Appearance
for Computer Vision. Technical report, The University of Manchester School of Medicine, 2004.
4. T. F. Cootes, C. J. Taylor, D. Cooper, and J. Graham. A Trainable Method of Parametric Shape Description. 2nd British Machine Vision Conference, pages 54–61, 1991.
5. S. E. Lim, Y. Xing, Y. Chen, W. K. Leow, T. S. Howe, and M. A. Png. Detection of Femur and Radius Fractures in X-Ray Images. 2nd International Conference on Advances in Medical Signal and Information, pages 249–256, 2004.
6. V. L. F. Lum, W. K. Leow, Y. Chen, T. S. Howe, and M. A. Png. Combining classifiers for bone fracture detection in X-ray Images.
22Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
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References8. T. P. Tian, Y. Chen, W. K. Leow, W. Hsu, T. S. Howe, and M. A. Png. Computing
neck- shaft angle of femur for x-ray fracture detection. International Conference on Computer Analysis of Images and Patterns, 2003.
9. T. T. Peng. Detection of Femur Fractures in X-ray images. PhD thesis, National University of Singapore, 2002.
23Mayuresh Kulkarni (BSc. Elec. Eng. UCT)