X-ray Image Segmentation using
Active Shape Models
Mayuresh Kulkarni
(KLKMAY001)
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
1
Presentation Overview
•
•
•
•
•
•
Introduction
Problem Description
Basic Segmentation Techniques
Active Shape Models (ASMs)
Performance Evaluation of ASMs
Conclusions
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
2
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
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
3
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
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
4
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
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
5
Filtering and Thresholding
• 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
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
6
Filtering and Thresholding
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
7
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
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
8
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
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
9
Training Images
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
10
The Mean Shapes
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
11
Creating profile
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
12
Searching the shape
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
13
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?
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
14
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
15
Performance Evaluation
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
16
Performance Evaluation
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
17
Performance Evaluation
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
18
Performance Evaluation
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
19
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
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
20
THANK YOU
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
21
References
1.
2.
3.
4.
5.
6.
M. Donnelley. Computer aided Long-bone Segmentation and Fracture Detection.
PhD thesis, Flinders University of South Australia, January 2008.
C. Ying. Model-Based Approach for Extracting Femur Contours in X-ray Images.
Master’s thesis, National University of Singapore, 2005.
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.
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.
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.
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.
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
22
References
8. T. P. Tian, Y. Chen, W. K. Leow, W. Hsu, T. S. Howe, and M. A. Png. Computing neckshaft 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.
Mayuresh Kulkarni (BSc. Elec. Eng. UCT)
23