Nowadays, advanced medical imaging is crucial for the diagnosis of various internal injuries. For instance, in bone fracture detection, X-ray images are among the fastest and cheapest medical-imaging techniques. However, various criteria such as varying brightness, presence of noise, and artificial effects make the bone segmentation and fracture detection a cumbersome task. These difficulties often lead to misdiagnosis and further inconvenience for the patients. This thesis proposes three ideas for quality enhancement, bone segmentation, and fracture detection in long bone X-ray images. We show that an edge-preserving smoothing filter, in addition to local contrast enhancement, enhances the quality of the image. Then, considering the tubular structure of long bone, we analyze the intensity fluctuations of the image to recognize the bone boundary. We show that our proposed algorithm provides more robust and accurate bone segmentation compared with former methods. For fracture detection, we extracted the skeleton of the segmented bone and searched for disjointed parts. An empirical evaluation of our method showed 96% accuracy on 100 images taken from public datasets.

1. Long Bone Segmentation and
Fracture Detection in X-ray Images
by:
Salome Kazeminia
supervisors:
Prof. Shadrokh Samavi
Dr. Nader Karimi
January 2016
Isfahan University of Technology

2. Table of Contents
‣ Introduction
‣ Background
‣ Preprocessing
‣ Segmentation
‣ Fracture Detection
‣ Conclusion
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3. INTRODUCTION

4. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Different tasks require different structures
‣ Long Bone Structure
‣ Long Bone Fracture Types
Bone
4
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
✓ http://cnx.org/contents/9306de62-3f52-46f8-ab1a-94263c480eda@4/bone-structure.
✓ Müller,MauriceE,Koch,Peter,Nazarene,Serge,andSchatzker,Joseph.”Thecomprehensiveclassification of fractures of long bones”. Springer Science
& Business Media, 1990.
Simple Wedge Complex
Sprial Oblique Transverse Sprial Bending Fragmented Sprial Segmental Irregular
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5. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Role of medical imaging
‣ X-ray imaging
• Discovered by William Roentgen in 1895
• Radiation
• Absorption
• Radiography with X-Ray
X-Ray Imaging
5
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
✓ https://www.medicalradiation.com/types-of-medical-imaging/imaging-using-x-rays/radiography-plain-x- rays/
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6. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Gray Scale Images
‣ Rough texture
‣ Gaussian & Poisson Noise
Bone X-Ray Images Properties
6
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
✓ https://en.wikipedia.org/wiki/.
✓ Ribeiro, Maria Isabel. ”gaussian probability density functions: Properties and error characterization”. Instituto Superior Tcnico, Lisboa, Portugal, Tech.
Rep, pp. 1049–001, 2004.
✓ https://www.umass.edu/wsp/resources/poisson/.
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‣ Variable Size & Variable Magnification Zone

7. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Radiography Effects
• High brightness or darkness in images
• Misty and foggy images
• Low contrast images
• Presence of meaningless shadows in image
Bone X-Ray Images Properties (cont.)
7
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
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8. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Time Importance in Efficient treatment
‣ Invisible Fractures
• Low quality of bone X-Ray images
• Satisfaction of Search Error
• Other Medical Errors
‣ 31% to 60% Undetected Bone Fracture
Importance of Automatic Fracture Detection in Bone X-Ray
Images
8
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
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9. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Automation Steps
- Pre-Processing
• Quality enhancement
• Other Steps Requirements
- Bone Segmentation
- Fracture Detection
- Fracture Classification
Long Bone Fracture Automation
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10. BACKGROUND

11. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
! Bone boundaries contain important information.
‣ Edge Preserving Smoothing Filters
• Anisotropic Diffusion Filter
• Non Local Mean Filter
• Bilateral Filter
‣ Morphology Functions
• Top-Hat
• Closing and Opening
Previous Approaches : Pre-processing
11
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
✓ Kundu, Rupam. ”structural enhancement of digital x-ray image of bone with a suitable denoising tech- nique”. in Indian Conference on Medical Informatics
and Telemedicine (ICMIT), pp. 17–22. 2013. /48

12. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Contrast Enhancement
- Histogram Equalization
• Partitioning pixels according to their brightness
• Local Processing
- Gamma Correction
Previous Approaches : Pre-processing (cont.)
12
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
✓ Chang, Yung-Tseng, Wang, Jen-Tse, Yang, Wang-Hsai, and Chen, Xiang-Wei. ”contrast enhancement in palm bone image using quad-histogram
equalization”. in International Symposium on Computer, Consumer and Control (IS3C), pp. 1091–1094, 2014.
✓ /48

13. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Active Contour Methods
✓Successful in low contrast images.
! Sensitive to Noise
! Only can detect a closed curve.
‣ Active Shape Methods
! Sensitive to noise and deformation of Bone
! Need Special Pattern
‣ Hough Transform
! Bone marrow is not always a straight line.
! Need suitable threshold in wedge and complex fractures
Previous Approaches : Segmentation
13
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
✓ Niroshika, UAA and Meegama, RGN. ”a deformable model to segment discontinuous boundaries of bone fractures in x-ray images”. in IEEE International
Conference on Industrial and Information Systems (ICIIS), pp. 309–314. IEEE, 2013.
✓
✓
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14. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Thresholding
! Challenge of variety of quality and brightness.
• Brightness
! Doesn’t work in challenging images.
• Standard deviation
• Entropy
Previous Approaches : Segmentation (cont.)
14
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
• Morphology - Thresholding
• Thresholding - Contour generation
• Simplification Contour
✓ Bandyopadhyay, Oishila, Chanda, Bhabatosh, and Bhattacharya, Bhargab B. ”entropy-based automatic segmentation of bones in digital x-ray images”. in
Springer, Pattern Recognition and Machine Intelligence, pp. 122–129. 2011.
✓
✓
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15. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Changes in Angle of Bone Marrow
• Joints are detected as fracture wrongly.
‣ Angle and Strength of Bone Internal Edges
• Only internal fracture can be detected.
‣ Machine Learning
• Need to appropriate feature
‣ Other techniques
Previous Approaches : Fracture Detection
15
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
✓ Wei, Zheng and Liming, Zhang. ”study on recognition of the fracture injure site based on x-ray images”. in IEEE, International Congress on Image and
Signal Processing (CISP), vol. 4, pp. 1947–1950, 2010. /48

16. Proposed Methods

17. introduction Background Proposed Method Conclusionintroduction Background Proposed Method Conclusion
‣ We worked on 3 steps:
- Preprocessing
- Long Bone Segmentation
- Fracture Detection
Proposed Methods
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18. Pre-processing

19. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
! Effective contrast enhancement requires a local processing
! Especial Pattern Existence in a Cross Section of Long Bone
PreProcessing
19
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
Rotation
Contrast Enhancement
Denoising & Smoothing
Input Image
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20. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Tissue Boundaries Elimination
✴ Gamma Correction
Rotation - Enhancement
20
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
Original Image Gamma = 1.5 Gamma = 2 Gamma = 2.5
Gamma = 1.75
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21. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Rough Texture Smoothing
‣ Edge Preserving Filters
✴Guided Filter
Rotation - Smoothing
21
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
r = 8 ε = 0.01
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22. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
Rotation - Smoothing
22
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
Guided Filter Bilateral Filter Morphology Gaussian
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23. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Bone Boundaries extraction
Rotation - Edge Detection
23
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
Canny Edge Detector
Original Image
Standard Deviation = 2
&
Threshold = 0.2
Standard Deviation = 2
&
Threshold = 0.4
Standard Deviation = 4
&
Threshold = 0.2
Standard Deviation = 4
&
Threshold = 0.4
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24. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Rotation Angle Estimation
• Variety of Edge Magnitude on Bone Boundaries
• Same Boundary Direction of Bone and Tissue
! Rotated Image shouldn’t be Cropped
Rotation
24
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
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25. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Bone: always Brighter than its neighbors
• Local Brightness Normalization
• Local Gamma Correction
! (Max Brightness - min Brightness > 0.7) & (max Brightness > 0.1)
Local Contrast Enhancement
25
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
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Original Image Global Gamma Local Gamma

26. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
Guided Filter
26
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
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27. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
PreProcess Effect on Segmentation Methods
27
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
Region Growing Global Otsu
on
Original Image
on
PreProcessed Image
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28. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
28
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
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‣ Need to a Stable Property what doesn’t change in different conditions!
✴Existence of a spacial Pattern in a Cross Section of Long Bone
Long Bone Structure in a Cross Section of it
28

29. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
29
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
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! Maximum brightness value is somewhere near to bone boundary.
Bone Boundary Extraction 1
29

30. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
30
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
✓ Bandyopadhyay, Oishila, Chanda, Bhabatosh, and Bhattacharya, Bhargab B. ”entropy-based automatic segmentation of bones in digital x-ray images”. in Pattern
Recognition and Machine Intelligence, pp. 122–129. Springer, 2011.
✓ Bandyopadhyay,Oishila,Biswas,Arindam,Chanda,Bhabatosh,andBhattacharya,BhargabB.”bonecon- tour tracing in digital x-ray images based on adaptive
thresholding”. in Pattern Recognition and Machine Intelligence, pp. 465–473. Springer, 2013.
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Bone Boundary Extraction(1) Vs Previous Methods
30

31. introduction Background PreProcessing Segmentation Fracture Detection Conclusionintroduction Background PreProcessing Segmentation Fracture Detection Conclusion
Bone Boundary Extraction(1) on Challenging Images
31
‣ Successful in Bone Boundary Detection 40%
‣ Additional Internal Edges Detection 40%
‣ Additional External Edges Detection 18%
‣ Unsuccessful in Bone Boundary detection 2%
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32. introduction Background PreProcessing Segmentation Fracture Detection Conclusionintroduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Local Maxima Detection and Thresholding is not Enough.
- There are many local maximum in different areas.
• Smoothed intense noise
• Smoothed rough texture
- Thresholding only can remove some of them.
• Internal edges remain
‣ Stronger Edge Detection
- Otsu is blind to location.
Bone Boundary Extraction(1) Weak Points
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33. introduction Background PreProcessing Segmentation Fracture Detection Conclusionintroduction Background PreProcessing Segmentation Fracture Detection Conclusion
Bone Boundary Extraction(2)
33
‣ Select 4 Marked Vertical Edges
‣ Strongest Edges Reamin - 95%
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34. introduction Background PreProcessing Segmentation Fracture Detection Conclusionintroduction Background PreProcessing Segmentation Fracture Detection Conclusion
Bone Boundary Extraction(2) Evaluation
34
‣ Successful in 78%
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35. introduction Background PreProcessing Segmentation Fracture Detection Conclusionintroduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Failure in Some Wedge and Complex Bone Fracture
- Limit of 4 vertical edge selection
‣ Non Boundary Edges Along Bone Boundaries
‣ Other Organs Existence
✓Long Bone Pattern Extraction
✓Consider The Properties of Individual or Group of Connected
Edges
Bone Boundary Extraction(2) - Weak Points
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36. introduction Background PreProcessing Segmentation Fracture Detection Conclusionintroduction Background PreProcessing Segmentation Fracture Detection Conclusion
Bone Boundary Extraction(3) and Bone Segmentation
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37. introduction Background PreProcessing Segmentation Fracture Detection Conclusionintroduction Background PreProcessing Segmentation Fracture Detection Conclusion
Bone Boundary Extraction(3) - Tubular Pattern Extraction
37
✓ Frangi, Alejandro F, Niessen, Wiro J, Vincken, Koen L, and Viergever, Max A. ”multiscale vessel en- hancement filtering”. in Medical Image Computing and
Computer-Assisted Interventation (MICCAI), Springer, pp. 130–137, 1998. /48

38. introduction Background PreProcessing Segmentation Fracture Detection Conclusionintroduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Continuous Values
‣ Otsu Thresholding
Bone Boundary Extraction(3) - Tubular Pattern Quantization
38
Global Otsu
Local Otsu
PreProcesse
d
Image
✓ http://cse19-iiith.vlabs.ac.in/segment/images/image001.gif
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39. introduction Background PreProcessing Segmentation Fracture Detection Conclusionintroduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Detect all Near Edges to Tubular Pattern
- Internal Bone Edges elimination
• Internal Edges are weaker than Bone Boundary.
➡ Individual and in Group of Connected Edges
• Internal Edges are Surrounded by Bone Boundary.
• The Number of Edges is not an odd number!
Bone Boundary Extraction(3) - Strong Boundaries Extraction
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40. introduction Background PreProcessing Segmentation Fracture Detection Conclusionintroduction Background PreProcessing Segmentation Fracture Detection Conclusion
Bone Boundary Extraction(3) Vs. Bone Boundary
Extraction(2)6
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version3version2

41. FRACTURE DETECTION

42. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Accurate Segmentation
‣ Black and White Mask Generation
• All edges extraction
• Bone marrow detection by eye diagnosis
‣ Mask Boundary area Correction
• Morphology Opening Function (Erosion - Dilation)
• Structure Element : Disk (r=5)
Segmented Mask Generation
42
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
✓ http://aishack.in/tutorials/mathematical-morphology-composite-operations/
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43. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Morphological Thinning
‣ Disconnected Bone Parts
‣ Junctions of Skeleton
- Fractures
- Expanded area of End of the Components
• 25% of the top and bottom of the Component - check Size
Mask Skeleton Extraction
43
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
✓ http://homepages.inf.ed.ac.uk/rbf/HIPR2/thin.htm
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44. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
Fracture Detection Results
44
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45. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
Fracture Detection Evaluation
45
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ 96% Successful
‣ Unsuccessful Results
- Fractures cause to compress Bone
- Joint adjacent areas out of 25% range
- Too small Image
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46. CONCLUSION

47. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
✓Long Bone Structure and Types of its fracture
✓X-ray Imaging
✓Bone X-ray Images Properties
✓Importance Of Automatic Fracture Detection
✓Automatic Fracture Detection Steps
✓Previous Proposed Methods
Summery
47
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48. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Proposed Method
- PreProcessing
• Rotation - Contrast Enhancement - Denoising and Smoothing
- Segmentation
• 3 Methods for Bone Marrow Extraction
- Fracture Detection
• Simplification
Summery
48
introduction Background PreProcessing Segmentation Fracture Detection Conclusion
Subjective quality improvement
More accuracy - 38% improvement - Trade Off
96% Successfull
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49. introduction Background PreProcessing Segmentation Fracture Detection Conclusion
‣ Try Other Segmentation Methods
• Introduce Efficient Features to Applying Strong Segmentation Methods
‣ Try Other Fracture Detection Methods
• Detect more Efficient Features and Using Learning Methods
‣ Long Bone Fracture Classification
‣ Automatic Fracture Detection for Other Types of Bone
‣ Automatic Diagnosis of Other Bone Sickness Types
Suggestions
49
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50. TANK YOU!
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