This document describes a framework for breast lesion segmentation in ultrasound images. It consists of pre-processing using median filtering to reduce speckle noise. Segmentation is done using normalized cut to identify four segments, which are then classified using k-means clustering. The best segmented lesion region is selected based on minimum area. The framework was tested on 14 images, achieving correct segmentation on 11 images based on Jaccard and Dice similarity metrics. While speckle noise reduction is important, the median filter was not effective for edge enhancement.

1. Breast Lesion Segmentation in
Ultrasound Images
Group Members
Ibrahim Sadek
Mohamed Elawady
Viktor Stefanovski
Medical Imaging Analysis Module 1

2. Outline
1. Introduction
2. Problem Definition
3. Framework
4. Results
5. Conclusion
Medical Imaging Analysis Module 2

3. Outline
1. Introduction
2. Problem Definition
3. Framework
4. Results
5. Conclusion
Medical Imaging Analysis Module 3

4. Introduction
Medical Imaging Analysis Module 4
Breast Lesion
Segmentation
Digital
Mammography
(DM)
Ultra-Sound
(US) imaging
Magnetic
Resonance
Image (MRI)
• Harmless and painless examination method
• Perfect early-stage cancer detection
• Reduce the potential number of unnecessary
biopsies

5. Outline
1. Introduction
2. Problem Definition
3. Framework
4. Results
5. Conclusion
Medical Imaging Analysis Module 5

6. Problem Definition
Medical Imaging Analysis Module 6
Breast
Lesion
Segmentation
In Ultrasound
Images
Low
Contrast
Inherent
Speckle
Noise

7. Outline
1. Introduction
2. Problem Definition
3. Framework
4. Results
5. Conclusion
Medical Imaging Analysis Module 7

8. Framework
Medical Imaging Analysis Module 8
Input Image (Ultrasound Image)
Pre-processing Step (Median Filter)
Segmentation (Normalized Cut)
Post-processing Classification (K-means Clustering)
Output Image (Segmented Lesion)

9. Framework: Pre-processing Step
Medical Imaging Analysis Module 9
imadjust
Optional Intensity
Adjustment
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imadjust
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Input
Input Image
(Gray Scale)
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Input
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medfilt2
2D Median Filter
(7x7 Window Size)
histeq
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histeq
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Optional Histogram
Equalization
im2bw
Binary Thresholding
(Level=0.2)
Remove
Speckle
Noise
Enhance
Image
Quality

10. Framework: Segmentation
Medical Imaging Analysis Module 10
Enhanced Image
im2bw
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Normalized Cut
Method
(4 Segments)
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ncut eigenvectors of 1
Ncut eigenvectors of 2
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Ncut eigenvectors of 3
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Ncut eigenvectors of 4
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Allocate
Region Of
Interest
(ROI)
Input Image

11. Framework: Classification
Medical Imaging Analysis Module 11
Norm
Input Image
One of
Segmented Images
kmeans
K-means Clustering
(2 clusters)
Contour Selection
Contour Selection with
Minimum Length
1st Approach
Main
2nd Approach
Backup
Contour Selection
Otsu Binary
Thresholding
Selection of
Best Classified
Image based on
Minimum Area
Extract
Lesion
Region

12. Outline
1. Introduction
2. Problem Definition
3. Framework
4. Results
5. Conclusion
Medical Imaging Analysis Module 12

13. Results
Medical Imaging Analysis Module 13
14 Dataset
Images
11 Correct
Segmentation
3 Incorrect
Segmentation
No Intensity
Adjustment
No Histogram
Equalization
Jaccard 0.8235
Dice 0.9032
FPR 0.0616
FNR 0.1257
Jaccard 0
Dice 0
FPR 75.488
FNR 100
Results GT

14. Results
Medical Imaging Analysis Module 14
Image Name Jaccard Dice RFP RFN
000018(F,F) 0.8235 0.9032 0.0616 0.1257
000032(F,F) 0.8107 0.8954 0.0017 0.1879
000031(T,F) 0.5857 0.7387 0.0294 0.3971
000025(T,F) 0.4410 0.6121 0.3541 0.4029
000023(T,F) 0.7143 0.8333 0.0687 0.2366
000011(F,F) 0.5338 0.6960 0.0207 0.4552
000001(F,F) 0.5206 0.6847 0 0.4794
000002(F,F) 0.7693 0.8696 0 0.2307
000022(F,F) 0.4869 0.6549 0.0034 0.5115
000010(T,T) 0.5061 0.6721 0.4162 0.2832
000007(T,T) 0.4365 0.6077 0 0.5635
000019
000014
000030

15. Outline
1. Introduction
2. Problem Definition
3. Framework
4. Results
5. Conclusion
Medical Imaging Analysis Module 15

16. Conclusion
Medical Imaging Analysis Module 16
 Speckle noise reduction:
It is an important prerequisite , whatever ultrasound imaging
techniques is used for tissue characterization.
 preprocessing step:
The median filter in the preprocessing step is not an effective method
to enhance the edges and lines in the images.

17. Bibliography
Medical Imaging Analysis Module 17
 “Automated breast cancer detection and classification using
ultrasound images: A survey”, H.D. Cheng, J. Shan, W. Ju, Y. Guo,
and L. Zhang, Pattern Recognition, Volume 43, Issue 1, January
2010, Pages 299-317.
 “Automated segmentation of breast lesions in ultrasound images”, X.
Liu, Z. M. Huo, and J. W. Zhang, IEEE Comput. Soc., Shanghai,
China, 2005, pp. 7433–7435.
 “Image Segmentation with Normalized Cuts”, Jianbo Shi,
Department of Computer and Information Science, University of
Pennsylvania.

18. Medical Imaging Analysis Module 18
Thanks for
Listening!

19. Medical Imaging Analysis Module 19
Questions?!