Breast Lesion Segmentation in

Ultrasound Images

Group Members

Ibrahim Sadek

Mohamed Elawady

Viktor Stefanovski

Medical Imaging Analysis Module 1

Outline

1. Introduction

2. Problem Definition

3. Framework

4. Results

5. Conclusion

Medical Imaging Analysis Module 2

Outline

1. Introduction

2. Problem Definition

3. Framework

4. Results

5. Conclusion

Medical Imaging Analysis Module 3

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

Outline

1. Introduction

2. Problem Definition

3. Framework

4. Results

5. Conclusion

Medical Imaging Analysis Module 5

Problem Definition

Medical Imaging Analysis Module 6

Breast

Lesion Segmentation

In Ultrasound Images

Low Contrast

Inherent Speckle Noise

Outline

1. Introduction

2. Problem Definition

3. Framework

4. Results

5. Conclusion

Medical Imaging Analysis Module 7

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)

Framework: Pre-processing Step

Medical Imaging Analysis Module 9

imadjust

Optional Intensity

Adjustment

0

500

1000

1500

2000

2500

3000

3500

imadjust

0 50 100 150 200 250

Input

Input Image

(Gray Scale)

0

500

1000

1500

2000

2500

3000

3500

Input

0 50 100 150 200 250

medfilt2

2D Median Filter

(7x7 Window Size)

histeq

0

500

1000

1500

2000

2500

3000

3500

4000

4500

histeq

0 50 100 150 200 250

Optional Histogram

Equalization

im2bw

Binary Thresholding

(Level=0.2)

Remove

Speckle

Noise

Enhance

Image

Quality

Framework: Segmentation

Medical Imaging Analysis Module 10

Enhanced Image

im2bw

20 40 60 80 100 120 140 160

20

40

60

80

100

120

140

160

Normalized Cut

Method

(4 Segments)

0

500

1000

1500

2000

2500

3000

3500

4000

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

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Ncut eigenvectors of 3

0

500

1000

1500

2000

2500

3000

3500

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Ncut eigenvectors of 4

0

500

1000

1500

2000

2500

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Allocate

Region Of

Interest

(ROI)

Input Image

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

Outline

1. Introduction

2. Problem Definition

3. Framework

4. Results

5. Conclusion

Medical Imaging Analysis Module 12

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

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

Outline

1. Introduction

2. Problem Definition

3. Framework

4. Results

5. Conclusion

Medical Imaging Analysis Module 15

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.

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.

Medical Imaging Analysis Module 18

Thanks for

Listening!

Medical Imaging Analysis Module 19

Questions?!