ICIAR 2016 Poster: Automatic Nonlinear Filtering and Segmentation for Breast Ultrasound Images
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13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel, Póvoa de Varzim, Portugal, July 13-15, 2016, Proceedings DOI: 10.1007/978-3-319-41501-7_24 Link: http://link.springer.com/chapter/10.1007/978-3-319-41501-7_24
![UMR • CNRS • 5516 • SAINT-ETIENNE
Automatic Nonlinear Filtering and Segmentation
for Breast Ultrasound Images
Mohamed Elawady, Ibrahim Sadek, Abd El Rahman
Shabayek, Gerard Pons, Sergi Ganau
PROBLEM DEFINITION
The ultrasound image contrast between the ab-
normality and the surrounding breast tissue is
insufficient for direct lesion detection.
CONTRIBUTION
• Introducing a fully automatic lesion ex-
traction algorithm.
• Proposing a fast segmentation step by
means of Quick Shift; against frequently
used Normalized Cut.
• Conducting a comparative study on the
most common preprocessing nonlinear
techniques.
METHOD
Figure 1: The proposed framework used for lesion segmentation in BUS images.
RESULTS I
Performance results across all proposed methods (segmentation [QS: Quick Shift and NC: Normal-
ized Cut] and preprocessing [FR: Frost Filter, DPAD: Detail Preserving Anisotropic Diffusion and
PPB: Probabilistic Patch-Based]):
Percentage(%)
0
10
20
30
40
50
60
70
Methods
QS-FR QS-DPAD QS-PPB NC-FR NC-DPAD NC-PPB
Dice
Jaccard
Sensitivity
Figure 2: Statistical metrics calculated in average. Figure 3: Box plot of Dice similarity coefficient.
RESULTS II
Results of some successful lesion extraction.
First row represents some of the input images.
Second, third and fourth rows show the output
results of [QS-FR, NC-DPAD, NC-PPB] respec-
tively, in which white color is true segmented
lesion, green color is false positive, red color is
false negative and black color is true negative.
The computation time of Quick Shift method
is 8x faster than Normalized Cut method.
The failure cases exist in all methods due to
the intensity similarity of surrounding tissues
around the target lesion, leading to incorrect
segmentation.
REFERENCES
[1] H.D. Cheng, Juan Shan, Wen Ju, Yanhui Guo, and Ling Zhang. Automated breast cancer detection and
classification using ultrasound images: A survey. Pattern Recognition, 43(1):299 – 317, 2010.
[2] Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern
Analysis and Machine Intelligence, 22(8):888–905, August 2000.
[3] A. Vedaldi and S. Soatto. Quick shift and kernel methods for mode seeking. In European Conference on
Computer Vision, 2008.
[4] Ju Zhang, Chen Wang, and Yun Cheng. Comparison of despeckle filters for breast ultrasound images.
Circuits, Systems, and Signal Processing, 34(1):185–208, 2015.
CONCLUSION
• Best performance: FR with QS, DPAD
with NC and PPB with NC.
• QS is a more preferable choice in real time
applications.
• Future work: use superpixel segmenta-
tion approaches for robust results.
PARTNERS](https://image.slidesharecdn.com/conferenceposter4-edited-170206070852/85/ICIAR-2016-Poster-Automatic-Nonlinear-Filtering-and-Segmentation-for-Breast-Ultrasound-Images-1-320.jpg)
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ICIAR 2016 Poster: Automatic Nonlinear Filtering and Segmentation for Breast Ultrasound Images
- 1. UMR • CNRS • 5516 • SAINT-ETIENNE Automatic Nonlinear Filtering and Segmentation for Breast Ultrasound Images Mohamed Elawady, Ibrahim Sadek, Abd El Rahman Shabayek, Gerard Pons, Sergi Ganau PROBLEM DEFINITION The ultrasound image contrast between the ab- normality and the surrounding breast tissue is insufficient for direct lesion detection. CONTRIBUTION • Introducing a fully automatic lesion ex- traction algorithm. • Proposing a fast segmentation step by means of Quick Shift; against frequently used Normalized Cut. • Conducting a comparative study on the most common preprocessing nonlinear techniques. METHOD Figure 1: The proposed framework used for lesion segmentation in BUS images. RESULTS I Performance results across all proposed methods (segmentation [QS: Quick Shift and NC: Normal- ized Cut] and preprocessing [FR: Frost Filter, DPAD: Detail Preserving Anisotropic Diffusion and PPB: Probabilistic Patch-Based]): Percentage(%) 0 10 20 30 40 50 60 70 Methods QS-FR QS-DPAD QS-PPB NC-FR NC-DPAD NC-PPB Dice Jaccard Sensitivity Figure 2: Statistical metrics calculated in average. Figure 3: Box plot of Dice similarity coefficient. RESULTS II Results of some successful lesion extraction. First row represents some of the input images. Second, third and fourth rows show the output results of [QS-FR, NC-DPAD, NC-PPB] respec- tively, in which white color is true segmented lesion, green color is false positive, red color is false negative and black color is true negative. The computation time of Quick Shift method is 8x faster than Normalized Cut method. The failure cases exist in all methods due to the intensity similarity of surrounding tissues around the target lesion, leading to incorrect segmentation. REFERENCES [1] H.D. Cheng, Juan Shan, Wen Ju, Yanhui Guo, and Ling Zhang. Automated breast cancer detection and classification using ultrasound images: A survey. Pattern Recognition, 43(1):299 – 317, 2010. [2] Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888–905, August 2000. [3] A. Vedaldi and S. Soatto. Quick shift and kernel methods for mode seeking. In European Conference on Computer Vision, 2008. [4] Ju Zhang, Chen Wang, and Yun Cheng. Comparison of despeckle filters for breast ultrasound images. Circuits, Systems, and Signal Processing, 34(1):185–208, 2015. CONCLUSION • Best performance: FR with QS, DPAD with NC and PPB with NC. • QS is a more preferable choice in real time applications. • Future work: use superpixel segmenta- tion approaches for robust results. PARTNERS