Image segmentation is an important task in the image processing field. Efficient segmentation of images considered important for further object recognition and classification. This paper presents a novel segmentation approach based on Particle Swarm Optimization (PSO) and an adaptive Watershed algorithm. An application of liver CT imaging has been chosen and PSO approach has been applied to segment abdominal CT images. The experimental results show the efficiency of the proposed approach and it obtains overall accuracy 94% of good liver extraction.

1. Abdominal CT Liver Parenchyma Segmentation
Based
on Particle Swarm Optimization
SRGE Workshop, Cairo University Conference Hall (28-November-2015)
Gehad Ismail Sayed
http://www.egyptscience.net

2. Overview
 Introduction
Problem Definition
Motivation
 Related Work
 Proposed Approache
 Results and Discussion
 Conclusion and Future Works
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SRGE Workshop, Cairo University Conference Hall (28-November-2015)

3. Introduction
 Problem Definition
 Liver cancer is one of the most leading death in the world.
 Early detection and accurate staging of liver cancer is considered and
important issue
 Image segmentation is an important task in the image processing field.
Efficient segmentation of images considered important for further object
recognition and classification.
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SRGE Workshop, Cairo University Conference Hall (28-November-2015)

4. Introduction
 Motivation
 Liver segmentation is essential step for diagnosis liver disease
 Manual segmentation of Computed Tomography (CT) scans are
tedious and prohibitively time consuming
 Automatic Liver segmentation in CT image is a difficult task due to:-
 Low level of contrast and blurry edges which characterize the CT images
 Gray levels similarity between neighbor organs like spleen, liver and stomach
 Variety of liver shape and size
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SRGE Workshop, Cairo University Conference Hall (28-November-2015)

5. Related Work
 Several approaches for liver segmentation have been proposed,
which can be categorized based on the degree of automation:-
 Fully automatic
 Most of these methods respond identically to different patients. They usually produce over
segmentation and also give unsatisfied results
 Semi or interactive automatic
 It requires a limited user intervention to complete the task.
 i.e. Snake model, Active contour, …
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SRGE Workshop, Cairo University Conference Hall (28-November-2015)

6. Proposed Approach
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SRGE Workshop, Cairo University Conference Hall (28-November-2015)

7. Particle Swarm Optimization
SRGE Workshop, Cairo University Conference Hall (28-November-2015)

8. Particle Swarm Optimization
SRGE Workshop, Cairo University Conference Hall (28-November-2015)

9. Particle Swarm Optimization
SRGE Workshop, Cairo University Conference Hall (28-November-2015)

10. Particle Swarm Optimization
SRGE Workshop, Cairo University Conference Hall (28-November-2015)

11. Particle Swarm Optimization
SRGE Workshop, Cairo University Conference Hall (28-November-2015)

12. Particle Swarm Optimization
SRGE Workshop, Cairo University Conference Hall (28-November-2015)

13. Particle Swarm Optimization
SRGE Workshop, Cairo University Conference Hall (28-November-2015)

14. Particle Swarm Optimization
SRGE Workshop, Cairo University Conference Hall (28-November-2015)

15.  43 images are middle slice frontal images in JPEG format, selected from a
DICOM from different patients
 Image dimensions: 630x630
 Image resolution: 72 DPI, and bit depth of 24 bits.
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Dataset Description
SRGE Workshop, Cairo University Conference Hall (28-November-2015)

16. Dataset Samples
SRGE Workshop, Cairo University Conference Hall (28-November-2015)

17. 17
a) Original Image b) Median Filter Results c) Cluster-1 d) Cluster-2
e) Cluster-3
SRGE Workshop, Cairo University Conference Hall (28-November-2015)
Results and Discussion

18. 18
a) Binarized Clustered Image b) Open Morphology
Results
c) Image After Selecting Largest Region d) Close Morphology Results
e) Image After Filling HolesSRGE Workshop, Cairo University Conference Hall (28-November-2015)
Results and Discussion

19. 19
a) Gradient Image b) Gradient Image After
Normalization
d) Image After Applying Watershed and e) Visualization of Extracted Liver
Taking the Largest RegionSRGE Workshop, Cairo University Conference Hall (28-November-2015)
Results and Discussion

20. 20
Parameter Value (s)
Population Size 150
Number of Iterations 10
0.6
0.6
255
0
2
-2
w 0.4
Number of Levels 3
PSO Parameters Settings
1
2
maxX
minX
maxV
minV
SRGE Workshop, Cairo University Conference Hall (28-November-2015)
Results and Discussion

21. 21
Authors Year Accuracy
Jeongjin et al. 2007 70%
Ruchaneewan et al. 2007 86%
M. Abdallal 2012 84%
Z. Abdallal 2012 92%
M. Anter 2013 93%
N. Aldeek 2014 87%
Proposed Approach 2015 94%
Comparison Between the Proposed Approach and The Previous Approaches
SRGE Workshop, Cairo University Conference Hall (28-November-2015)
Results and Discussion

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Dice (%) Correlation (%) True Positive (%)
Using Watershed 91.89 90.62 94.62
Without Using Watershed 89.12 87.94 90.23
Comparison Between Using Watershed in The Proposed Approach and
Without Using It
SRGE Workshop, Cairo University Conference Hall (28-November-2015)
Results and Discussion

23. 23
Dice (%) Correlation (%) True Positive (%) CPU Process Time in
Seconds
2 78.80 76.51 71.71 50.26
3 91.89 90.62 94.62 56.66
4 87.98 86.83 93.36 62.39
5 80.48 80.62 88.44 75.69
Comparison Between The Results Obtained From Different Levels
SRGE Workshop, Cairo University Conference Hall (28-November-2015)
Results and Discussion

24. 24
Dice (%) Correlation (%) True Positive (%)
Active Contour 71.87 69.22 72.43
Global Threshold 81.34 79.41 81.19
Proposed Approach 91.89 90.62 94.62
Comparison Between The Proposed Approach and The Other Approaches
SRGE Workshop, Cairo University Conference Hall (28-November-2015)
Results and Discussion

25. Conclusion and Future Works
 Conclusion
 The experimental results show that the proposed approach gives better result
compared with other approaches and obtained over all accuracy about 94% of
good liver extraction.
 These results from proposed approach can help for further diagnosis and
treatment planning
 Future Works
 Increase the number of CT images dataset to evaluate the performance of the
proposed approach
 Test new versions of PSO
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SRGE Workshop, Cairo University Conference Hall (28-November-2015)

26. Thanks and Acknowledgement
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SRGE Workshop, Cairo University Conference Hall (28-November-2015)