This presentation was deviled at the Intelligent Systems and Application held at Zewail University for Science and Technology on Saturday 7 March 2015

1. CT Computer-Aided Diagnosis
System
PRESENTED BY
ABDALLA MOSTAFA ABDALLA
Scientific Research Group in Egypt http://www.egyptscience.net
7 March 2015 - Zewail University for Science and Technology

2. SRGE Members
Group founder and chair: Professor Aboul Ella Hassanien

3. Scientific Research
Group in Egypt
Agenda  Introduction
 Problem Definition
 Objective
 Liver and Medical imaging
 Pre-processing
 Segmentation
 Proposed Approach
 Experiments and Results
 Conclusion

4. Introduction
◦ Liver is an important organ in human
body.
◦ It may have different colors (dark
blue cyst, dark brown - cirrhosis,
yellow - fatty, green – billary
cirrhosis)
◦ It is common to use Computed
Tomography (CT) in Computer aided
diagnosis systems (CAD)

5. Problem Statement
Difficulties associated with liver image segmentation
◦ Liver has different shapes.
◦ Similarities to other organs (muscles, flesh, kidney,
spleen).
◦ Similarity between Vessels and cyst.

6. Objective
6
We aim to
• Enhance Region growing technique.

7. We have chosen
Liver
CT
Images
Computer
Manipulation
CAD
Computer-Aided Diagnosis System

8. Liver
8

9. Why Liver?
9
The statistics of liver diseases shows that
• The ratio of virus C infection is 12.8 % in Egypt.
• The ratio of virus C infection is almost 1.2% in
Europe.
• 130 thousands people need liver transplantation
In Egypt.

10. Liver diseases
Cyst
Fatty Liver
Fibrosis
Billary Cirrhosis
Carcinoma
10
Different diseases may have different colors
According to
• Liver bible
• Pathology Atlas
• Oncology ref.
So, Image can help in diagnosis

11. Biopsy
11
• It may puncture the lung.
• It may fracture rib.
• Liver bleeding.
• The worst of all
the sample might not represent the
lesion.
Biopsy has its limitations and risks

12. CT Image Slicing
12
Slicing technique
Liver sliced imageCT machine moves through the abdomen and
records the details of liver tissues

13. Proposed approach
1
• Preprocessing
2
• Segmentation
13
The proposed approach has main two phases

14. Preprocessing
14
The main objective of image preprocessing is to improve the
quality of the image being processed by:
• Removing noise.
• Emphasizing certain features.
• Isolating regions of interests.

15. Liver Segmentation
15
Liver segmentation depends on :
• The difficulty of the anatomy of liver.
• Liver is surrounded by many organs, similar to its intensity as
spleen, stomach, and kidney.
• The nature of liver tissues, and blood vessels.
.

16. Region Growing Segmentation
16
Based on the growth of a
homogeneous region
according to certain
features as intensity, color
or texture.

17. 17
Now
Let us go to the
Proposed Approach

18. Phases of Proposed Approach
18
Segmentation
Region growing
Preprocessing
Using morphological
operations
Connecting Ribs

19. Morphological operations
Morphological Operations are :-
◦ Structure element.
◦ Dilation.
◦ Erosion.
◦ Opening.
◦ Closing.
19

20. Morphological operations
Structure element
has a shape of
square,
diamond and
cross
20

21. Morphological operations
Dilation
The basic effect of the operator on a binary image is to gradually enlarge
the boundaries (thicking) of regions of foreground pixels.
21

22. Morphological operations
Erosion
The basic effect of the operator on a binary image is to shrink
(erode away ) the boundaries of regions of foreground pixels
22

23. Morphological operations
Opening
It is an erosion followed by a dilation.
It can open up a gap between objects connected by a thin
bridge of pixels.
23

24. Morphological operations
Closing
is a dilation followed by erosion, it fills some gabs.
24

25. Connecting ribs
25
Using contrast stretching to emphasize the ribs boundaries. The
ribs will be connected as follows:
Now the image is prepared for the next phase

26. Segmentation
26
•It is partitioning an image into homogeneous
regions with respect to intensity, or texture.
•Image segmentation methods can be categorized
as
• Edge-based methods (discontinuity )
• Region-based methods (similarity)

27. Liver Segmentation
27
So, there is a need to get
Separated Liver Regions of Interest

28. Proposed algorithm
28

29. Experiments and results
29
Cleaning image is a process of removing annotation and bed from
the image

30. Experiments and results
30
Preparation phase

31. 31
Similarity Index
Validation measure

32. Experiments and results
32
Difference between
segmented and annotated image .

33. Experiments and results
33
Normal Region Growing vs Proposed approach

34. Experiments and results
34
Using the proposed method showed that:
 The accuracy result using similarity index measure is
(SI=91.2% ).
 The method could segment images that was difficult to
segmented before.

35. Conclusion
35
Testing proposed approach, with region growing showed that:
Normal Region growing has the result of 82% accuracy.
Proposed approach has the result of 91.2% accuracy.

36. Future Work
36
The future work would be the change of the approach of
classification to use Bio-Inspired methodology to :-
• Eliminate the liver separation computational cost.
• Generalize the approach for other organs as spleen
and stomach.