CT computer aided diagnosis system

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. What To do? We have chosen Liver CT Images Computer Manipulation CAD Computer-Aided Diagnosis System

8. Liver 8

9. Why Liver? 9 T he 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 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 image CT machine moves through the abdomen and records the details of liver tissues

13. Proposed approach 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 P roposed Approach

18. Phases of Proposed Approach 18

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 .