1. Speckle Reduction in Ultrasound Image Prepared by: Osama O. AbuSalah & Almoutaz Alhumaid Osama O. AbuSalah & Almoutaz Alhumaid Supervised by: Dr. Ali Saad

2. Objective provide the radiologist with better view of the Ultrasound image through reducing the noise without destroying important features. provide the radiologist with better view of the Ultrasound image through reducing the noise without destroying important features.

3. Outline 1)Introduction. 2)Ultrasound equipment. 3)Some Definitions. 4)Programs method. 5)Results. 6)comparison Between different filters. 7)conclusion & future work.

4. Introduction ultrasound machine participates strongly in the assistance of medical diagnosis and treatment. ultrasound machine participates strongly in the assistance of medical diagnosis and treatment. Ultrasound imaging is gaining more and more importance in medical practice nowadays. Ultrasound imaging is gaining more and more importance in medical practice nowadays. So its very important to gain the best results possible. So its very important to gain the best results possible.

5. Ultrasound Equipment

6. definitions Speckle: A form of multiplicative noise corrupts medical Ultrasounds imaging making visual observation difficult. Filter: speckle noise removal by means of digital image processors to extract useful information about the scene being imaged.

7. Program method We used a Read & Write program that works on TIF images only. We used a Read & Write program that works on TIF images only. Read program converts image gray scale pixels into digital values. Read program converts image gray scale pixels into digital values. The filter deals with numeric values The filter deals with numeric values Write program converts back digital value into gray scale. Write program converts back digital value into gray scale.

8. Spatial Filter method The method works by using a window as a sub image to estimate statistical measures. The method works by using a window as a sub image to estimate statistical measures. The sub image consist of neighborhood pixels which is called window (3x3, 5x5, 7x7,……). The sub image consist of neighborhood pixels which is called window (3x3, 5x5, 7x7,……).

9. C:\osama\ultrasound filtering\presentation\osssss.swf C:\osama\ultrasound filtering\presentation\osssss.swf

12. Noisy program A program that applies noise to the Image. A program that applies noise to the Image. Used to check the efficiency of our filter under different noise levels. Used to check the efficiency of our filter under different noise levels. Using a random function for applying additive or multiplicative noise to the image. Using a random function for applying additive or multiplicative noise to the image.

13. Noise method Random function (0-32700). Random function (0-32700). Random/32700=(0-1). Random/32700=(0-1). N=(0-1) x level of noise. N=(0-1) x level of noise. Additive noise: + N Additive noise: + N Multiplicative noise: x N Multiplicative noise: x N

14. Multiplicative noise (x3)

15. Evaluation program Calculates the RMSE value between the original image and the filtered image that we applied noise to it. Calculates the RMSE value between the original image and the filtered image that we applied noise to it. The output image presents the difference between the two images. The output image presents the difference between the two images.

16. Results

17. Additive noise applied (+100)

18. Additive noise applied (+150)

19. Multiplicative noise applied (x2)

20. Multiplicative noise applied (x4)

21. Value of error RMSE value Amount of noise 0.248865 0.248865+100 0.316899+150 0.222866 x 2 0.231315 x 4

22. Results The value of error increases proportional to the applied noise either additive or multiplicative. The value of error increases proportional to the applied noise either additive or multiplicative. when applying multiplicative noise The darkness of the image and its boundaries increases with decreasing level of noise. when applying multiplicative noise The darkness of the image and its boundaries increases with decreasing level of noise.

23. High level of multiplicative noise produces very light boundaries making the detail's of the image to disappear. High level of multiplicative noise produces very light boundaries making the detail's of the image to disappear. The filter gives better result with multiplicative noise. The filter gives better result with multiplicative noise.

24. Comparison Between different filters

25. .

26. Comparison between Values of error RMSE value Filter 0.316899 Spatial filter 0.316975 Average filter 0.344035 Median filter

27. Conclusion & future work We had a result for different filtered images with different window sizes that showed a good reduction of noise compared to other filters and a RMSE value very close to them. We had a result for different filtered images with different window sizes that showed a good reduction of noise compared to other filters and a RMSE value very close to them. May be other students in the future could complete the work we started and compare other methods with the method we worked on. May be other students in the future could complete the work we started and compare other methods with the method we worked on.

28. Acknowledgment  we would like to thank Dr. Ali Saad for his Support and time and the worthy knowledge that we learned from him.  Also we thank Dr. Ali Zerit for providing us with the Read & write program and for willing to help us with any help we need.  Big thanks for all our teachers that worked with us until we finally came to graduation.