1. Resolution Mosaic EM Algorithm for Medical Image Segmentation Mohammed A-Megeed Salem, Beate Meffert High Performance Computing & Simulation(HPCS)2009 IEEE

2. Outline Introduction Method – Multiresolution analysis – Resolution Mosaic EM Algorithm – Application for Medical Image Segmentation Result Conclusion

3. Introduction Segmentation is an unconscious activeness by human but in computer it is a logically non-trivial. Multiresolution analysis builds different representations of an image with a spatial resolution. Multiresolution analysis simplified and improve the segmentation.

4. Multiresolution Analysis 2D Wavelet transform 第一級解析度 LL A 第一級解析度 HL H 第一級解析度 LH V 第一級解析度 HH D 高頻分析濾波器 低頻分析濾波器 高頻合成濾波器 低頻合成濾波器 低頻數列 高頻數列 原始數列 還原成原始數列 第二級 解析度 LL 第二級 解析度 HL 第一級解析度 HL 第二級 解析度 LH 第二級 解析度 HH 第一級解析度 LH 第一級解析度 HH

5. Resolution Mosaic EM Algorithm Motivation – The interesting regions could be displayed in a higher resolution than the non-interesting regions.

6. Resolution Mosaic EM Algorithm Generating the Mosaic Map – A label image – The non-relevant parts :high numbers with a lower resolution. The relevant parts :low numbers indicating a higher resolution.

7. Resolution Mosaic EM Algorithm Generating the Mosaic Map – Step 1 :Performing two levels of wavelet analysis. The three detail images of each level are combine together to create a new image, the mask image. – Step 2: label Mosaic map

8. Resolution Mosaic EM Algorithm Generating the Resolution Mosaic Image – The mosaic map divided into blocks – Do the loop according to (a) (b) If min(MAP(t,l,b,r))>=CurrentLevel If min(MAP(t,l,b,r)<CurrentLevel)

9. Resolution Mosaic EM Algorithm Image Segmentation – The Gaussian Mixture Model (GMM)

10. Resolution Mosaic EM Algorithm Image Segmentation – Use EM(Expectation-Maximization) algorithm to estimate Gaussian distribution parameter. (1)E: (2)M: ，

11. Resolution Mosaic EM Algorithm Image Segmentation – EM Algorithm for image segmentation Step1: Input image I and the number of class K. Step2:Set the initial parameters Θ (0) Step3:Update the parameters by using Eqs. (1)(2) iteratively until convergence. Step4:Use Θ ML in a classifier to generate classification matrix. K i = arg max( f i (x i, Θ k ))

12. Resolution Mosaic EM Algorithm

13. Application for Medical Image Segmentation Test Data Sets

14. Application for Medical Image Segmentation Test Data Sets : Mean=50,150,200 Std=10,15,20

15. Application for Medical Image Segmentation Mosaic map example

16. Application for Medical Image Segmentation Mosaic map Resolution level White :0 Light grey:1 Dark:2

17. Segmentation Result

18. STD=10STD=15STD=20 EM99.03%92.06%84.96% RE-ME99.17%96.34%94.55% Table1.Overall Accuracies for Simulated MRI STD=10STD=15STD=20 EM96.15%73.42%59.64% RE-ME98.01%87.61%86.31% Table2.Precisions of the Grey Matter class for Simulated MRI

19. Conclusion A new image segmentation algorithm has been proposed based on the resolution mosaic and the EM algorithm. The number of iteration needed by the algorithm is reduced from 737 to 25. The resolution mosaic introduced here can be used in a wide range of applications.