1. PVE for MRI Brain Tissue Classification Zeng Dong SLST, UESTC 6-9

2. PVE Partial Volume Effect

3. Contents Overview Method Nei. PVE Model MAP Result Discussion Conclusion

4. Overview 1-role Roles of Segmentation in qualitative in visualization

5. Overview 2 – difficulty difficulty inhomogeneous PVE hard soft segmentation Statistic MAP MRF

6. Overview 3 - PVE IEE95 NOISE MODELS based Sampling noise Material-dependent Noise PVE direct indirect

7. Direct Determine PVC directly IEEE91 multichannel

8. continuous IEE03 Only tow types

9. continuous 02 Fuzzy Markov discrete PVC

10. Indirect PV class Determine PVC based on PV voxels

11. continuous More Tow Not Multi-channal Discrete PVC Boundary voxels More accurate, more efficient

12. Method-Nei. PVE N: numbers of pixels Assume: mask

13. continuous K: numbers of pure g(.,.): Gaussian function

14. continue Observed is mixed with its nei.s meanly during sampling Nei. Size M

15. continue

16. continue L kinds of mixed types: Mixed set Assume A mixed type

17. continue

18. PVE Segmentation MAP Y: observed images X: segmentation images

19. Likelihood term Assume that the intensity at voxel i does not depend on the tissue content of the other voxels.

20. Prior Assume X is MRF on nei. System C, and x is a realization RF X Z: Partition function U(x): Potential energy function

21. continue

22. continue

23. continue

24. ICM Iterative Constrained Mode local combination

25. continue

26. continue

27. continue 1. Init X (beta = 0, M=1) 2. Update mixel mean and variance 3. ICM 4. goto 2

28. Result Brain tissue classification K=3: CSF, GM, WM N=1,3,7,27

29. continue Generate Mixed types: for CSF=M:0 for GM=(M-CSF):0 { WM=M-CSF-GM … }

30. continue Example(K=3,M=7) Reduce: Not tow maximization( 3) CSF !=0 && GM != 0 (18) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 CSF 7 6 6 5 5 5 4 4 4 4 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 GM 0 1 0 2 1 0 3 1 2 0 4 3 2 1 0 5 4 3 2 1 0 6 5 4 3 2 1 0 7 6 5 4 3 2 1 0 WM0 0 1 0 1 2 0 1 2 3 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 6 0 1 2 3 4 5 6 7

31. continue Ori seg Ori Seg with b Seg without b

32. continue compare 数据 100_23 1_24 11_3 110_3 111_2 112_2 12_3 13_3 15_3* 17_3* CSF 0.3018 0.1089 0.1159 0.3101 0.3418 0.3072 0.1182 0.3097 GM 0.8880 0.8460 0.8781 0.8221 0.8195 0.8056 0.9035 0.9022 WM 0.8339 0.8170 0.8356 0.7757 0.7956 0.7649 0.8583 0.8529 数据 17_3 191_3 202_3 205_3 CSF 0.1031 0.1296 0.0891 GM 0.642 0.8541 0.8924 0.8781 WM 0.782 0.8263 0.8403 0.8433 Mean: csf 20% GM 86% WM :83%

33. Discussion 4-1 Mixel of CSF and WM 0:76.614136 297.295471 1:84.838821 316.580113 2:93.063507 335.864755 3:117.737564 393.718680 4:125.962250 413.003322 5:134.186935 432.287964 6:139.243421 377.461605 7:144.299907 322.635246 8:159.469365 158.156170 9:164.525851 103.329811 10:169.582336 48.503452 mix mean and var 0:76.614136 297.295471 1:84.838821 316.580113 2:89.895307 261.753754 3:93.063507 335.864755 4:98.119993 281.038396 5:103.176479 226.212037 6:106.344679 300.323038 7:111.401164 245.496679 8:117.737564 393.718680 9:122.794050 338.892321 10:137.963508 174.413245 11:143.019993 119.586886 12:125.962250 413.003322 13:131.018736 358.176963 14:136.075221 303.350604 15:146.188193 193.697887 16:151.244679 138.871528 17:156.301165 84.045169 18:134.186935 432.287964 19:139.243421 377.461605 20:144.299907 322.635246 21:159.469365 158.156170 22:164.525851 103.329811 23:169.582336 48.503452

34. continous Mixel of CSF and WM

35. Discussion 4-2 Measure Parameter estimation csf mean : 89.168350 csf var : 836.750610 gm mean : 124.758827 gm var : 385.225983 wm mean : 162.237137 wm var : 186.776108 csf mean : 111.797281 csf var : 273.691200 gm mean : 124.073118 gm var : 359.172593 wm mean : 159.252710 wm var : 212.954779 Result para:Ori para Csf mean:76.614136 Csf var:297.295471 gm mean:134.186935 gm var:432.287964 wm mean:169.582336 wm var:48.503452 Init para(ML)

36. Discussion 4-3 Prior Parameter estimation

37. Discussion 4-4 Intensity inhomogeneous

38. Conclusion More accurate, more efficient Unify framework generalization

39. Thanks