1. 蛋白質二維凝膠電泳影像內容 特徵研究之技術 指導老師：陳同孝教授 學 生 : 唐仕珊、林玉琪

2. 技術運用項目 技術運用項目 運用 ACM 技術於蛋白質二維凝膠電泳影像 篩選蛋白質點 運用 ACM 技術於蛋白質二維凝膠電泳影像 篩選蛋白質點 運用 Gaussian Model 與 Diffusion Model 於內容 特徵之蛋白質二維凝膠電泳影像漸近式傳 輸 運用 Gaussian Model 與 Diffusion Model 於內容 特徵之蛋白質二維凝膠電泳影像漸近式傳 輸 運用 LBG 演算法及向量量化調整影像之還 原 運用 LBG 演算法及向量量化調整影像之還 原

3. 運用 ACM 技術於蛋白質二維凝膠電泳 影像篩選蛋白質點

4. Outline Introducing the 2DGE Introducing the 2DGE Flow for the proposed method Flow for the proposed method Normalization Normalization Local threshold transform Local threshold transform Refining with ACM Refining with ACM Experimental results Experimental results Conclusions Conclusions

5. Introducing the 2DGE 2DGE image protein block

6. Flow for the proposed method Flow for the proposed method 2DGE image NormalizationLocal threshold ACM Normalized image Black and white image

7. Normalization(1/2) X11X12X13 X21 X22 X23 X31X32X33 pixel S S 33 block (1) Block average a1 (2) Apply Rules t1= threshold by Otsu ’ s algorithm

8. Normalization(2/2) original image Normalized image

9. Normalized image Protein spots with white centersProtein spots with black centers Local threshold transform (1/2) Black and white image Local threshold image X ij = pixel from the original image X b ij = pixel from the black and white image Rules applied (1)block average a2 (2)Rules applied t2 => also using Otsu ’ s algorithm (white) (black)

10. Local threshold transform (2/2) original image Block of protein spot Local threshold image

11. Refining with ACM Initial contour for the protein spot Refined contour after using ACM

12. Experimental results (1/3) Locating for protein spot(r=9)Locating for protein spot(r=3)

13. Experimental results (2/3) Locating for protein spot(r=2) Locating for protein spot(r=1)

14. Experimental results (3/3) Z3our method

15. Conclusions Protein spots can be accurately identified. Protein spots can be accurately identified. More lighter-colored protein spots can be successfully identified. More lighter-colored protein spots can be successfully identified.

16. 運用 Gaussian Model 與 Diffusion Model 於內容特徵之蛋白質二維凝膠 電泳影像漸近式傳輸

17. Outline JPEG JPEG Bit-plane Bit-plane Flow for the proposed method Flow for the proposed method Our Method Our Method Experimental result Experimental result Conclusion Conclusion

18. JPEG progressive compression model 8 8 DCT Zig-Zag scan order

19. Bit-plane Method 第一階段 第二階段 第三階段.

20. Flow for the proposed method 2DGE image Normalization Normalized image Watershed Gaussian or Diffusion function

21. Our method (1/7) Normalization Normalization original image Normalized image

22. Our method (2/7) Watershed Watershed 2DGE with watershed boundaries

23. Our method (3/7) original protein block Apply Gaussian function I ： spot height (L1,L2) ： protein block center location δx 、 δy ： standard deviation in x and y directions Gaussian protein block Gaussian Model Gaussian Model

24. Our method (4/7) Diffusion Model Diffusion Model Apply Diffusion function B ： background intensity ， C 0 ： initial concentration D x, D y ： related to the diffusion constants in the two main directions of diffusion x 0, y 0 ： control location ， a ： the area of the disc containing the protein material and original protein blockDiffusion protein block

25. Our method (5/7) Gaussian 2DGE … Diffusion 2DGE

26. Transmitting process Transmitting process Our method (6/7) - = Original 2DGE Gaussian 2DGE Difference image Diffusion 2DGE -= Difference image

27. Our method (7/7) Transmitting process Transmitting process Every Gaussian or Diffusion protein block variable With Bit-plane Gaussian Diffuion or

28. Experimental result (1/8) 512x512 original 2D image

29. Experimental result (2/8) Phase1: Bit-plane Method JPEG model Gaussian model Diffusion m odel

30. Experimental result (3/8) Phase2: Bit-plane Method JPEG model Gaussian model Diffusion m odel

31. Experimental result (4/8) Phase3: Bit-plane Method JPEG model Gaussian model Diffusion m odel

32. Experimental result (5/8) Phase4: Bit-plane Method JPEG model Gaussian model Diffusion m odel

33. Experimental result (6/8) Phase5: Bit-plane Method JPEG model Gaussian model Diffusion m odel

34. Experimental result (7/8) Phase6: Bit-plane Method JPEG model Gaussian model Diffusion m odel

35. Experimental result (8/8) PSNR

36. Conclusion When the transmission is finished using our method, the total amount of transmission is less than the original image size. When the transmission is finished using our method, the total amount of transmission is less than the original image size.

37. 運用 LBG 演算法及向量量化調 整影像之還原

38. Outline Introduction Introduction Related Literatures Related Literatures Our Proposed Method Our Proposed Method Experimental results Experimental results Conclusions Conclusions

39. Introduction Different settings in a digital camera like automatic exposure or automatic focus may result in different photographic effects when focusing on a static or dynamic object. Different settings in a digital camera like automatic exposure or automatic focus may result in different photographic effects when focusing on a static or dynamic object. Experimental tests will be carried out to photograph subjects with different shutter speeds. Experimental tests will be carried out to photograph subjects with different shutter speeds. (a) longer shutter speed (b) shorter shutter speed

40. Related Literatures (1/2) In high dynamic range (HDR) technique, pictures with different exposures can be combined to create a single image with combined information. In high dynamic range (HDR) technique, pictures with different exposures can be combined to create a single image with combined information. HDR can be used to forecast changes in the brightness and color in the pictures, which may result in loss of some information. HDR can be used to forecast changes in the brightness and color in the pictures, which may result in loss of some information.

41. Related Literatures (2/2)

42. Our Proposed Method (1/2) System flow System flow

43. Our Proposed Method (2/2) System flow System flow Next

44. Original image Original image N×N is the image size, n is the total number of images, (P 1, P 2, …, P n ) is a series of images with the same background, (f 1, f 2, …, f n ) is different shutter speeds BACK

45. VQ Encoder BACK

46. VQ Decoder BACK Input the known shutter speed (ex: 3 )

47. Linear interpolation BACK Ｎ Ｎ Index table Recover to original image 251=> Ｎ Ｎ The value in the known shutter speed range is evaluated for the range (ex: 45 ) (45-30) × (45-75)/(60-30)+75=60

48. Experimental results (1/4) (a) Original image (b) Our method (c) HDR

49. Experimental results (2/4) PSNRs is used to compare for experiments with HDR. PSNRs is used to compare for experiments with HDR.

50. Experimental results (3/4) For the image not within the known shutter speed range. For the image not within the known shutter speed range. The known shutter speeds are 1/4, 1/8, 1/15, 1/30, 1/60, 1/125, 1/250, The known shutter speeds are 1/4, 1/8, 1/15, 1/30, 1/60, 1/125, 1/250, 1/500, 1/1000 and 1/2000. 1/500, 1/1000 and 1/2000. (a) Our method (b) HDR The shutter speed is 1 The shutter speed is 1/2 (a) Our method (b) HDR

51. Experimental results (4/4) (a) Our method (b) HDR The shutter speed is 1/4000 For the image not within the known shutter speed range. For the image not within the known shutter speed range. The known shutter speeds are 1/4, 1/8, 1/15, 1/30, 1/60, 1/125, 1/250, The known shutter speeds are 1/4, 1/8, 1/15, 1/30, 1/60, 1/125, 1/250, 1/500, 1/1000 and 1/2000. 1/500, 1/1000 and 1/2000.

52. Conclusions Our method is applied to simulate the effects on the human visual system. It can be used to simulate the smoothing and exquisite image which have a higher (PSNRs) than HDR. Our method is applied to simulate the effects on the human visual system. It can be used to simulate the smoothing and exquisite image which have a higher (PSNRs) than HDR. It can be applied on the two-dimensional gel electrophoresis image for clearer identification of protein spots. It can be applied on the two-dimensional gel electrophoresis image for clearer identification of protein spots.