Segmentation and Registration of Echo-cardiographic Images Using Radon Transform

1. A NOVEL APPROACH TO SEGMENTATION AND REGISTRATION OF ECHO-CARDIOGRAPHIC IMAGES Vidhyadhari Gondle Supervisor: Jayanthi Sivaswamy CVIT, IIIT Hyderabad, Hyderabad, India

2. Echo-cardiographic Images • Echo-cardiogram is the sonogram of heart. It is one of the primary diagnostics conducted due to its non-invasive nature. Ultrasound Imaging Echo-cardiogram • Analysis of Echo-cardiographic Images is challenging because of presence of speckle.

3. Echo-cardiographic images • Phenomena that occur during image acquisition: • Absorption • Specular Reflection • Diffuse Reflection Speckle noise is caused by diffuse reflection which occurs because of back-scattering

4. Motivation • The challenges that occur during processing the echo-cardiographic images are due to the physics involved in acquisition of the images. • Problems in processing of echo-cardiographic images are: • Presence of speckle noise. • Blurring of spatial information. • Discontinuities in the contours. • Image analysis algorithm aimed for ultrasound images must be robust to speckle noise and should be able to detect the image features in low contrast.

5. Echo-cardiographic Image Analysis. Present Methods Pre-processing based approaches. Use of noise model in the formulation of algorithm. Loss of information like speckle pattern. Difficulties in the designing of the algorithm. Complex formulation.

6. Problem Statement Objective: To devise a solution for Segmentation and Registration of echo-cardiographic images that can handle noise naturally. Proposed Solution: • A noise-robust representation of an image. • Image is mapped from intensity space to feature space.

7. Feature Descriptor • Requirements of the Feature Descriptor: • Capture local context of a pixel since it gives information about speckle. • Stability with respect to small distortions and invariance to geometric distortions. • Noise Robustness. • We design Radon Transform based feature vector.

8. Radon Transform

9. Feature Descriptor

10. Strengths of R-Transform • Gives high response in the presence of high intensities hence can be used to differentiate between bright regions and dark regions. • Good Representation of shape or context around the pixel. • Resistant to the blurring effect present in the image because blurring spreads the intensity values in neighbourhood.

11. Proposed Methods: We proposed two algorithms based on this feature descriptor for echo-cardiographic images. • Image Segmentation • Pixels are grouped into regions based upon certain properties • Image Registration • Aligning two Images I1, I2taken at different times/different modalities/different view points using Mapping function T where I1(x) ~ I2(T(x)).

12. Segmentation

13. Segmentation Outline

14. Segmentation K-Means Clustering • K-Means clustering is used to group the pixels with similar feature-vectors. • K-Means is one of simplest and efficient ways to clustering when compared to other clustering methods like Agglomerative, Fuzzy C-Means, and Mixture of Gaussians. • The third part of the algorithm is mapping the assigned labels back to the pixels.

15. Segmentation Assessment • The proposed method is tested on synthetic and real Ultrasound Images. • The results obtained are compared with other feature-descriptors to test its efficiency: • Geometric Blur. (GC) • Histogram of Oriented Gradients.(HC) • DAISY.(DC)

16. Segmentation Settings • The proposed method is implemented using the following parameter setting. • The Radon Transform is computed on 10X10 image patches in 37 different orientations. • Radon Transform is 37X10. • Final Feature Descriptor is 37X1

17. Segmentation Evaluation Measures

18. Segmentation Evaluation Measures • The range of Rate of Misclassification is [0-1]. Low rate of misclassification indicates better performance of the algorithm. • The range of Dice Coefficient is from [0-1]. High value indicates better performance of the algorithm.

19. Segmentation Synthetic Data • A synthetic image is generated to have a dark circle on a white background with the intensity of the pixel varying inversely with the radius. • Speckle noise with different parameters is added to different regions to model ultrasound images.

20. Segmentation HC) FSC) DC) GC where HC = Histogram of Oriented gradients; DC = Daisy; GC = Geometric Blur and FSC = Proposed Feature Descriptor

21. Quantitative Results Segmentation where DC = Daisy Feature Descriptor; GC = Geometric Blur and FSC = Proposed Feature Descriptor

22. Segmentation Real Data • Echocardiographic data is generally acquired in a video form. We have evaluated our results on 3 such data sets. • The ground truth was marked by an expert.

23. Segmentation Segmented Result overlaid on Ground Truth Original Image Labelled Image

24. Segmentation DAISY Geometric Blur Histogram of Oriented Gradients Proposed Feature Descriptor Labels used for representing classes

25. Segmentation Quantitative Results

26. Image Registration

27. Registration Problem Statement and Approach • Image Registration is alignment of any two Images I1, I2taken at different times/different modalities/different view points using Mapping function T where I1(x) ~ I2(T(x)). • We adapted Demon’s algorithm since it well suits complex motion involved in echo-cardiographic images.

28. Registration Motivation • Demons is a popular algorithm for non-rigid medical image registration. • Drawbacks of Demon’s algorithm: • Displacement field computation based on brightness constraint. • Sensitive to noise.

29. Registration Considerations in Noise Adaption Desirables: • Displacement field shouldn’t get affected by noise. • Pixels in Moving Image must be mapped to similar regions in Source Image. • Similarity(as measured by SSD) between Moving Image and Source Image must be optimized.

30. Registration Computing Similarity We need a measure which: • Is independent of noise • Gives the information of local context if the image, so that similar regions can be mapped.

31. Registration Modified Demon’s Algorithm

32. Registration Distance Information • We use distance measure information computed after k-means clustering of feature-space representation of the image • Intensity in Demon’s algorithm is now replaced with this distance measure Cluster 2 Clusters in Feature space x Cluster 3 D Ck Cluster 1

33. Registration Distance Measure

34. Registration Significance of using Distance Measure • This Distance Measure represents the variance of the feature vector. In Ideal case we could have used both mean and variance but to simplify the process we use only variance. • Also since Demon’s algorithm proceeds by mapping nearest pixels first, nearest regions will be mapped initially.

35. Registration Strengths of Distance Measure • Representation of Local Context: This measure is computed from Radon-Transform which gives good information about local context. Also it doesn’t get affected with high speckle noise. • Localization: The localization is handled internally by brightness constraint in Demon’s algorithm.

36. Registration Settings • The proposed method is tested on synthetic and real data. • Radon Transform is computed on 10 X 10 image patches in 37 different orientations. • The dimensions of Radon Transform obtained is 37 X 10. • Final feature vector obtained is 37 X 1

37. Registration Evaluation Measures

38. Registration Synthetic Data • Synthetic Images generated in evaluating Segmentation algorithm are used. • Speckle noise, Gaussian noise and Salt & pepper noise with different parameters is added to different regions to validate the algorithms.

39. Registration Image Registered using Proposed Algorithm Image Registered using Demons Algorithm Image Registered using Optical flow Moving Image Source Image Images Without Noise GaussianNoise Speckle Noise Salt and Pepper Noise

40. Quantitative Results Registration

41. Registration Real Data • Echocardiographic data is generally acquired in a video form. We evaluated experiments on 3 sets of echo-cardiographic data.

42. Registration Input Images .(a) Source Image Moving Image is to be aligned with Source Image . (b) Moving Image

43. Registration (b) Demon’s algorithm) (c)Optical Flow (a) Proposed algorithm)

44. Registration Quantitative Results We present the results only in terms of SSD for 3 data-sets s

45. Conclusion • A feature-space representation of an image is presented. • Proposed Radon Transform based Feature descriptor. • This feature-space representation was used in Segmentation and Registration algorithms.

46. Conclusion • Segmentation results have out-performed when compared to other feature descriptors. • Image Registration results are comparable to Demon’s algorithm with slight improvement.

47. Future Work • Time complexity can be improved by computing the feature descriptor at required pixels rather than every pixel of the image. • There is a requirement for post-processing of these images. • Comparison between normal and abnormal sequences also can be taken as further step to aid in diagnosis.

48. References [1] J.-P. Thirion, “Image matching as a diffusion process: an analogy with maxwell’s demons,” Medical Image Analysis, vol. 2, no. 3, pp. 243–260, 1998. [2] B. K. Horn and B. G. Schunck, “Determining optical flow,” 1980. [3] I. Dydenko, F. Jamal, O. Bernard, J. D’hooge, I. E. Magnin, and D. Friboulet, “A level set framework with a shape and motion prior for segmentation and region tracking in echocardiography,” Medical Image Analysis, vol. 10, no. 2, pp. 162–177, 2006.

49. Publications • Vidhyadhari Gondle and Jayanthi Sivaswamy, “Echo-Cardiographic Image Segmentation : Via Feature Space Clustering“,in Proceedings of the National Conference on Communications, Bangalore, India, 2011.

50. Questions ??? Thank you