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Perceptual Grouping: The Closure of Gaps within Elongated Structures in Medical Images

1 2. Perceptual Grouping: The Closure of Gaps within Elongated Structures in Medical Images. Renske de Boer March 23 rd , 2006. Committee: prof. dr. ir. B.M. ter Haar Romeny prof. dr. ir. F.N. van de Vosse dr. L.M.J. Florack dr. ir. R. Duits ir. E.M. Franken. /mhj. 1 2. Contents. 1 2.

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Perceptual Grouping: The Closure of Gaps within Elongated Structures in Medical Images

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  1. 12 Perceptual Grouping: The Closure of Gapswithin Elongated Structures in Medical Images Renske de Boer March 23rd, 2006 Committee: prof. dr. ir. B.M. ter Haar Romenyprof. dr. ir. F.N. van de Vossedr. L.M.J. Florackdr. ir. R. Duitsir. E.M. Franken /mhj

  2. 12 Contents

  3. 12 Introduction http://www.cps.utexas.edu/Research/Geisler/Projects/perceptualgrouping.html

  4. 12 Ts’o et al., 1990 Bosking et al., 1997 Introduction

  5. 12 Orientation scores 2D image f(x,y)  ‘2D + orientation’ score Uf(x,y,q) with position (x,y) and local orientation q.

  6. 12 with and the rotation matrix: Orientation scores Orientation score is obtained by wavelet transformation of image: Reconstruction of image is possible.

  7. 12 Orientation scores Different possibilities for kernel For example cake kernels Advantage: easy reconstruction of image.

  8. 12 Linear operations Normal convolution G-convolution, where G is the Euclidean motion group

  9. 12 Linear operations Stochastic completion kernel creates probability density field for line continuation.

  10. 12 Linear operations Filling gaps in line structures

  11. 12 Non-linear operations • Probability density function (PDF) • Stochastic completion kernel shouldbe applied to an orientation score containing the probability density of lines in the image. • Probability is obtained by creating 2D-histogram of 2 features: gray values of the image and orientation score values. Only for line structures. • The Bayesian theorem is used to calculate the desired probability. • PDF is estimated by kernel density estimation of the histogram.

  12. 12 Non-linear operations 1 Orientation score thinning Thinning with a certain number of pixels of the orientation score in both the spatial dimensions and the orientation dimension. 2 Angular thinning The two orientations that give maximum orientation score responses get values, all other orientations are put to zero.

  13. 12 Non-linear operations 3 Pyramid thinning

  14. 12 Non-linear operations 4 Normal power enhancement 5 Power enhancement

  15. 12 Experiments • Measures for gap filling: • mean filling Ratio of mean gap filling and mean of line structure. • min filling Ratio of minimal gap filling and mean line structure. • background ratio Ratio of mean background and mean of line structure and gap. • fill back Ratio of mean gap filling and mean background.

  16. 12 Experiments Evaluation of non-linear operations

  17. 12 Experiments Noise robustness

  18. 12 Filling measure Experiments Noise robustness NSR is noise to signal ratio sN is scale of Gaussian correlation of noise Correlation of noise

  19. 12 Used ground truth Obtained PDF Experiments Probability density function

  20. 12 Experiments Examples of artificial images

  21. 12 Experiments Examples of artificial images

  22. 12 – Examples of medical images Experiments [1] Original After preprocessing Result [2]

  23. 12 – Examples of medical images Experiments [3] Original After preprocessing Result

  24. 12 Conclusion • Conclusions • New method is successfully generated. • Method performs reasonable on lower noise levels. • PDF detects line structures but filling of gap is weak due to enlargement of the gap. • Larger gaps or high curvature result in weaker filling. • Some undesired filling might be caused by line structures that are close together and are not part of the same line. • Medical images need a lot of preprocessing to prevent background artifacts. Overall the method gives reasonable results for filling gaps and enhancing line structures.

  25. 12 Conclusion • Recommendations • Deblurring afterwards is necessary! • Include curvature in stochastic completion kernel to fill gaps in lines with high curvature. • Adjust width of cake kernels to detect lines at different scales. • Optimize parameters of stochastic completion kernel. • Find a better non-linear enhancement operation. • Improve PDF results by creating extra PDF for line endings. • Preprocess (medical) images!

  26. 12 Acknowledgment Thanks to: Committee, especially Erik, Remco and Markus. Family, friends and housemates. [1] http://www.vistaradiology.com/NewFiles/Ultrasound.html [2] J.J. Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever, and B. van Ginneken. Ridge based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging, 23(4): 501–509, 2004. [3] Philips StentBoost Questions?

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