A robust method based on ICA and mixture sparsity for edge detection in medical images
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In this paper, a robust edge detection method based on independent component analysis (ICA) was proposed. It is known that most of the ICA basis functions extracted from images are sparse and similar to localized and oriented receptive fields. In this paper, the L p norm is used to estimate sparseness of the ICA basis functions, and then, the sparser basis functions were selected for representing the edge information of an image. In the proposed method, a test image is first transformed by ICA basis functions, and then, the high-frequency information can be extracted with the components of the selected sparse basis functions. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in the ICA domain, we can readily obtain the sparse components of the noise-free image, resulting in a kind of robust edge detection even for a noisy image with a very low SN ratio. The efficiency of the proposed method for edge detection is demonstrated by experiments with some medical images.
KeywordsIndependent component analysis Edge detection Medical images
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- 1.Stella Atkins M., Mackiewich B.T.: Handbook of Medical Imaging, Processing and Analysis, chap 11, pp. 171–183. Academic Press, Orlando (2000)Google Scholar
- 3.Canny J.F.: A computation approach to edge detection. IEEE PAMI 8(6), 679–698 (1987)Google Scholar
- 4.Haralick R.M.: Digital step edges from zero crossings of the second directional derivative. IEEE PAMI 6(1), 58–68 (1984)Google Scholar
- 5.Pratt W.K.: Digital Image Processing. Wiley, New York (1978)Google Scholar
- 10.Hoyer, P.: Independent component analysis in image denoising. Master’s thesis, Helsinki University of Techonology (1999)Google Scholar
- 13.Smith, J.O., III: Mathematics of the Discrete Fourier Transform (DFT). W3K Publishing, Stanford. ISBN 0-9745607-0-7. http://www.w3k.org/books/ (2003)
- 14.Karvanen, J., Cichocki, A.: Measuring sparseness in noisy signals. In: Proceedings of the ICA 2003, Nara, Japan, April 2003, pp. 125–130 (2003)Google Scholar
- 16.Hyvarinen, A., Hoyer, P., Oja, E.: Sparse code shrinkage for image denoising, vol. 2. In: The 1998 IEEE International Joint Conference on Neural Networks Proceedings, Anchorage, AK, May 1998, pp. 859–864 (1998)Google Scholar
- 18.Han, X.-H., Chen, Y.-W., Nakao, Z.: An ICA-Based Method for Poisson Noise Reduction. Lecture Notes in Artificial Intelligence, vol. 2773, pp. 1449–1454. Springer, Berlin (2003)Google Scholar
- 20.Reza A.M.: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J. VLSI Signal Process. 33(1), 35–44 (2004)Google Scholar