Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
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Biological Image Edge Extraction Based on Adaptive Beamlet Transform

  • Received : 2010.11.09
  • Accepted : 2011.04.30
  • Published : 2011.04.30
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Abstract

In cell biology area, microscopy enables detecting objects inside cells that are stained or fluorescently tagged. It is disadvantageous for observing these objects because of the noisy characteristics of their environmental surrounding. In this paper, a framework is proposed to increase the throughput and reliability for analysis of these images. First, we apply adaptive beamlet transform to extract edges meaningfully followed by orientation, location, and length in different scales. Then, a post-process is implemented to extend and map them onto original image. Our proposed scheme is compared with Canny edge detector and conventional beamlet transform from four evaluation aspects. It produces better results when experiments are conducted on real images. Much better results for observing internal parts make this framework competitive for analysis of cell images.

Keywords

Acknowledgement

Supported by : University of Ulsan

References

  1. Meijering E, van Cappellen G, "Quantitative biological image analysis," in Imaging cellular and molecular biological functions, Edited by: Shorte SL, Frischknecht F. Berlin: Springer Verlag, pp. 45-70, 2007.
  2. Murashov, D.M. "A Two-level Method for Segmenting Cytological Images Based on Active Contour Model," Pattern Recognition and Image Analysis, Volume 18, pp. 177 - 192, 2008. https://doi.org/10.1134/S1054661808010215
  3. Yang Qihua, Wang Qiang, "Tissue Cell Boundaries Detection based on Curvelet-based Snake Model in Electrorotation Bio-chip Control System," BioMedical Engineering and Informatics, BMEI 2008, vol.1, pp. 728-732, 2008.
  4. Changrning Sun, Pascal Vallotton, Dadong Wang, Jamie Lopez, Yvonne Ng, David James, "Membrane boundary extraction using circular multiple paths," Pattern Recognition in Computational Life Sciences, Volume 42, Issue 4, pp. 523-530, 2009. https://doi.org/10.1016/j.patcog.2008.09.029
  5. Hui Zhang, Jason E. Fritts, Sally A. Goldman, "Image segmentation evaluation: A survey of unsupervised methods," Computer Vision and Image Understanding, Volume 110, Issue 2, pp. 260-280, May 2008.
  6. Parker, James R, Algorithms for Image Processing and Computer Vision, New Yark, John Wiley & Sons, Inc., pp. 23-29, 1997.
  7. Mehrotra R, Namuduri KR, Ranganathan N, "Gabor filter-based edge detection," Pattern Recogn, 25(12 pp. 1479-1494, 1992. https://doi.org/10.1016/0031-3203(92)90121-X
  8. Kass M, Witkin A, Terzopolous D, "Snakes- active contour models," Int J Comput Vision, 1(4) pp. 321-331, 1987.
  9. Candes EJ, Donoho DL, "New tight frames of curvelets and optimal representations of objects with piecewise C-2 singularities," Commun Pur Appl Math., 57(2) pp. :219-266, 2004. https://doi.org/10.1002/cpa.10116
  10. Donoho D L, "Beamlet pyramids: A new form of multi-resolution analysis, suited for extracting lines, curves, and objects from very noisy image data," in Proceeding of SPIE, volume 4119, July 2000.
  11. A. Karathanou, J.-L. Buessler, H. Kihl and J.-P. Urban R, "Background Extraction in Electron Microscope Images of Artificial Membranes,"in IFIP Advances in Information and Communication Technology, Volume 296/2009, pp. 165-173, 2009.
  12. Campilho, Aurelio, Coudray, Nicolas, Buessler, Jean-Luc, Kihl Hubert, Urban, Jean-Philippe, "Multi-scale and First Derivative Analysis for Edge Detection in TEM Images," Image Analysis and Recognition, Lecture Notes in Computer Science, Springer 2007, pp. 1005-1016, 2007.
  13. A. Averbuch, R.R. Coifman, D.L. Donoho, M. Israeli, and J. Walden, "Fast slant stack: A notion of radon transform for data on a Cartesian grid which is rapidly computable, algebraically exact, geometrically faithful, and invertible," Tech Rep., Stanford University, 2001.
  14. D. L. Donoho and X. Huo, "Bearnlets and multiscale image analysis," in Proc. Multiscale and Multiresolution Methods, vol. 20, pp. 149-196, Lecture Notes in Computational Science and Engineering, 2002.
  15. Canny J, "A computational approach to edge-detection," IEEE T Pattern Anal., 8(6) pp. 679-698, 1986.
  16. R. Deriche, "Using Canny's Criteria to Derive a Recursively Implemented Optimal Edge Detector," Int'l J. Computer Vision, pp. 167- 187, 1987.
  17. Xuan Yang and Dequn Liang, "A New Edge Evaluation Using Region Homogeneous Measure," Journal of Image and Graphics, vol. 4, no. 3, pp. 234-238, Mar. 1999.
  18. Ling, Wing-Kuen and Tam, Kwong-Shun, "Closed boundary extraction of cancer cells using fuzzy edge linking technique," In SPIE Symposium-Image Processing: Algorithms and Systems, San Jose, 2002.