Biological Image Edge Extraction Based on Adaptive Beamlet Transform
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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
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
참고문헌 (18)
- Meijering E, "Quantitative biological image analysis", pp.45~70 (2007), Springer
- Murashov, D.M, "A Two-level Method for Segmenting Cytological Images Based on Active Contour Model", Pattern Recognition and Image Analysis, 18, pp.177~192 (2008)
- Yang Qihua, "Tissue Cell Boundaries Detection based on Curvelet-based Snake Model in Electrorotation Bio-chip Control System", BioMedical Engineering and Informatics, 1(1), pp.728~732 (2008)
- Changming Sun, "Membrane boundary extraction using circular multiple paths", Pattern Recognition in Computational Life Sciences, 42(4), pp.523~530 (2009)
- Hui Zhang, "Image segmentation evaluation: A survey of unsupervised methods", Computer Vision and Image Understanding, 110(2), pp.260~280 (2008)
- Parker, "Algorithms for Image Processing and Computer Vision", pp.23~29 (1997), John Wiley & Sons, Inc
- Mehrotra R, "Gabor filter-based edge detection", Pattern Recogn, 25(12), pp.1479~1494 (1992)
- Kass M, "Snakes- active contour models", Int J Comput Vision, 1(4), pp.321~331 (1987)
- Candès EJ, "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)
- 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, 4119 (2000)
- A. Karathanou, "Background Extraction in Electron Microscope Images of Artificial Membranes", IFIP Advances in Information and Communication Technology, 296, pp.165~173 (2009)
- Campilho, "Multi-scale and First Derivative Analysis for Edge Detection in TEM Images", pp.1005~1016 (2007), Springer
- A. Averbuch, "Fast slant stack: A notion of radon transform for data on a Cartesian grid which is rapidly computable, algebraically exact, geometrically faithful, and invertible" (2001), Tech. Rep., Stanford University
- D. L. Donoho, "Beamlets and multiscale image analysis", Proc. Multiscale and Multiresolution Methods, 20, pp.149~196 (2002)
- Canny J, "A computational approach to edge-detection", IEEE T Pattern Anal, 8(6), pp.679~698 (1986)
- R. Deriche, "Using Canny’s Criteria to Derive a Recursively Implemented Optimal Edge Detector", Int’l J. Computer Vision, pp.167~187 (1987)
- Xuan Yang, "A New Edge Evaluation Using Region Homogeneous Measure", Journal of Image and Graphics, 4(3), pp.234~238 (1999)
- Ling, Wing-Kuen, "Closed boundary extraction of cancer cells using fuzzy edge linking technique", In SPIE Symposium-Image Processing: Algorithms and Systems (2002)