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An Efficient Method for Noisy Cell Image Segmentation Using Generalized α-Entropy

  • Conference paper
Signal Processing, Image Processing and Pattern Recognition (SIP 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 61))

Abstract

In 1953, a functional extension by A. Rènyi to generalize traditional Shannon’s entropy known as α-entropies was proposed. The functionalities of α-entropies share the major properties of Shannon’s entropy. Moreover, these entropies can be easily estimated using a kernel estimate. This makes their use by many researchers in computer vision community highly appealing . In this paper, an efficient and fast entropic method for noisy cell image segmentation is presented. The method utilizes generalized α-entropy to measure the maximum structural information of image and to locate the optimal threshold desired by segmentation. To speed up the proposed method, computations are carried out on 1D histograms of image. Experimental results show that the proposed method is efficient and much more tolerant to noise than other state-of-the-art segmentation techniques.

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Author information

Authors and Affiliations

  1. Institute for Electronics, Signal Processing and Communications, Otto-von-Guericke-University, Magdeburg, Germany

    Samy Sadek, Ayoub Al-Hamadi & Bernd Michaelis

  2. Assiust University, Egypt

    Usama Sayed

Authors
  1. Samy Sadek
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  2. Ayoub Al-Hamadi
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  3. Bernd Michaelis
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  4. Usama Sayed
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Editor information

Editors and Affiliations

  1. University of Warsaw and Infobright Inc., Poland

    Dominik Ślęzak

  2. Indian Statistical Institute, India

    Sankar K. Pal

  3. University of Tasmania, 7001, Hobart, Australia

    Byeong-Ho Kang

  4. Lab. of Multimedia Information Technology (MMIT), East China Normal University (ECNU), P.O. Box, 200062, Shanghai, China

    Junzhong Gu

  5. Nagasaki University, Japan

    Hideo Kuroda

  6. Hannam University, 306-791, Daejeon, South Korea

    Tai-hoon Kim

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© 2009 Springer-Verlag Berlin Heidelberg

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Sadek, S., Al-Hamadi, A., Michaelis, B., Sayed, U. (2009). An Efficient Method for Noisy Cell Image Segmentation Using Generalized α-Entropy. In: Ślęzak, D., Pal, S.K., Kang, BH., Gu, J., Kuroda, H., Kim, Th. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2009. Communications in Computer and Information Science, vol 61. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10546-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-10546-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10545-6

  • Online ISBN: 978-3-642-10546-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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