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Machine Learning Techniques for Prostate Ultrasound Image Diagnosis

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Advances in Machine Learning I

Part of the book series: Studies in Computational Intelligence ((SCI,volume 262))

Abstract

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this chapter, we present a machine learning scheme, employing a combination of fuzzy sets, wavelets and rough sets, for analyzing prostrate ultrasound images in order diagnose prostate cancer. To address the image noise problem we first utilize an algorithm based on type-II fuzzy sets to enhance the contrast of the ultrasound image. This is followed by performing a modified fuzzy c-mean clustering algorithm in order to detect the boundary of the prostate pattern. Then, a wavelet features are extracted and normalized, followed by application of a rough set analysis for discrimination of different regions of interest to determine whether they represent cancer or not. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach is high compared with other machine learning techniques including decision trees, discriminant analysis, rough neural networks, and neural networks.

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Author information

Authors and Affiliations

  1. Information Technology Department, FCI, Cairo University, 5 Ahamed Zewal Street, Orman, Giza, Egypt

    Aboul Ella Hassanien

  2. System Department, CBA, Kuwait University, Kuwait

    Hameed Al-Qaheri

  3. Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VŠB - Technical University of Ostrava, 17. listopadu 15, 708 33, Ostrava - Poruba, Czech Republic

    Václav Snášel

  4. Computational Intelligence Laboratory, Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, Manitoba, R3T 5V6, Canada

    James F. Peters

Authors
  1. Aboul Ella Hassanien
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  2. Hameed Al-Qaheri
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  3. Václav Snášel
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  4. James F. Peters
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Editor information

Editors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, ul.Ordona 21, 01-237, Warsaw, Poland

    Jacek Koronacki  & Sławomir T. Wierzchoń  & 

  2. Woodward Hall 430C University of North Carolina, 9201 University City Blvd., N.C. 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Systems Research Institute, Polish Academy of Sciences, ul.Newelska 6, 01-447, Warsaw, 01-447

    Janusz Kacprzyk

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Ella Hassanien, A., Al-Qaheri, H., Snášel, V., Peters, J.F. (2010). Machine Learning Techniques for Prostate Ultrasound Image Diagnosis. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning I. Studies in Computational Intelligence, vol 262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05177-7_19

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  • DOI: https://doi.org/10.1007/978-3-642-05177-7_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05176-0

  • Online ISBN: 978-3-642-05177-7

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