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Local Binary Patterns, Haar Wavelet Features and Haralick Texture Features for Mammogram Image Classification Using Artificial Neural Networks

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Advances in Computing and Information Technology (ACITY 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 198))

Abstract

The objective of this study is the classification of mammogram images into benign and malignant using Artificial Neural Network. This framework is based on combining Local Binary Patterns, Haar Wavelet features and Haralick Texture features. The study shows the importance of Computer Aided Medical Diagnosis in successful decision making by calculating the likelihood of a disease. This multi feature approach for classification obtains an average classification accuracy of 98.6% for training, validation and testing.

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Author information

Authors and Affiliations

  1. Department of Computer Applications, Cochin University of Science and Technology, Kochi, India

    Simily Joseph & Kannan Balakrishnan

Authors
  1. Simily Joseph
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  2. Kannan Balakrishnan
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Editor information

Editors and Affiliations

  1. Southeastern Louisiana University, 70402, Hammond, LA, USA

    David C. Wyld

  2. Chair of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michal Wozniak

  3. University of Calcutta, Calcutta, India

    Nabendu Chaki

  4. Jackson State University Jackson, 39217, MS, USA

    Natarajan Meghanathan

  5. Wireilla Net Solutions PTY Ltd, Melbourne, Victoria, Australia

    Dhinaharan Nagamalai

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© 2011 Springer-Verlag Berlin Heidelberg

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Joseph, S., Balakrishnan, K. (2011). Local Binary Patterns, Haar Wavelet Features and Haralick Texture Features for Mammogram Image Classification Using Artificial Neural Networks. In: Wyld, D.C., Wozniak, M., Chaki, N., Meghanathan, N., Nagamalai, D. (eds) Advances in Computing and Information Technology. ACITY 2011. Communications in Computer and Information Science, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22555-0_12

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  • DOI: https://doi.org/10.1007/978-3-642-22555-0_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22554-3

  • Online ISBN: 978-3-642-22555-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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