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Pixel N-Grams Representation for Medical Image Classification

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Hybrid Machine Intelligence for Medical Image Analysis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 841))

Abstract

Image classification has wide applications in many fields including medical imaging. A major aspect of classification is to extract features that can correctly represent important variations in an image. Global image features commonly used for classification include Intensity Histograms, Haralick’s features based on Gray-level co-occurrence matrix, Local Binary Patterns and Gabor filters. A novel feature extraction and image representation technique ‘Pixel N-grams’ inspired from ‘Character N-grams’ concept in text categorization is described in this chapter. The classification performance of Pixel N-grams is tested on the various datasets including UIUC texture dataset, binary shapes dataset, miniMIAS dataset of mammography, and real-world high-resolution mammography dataset provided by an Australian radiology practice. The results are compared with other feature extraction techniques such as co-occurrence matrix features, intensity histogram, and bag of visual words. The results demonstrate promising classification accuracy in addition to reduced computational costs, enabling a new way for mammographic classification on low resource computers.

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Notes

  1. 1.

    Truly digital mammograms/primary digital mammograms are digital mammograms directly generated with the help of advanced imaging equipment.

  2. 2.

    Secondary digital mammograms are conventional film-based mammograms digitised with the help of a scanner.

  3. 3.

    MATLAB 7.9.0.

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Authors and Affiliations

  1. MIT World Peace University, Pune, India

    Pradnya Kulkarni

  2. Federation University, Ballarat, Australia

    Pradnya Kulkarni & Andrew Stranieri

Authors
  1. Pradnya Kulkarni
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  2. Andrew Stranieri
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Correspondence to Pradnya Kulkarni .

Editor information

Editors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Beliaghata, West Bengal, India

    Siddhartha Bhattacharyya

  2. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Gangtok, Sikkim, India

    Debanjan Konar

  3. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Jan Platos

  4. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Gangtok, Sikkim, India

    Chinmoy Kar

  5. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Gangtok, Sikkim, India

    Kalpana Sharma

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Kulkarni, P., Stranieri, A. (2020). Pixel N-Grams Representation for Medical Image Classification. In: Bhattacharyya, S., Konar, D., Platos, J., Kar, C., Sharma, K. (eds) Hybrid Machine Intelligence for Medical Image Analysis. Studies in Computational Intelligence, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-8930-6_2

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  • DOI: https://doi.org/10.1007/978-981-13-8930-6_2

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