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Ovarian Tumor Characterization and Classification Using Ultrasound: A New Online Paradigm

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Ovarian Neoplasm Imaging

Abstract

Among gynecological malignancies, ovarian cancer is the most frequent cause of death. Image mining algorithms have been predominantly used to give the physicians a more objective, fast, and accurate second opinion on the initial diagnosis made from medical images. The objective of this work is to develop an adjunct Computer-Aided Diagnostic (CAD) technique that uses 3D ultrasound images of the ovary to accurately characterize and classify benign and malignant ovarian tumors. In this algorithm, we first extract features based on the textural changes and higher-order spectra (HOS) information. The significant features are then selected and used to train and evaluate the decision tree (DT) classifier. The proposed technique was validated using 1,000 benign and 1,000 malignant images, obtained from ten patients with benign and ten with malignant disease, respectively. On evaluating the classifier with tenfold stratified cross validation, the DT classifier presented a high accuracy of 97 %, sensitivity of 94.3 %, and specificity of 99.7 %. This high accuracy was achieved because of the use of the novel combination of the four features which adequately quantify the subtle changes and the nonlinearities in the pixel intensity variations. The rules output by the DT classifier are comprehensible to the end user and, hence, allow the physicians to more confidently accept the results. The preliminary results show that the features are discriminative enough to yield good accuracy. Moreover, the proposed technique is completely automated and accurate and can be easily written as a software application for use in any computer.

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Author information

Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya PhD, DEng

  2. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    U. Rajendra Acharya PhD, DEng

  3. Department of Radiology, Azienda Ospedaliero Universitaria di Cagliari, Cagliari, Italy

    Luca Saba MD

  4. Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy

    Filippo Molinari PhD

  5. Department of Obstetrics and Gynecology, University of Cagliari, Ospedale San Giovanni di Dio, Via Ospedale 46, Cagliari, 09124, Italy

    Stefano Guerriero MD

  6. Department of Biomedical Engineering, CTO, Global Biomedical Technologies, Roseville, CA, USA

    Jasjit S. Suri PhD, MBA, Fellow AIMBE

  7. Department of Biomedical Engineering, Idaho State University (Aff.), Pocatello, ID, USA

    Jasjit S. Suri PhD, MBA, Fellow AIMBE

Authors
  1. U. Rajendra Acharya PhD, DEng
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  2. Luca Saba MD
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  3. Filippo Molinari PhD
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  4. Stefano Guerriero MD
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  5. Jasjit S. Suri PhD, MBA, Fellow AIMBE
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Corresponding author

Correspondence to Jasjit S. Suri PhD, MBA, Fellow AIMBE .

Editor information

Editors and Affiliations

  1. University of Cagliari, Cagliari, Italy

    Luca Saba

  2. Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  3. University of Cagliari, Cagliari, Italy

    Stefano Guerriero

  4. Biomedical Technologies, Inc, Roseville, California, USA

    Jasjit S. Suri

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Acharya, U.R., Saba, L., Molinari, F., Guerriero, S., Suri, J.S. (2013). Ovarian Tumor Characterization and Classification Using Ultrasound: A New Online Paradigm. In: Saba, L., Acharya, U., Guerriero, S., Suri, J. (eds) Ovarian Neoplasm Imaging. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-8633-6_26

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  • DOI: https://doi.org/10.1007/978-1-4614-8633-6_26

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4614-8632-9

  • Online ISBN: 978-1-4614-8633-6

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