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Automated Renal Cell Carcinoma Subtype Classification Using Morphological, Textural and Wavelets Based Features

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Abstract

We present a new image quantification and classification method for improved pathological diagnosis of human renal cell carcinoma. This method combines different feature extraction methodologies, and is designed to provide consistent clinical results even in the presence of tissue structural heterogeneities and data acquisition variations. The methodologies used for feature extraction include image morphological analysis, wavelet analysis and texture analysis, which are combined to develop a robust classification system based on a simple Bayesian classifier. We have achieved classification accuracies of about 90% with this heterogeneous dataset. The misclassified images are significantly different from the rest of images in their class and therefore cannot be attributed to weakness in the classification system.

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Acknowledgement

This research was supported by grants from the National Institutes of Health (R01CA108468, P20GM072069, and U54CA119338) to M.D.W., Georgia Cancer Coalition (Distinguished Cancer Scholar Award to M.D.W.), Hewlett Packard, and Microsoft Research.

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Authors and Affiliations

  1. Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Qaiser Chaudry, Syed Hussain Raza & May D. Wang

  2. Biomedical Engineering, Winship Cancer Institute, Georgia Institute of Technology and Emory University, Atlanta, GA, USA

    May D. Wang

  3. Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA

    Andrew N. Young

Authors
  1. Qaiser Chaudry
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  2. Syed Hussain Raza
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  3. Andrew N. Young
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  4. May D. Wang
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Correspondence to May D. Wang.

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Chaudry, Q., Raza, S.H., Young, A.N. et al. Automated Renal Cell Carcinoma Subtype Classification Using Morphological, Textural and Wavelets Based Features. J Sign Process Syst Sign Image Video Technol 55, 15–23 (2009). https://doi.org/10.1007/s11265-008-0214-6

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  • DOI: https://doi.org/10.1007/s11265-008-0214-6

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