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Classification of hematologic malignancies using texton signatures

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Abstract

We describe a decision support system to distinguish among hematology cases directly from microscopic specimens. The system uses an image database containing digitized specimens from normal and four different hematologic malignancies. Initially, the nuclei and cytoplasmic components of the specimens are segmented using a robust color gradient vector flow active contour model. Using a few cell images from each class, the basic texture elements (textons) for the nuclei and cytoplasm are learned, and the cells are represented through texton histograms. We propose to use support vector machines on the texton histogram based cell representation and achieve major improvement over the commonly used classification methods in texture research. Experiments with 3,691 cell images from 105 patients which originated from four different hospitals indicate more than 84% classification performance for individual cells and 89% for case based classification for the five class problem.

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Acknowledgments

This research was funded, in part, by grants from the NIH through contracts 5R01LM007455-03 from the National Library of Medicine and 5R01EB003587-02 from the National Institute of Biomedical Imaging and Bioengineering.

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Authors and Affiliations

  1. Department of Computer Science, Rutgers University, Piscataway, NJ, 08854, USA

    Oncel Tuzel & Peter Meer

  2. Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA

    Lin Yang & Peter Meer

  3. Center for Biomedical Imaging and Informatics, University of Medicine and Dentistry of New Jersey, Piscataway, NJ, 08854, USA

    Lin Yang & David J. Foran

  4. CAIP Center, 96 Frelinghuysen Road, Piscataway, NJ, 08854, USA

    Oncel Tuzel

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  1. Oncel Tuzel
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  2. Lin Yang
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  4. David J. Foran
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Correspondence to Oncel Tuzel.

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Tuzel, O., Yang, L., Meer, P. et al. Classification of hematologic malignancies using texton signatures. Pattern Anal Applic 10, 277–290 (2007). https://doi.org/10.1007/s10044-007-0066-x

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  • DOI: https://doi.org/10.1007/s10044-007-0066-x

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