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Three-dimensional Texture Analysis of Renal Cell Carcinoma Cell Nuclei for Computerized Automatic Grading

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Abstract

The extraction of important features in cancer cell image analysis is a key process in grading renal cell carcinoma. In this study, we analyzed the three-dimensional chromatin texture of cell nuclei based on digital image cytometry. Individual images of 2,423 cell nuclei were extracted from 80 renal cell carcinomas (RCCs) using confocal laser scanning microscopy (CLSM). First, we applied the 3D texture mapping method to render the volume of entire tissue sections. Then, we determined the chromatin texture quantitatively by calculating 3D gray level co-occurrence matrices and 3D run length matrices. Finally, to demonstrate the suitability of 3D texture features for classification, we performed a discriminant analysis. In addition, we conducted a principal component analysis to obtain optimized texture features. Automatic grading of cell nuclei using 3D texture features had an accuracy of 78.30%. Combining 3D textural and 3D morphological features improved the accuracy to 82.19%.

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Authors and Affiliations

  1. School of Computer Engineering, Inje University, Obang-dong 607, Gimhae, Gyungnam, 621-749, Republic of Korea

    T. Y. Kim, H. G. Hwang & H. K. Choi

  2. Centre for Image Analysis, Uppsala University, Uppsala, Sweden

    H. J. Choi

  3. Ubiquitous Healthcare Research Center, Inje University, Gimhae, Republic of Korea

    H. K. Choi

Authors
  1. T. Y. Kim
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  2. H. J. Choi
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  3. H. G. Hwang
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  4. H. K. Choi
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Corresponding author

Correspondence to H. K. Choi.

Additional information

This work is supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Research Promotion Fund) (KRF-2006-311-D00840).

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Kim, T.Y., Choi, H.J., Hwang, H.G. et al. Three-dimensional Texture Analysis of Renal Cell Carcinoma Cell Nuclei for Computerized Automatic Grading. J Med Syst 34, 709–716 (2010). https://doi.org/10.1007/s10916-009-9285-6

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  • DOI: https://doi.org/10.1007/s10916-009-9285-6

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