Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma

  • Peter J. SchüfflerEmail author
  • Thomas J. Fuchs
  • Cheng Soon Ong
  • Volker Roth
  • Joachim M. Buhmann
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.


Support Vector Machine Renal Cell Carcinoma Local Binary Pattern Renal Clear Cell Carcinoma Multiple Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Aydın Ulaş, Umberto Castellani, Vittorio Murino, Mehmet Gönen, Manuele Bicego, Pasquale Mirtuono, André Martins, Pedro M.Q. Aguiar and Mário A.T. Figueiredo for successful collaborations and inspiring ideas. We want to thank all our co-workers and SIMBAD partners for fruitful discussions.


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Peter J. Schüffler
    • 1
    Email author
  • Thomas J. Fuchs
    • 2
  • Cheng Soon Ong
    • 3
  • Volker Roth
    • 4
  • Joachim M. Buhmann
    • 1
  1. 1.Swiss Federal Institute of Technology ZurichZurichSwitzerland
  2. 2.California Institute of TechnologyPasadenaUSA
  3. 3.National ICT AustraliaMelbourneAustralia
  4. 4.Computer Science DepartmentUniversity of BaselBaselSwitzerland

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