Nucleolus detection in the Fuhrman grading system for application in CCRC

Michal Kruk; Stanislaw Osowski; Tomasz Markiewicz; Wojciech Kozlowski; Robert Koktysz; Janina Slodkowska; Bartosz Swiderski

doi:10.1515/bmt-2013-0053

Article

Nucleolus detection in the Fuhrman grading system for application in CCRC

Michal Kruk , Stanislaw Osowski , Tomasz Markiewicz , Wojciech Kozlowski , Robert Koktysz , Janina Slodkowska and Bartosz Swiderski

Published/Copyright: August 14, 2013

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information Explore this Subject

From the journal Biomedizinische Technik/Biomedical Engineering Volume 59 Issue 1

Abstract

The paper presents a method for nucleolus detection in images of nuclei in clear-cell renal carcinoma (CCRC). The method is based on the similarity of the nuclei image and the two-dimensional paraboloidal window function. The results of numerical experiments performed on almost 2600 images of CCRC nuclei have confirmed the good accuracy of the method. The developed algorithm will be used to accelerate further research in computer-assisted diagnosis of CCRC.

Keywords: Fuhrman grading; image processing; nucleolus detection; renal carcinoma cells

Corresponding author: Stanislaw Osowski, Warsaw University of Technology, ul. Koszykowa 75, Warsaw, Poland; and Military University of Technology, Kaliskiego 2, 00-908 Warsaw, Poland, Phone: +48-22-234-7235, Fax: +48-22-234-5642, E-mail: sto@iem.pw.edu.pl

References

[1] Demirkaya O, Asyali MH, Sahoo PH. Image processing with MATLAB: applications in medicine and biology. Boca Raton, FL: CRC Press 2009.10.1201/9781420008937Search in Google Scholar

[2] Finley D, Pantuck A, Belldegrun A. Tumor biology and prognostic factors in renal cell carcinoma. Oncologist 2011; 16(Suppl 2): 4–13.10.1634/theoncologist.2011-S2-04Search in Google Scholar

[3] Fuhrman SA, Lasky LC, Limas C. Prognostic significance of morphologic parameters in renal cell carcinoma. Am J Surg Pathol 1982; 6: 655–663.10.1097/00000478-198210000-00007Search in Google Scholar

[4] Gonzalez R, Woods R. Digital image processing. Englewood Cliffs, NJ: Prentice Hall 2008.Search in Google Scholar

[5] Kittler J, Illingworth J. Minimum error thresholding. Pattern Recogn 1986; 19: 41–47.10.1016/0031-3203(86)90030-0Search in Google Scholar

[6] Kontak JA, Campbell SC. Prognostic factors in renal cell carcinoma. Urol Clin North Am 2003; 30: 467–480.10.1016/S0094-0143(03)00020-XSearch in Google Scholar

[7] MatLab user manual – Image processing toolbox. Natick, MA: MathWorks 2012.Search in Google Scholar

[8] Soille P. Morphological image analysis, principles and applications. Berlin: Springer 2003.10.1007/978-3-662-05088-0Search in Google Scholar

[9] Soille P, Otsu N. A threshold selection method from grey-level histograms. IEEE Trans Sys Man Cyber 1979; 9: 62–66.10.1109/TSMC.1979.4310076Search in Google Scholar

Received: 2013-5-8

Accepted: 2013-7-11

Published Online: 2013-08-14

Published in Print: 2014-02-01

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.1515/bmt-2013-0053

Keywords for this article

Fuhrman grading; image processing; nucleolus detection; renal carcinoma cells