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Journal of Medical Systems
Published in: Journal of Medical Systems 4/2010

01-08-2010 | Original Paper

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

Authors: T. Y. Kim, H. J. Choi, H. G. Hwang, H. K. Choi

Published in: Journal of Medical Systems | Issue 4/2010

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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%.
Literature
1.
Kim, D. S., and Lee, S. J., Diagnostic pathology of the breast. Academia. 139–172, 1990.
2.
Walker, R.F., Jackway, P.T., Statistical geometric features-extension for cytological texture analysis. IEEE Proc of ICP R'96. 790–794, 1996.
3.
François, C., Remmelink, M., Petein, M., van Velthoven, R., Danguy, A., Wespes, E., Salmon, I., Kiss, R., and Decaestecker, C., The chromatin pattern of cell nuclei is of prognostic value for in renal cell carcinoma. Anal. Cell. Pathol. 16:161–175, 1998.
4.
5.
6.
7.
Jafari-Khouzani, K., Soltanian-Zadeh, H., Elisevich, K., and Patel, S., Comparison of 2D and 3D wavelet features for TLE lateralization. Proc. of SPIE Medical Imaging 2004-Physiology, Function and Structure from Medical Images. 5369:593–601, 2004.
8.
Madabhushi, A., Feldman, M., Metaxas, D., Chute, D., and Tomaszewski, J., A novel stochastic combination of 3D texture features for automated segmentation of prostatic adenocarcinoma from high resolution MRI. Med. Image Comput. Computer-assisted Intervention. 2878:581–591, 2003.
9.
Kurani, A. S., Xu, D. H., Furst, J. D., Raicu, D. S., Co-occurrence matrices for volumetric data. 7th IASTED Int'l Conf on Computer Graphics and Imaging, Kauai, Hawaii, USA, in August 16–18. 2004.
10.
Xu, D. H., Kurani, A. S., Furst, J. D., Raicu, D. S., Run-length encoding for volumetric texture. 4th IASTED Int'l Conf on Visualization, Imaging and Image Processing, Marbella, Spain, September 6–8. 2004.
11.
Huisman, A., Ploeger, L. S., Dullens, H. F. J., Poulin, N., Grizzle, W. E., and Diest, P. J., Development of 3D chromatin texture analysis using confocal laser scanning microscopy. Cell. Oncol. 27:335–345, 2005.
12.
Parker, J. R., Algorithms for Image Processing and Computer Vision. John Wiley & Sons, Inc, New York, 1997.
13.
Gonzalez, R. C., and Woods, R. E., Digital Image Processing, 2nd edition. Prentice-Hall, Upper Saddle River, NJ, 2002.
14.
Rodenacker, K., and Bengtsson, E., A feature set for cytometry on digitized microscopic images. Anal. Cell. Pathol. 24:1–36, 2003.
15.
16.
17.
Thiran, J. P., and Macq, B., Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Trans. Biomed. Eng. 43:1011–1020, 1996. doi:10.​1109/​10.​536902.CrossRef
18.
Rost, R. J., OpenGL Shading Language. Addison Wesley, Boston, 2004.
19.
Johnson, R. A., and Wichern, D. W., Applied Multivariate Statistical Analysis. Prentice-Hall, Upper Saddle River, NJ, 2002.
20.
Johnsonbaugh, R., and Jost, S., Pattern Recognition and Image Analysis. Prentice-Hall, Upper Saddle River, NJ, 1996.
21.
Choi, H. J., Choi, I. H., Kim, T. Y., Cho, N. H., and Choi, H. K., Three-dimensional Visualization and Quantitative Analysis of Cervical Cell Nuclei with Confocal Laser Scanning Microscopy. Anal. Quant. Cytol. Histol. 27:174–180, 2005.
Metadata
Title
Three-dimensional Texture Analysis of Renal Cell Carcinoma Cell Nuclei for Computerized Automatic Grading
Authors
T. Y. Kim
H. J. Choi
H. G. Hwang
H. K. Choi
Publication date
01-08-2010
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 4/2010
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-009-9285-6

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