Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-18T23:44:20.713Z Has data issue: false hasContentIssue false

Gray Level Co-Occurrence Matrix Texture Analysis of Germinal Center Light Zone Lymphocyte Nuclei: Physiology Viewpoint with Focus on Apoptosis

Published online by Cambridge University Press:  23 March 2012

Igor Pantic*
Affiliation:
Institute of Medical Physiology, Faculty of Medicine, University of Belgrade, Visegradska 26/II, 11000 Belgrade, Serbia
Senka Pantic
Affiliation:
Institute of Histology and Embryology, Faculty of Medicine, University of Belgrade, Visegradska 26/II, 11000 Belgrade, Serbia
Gordana Basta-Jovanovic
Affiliation:
Institute of Pathology, Faculty of Medicine, University of Belgrade, Dr Subotica 1, 11000 Belgrade, Serbia
*
Corresponding author. E-mail: [email protected]
Get access

Abstract

In our study we investigated the relationship between conventional morphometric indicators of nuclear size and shape (area and circularity) and the parameters of gray level co-occurrence matrix texture analysis (entropy, homogeneity, and angular second moment) in cells committed to apoptosis. A total of 432 lymphocyte nuclei images from the spleen germinal center light zones (cells in early stages of apoptosis) were obtained from eight healthy male guinea pigs previously immunized with sheep red blood cells (antigen). For each nucleus, area, circularity, entropy, homogeneity, and angular second moment were determined. All measured parameters of gray level co-occurrence matrix (GLCM) were significantly correlated with morphometric indicators of nuclear size and shape. The strongest correlation was observed between GLCM homogeneity and nuclear area (p < 0.0001, rs = 0.61). Angular second moment values were also highly significantly correlated with nuclear area (rs = 0.39, p < 0.0001). These results indicate that the GLCM method may be a powerful tool in evaluation of ultrastructural nuclear changes during early stages of the apoptotic process.

Type
Biological Applications
Copyright
Copyright © Microscopy Society of America 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Al-Janobi, A. (2001). Performance evaluation of cross-diagonal texture matrix method of texture analysis. Pattern Recogn 34, 171180.Google Scholar
Alvarenga, A.V., Teixeira, C.A., Ruano, M.G. & Pereira, W.C. (2010). Influence of temperature variations on the entropy and correlation of the grey-level co-occurrence matrix from B-mode images. Ultrasonics 50, 290293.Google Scholar
Assefa, D., Keller, H., Menard, C., Laperriere, N., Ferrari, R.J. & Yeung, I. (2010). Robust texture features for response monitoring of glioblastoma multiforme on T1-weighted and T2-FLAIR MR images: A preliminary investigation in terms of identification and segmentation. Med Phys 37, 17221736.CrossRefGoogle ScholarPubMed
Carvalho, L.J., Ferreira-da-Cruz, M.F., Daniel-Ribeiro, C.T., Pelajo-Machado, M. & Lenzi, H.L. (2007). Germinal center architecture disturbance during Plasmodium berghei ANKA infection in CBA mice. Malar J 6, 59.Google Scholar
Chan, H.P., Wu, Y.T., Sahiner, B., Wei, J., Helvie, M.A., Zhang, Y., Moore, R.H., Kopans, D.B., Hadjiiski, L. & Way, T. (2010). Characterization of masses in digital breast tomosynthesis: Comparison of machine learning in projection views and reconstructed slices. Med Phys 37, 35763586.CrossRefGoogle ScholarPubMed
Daniel, B. & DeCoster, M.A. (2004). Quantification of sPLA2-induced early and lateapoptosis changes in neuronal cell cultures using combined TUNEL and DAPI staining. Brain Res Brain Res Protoc 13, 144150.Google Scholar
Falcieri, E., Gobbi, P., Zamai, L. & Vitale, M. (1994). Ultrastructural features of apoptosis. Scanning Microsc 8, 653665.Google Scholar
Haralick, R.M., Shanmugam, K. & Dinstein, I. (1973). Textural features for image classification. IEEE T Syst Man Cyb 3, 610621.CrossRefGoogle Scholar
Hasdai, D., Sangiorgi, G., Spagnoli, L.G., Simari, R.D., Holmes, D.R. Jr., Kwon, H.M., Carlson, P.J., Schwartz, R.S. & Lerman, A. (1999). Coronary artery apoptosis in experimental hypercholesterolemia. Atherosclerosis 142, 317325.Google Scholar
Huber, M.B., Nagarajan, M.B., Leinsinger, G., Eibel, R., Ray, L.A. & Wismüller, A. (2011). Performance of topological texture features to classify fibrotic interstitial lung disease patterns. Med Phys 38, 20352044.CrossRefGoogle ScholarPubMed
Hur, D.Y., Kim, D.J., Kim, S., Kim, Y.I., Cho, D., Lee, D.S., Hwang, Y., Bae, K., Chang, K.Y. & Lee, W.J. (2000). Role of follicular dendritic cells in the apoptosis of germinal center B cells. Immunol Lett 72, 107111.Google Scholar
Jambawalikar, S., Li, H., Shah, S., Fisher, P. & Button, T. (2011). SU-E-I-04: Texture feature based CAD for breast cancer detection. Med Phys 38, 3396.CrossRefGoogle Scholar
Kao, E.F., Kuo, Y.T., Hsu, J.S., Chou, M.C. & Liu, G.C. (2011). Zone-based analysis for automated detection of abnormalities in chest radiographs. Med Phys 38, 42414250.Google Scholar
Kihlmark, M., Imreh, G. & Hallberg, E. (2001). Sequential degradation of proteins from the nuclear envelope during apoptosis. J Cell Sci 114, 36433653.CrossRefGoogle ScholarPubMed
Klein, U., Tu, Y., Stolovitzky, G.A., Keller, J.L., Haddad, J. Jr., Miljkovic, V., Cattoretti, G., Califano, A. & Dalla-Favera, R. (2003). Transcriptional analysis of the B cell germinal center reaction. Proc Natl Acad Sci USA 100, 26392644.CrossRefGoogle Scholar
Liao, Y.Y., Tsui, P.H., Li, C.H., Chang, K.J., Kuo, W.H., Chang, C.C. & Yeh, C.K. (2011). Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and Nakagami-parameter images. Med Phys 38, 21982207.Google Scholar
Losa, G.A. & Castelli, C. (2005). Nuclear patterns of human breast cancer cells during apoptosis: Characterisation by fractal dimension and co-occurrence matrix statistics. Cell Tissue Res 322, 257267.CrossRefGoogle ScholarPubMed
MathWorks, Inc. (1984–2012). Properties of gray-level co-occurrence matrix. MATLAB software product documentation. Natwick, MA: The MathWorks, Inc. Available at www.mathworks.com/help/toolbox/images/ref/graycoprops.html (accessed Oct. 26, 2011).Google Scholar
Pantic, V.S. & Pantic, S.M. (1992). Opposite actions of alpha-adrenergic vs beta-adrenergic influences on humoral immune response in guinea pigs. Ann NY Acad Sci 650, 165169.CrossRefGoogle ScholarPubMed
Ranjanomennahary, P., Ghalila, S.S., Malouche, D., Marchadier, A., Rachidi, M., Benhamou, C. & Chappard, C. (2011). Comparison of radiograph-based texture analysis and bone mineral density with three-dimensional microarchitecture of trabecular bone. Med Phys 38, 420428.Google Scholar
Saitoh, H.A., Maeda, K. & Yamakawa, M. (2006). In situ observation of germinal center cell apoptosis during a secondary immune response. J Clin Exp Hematop 46, 7382.CrossRefGoogle ScholarPubMed
Shamir, L., Wolkow, C.A. & Goldberg, I.G. (2009). Quantitative measurement of aging using image texture entropy. Bioinformatics 25, 30603063.CrossRefGoogle ScholarPubMed
Vince, D.G., Dixon, K.J., Cothren, R.M. & Cornhill, J.F. (2000). Comparison of texture analysis methods for the characterization of coronary plaques in intravascular ultrasound images. Comput Med Imaging Graph 24, 221229.Google Scholar
Waugh, S.A., Lerski, R.A., Bidaut, L. & Thompson, A.M. (2011). The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms. Med Phys 38, 5058.CrossRefGoogle ScholarPubMed
Winter, D.B., Gearhart, P.J. & Bohr, V.A. (1998). Homogeneous rate of degradation of nuclear DNA during apoptosis. Nucleic Acids Res 26, 44224425.Google Scholar
Zhang, J.H. & Xu, M. (2000). DNA fragmentation in apoptosis. Cell Res 10, 205211.Google Scholar