JBM  Vol.4 No.3 , March 2016
3D Gray Level Co-Occurrence Matrix Based Classification of Favor Benign and Borderline Types in Follicular Neoplasm Images
Abstract: Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation of experienced pathologists. However, it is difficult to separate the favor benign from borderline types. Thus, this paper presents a classification approach based on 3D nuclei model to classify favor benign and borderline types of follicular thyroid adenoma (FTA) in cytological specimens. The proposed method utilized 3D gray level co-occurrence matrix (GLCM) and random forest classifier. It was applied to 22 data sets of FN images. Furthermore, the use of 3D GLCM was compared with 2D GLCM to evaluate the classification results. From experimental results, the proposed system achieved 95.45% of the classification. The use of 3D GLCM was better than 2D GLCM according to the accuracy of classification. Consequently, the proposed method probably helps a pathologist as a prescreening tool.
Cite this paper: Boonsiri, O. , Washiya, K. , Aoki, K. and Nagahashi, H. (2016) 3D Gray Level Co-Occurrence Matrix Based Classification of Favor Benign and Borderline Types in Follicular Neoplasm Images. Journal of Biosciences and Medicines, 4, 51-56. doi: 10.4236/jbm.2016.43009.

[1]   Kennichi, K., Kaori, K., Mitsuyoshi, H., Ryohei, K. and Hirotoshi, N. (2015) Subclassification of Follicular Neoplasms Recommended by the Japan Thyroid Association Reporting System of Thyroid Cytology. International Journal of Endocrinology, Article ID: 938305.

[2]   Kong, J., Wang, F., Teodoro, G., Liang, Y., Zhu, Y., Tucker-Burden, C. and Brat, D. (2015) Automated Cell Segmentation with 3D Fluorescence Microscopy Images. IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[3]   Indhumathi, C., Cai, Y., Guan, Y. and Opas, M. (2011) An Automatic Segmentation Algorithm for 3D Cell Cluster Splitting Using Volumetric Confocal Images. Journal of Microscopy, 243, 60-76.

[4]   Quelhas, P., Marcuzzo, M., Mendon?a, A.M., Oliveira, M.J. and Campilho, A.C. (2009) Cancer Cell Detection and Invasion Depth Estimation in Brightfield Images. BMVC.

[5]   Straka, M., Cruz, A.L., K?chl, A., ?rámek, M., Fleischmann, D. and Gr?ller, E. (2003) 3D Watershed Transform Combined with a Probabilistic Atlas for Medical Image Segmentation. Proceedings of MIT.

[6]   Otsu, N. (1979) A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man and Cyber-netics, 9, 62-66.

[7]   Breiman, L. (2001) Random Forests. Mach. Learn., 45, 5-32.

[8]   Yang, X., Li, H. and Zhou, X. (2006) {Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Mi-croscopy. Microscopy, 53, 2405-2414.

[9]   Kurani, A.S., Xu, D.H., Furst, J. and Raicu, D.S. (2004) Raicu. Co-Occurrence Matrices for Volumetric Data. 7th IASTED International Conference on Computer Graphics and Imaging, Kauai.

[10]   Tsai, F., Chang, C.-K., Rau, J.-Y., Lin, T.-H. and Liu, G.-R. (2007) 3D Computation of Gray Level Co-Occurrence in Hyperspectral Image Cubes. In: Yuille, A., Zhu, S., Cremers, D. and Wang, Y., Eds., Energy Mini-mization Methods in Computer Vision and Pattern Recognition, 4679, Springer, Berlin Heidelberg, 429-440.

[11]   Haralick, R. (1979) Statistical and Structural Approaches to Texture. Proceedings of the IEEE, 67, 786-804.

[12]   Haralick, R.M. and Shapiro, L.G. (1992) Computer and Robot Vision. Addison-Wesley Longman Publishing Co., Inc., Boston.