A Benign and Malignant Pattern Identification in Cytopathological Images of Thyroid Nodules using Gabor Filter and Neural Networks

  • B. Gopinath


This research work presents an automated pattern recognition system to discriminate benign and malignant thyroid nodules using Gabor features based Neural Network classifiers. In the preprocessing step, the required regions of multi-stained Fine Needle Aspiration Cytology images of thyroid nodules are automatically cropped. Then, the segmentation of foreground information is performed using mathematical morphology technique. In the post processing step, the significant statistical features are extracted from the segmented images with the help of Gabor filter under various frequencies and orientations. Finally, the benign and malignant image patterns are discriminated using Elman Neural Network and Auto-associative Neural Network. Based on the performance analysis, the discrimination accuracy of 93.33% is obtained by the Elman Neural Network classifier for the statistical features extracted by Gabor filter bank. However, the auto-associative Neural Network classifier reports a highest discrimination accuracy of 96.66% for the same configuration. This automated discrimination system can be used as a second opinion tool for thyroid nodule analysis by the pathologists.

Keywords: Cytology; Fine Needle Aspiration; Gabor Filter; Morphology; Neural Network; Segmentation


[1] S.A. Mahar, A. Husain, and N. Islam, “Fine needle aspiration cytology of thyroid nodule: Diagnostic accuracy and pitfalls,” Journal of Ayub Medical College, vol. 18, pp. 26-29, 2006. [2] P. Karakitsos, B. Cochand-Priollet, P.J. Guillausseau, and A. Pouliakis, “Potential of the back propagation neural network in the morphologic examination of thyroid lesions,” Anal. Quant. Cytol. Histol., vol. 18, pp. 494-500, 1996. [3] T. Wurflinger, J. Stockhausen, D. Meyer-Ebrecht, and A. Bocking, “Robust automatic coregistration, segmentation and classification of cell nuclei in multimodal cytopathological microscopic images,” Comput. Med. Imaging Graph., vol. 28, pp. 87–98, 2003. [4] O. Lezoray, and M. Lecluse, “Automatic segmentation and classification of cells from Bronchoalveolar lavage,” Image Anal. Stereol., vol. 26, pp. 111–119, 2007. [5] N.A. Shapiro, T.L. Poloz, V.A. Shkurupij, M.S. Tarkov, V.V. Poloz, and A.V. Demin, “Application of artificial neural network for classification of thyroid follicular tumors,” Anal. Quant. Cytol. Histol., Vol. 29, pp. 87–94, 2007. [6] X. Wang, S. Li, H. Liu, M. Wood, W.R. Chen, and B. Zheng, “Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images,” J. Biomed. Inform., vol. 41, pp. 264–271, 2008. [7] A. Daskalakis, S. Kostopoulos, P. Spyridonos, D. Glotsos, P. Ravazoula, M. Kardari, I. Kalatzis, D. Cavouras, and G. Nikiforidis, “Design of a multi-classifier system for discriminating benign from malignant thyroid nodules using routinely H&E-stained cytological images,” Comput. Biol. Med., vol. 38, pp. 196–203, 2008. [8] B. Gopinath, and N. Shanthi, “Development of an Automated Medical Diagnosis System for Classifying Thyroid Tumor Cells using Multiple Classifier Fusion,” Technology in Cancer Research and Treatment, vol. 14, pp. 653–662, 2015.
92 Views | 67 Downloads
How to Cite
Gopinath, B. (2018). A Benign and Malignant Pattern Identification in Cytopathological Images of Thyroid Nodules using Gabor Filter and Neural Networks. Asian Journal For Convergence In Technology (Founded by ISB &M School of Technology )), 4(I). https://doi.org/10.33130/asian journals.v4iI.414
Electronics and Telecommunication