Abstract
In teeth-related radiograph research, the information of teeth shape is the most critical factor for achieving highly automated diagnosis. Therefore, accurate segmentation is an essential but difficult task due to low contrast and uneven exposure of the dental X-ray image. In this paper, we propose a novel scheme to automatically segment teeth by using texture characteristics instead of primitive intensity or edge used in previous researches. At first, image enhancement based on homogeneity measurement is applied to accentuate the texture of gums while smoothing the teeth so that a coarse clustering result can be obtained. Then, fuzzy inference is applied to speculate degrees of pixel belonging to either part. Finally, region growing based on inferences is performed to obtain the complete shape of teeth. The experimental results show that our proposed method indeed outperforms the methods using direct intensity or edge in segmenting complete teeth from X-ray dental images.
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References
Cheng, H.D., Xue, M., Shi, X.J.: Contrast Enhancement Based on a Novel Homogeneity Measurement. Pattern Recognition 36, 2687–2697 (2003)
de Boor, C.: B-Spline Basics, Fundamental Developments of Computer-Aided Geometric Modeling, pp. 27–49. Academic Press, New York (1993)
Fan, J., Zeng, G., Body, M., Hacid, M.: Seeded Region Growing: An Extensive and Comparative Study. Pattern Recognition Letter 26, 1139–1156 (2005)
Gonzalez, Woods: Digital Image Processing, 2 edn. Prentice Hall, Englewood Cliffs (2002)
Jain, A.K., Chen, H.: Matching of Dental X-ray Images for Human Identification. Pattern Recognition 37, 1519–1532 (2004)
Kanungo, T., Mount, D.M., Netanyahu, N., Piatko, C., Silverman, R., Wu, A.Y.: An Efficient K-means Clustering Algorithm: Analysis and Implementation. IEEE Transaction on Pattern Analysis and Machine Intelligence 24, 881–892 (2002)
Keshtkar, F., Gueaieb, W.: Segmentation of Dental Radiographs Using a Swarm Intelligence Approach. In: IEEE Canadian Conference on Electrical and Computer Engineering, pp. 328–331 (2006)
Li, S., Fevens, T., Krzyzak, A., Jin, C., Li, S.: Semi-automatic Computer Aided Lesion Detection in Dental X-rays Using Variational Level Set. Pattern Recognition 40, 2861–2873 (2007)
Miosso, C.J., Bauchspiess, A.: Fuzzy Inference System Applied to Edge Detection in Digital Images. In: Proceedings of the V Brazilian Conference on Neural Networks, pp. 481–486 (2001)
Moragas, A., Garcia-Bonafe, M., de Torres, I., Sans, M.: Textural Analysis of Lymphoid Cells in Serous Effusions. A Mathematical Morphologic Approach. Analytical and Quantitative Cytology and Histology 15, 165–170 (1993)
Mukhopadhyay, S., Chanda, B.: A Multiscale Morphological Approach to Local Contrast Enhancement. Signal Processing 80, 685–696 (2000)
Nomir, O., Abdel-Mottaleb, M.: A System for Human Identification from X-ray Dental Radiographs. Pattern Recognition 38, 1295–1305 (2005)
Pham, D.L., Xu, C., Prince, J.: A Survey of Current Methods in Medical Image Segmentation. Annual Review of Biomedical Engineering 2, 315–337 (2000)
Said, E.H., Nassar, D.E.M., Fahmy, G., Ammar, H.H.: Teeth Segmentation in Digitized Dental X-Ray Films Using Mathematical Morphology. IEEE Transactions on Information Forensics and Security 1, 178–189 (2006)
Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice-Hall, Englewood Cliffs (2001)
Shih, F.Y., Cheng, S.: Automatic Seeded Region Growing for Color Image Segmentation. Image and Vision Computing 23, 844–886 (2005)
Siler, W., Buckley, J.J.: Fuzzy Expert Systems and Fuzzy Reasoning. John Wiley, Chichester (2005)
Zhou, J., Abdel-Mottaleb, M.: A Content-based System for Human Identification Based on Bitewing Dental X-ray Images. Pattern Recognition 38, 2132–2142 (2005)
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Lai, Y.H., Lin, P.L. (2008). Effective Segmentation for Dental X-Ray Images Using Texture-Based Fuzzy Inference System. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_85
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DOI: https://doi.org/10.1007/978-3-540-88458-3_85
Publisher Name: Springer, Berlin, Heidelberg
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