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dc.contributor.authorLiao, Hsin-Yien_US
dc.identifier.citation[1] G. Fahmy, D. Nassar, E. Haj-Said, H. Chen, O. Nomir, J. D. Zhou, R. Howell, H. H. Ammar, M. Abdel-Mottaleb, and A. K. Jain, “Towards an Automated Dental Identification System (ADIS),” Proc. ICBA, pp. 789-796, 2004. [2] H. Chen and A. K. Jain, “Tooth Contour Extraction for Matching Dental Radiographs,” Proc. 17th Int'l Conf. Pattern Recognition, Vol. III, pp. 522-525, 2004. [3] A. K. Jain and H. Chen, “Dental Biometrics: Alignment and Matching of Dental Radiographs,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 24, pp. 1319 - 1326, 2005. [4] A. K. Jain and H. Chen, “Matching of Dental X-ray Images for Human Identification,” Pattern Recognition, Vol. 37, pp. 1519-1532, 2004. [5] O. Nomir and M. Abdel-Mottaleb, “A System for Human Identification from X-ray Dental Radiographs,” Pattern Recognition, Vol. 38, pp. 1295-1305, 2005. [6] J. D. Zhou and M. Abdel-Mottaleb, “A Content-based System for Human Identification Based on Bitewing Dental X-ray Images,” Pattern Recognition, Vol. 38, pp. 2132-2142, 2005. [7] A. K. Jain, H. Chen, and S. Minut, “Dental Biometrics: Human Identification Using Dental Radiographs,” Proc. 4th International Conference on AVBPA, pp. 429-437, 2003. [8] E. H. Said, D. E. M. Nassar, H. H. Ammar, “Teeth Segmentation in Digitized Dental X-Ray Films Using Mathematical Morphology,” IEEE Trans. Information Forensics And Security, Vol. 1, pp. 178-189, 2006. [9] M. Mahoor, M. Abdel-Mottaleb, “Classification and Numbering of Teeth in Bitewing Dental Images,” Pattern Recognition, Vol. 38, pp. 577-586, 2005. [10] R. Gonzalez and R. Wood, Digital Image Processing, Addison Wesley, Reading, MA, 1993. [11] Linda G. Shapiro and George C. Stockman, Computer Vision, Prentice Hall, 2001. [12] Alasdair McAndrew, Introduction to Digital Image Processing with MATLAB, Thomson Course Technology, 2004. [13] D. Zhang and G. Lu, “A Comparative Study on Shape Retrieval Using Fourier Descriptors with Different Shape Signatures,” Proc. Conf. Intelligent Multimedia and Distance Education, pp. 1-9, 2001. [14] C. deBoor, “B-Spline Basics,” Fundamental Developments of Computer-Aided Geometric Modeling, Academic Press, New York, pp. 27-49, 1993. [15] Weisstein, Eric W. "B-Spline." From MathWorld--A Wolfram Web Resource. [16] Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Introduction to Data Mining, Addison-Wesley, 2006. [17] Christopher J.C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, Vol. 2, pp. 121-167, 1998. [18] Chih-Wei Hsu and Chih-Jen Lin, “A Comparison on Methods for Multiclass Support Vector Machines,” IEEE Transactions on Neural Networks, Vol. 13, pp. 415-425, 2002. [19] American Dental Association, “Current Dental Terminology Third Edition (CDT-3),” 1999. [20] J.C. Fu et al., “Image Segmentation Feature Selection and Pattern Classification for Mammographic Microcalcifications,” Computerized Medical Imaging and Graphics, Vol. 29, pp. 419-429, 2005.zh_TW
dc.description.abstract在本篇論文中,我們提出一個新的切割演算法與牙齒x光片(bite-wing images)自動分類系統,相較於以往的研究只擷取部分的牙齒形狀來進行分類,我們則擷取完整的牙齒形狀做為分類的標準。本論文最終的目標是希望藉由此分類的結果來改善牙齒的辨識系統,進而提升辨識率和有效率的牙齒資料比對。 本論文使用Support Vector Machine (SVM)將bite-wing images中所涵蓋的牙齒分為臼齒和前臼齒兩類,並將分類結果配合空間關係(如牙齒出現在圖片中的位置、牙齒間的相對位置等)對圖片中的牙齒做編號,此外我們對所淬取的特徵值做Sequential Forward Selection (SFS)分析,希望從中獲得每個特徵值的重要性,實驗結果顯示,我們從62張bite-wing圖片裡,切出375顆牙齒,獲得相當高的分類正確率,表示本研究能有效及正確的進行牙齒的分類和編號。zh_TW
dc.description.abstractWe present an automatic segmentation and classification system to segment and classify teeth in bite-wing images. Unlike other methods, we extract the complete tooth contour. Our final goal is to classify teeth in bite-wing images more correctly and efficiently. We use Support Vector Machine to classify teeth in bite-wing images into molars and premolars. After classifying teeth, we utilize their spatial relationships to automatically number the teeth based on the universal numbering system. Furthermore, we use Sequential Forward Selection (SFS) method to rank the features and analyze the degree of importance for each feature in classification. In a set of 62 bite-wing images containing 375 teeth, experimental results show that our method is capable of classifying and numbering the teeth with high accuracy.en_US
dc.description.tableofcontents摘要 I ABSTRACT II CHAPTER 1 INTRODUCTION 1 1.1 Research Scope 1 1.2 The goal of our research 2 1.3 Framework 3 CHAPTER 2 RELATED WORKS 4 CHAPTER 3 TEETH SEGMENTATION 6 3.1 Image enhancement 7 3.2 Entropy computation 9 3.3 K-means clustering 10 3.4 Region growing 12 3.4.1 Choosing seed points 13 3.4.2 Criteria 14 3.5 Contour extraction 14 3.5.1 Normalization 17 3.5.2 B-spline 18 CHAPTER 4 FEATURE EXTRACTION 20 4.1 Length/width ratio 20 4.2 Eccentricity 21 4.3 Signature 22 CHAPTER 5 CLASSIFICATION AND NUMBERING 24 5.1 Support Vector Machine (SVM) 24 5.2 Numbering 26 CHAPTER 6 EXPERIMENTAL RESULTS 28 6.1 Segmentation result 28 6.2 Classification result 30 6.3 Numbering result 32 6.4 Feature analysis 33 CHAPTER 7 CONCLUSION AND FUTURE WORK 35 REFERENCES 36zh_TW
dc.subjectDental Identification Systemen_US
dc.subjectBite-wing Imageen_US
dc.subjectK-means Clusteringen_US
dc.subjectRegion Growingen_US
dc.subjectSupport Vector Machineen_US
dc.titleAn Effective Classification System for Dental Bite-wing images Using Entire Tooth Shapeen_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
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