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Bibliografia

 

[1] Maurence M. Anguh and Aristófanes C. Silva. Multislice segmentation and enhancement in mammograms. In X Simpósio Brasileiro de Computaçao Gráfica e Processamento de Imagem, pages 136-139. IEEE Transactions on Computers, 1997.
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[27] David W. Henderson. Differental Geometry: A Geometric Introduction. Prentice-Hall, Upper Saddle River, New Jersey, 1998.
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[39] Y. Kawata, N. Niki, , H. Ohmatsu, R. Kakinuma, K. Eguchi, M. Kaneko, and N. Moriyama. Classification of pulmonary nodules in thin-section CT images based on shape characterization. In International Conference on Image Processing, volume 3, pages 528-530. IEEE, 1997.
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[40] Y. Kawata, N. Niki, H. Ohmatsu, R. Kakinuma, K. Eguchi, M. Kaneko, and N. Moriyama. Quantitative surface characterization of pulmonary nodules based on thin-section CT images. IEEE Transactions on Nuclear Science, 45(4):2132-2138, august 1998.
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[41] Y. Kawata, N. NIKI, H. Ohmatsu, M. Kusumoto, R. Kakinuma, K. Mori, H. Nishiyama, K. Eguchi, M. Kaneko, and N. Moriyama. Internal structure analysis of pulmonary nodules in topological and histogram feature spaces. In International Conference on Image Processing, volume 1, pages 168-171. IEEE, 2000.
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[42] Y. Kawata, N. Niki, H. Ohmatsu, M. Kusumoto, R. Kakinuma, K. Mori, H. Nishiyama, K. Eguchi, M. Kaneko, and N. Moriyama. Computer aided differential diagnosis of pulmonary nodules using curvature based analysis. In International Conference on Image Analysis and Processing, volume 2, pages 470-475. IEEE, 1999.
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[43] Y. Kawata, N. Niki, H. Ohmatsu, M. Kusumoto, R. Kakinuma, K. Mori, H. Nishiyama, K. Eguchi, M. Kaneko, and N. Moriyama. Computerized analysis of 3-d pulmonary nodule images in surrounding and internal structure feature spaces. In International Conference on Image Processing, volume 2, pages 889-892. IEEE, 2001.
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[44] Jane P. Ko and Magrit Betke. Chest CT : Automated nodule detection and assessment of change over time - preliminary experience. Radiologic Clinics of North America, 218:267-273, 2001.
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[45] Jan J. Koenderink. Solid Shape. MIT Press, Cambridge, MA, USA, 1990.
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[46] Jan J. Koenderink and Andrea J. Van Doorn. Surface shape and curvature scales. Image and Vision Computing, 10(8):557-565, october 1992.
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[47] S. Krass, D. Bohm, and D. Selle. Determination of bronchopulmonary segments based on HRCT data. pages 584-589, Amsterdan:Elsevier, 2000.
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[48] Stefan Krass, Dominik Bohm, and Dirk Selle. Determination of bronchopulmanary segments based on chest CT, 2000.
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[49] K. Kubota, M. Kubo, Y. Kawata, N. NIKI, K. Eguchi, H. Ohmatsu, R. Kakinuma, M. Kaneko, and N. Moriyama. The results in the clinical trial of CAD system for lung cancer using helical CT images. In International Conference on Image Processing, volume 1, pages 313-316. IEEE, 2001.
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[51] Wen-Yau Liang and Peter O'Grady. A formalism for the creation of virual reality worlds from radiology imaging data. Technical Report TR98-06, University of Iowa, 1998.
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[53] J. B. Antoine Maintz and Max A. Viergever. An overview of medical image registration methods. IEEE, pages 1-20, 1999.
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[54] Michael F. McNitt-Gray, Eric M. Hart, Nathaniel Wyckoff, James W. Sayre, Jonathan G. Goldin, and Denise R. Aberle. The effects of co-occurrence matrix based texture parameters on the classification of solitary pulmonary nodules imaged on computed tomography. Computerized Medical Imaging and Graphics, 23:339-348, 1999.
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[55] Michael F. McNitt-Gray, Eric M. Hart, Nathaniel Wyckoff, James W. Sayre, Jonathan G. Goldin, and Denise R. Aberle. A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: Preliminary results. Medical Physics, 26(6):880-888, 1999.
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[56] Olli S. Miettinen. Screening for lung cancer. Radiologic Clinic North America, 38(3):479-485, Maio 2000.
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[58] Bryan S. Morse. Differential geometry, 2000.
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[59] Russell E. Muzzolini. A Volumetric Approach to Segmenation and Texture Characterisation of Ultrasound Images. PhD thesis, College and Graduate Studies and Research, 1996.
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