Abstract
In this paper, we describe a computer assisted automatic diagnosis system of lung cancer that detects tumor candidates in its early stage from the helical CT images. This automation of the process reduces the time complexity and increases the diagnosis confidence. Our algorithm consists of analysis part and diagnosis part. In the analysis part, we extract the lung regions and the pulmonary blood vessels regions and analyze the features of these regions using image processing technique. In the diagnosis part, we define diagnosis rules based on these features, and we detect the tumor candidates using these rules. We apply our algorithm to 224 patients data of mass screening. These results show that our algorithm detects lung cancer candidates successfully.
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T.Iinuma, Y.Tateno, T.Matsumoto, S.Yamamoto, M.Matsumoto, “Preliminary Specification of X-ray CT for Lung Cancer Screening (LSCT) and its Evaluation on Risk-Cost-Effectiveness”, NIPPON ACTA RADIOLOGICA, Japan, vol. 52, no.2, pp. 182–190, 1992
J.Hasegawa, K.Mori, J.Toriwaki, H.Anno, K.Katada, “Automated Extraction of Lung Cancer Lesions from MultiSlice Chest CT Images by Using Tree-Dimensional Image Processing”, Trans.IEICE, Japan, vol. J76-D-II, no.8, pp. 1587–1594, 1993
M.L.Giger, K.T.Bae, and H. MacMahon,“Computerized Detection of Pulmonary Nodules in computed Tomography Images”, Invest Radiol,vol. 29, no.4, pp. 459–465, 1994
M.M.Trivede, J.C.Bezdek, “Low-Level segmentation of aerial images with Fuzzy clustering”, IEEE Trans.Syst., Man. & Cybern., SMC-16, 4, pp. 589–59, 1986
N.Niki, Y.Kawata, H.Satoh, “A 3-D Display Method of Fuzzy Shapes Obtained from Medical Images”, Trans.IEICE, Japan, vol. J73-D-II, no.10, pp. 1707–1715, 1990
J.Toriwaki, A.Fukumura, T.Maruse, “Fundamental Properties of the Gray Weighted Distance Transformation”, Trans.IEICE, Japan, vol. J60-D, no.12, pp. 1101–1108, 1977
K.Kanazawa, Y.Kawata, N.Niki, H.Nishitani, H.Satoh, “Study of Diagnosis of Lung Cancer Using Cone-beam 3-D X-ray CT”, Technical Rept., JAMIT'93, Japan, pp.62–65, 1993
K.Kanazawa, N.Niki, H.Nishitani, H.Satoh, H.Omatsu, N.Moriyama, “Computer Assisted Diagnosis of Lung Cancer Using Helical X-ray CT”, IEEE Workshop on Biomédical Image Analysis, Seattle, pp.261–267, 1994
K.Kanazawa, M.Kubo, N.Niki, H.Satoh, H.Omatsu, N.Moriyama, “Computer-Assisted Lung Cancer Diagnosis Based on Helical CT Images”, Computer Assisted Radiology, Berlin, pp.369–374, 1995
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© 1995 Springer-Verlag Berlin Heidelberg
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Kanazawa, K. et al. (1995). Computer assisted lung cancer diagnosis based on helical images. In: Chin, R.T., Ip, H.H.S., Naiman, A.C., Pong, TC. (eds) Image Analysis Applications and Computer Graphics. ICSC 1995. Lecture Notes in Computer Science, vol 1024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60697-1_118
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DOI: https://doi.org/10.1007/3-540-60697-1_118
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