Abstract
Honeycombing is a disease pattern seen in High-Resolution Computed Tomography which allows a confident diagnosis of a number of diseases involving fibrosis of the lung. An accurate quantification of honeycombing allows radiologists to determine the progress of the disease process. Previous techniques commonly applied a classifier over the whole lung image to detect lung pathologies. This resulted in spurious classifications of honeycombing in regions where the presence of honeycombing was highly improbable. In this paper, we present a novel technique which uses a seeded region growing algorithm to guide the classifier to regions with potential honeycombing. We show that the proposed technique improves the accuracy of the honeycombing detection. The technique was tested using ten-fold cross validation on forty two images over eight different patients. The proposed technique classified regions of interests with an accuracy of 89.7%, sensitivity of 96.6% and a specificity of 88.6%.
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References
Webb, W.R., Müller, N.L., Naidich, D.P.: High-Resolution CT of the Lung, 3rd edn. Lippincott Williams & Wilkins, Philadelphia (2001)
Uppaluri, R., Hoffman, E.A., Sonka, M., Hartley, P.G.: Computer Recognition of Regional Lung Disease Patterns. American Journal of Respiratory and Critical Care Medicine 160(2), 648–654 (1999)
Uchiyama, Y., Katsuragawa, S., Abe, H., Shiraishi, J., Li, F., Li, Q., Zhang, C.T., Suzuki, K., Doi, K.: Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Medical Physics 30(9), 2440–2454 (2003)
Papasoulis, J.: LMIK — Anatomy and Lung Measurements using Active Contour Snakes. Undergraduate thesis, Computer Science and Engineering, University of New South Wales, Sydney, Australia (2003)
Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: ICML 2000: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 359–366. Morgan Kaufmann, San Francisco (2000)
Liu, H., Setiono, R.: A Probabilistic Approach to Feature Selection – A Filter Solution. In: Proceedings of the 13th Intl. Conf. Machine Learning, pp. 319–327 (1996)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Adams, R., Bischof, L.: Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(6), 641–647 (1994)
Zrimec, T., Busayarat, S., Wilson, P.: A 3D model of the human lung with lung regions characterization. In: Proc. IEEE Int. Conf. on Image Processing, ICIP 2004, vol. 2, pp. 1149–1152 (2004)
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© 2006 Springer-Verlag Berlin Heidelberg
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Wong, J.S.J., Zrimec, T. (2006). Classification of Lung Disease Pattern Using Seeded Region Growing. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_27
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DOI: https://doi.org/10.1007/11941439_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
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