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29 March 2007 Efficient detection of diffuse lung disease
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Automated methods of detecting lung disease typically involve the following: 1) Subdividing the lung into small regions of interest (ROIs). 2) Calculating the features of these small ROIs. 3) Applying a machine learnt classifier to determine the class of each ROI. When the number of features that need to be calculated is large, as in the case of filter bank methods or in methods calculating a large range of textural properties, the classification can run quite slowly. This is even more noticeable when a number of disease patterns are considered. In this paper, we investigate the possibility of using a cascade of classifiers to concentrate the processing power on promising regions. In particular, we focused on the detection of the honeycombing disease pattern. We used knowledge of the appearance and the distribution of honeycombing to selectively classify ROIs. This avoids the need to explicitly classify all ROIs in the lung; making the detection process more effcient. We evaluated the performance of the system over 42 HRCT slices from 8 different patients and show that the system performs the task of detecting honeycombing with a high degree of accuracy (accuracy = 86.2%, sensitivity = 90.0%, specificity = 82.2%).
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James S. J. Wong and Tatjana Zrimec "Efficient detection of diffuse lung disease", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65140Q (29 March 2007);

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