27 February 2009 Detection and classification of interstitial lung diseases and emphysema using a joint morphological-fuzzy approach
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Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 726031 (2009) https://doi.org/10.1117/12.811730
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Multi-detector computed tomography (MDCT) has high accuracy and specificity on volumetrically capturing serial images of the lung. It increases the capability of computerized classification for lung tissue in medical research. This paper proposes a three-dimensional (3D) automated approach based on mathematical morphology and fuzzy logic for quantifying and classifying interstitial lung diseases (ILDs) and emphysema. The proposed methodology is composed of several stages: (1) an image multi-resolution decomposition scheme based on a 3D morphological filter is used to detect and analyze the different density patterns of the lung texture. Then, (2) for each pattern in the multi-resolution decomposition, six features are computed, for which fuzzy membership functions define a probability of association with a pathology class. Finally, (3) for each pathology class, the probabilities are combined up according to the weight assigned to each membership function and two threshold values are used to decide the final class of the pattern. The proposed approach was tested on 10 MDCT cases and the classification accuracy was: emphysema: 95%, fibrosis/honeycombing: 84% and ground glass: 97%.
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Kuang-Che Chang Chien, Kuang-Che Chang Chien, Catalin Fetita, Catalin Fetita, Pierre-Yves Brillet, Pierre-Yves Brillet, Françoise Prêteux, Françoise Prêteux, Ruey-Feng Chang, Ruey-Feng Chang, } "Detection and classification of interstitial lung diseases and emphysema using a joint morphological-fuzzy approach", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726031 (27 February 2009); doi: 10.1117/12.811730; https://doi.org/10.1117/12.811730

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