Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a world health problem with high morbidity and mortality. High-Resolution Computed Tomography (HRCT), is an excellent tool for early detection of emphysema component of COPD. Despite this fact, HRCT presents limitations inherent to the subjective analysis of the gray scale image that directly compromises the accuracy for both diagnosis and precise determination of the disease extension. The objective of this paper is present a colored mask algorithm (CMA) to identify and quantify the emphysema, enhancing its visualization through pseudocolors. We studied 21 images of 7 patients with COPD and 1 healthy volunteer. The CMA applies colors to the segmented lungs according to pre-defined ranges of Hounsfield units. CMA automatically calculates the relative area occupied by tomographic densities within the pre-defined ranges, allowing precise quantification of diseased and normal parenchyma. Future works are needed in order to validate the incorporation of the CMA in the image assessment of emphysema in COPD patients.
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da Silva Felix, J.H., Cortez, P.C., Filho, P.P.R., de Alexandria, A.R., Costa, R.C.S., Holanda, M.A. (2008). Identification and Quantification of Pulmonary Emphysema through Pseudocolors. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_90
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DOI: https://doi.org/10.1007/978-3-540-88636-5_90
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