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9 May 1997 Automatic assessment of small airways disease with computed tomography
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Abstract
Bronchiolar obstruction is commonly manifested in computed tomographic (CT) images as areas of decreased attenuation relative to adjacent normal lung parenchyma. The certain identification of such areas is difficult in practice, particularly is such areas are poorly marginated. This paper presents a novel approach to the enhancement of feature differences between normal and diseased lung parenchyma so that reliable visual assessment can be made. The method relies on a hybrid structural filtering technique which removes pulmonary vessels appearing in the CT cross- sectional images without affecting intrinsic subtle intensity details of the lung parenchyma. In order to restore possible structural distortions introduced by the hybrid filter, a feature localization process based on wavelet reconstruction of feature extrema is used. After contrast enhancement the resultant images are used to delineate region borders of the diseased areas and quantification is made with regard to the extent of the disease. Clinically, the proposed technique is especially valuable for presymptomatic cases in which direct visual assessment of the unprocessed images by different observers often yields inconsistent results.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Guang-Zhong Yang, Michael Rubens, and David M. Hansell "Automatic assessment of small airways disease with computed tomography", Proc. SPIE 3033, Medical Imaging 1997: Physiology and Function from Multidimensional Images, (9 May 1997); https://doi.org/10.1117/12.274030
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