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 if 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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
R. Haralick, S. Sternberg, and X. Zhuang, “Image Analysis Using Mathematical Morphology,” IEEE Trans Pattern Analysis and Machine Intelligence, vol. 9, no. 4, pp. 691–696, 1987.
J. Serra, Image Analysis and Mathematical Morphology: Theoretical Advances, vol. 2. New York: Academic Press, 1988.
S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989.
S. Mallat and S. Zhong, “Characterization of signals from multiscale edges,” IEEE Trans Pattern Analysis and Machine Intelligence, vol. 14, pp. 71–732, 1992.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, GZ., Hansell, D.M. (1997). A hybrid approach for the detection of small airways disease from Computed Tomographic images. In: Duncan, J., Gindi, G. (eds) Information Processing in Medical Imaging. IPMI 1997. Lecture Notes in Computer Science, vol 1230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63046-5_41
Download citation
DOI: https://doi.org/10.1007/3-540-63046-5_41
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63046-3
Online ISBN: 978-3-540-69070-2
eBook Packages: Springer Book Archive