Automated characterization of normal and pathologic lung tissue by topological texture analysis of multidetector CT

H. F. Boehm; C. Fink; C. Becker; M. Reiser

doi:10.1117/12.702697

29 March 2007 Automated characterization of normal and pathologic lung tissue by topological texture analysis of multidetector CT

H. F. Boehm, C. Fink, C. Becker, M. Reiser

Author Affiliations +

Proceedings Volume 6514, Medical Imaging 2007: Computer-Aided Diagnosis; 65140N (2007) https://doi.org/10.1117/12.702697
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

Reliable and accurate methods for objective quantitative assessment of parenchymal alterations in the lung are necessary for diagnosis, treatment and follow-up of pulmonary diseases. Two major types of alterations are pulmonary emphysema and fibrosis, emphysema being characterized by abnormal enlargement of the air spaces distal to the terminal, nonrespiratory bronchiole, accompanied by destructive changes of the alveolar walls. The main characteristic of fibrosis is coursening of the interstitial fibers and compaction of the pulmonary tissue. With the ability to display anatomy free from superimposing structures and greater visual clarity, Multi-Detector-CT has shown to be more sensitive than the chest radiograph in identifying alterations of lung parenchyma. In automated evaluation of pulmonary CT-scans, quantitative image processing techniques are applied for objective evaluation of the data. A number of methods have been proposed in the past, most of which utilize simple densitometric tissue features based on the mean X-ray attenuation coefficients expressed in terms of Hounsfield Units [HU]. Due to partial volume effects, most of the density-based methodologies tend to fail, namely in cases, where emphysema and fibrosis occur within narrow spatial limits. In this study, we propose a methodology based upon the topological assessment of graylevel distribution in the 3D image data of lung tissue which provides a way of improving quantitative CT evaluation. Results are compared to the more established density-based methods.

Citation Download Citation

H. F. Boehm, C. Fink, C. Becker, and M. Reiser "Automated characterization of normal and pathologic lung tissue by topological texture analysis of multidetector CT", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65140N (29 March 2007); https://doi.org/10.1117/12.702697

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