Abstract
Emphysema is characterized by the destruction and over distension of lung tissue, which manifest on high resolution computer tomography (CT) images as regions of low attenuation. Typically, it is diagnosed by clinical symptoms, physical examination, pulmonary function tests, and X-ray and CT imaging. In this paper we discuss a quantitative imaging approach to analyze emphysema which employs low-level segmentations of CT images that partition the data into perceptually relevant regions. We constructed multi-dimensional histograms of feature values computed over the image segmentation. For each region in the segmentation, we derive a rich set of feature measurements. While we can use any combination of physical and geometric features, we found that limiting the scope to two features – the mean attenuation across a region and the region area – is effective. The subject histogram is compared to a set of canonical histograms representative of various stages of emphysema using the Earth Mover’s Distance metric. Disease severity is assigned based on which canonical histogram is most similar to the subject histogram. Experimental results with 81 cases of emphysema at different stages of disease progression show good agreement against the reading of an expert radiologist.
Chapter PDF
Similar content being viewed by others
Keywords
- Histogram Analysis
- Density Index
- Computer Tomography Image
- Emphysema Severity
- Cumulative Size Distribution
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Bankier, A.A., De Maertelaer, V., Keyzer, C., Gevenois, P.A.: Pulmonary emphysema: Subjective visual grading versus objective quantification with macroscopic morphometry and thin-section ct densitometry. Radiology 211, 851–858 (1999)
Muller, Staples, Miller, Abboud: Muller, Staples, Miller, Abboud: Density mask: An objective method to quantitate emphysema using computed tomography. Chest 94, 782–787 (1988)
Mishima, M.: Fractal analysis of emphysema in X-ray, CT and simulation. Medical Imaging Technology 18, 179–186 (2000)
Coxson, Rogers, Whittal, D’Yachkova, Pare, Sciurba, Hogg: A quantification of the lung surface area in emphysema using computed tomography. American Journal of Respiratory and Critical Care in Medicine 159, 851–856 (1999)
Uppaluri, R., Hoffman, E.A., Sonka, M., Hartley, P.G., Hunninghake, G.W., McLennan, G.: Computer recognition of regional lung disease patterns. American Journal Respiratory and Critical Care in Medicine 160, 648–654 (1999)
McCulloch, C.C., Kaucik, R.A., Mendonça, P.R.S., Walter, D.J., Avila, R.S.: Model-based detection of lung nodules in computed tomography exams. Academic Radiology 11, 258–266 (2004)
Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intell. 8, 679–698 (1986)
Elder, J.H., Zucker, S.W.: Local scale control for edge detection and blur estimation. IEEE Trans. Pattern Analysis and Machine Intell. 20, 699–716 (1998)
Rothwell, C., Mundy, J., Hoffman, W., Nguyen, V.D.: Driving vision by topology. In: IEEE International Symposium on Computer Vision, pp. 395–400 (1995)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. Journal of Computer Vision 40, 99–121 (2000)
Sutherland, W.A.: Introduction to Metric and Topological Spaces. Clarendon Press, Oxford (1975) (reprint)
Cohen, J.: A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20, 37–46 (1960)
Cohen, J.: Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin 70, 213–220 (1968)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mendonça, P.R.S., Padfield, D.R., Ross, J.C., Miller, J.V., Dutta, S., Gautham, S.M. (2005). Quantification of Emphysema Severity by Histogram Analysis of CT Scans. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_91
Download citation
DOI: https://doi.org/10.1007/11566465_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29327-9
Online ISBN: 978-3-540-32094-4
eBook Packages: Computer ScienceComputer Science (R0)