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Purpose: Automated image analysis tools have the potential to improve the objectivity of the diagnostic process. The study and improvement of the numerical methodologies behind these tools is, therefore, crucial. Volumetric, densitometric, and fractal analysis concepts were, thus, explored in the setting of computed tomography (CT) imaging of different lung morphologies. Material and methods: Thoracic CT scans were acquired for five sheep prior to and after smoke inhalation injury. Software was developed to segment the lungs from the digital image data and to estimate the morphometric parameters “volume”, “Hounsfield unit-density” (HU), and “fractal dimension”. These parameters were estimated for each scan, once from the complete dataset, covering the entire a range of -1000 to 399 HU, and once for 28 consecutive data subsets, with a width of 50 HU each. T-test statistics were used to investigate group differences “before” and “after” smoke inhalation, based on a 0.05 significance level. Results: For the complete data set, group differentiation into “before” and “after” smoke in-halation was feasible only with volumetric analysis. Analysis of 28 smaller HU subsets, on the other hand, allowed group differentiation with all three morphometric parameters. Conclusions: The analysis of small HU subsets can be helpful in differentiating groups and may be a useful approach for many image analysis projects. Correspondence to: Dr. Martin Obert, Justus-Liebig-University Giessen, Department of Radiology, University Hospital Giessen, Klinikstrasse 33; D-35392 Gießen; Germany, Tel: 049 (+641) 985-41836; Fax: 049 (+641) 98541809; E-mail: martin.obert@radiol.med.uni-gießen.de

Detailed investigation of volumetric analysis, densitometry, and fractal geometry based concepts in computer aided medical diagnoses