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Proceedings Paper

A novel multiscale topo-morphometric approach for separating arteries and veins via pulmonary CT imaging
Author(s): Punam K. Saha; Zhiyun Gao; Sara Alford; Milan Sonka; Eric Hoffman
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Paper Abstract

Distinguishing arterial and venous trees in pulmonary multiple-detector X-ray computed tomography (MDCT) images (contrast-enhanced or unenhanced) is a critical first step in the quantification of vascular geometry for purposes of determining, for instance, pulmonary hypertension, using vascular dimensions as a comparator for assessment of airway size, detection of pulmonary emboli and more. Here, a novel method is reported for separating arteries and veins in MDCT pulmonary images. Arteries and veins are modeled as two iso-intensity objects closely entwined with each other at different locations at various scales. The method starts with two sets of seeds -- one for arteries and another for veins. Initialized with seeds, arteries and veins grow iteratively while maintaining their spatial separation and eventually forming two disjoint objects at convergence. The method combines fuzzy distance transform, a morphologic feature, with a topologic connectivity property to iteratively separate finer and finer details starting at a large scale and progressing towards smaller scales. The method has been validated in mathematically generated tubular objects with different levels of fuzziness, scale and noise. Also, it has been successfully applied to clinical CT pulmonary data. The accuracy of the method has been quantitatively evaluated by comparing its results with manual outlining. For arteries, the method has yielded correctness of 81.7% at the cost of 6.7% false positives and 11.6% false negatives. Our method is very promising for automated separation of arteries and veins in MDCT pulmonary images even when there is no mark of intensity variation at conjoining locations.

Paper Details

Date Published: 27 March 2009
PDF: 10 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725910 (27 March 2009); doi: 10.1117/12.811339
Show Author Affiliations
Punam K. Saha, Univ. of Iowa (United States)
Zhiyun Gao, Univ. of Iowa (United States)
Sara Alford, Univ. of Iowa (United States)
Milan Sonka, Univ. of Iowa (United States)
Eric Hoffman, Univ. of Iowa (United States)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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