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29 April 2005 Expectation maximization approach to vessel enhancement in thoracic CT scans
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Vessel enhancement in volumetric data is a necessary prerequisite in various medical imaging applications. In the context of automated lung nodule detection in thoracic CT scans, segmented blood vessels can be used to resolve local ambiguities based on global considerations and so improve the performance of lung nodule detection algorithms. Segmenting the data correctly is a difficult problem with direct consequences for subsequent processing steps. Voxels belonging to vessels and nodules in thoracic CT scans are both characterized by high contrast with respect to a local neighborhood. Thus in order to enhance vessels while suppressing nodules, additional characteristics should be used. In this paper we propose a novel vessel enhancement filter that is capable of enhancing vessels and junctions in thoracic CT scans while suppressing nodules. The proposed filters are based on a Gaussian mixture model which is optimized through expectation maximization. The proposed filters are based on first order differential quantities and so are less sensitive to noise compared with known Hessian-based vessel enhancement filters. Moreover, the proposed filters utilize an adaptive window and so avoid the common need for multiple scale analysis. The proposed filters are evaluated and compared to known techniques qualitatively and quantitatively on both synthetic and actual clinical data and it is shown that the proposed filters perform better.
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Gady Agam and Changhua Wu "Expectation maximization approach to vessel enhancement in thoracic CT scans", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005);

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