Multi-scale feature extraction for learning-based classification of coronary artery stenosis

Matthias Tessmann; Fernando Vega-Higuera; Dominik Fritz; Michael Scheuering; Günther Greiner

doi:10.1117/12.811639

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27 February 2009 Multi-scale feature extraction for learning-based classification of coronary artery stenosis

Matthias Tessmann, Fernando Vega-Higuera, Dominik Fritz, Michael Scheuering, Günther Greiner

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Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 726002 (2009) https://doi.org/10.1117/12.811639
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Assessment of computed tomography coronary angiograms for diagnostic purposes is a mostly manual, timeconsuming task demanding a high degree of clinical experience. In order to support diagnosis, a method for reliable automatic detection of stenotic lesions in computed tomography angiograms is presented. Thereby, lesions are detected by boosting-based classification. Hence, a strong classifier is trained using the AdaBoost algorithm on annotated data. Subsequently, the resulting strong classification function is used in order to detect different types of coronary lesions in previously unseen data. As pattern recognition algorithms require a description of the objects to be classified, a novel approach for feature extraction in computed tomography angiograms is introduced. By generation of cylinder segments that approximate the vessel shape at multiple scales, feature values can be extracted that adequately describe the properties of stenotic lesions. As a result of the multi-scale approach, the algorithm is capable of dealing with the variability of stenotic lesion configuration. Evaluation of the algorithm was performed on a large database containing unseen segmented centerlines from cardiac computed tomography images. Results showed that the method was able to detect stenotic cardiovascular diseases with high sensitivity and specificity. Moreover, lesion based evaluation revealed that the majority of stenosis can be reliable identified in terms of position, type and extent.

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Matthias Tessmann, Fernando Vega-Higuera, Dominik Fritz, Michael Scheuering, and Günther Greiner "Multi-scale feature extraction for learning-based classification of coronary artery stenosis", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726002 (27 February 2009); https://doi.org/10.1117/12.811639

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