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.