Automated contour detection in X-ray left ventricular angiograms using multiview active appearance models and dynamic programming

• Published in: IEEE transactions on medical imaging 2006-09, Vol.25 (9), p.1158-1171
• Main Authors: Oost, E., Koning, G., Sonka, M., Oemrawsingh, P.V., Reiber, J.H.C., Lelieveldt, B.P.F.
• Format: Article
• Language: English
• Subjects: Active appearance model; Active appearance models (AAMs); Algorithms; Angiography; Angiography - methods; Artificial Intelligence; automatic left ventricle segmentation; Biomedical imaging; Dynamic programming; Heart Ventricles - diagnostic imaging; Humans; image analysis; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image segmentation; Information Storage and Retrieval - methods; multiple views; Pattern Recognition, Automated - methods; Radiology; Reproducibility of Results; Sensitivity and Specificity; Studies; Ventricular Dysfunction, Left - diagnostic imaging; X-ray angiography; X-ray detection; X-ray detectors; X-ray imaging
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2006.877094

T his paper describes a new approach to the automated segmentation of X-ray left ventricular (LV) angiograms, based on active appearance models (AAMs) and dynamic programming. A coupling of shape and texture information between the end-diastolic (ED) and end-systolic (ES) frame was achieved by constructing a multiview AAM. Over-constraining of the model was compensated for by employing dynamic programming, integrating both intensity and motion features in the cost function. Two applications are compared: a semi-automatic method with manual model initialization, and a fully automatic algorithm. The first proved to be highly robust and accurate, demonstrating high clinical relevance. Based on experiments involving 70 patient data sets, the algorithm's success rate was 100% for ED and 99% for ES, with average unsigned border positioning errors of 0.68 mm for ED and 1.45 mm for ES. Calculated volumes were accurate and unbiased. The fully automatic algorithm, with intrinsically less user interaction was less robust, but showed a high potential, mostly due to a controlled gradient descent in updating the model parameters. The success rate of the fully automatic method was 91% for ED and 83% for ES, with average unsigned border positioning errors of 0.79 mm for ED and 1.55 mm for ES