Left ventricle contour detection in x-ray angiograms using multiview active appearance models

Elco Oost; Boudewijn P. F. Lelieveldt; Gerhard Koning; Milan Sonka; Johan H. C. Reiber

doi:10.1117/12.481313

15 May 2003 Left ventricle contour detection in x-ray angiograms using multi-view active appearance models

Elco Oost, Boudewijn P. F. Lelieveldt, Gerhard Koning, Milan Sonka, Johan H. C. Reiber

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.481313
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

Automatic Left Ventricle (LV) border detection in X-ray angiograms for the quantitative assessment of cardiac function has proven to be a highly challenging task. The main difficulty is segmenting the End Systolic (ES) phase, in which much of the contrast dye has been squeezed out of the LV due to contraction, resulting in poor LV definition. 2D Active Appearance Models (AAMs) have shown utility for segmenting End Diastolic (ED) angiograms, but do not perform satisfactory in individual ES angiograms. In this work, we present a new Multi-view AAM in which we exploit the existing correlation in shape and texture between ED and ES phase to steer the segmentation of both frames simultaneously. Model position and orientation remain independent, whereas appearance statistics are coupled. In addition, an AAM is presented in which the gray-value information of the inner part of the LV is not taken into account. This so-called boundary AAM is applied mainly to enhance local boundary localization performance. Both models are applied in a combined manner and are validated quantitatively. In 61 out of 70 experiments good convergence for both ED and ES segmentation was achieved, with average border positioning errors of 1.86 mm (ED) and 1.93 mm (ES).

Citation Download Citation

Elco Oost, Boudewijn P. F. Lelieveldt, Gerhard Koning, Milan Sonka, and Johan H. C. Reiber "Left ventricle contour detection in x-ray angiograms using multi-view active appearance models", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.481313

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