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

Abstract

This 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.

Generalized Transformed Hough: Segmentation of Contours in Angiographic Images

“…The acquisition time average is around 8 to 10 s, covering 7 to 9 cardiac cycles. The distribution of the injected contrast media is considered optimal around the second or third cardiac cycle [4].…”

2D and 3D analysis of animal locomotion from biplanar X-ray videos using augmented active appearance models

“…Therefore, in our locomotion scenario, it is generally not possible to apply any of the methods mentioned above, as they rely on certain assumptions about the scene. However, it is still possible to exploit relationships between multiple camera views using multi-view AAMs [43,44], as shown in [30].…”

“…In addition, all parts of interest of an animal must remain in the field of view during the whole sequence, which generally implies the use of a treadmill. As the appearance of the animal is modeled using multi-view AAMs [43,44] (cf. Subsection 2.3), the camera setup must remain static during a recording.…”

Scale-Independent Spatio-Temporal Statistical Shape Representations for 3d Human Action Recognition

“…This was pointed out to be a generalization of the multi-view integration approach of (Lelieveldt et al, 2003) and (Oost et al, 2006). Instead of considering a skeleton configuration at a single time step t 0 to obtain the model parameters, they regard a series of sequential time steps t 0 < t 1 < .…”