Automatic contour detection by encoding knowledge into active contour models

An original method for an automatic detection of contours in difficult images is proposed. This method is based on a tight cooperation between a multi-resolution neural network and a hidden Markov model-enhanced dynamic programming procedure. This new method is able to overcome the three major drawbacks of the "standard" active contours, initialization dependency, exclusive use of local information and occlusion sensitivity. The driving idea is to introduce high-order a priori information in each step of the system. An application to the automatic detection of the left ventricle in digital X-ray images is proposed.