Entropy-based spatially-varying adjustment of active contour parameters

Parameter adjustment is a crucial, open issue in active contour methodology. Most state-of-the-art active contours are empirically adjusted on a trial and error basis. Such an empirical approach lacks scientific foundation, leads to suboptimal segmentation results and requires technical skills from the end-user. This work introduces a method for automatic adjustment of active contour parameters, which is based on image entropy. In addition, instead of being uniform, the parameter values calculated are spatially-varying, so as to reflect textural variations over the image. Experimental evaluation of the proposed method is conducted on thyroid US images, liver MRI images, as well as on real-world photographs. The results indicate that the proposed method is capable of identifying plausible object boundaries, obtaining a segmentation quality which is comparable to the one obtained with empirical parameter adjustment. Moreover, the applicability of the proposed method is not confined on a single active contour variation.