On active contour models and balloons
CVGIP: Image Understanding
ACM Computing Surveys (CSUR)
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
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An automatic and markerless tracking method of deformable structures (digestive organs) during laparoscopic cholecystectomy intervention that uses the (PSO) behavour and the preoperative a priori knowledge is presented The associated shape to the global best particles of the population determines a coarse representation of the targeted organ (the gallbladder) in monocular laparoscopic colored images The swarm behavour is directed by a new fitness function to be optimized to improve the detection and tracking performance The function is defined by a linear combination of two terms, namely, the human a priori knowledge term (H) and the particle's density term (D) Under the limits of standard (PSO) characteristics, experimental results on both synthetic and real data show the effectiveness and robustness of our method Indeed, it outperforms existing methods without need of explicit initialization (such as active contours, deformable models and Gradient Vector Flow) on accuracy and convergence rate.