Decoupled active contour (DAC) optimization using wavelet edge detection and curvature based resampling

Locating an accurate desired object boundary using active contours and deformable models plays an important role in computer vision, particularly in medical imaging applications. Powerful segmentation methods have been introduced to address limitations associated with initialization and poor convergence to boundary concavities. This paper proposes a method to improve one of the strongest and recent segmentation methods, called decoupled active contour (DAC). Here we apply Wavelet edge detection on the image which cause it to have more contrast to have more information about edges, followed by an optimum updating on the measurements using Hidden Markov Model (HMM) and the Viterbi search as an efficient solver. In order to have a more accurate boundary at each iteration more points are injected in the high curvature parts based on the snake curvature so we will have more precision in these part and also flat parts.