Local Binary Fitting Segmentation by Cooperative Quantum Particle Optimization
Abstract: Recently,
sophisticated segmentation techniques, such as level set method, which using
valid numerical calculation methods to process the evolution of the curve by
solving linear or nonlinear elliptic equations to divide the image availably,
has become being more popular and effective. In Local Binary Fitting (LBF)
algorithm, a simple contour is initialized in an image and then the
steepest-descent algorithm is employed to constrain it to minimize the fitting
energy functional. Hence, the initial position of the contour is difficult or
impossible to be well chosen for the final performance. To overcoming this
drawback, this work treats the energy fitting problem as a meta-heuristic
optimization algorithm and imports a varietal particle swarm optimization (PSO)
method into the inner optimization process. The experimental results of
segmentations on medical images show that the proposed method is not only
effective to both simple and complex medical images with adequate stochastic
effects, but also shows the accuracy and high efficiency.
Keywords: local binary
fitting; segmentation; particle swarm optimization; Lévy flights; active
contour
Author: Desheng Li, Qian He,
Liu Chunli, Yu Hongjie
Journal Code: jptkomputergg170019
