KEYWORDS: Particles, Quantum mechanics, Medical imaging, Mechanics, Quantum information, Polymers, Monte Carlo methods, Systems modeling, Statistical modeling, Digital filtering
Contour extraction is a key issue in many medical applications. A novel statistical approach based on quantum mechanics
to extract contour of the interested object of medical images was proposed in this paper. The natures of quantum
statistical concepts such as the quantum discontinuity and the wave function correspond to the discrete and gray
possibility of an image respectively. Contour extraction will be performed by the quantum particle movement, where the
particle will be moved forward to the position with high probability density edges in image potential field. Experimental
results with medical images demonstrated that the proposed approach has the capability to extract contours with arbitrary
initialization and handle topology changes as well as both the inner and outer contours by a single initialization.
Keywords: Statistical approach, contour extraction, path integral, quantum mechanics.ing
KEYWORDS: Particles, Quantum mechanics, Medical imaging, Monte Carlo methods, Digital imaging, Image segmentation, Mechanics, Lithium, Evolutionary algorithms, Systems modeling
A fast algorithm was proposed to decrease the computational cost of the contour extraction approach based on quantum mechanics. The contour extraction approach based on quantum mechanics is a novel method proposed recently by us, which will be presented on the same conference by another paper of us titled "a statistical approach to contour extraction based on quantum mechanics". In our approach, contour extraction was modeled as the locus of a moving particle
described by quantum mechanics, which is obtained by the most probable locus of the particle simulated in a large
number of iterations. In quantum mechanics, the probability that a particle appears at a point is equivalent to the square
amplitude of the wave function. Furthermore, the expression of the wave function can be derived from digital images,
making the probability of the locus of a particle available. We employed the Markov Chain Monte Carlo (MCMC) method to estimate the square amplitude of the wave function. Finally, our fast quantum mechanics based contour extraction algorithm (referred as our fast algorithm hereafter) was evaluated by a number of different images including synthetic and medical images. It was demonstrated that our fast algorithm can achieve significant improvements in accuracy and robustness compared with the well-known state-of-the-art contour extraction techniques and dramatic reduction of time complexity compared to the statistical approach to contour extraction based on quantum mechanics.
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