Paper
27 March 2009 A fast quantum mechanics based contour extraction algorithm
Author Affiliations +
Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72594C (2009) https://doi.org/10.1117/12.811319
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tian Lan, Yangguang Sun, and Mingyue Ding "A fast quantum mechanics based contour extraction algorithm", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72594C (27 March 2009); https://doi.org/10.1117/12.811319
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Cited by 2 scholarly publications.
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KEYWORDS
Particles

Quantum mechanics

Medical imaging

Monte Carlo methods

Digital imaging

Image segmentation

Lithium

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