An fMCMC Medical Image Segmentation Algorithm

According to medical image characteristics, an fMCMC algorithm (the fuzzy entropy measurement and Markov Chain and Monte Carlo based medical image segmentation algorithm) is proposed based on stochastic Markov chain model combining with the fuzzy entropy measurement. To address these challenges by taking the complexity and uncertainty of the medical images, the fuzzy entropy edge measurement of curves is designed via fuzzy entropy to describe the features of medical image firstly. Secondly, the random sequence closed curves are generated as Markov chains by shifting probability. Following, the Monte Carlo method is utilized to simulate and accelerate the convergence of the designed image segmentation model. Lastly, the ideal boundary curve is adopted as the closed region edge with maximal edge probability value. The experiment results demonstrate the validity of the presented algorithm. Moreover, it also indicates that the segmentation algorithm has higher ability of anti-noise and can achieve accurate medical image segmentation more quickly and exactly.