基于信息熵和改进粒子群算法的医学图像分割方法研究

针对传统的医学图像分割算法存在组织边缘模糊、灰度不均匀和图像噪声高的问题,将信息熵和改进的粒子群算法相结合,提出了一种基于信息熵和改进的粒子群算法的医学图像分割方法,在确保信息熵最大的条件下,实现医学图像的最佳阈值分割.将信息熵最大化作为适应度函数,通过改进的粒子群算法优化获得最佳分割门限,实现医学图像的最佳阈值分割.选择不合噪声和含噪声的脑部图像为研究对象,通过直观分析、客观分析和分割速度分析发现,提出的新方法在很大程度上克服了传统医学图像分割算法存在的缺陷,分割速度和精度得到显著提升;与此同时,新的算法具有很强的鲁棒性和抗噪声能力.

Medical Image Segmentation Method Based on Information Entropy and Improved Particle Swarm Algorithm

In view of the problems of dge blur, uneven gray level and high image noise presenting in traditional medical image segmentation algorithm, a medical image segmentation method was put forward based on combining the information entropy and the improved particle swarm algorithm, thus the best threshold for the medical image segmentation can be realized under the condition of ensuring the maximum information entropy. Using the maximum information entropy as the fitness function, optimal threshold segmentation of medical image was achieved by improving the particle swarm algorithm to optimize the optimal segmentation threshold. Choosing the brain images with noise or not as the research objects, intuitive and objective analysis and segmentation speed analysis indicate that, the proposed new method overcomes the defects existing in traditional segmentation algorithm to a large extent, thus the segmentation speed and accuracy are improved significantly. And also, the new algorithm has strong robustness and the ability of resisting noise.