摘要
传统的高斯混合模型对于含有噪声的图像不能进行有效的分割。针对有噪声图像的分割问题,提出了一种基于狄利克雷分布和参数分析的高斯混合模型图像分割算法。首先采用高斯函数对像素计算先验概率值,然后采用狄利克雷分布和定律关联像素间的邻域信息,并利用梯度下降法优化参数。实验结果表明,本文算法对无噪声和有噪声图像的分割结果比传统方法更有效,误分率更低。
With the development of image processing field,more and more effective image segmentation algorithms,in medical image processing,brain and blood vessel magnetic resonance image segmentation,lung nodules automatic detection and tumors detection have been recently proposed.This paper will consider the brain MRI segmentation problems.It is worthy of noting that although many traditional clustering algorithms of medical image segmentation like K-means,Fuzzy C-means（FCM）and general Gaussian mixture model can effectively deal with these medical images without noise,they cannot perform well in noisy medical images which often exist in original medical image processing.Therefore,it is quite necessary and actually required for researching the image segmentation algorithm with noise elimination.To this end,this paper propses an image segmentation method based on Gaussian mixture model with Dirichlet distribution and parameter analysis,which can effectively denoise the medical images.Firstly,Gaussian function is adopted to calculate pixels＇ prior probabilities.Dirichlet distribution and Dirichlet law are utilized to construct the local spatial information among pixels.And then,the gradient descent method is used to optimize the loss function and update model parameters at the same time.Finally,the probability of each pixel for each label can be obtained and the pixel＇s highest prediction probability in all labels can be selected as the clustering result.This paper performs four clustering segmentation algorithms on normal creatures and brain MR images with Gaussian noise and salt pepper noise.The experiment results show that image segmentation on normal creatures with three and four labels have a good result to cluster pixels which especially belong to background.Also,the proposed algorithm used to segment brain MR images with Gaussian noise and salt pepper noise is more precisely and clearer than other three traditional algorithms.In conclusion,the experiment results show that the proposed method is better than the
作者
赖嘉伟
朱宏擎
LAI Jia-wei;ZHU Hong-qing(School of Information Science and Engineering,East China University Science and Technology,Shanghai 200237,China)
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2018年第3期418-424,共7页
Journal of East China University of Science and Technology
基金
国家自然科学基金(61371150)
关键词
高斯混合模型
邻域信息
狄利克雷参数
参数分析
Gaussian mixture model
neighborhood information
Dirichlet parameters
parameters analysis
