基于MICO+FCM的MRI脑组织分割

针对MRI中存在的强度不均匀问题以及颅骨组织对于脑部组织提取所造成的影响,为了解决对特定脑部组织的研究问题,提出一个MICO+FCM脑组织分割算法。算法首先利用基于MICO的能量最小化算法对脑部MRI进行强度不均匀性估计和矫正,并且通过该算法完成对图像的初步分割,然后通过区域生长算法对图像中的颅骨组织进行去除,再利用FCM算法完成脑部组织中脑白质和脑灰的分割提取。通过仿真表明,相对于传统FCM算法及其它图像分割算法,提出的MICO+FCM脑组织分割算法在分割准确率和分割效率上均有所提升。

MRI Brain Tissue Segmentation Based on MICO+FCM

In view of the intensity inhomogeneity problem in MRI and the influence of skull tissue on brain tissue extraction,in order to solve the research problem of specific brain tissue,this paper proposes a MICO+FCM brain tissue segmentation algorithm.Firstly,the MICO-based energy minimization algorithm was used to estimate and correct the intensity non-uniformity of the brain MRI,and the initial segmentation of the image is completed by the algorithm,and then the skull tissue in the image was removed by the region growing algorithm.The FCM algorithm was used to complete the segmentation and extraction of white matter and brain gray in brain tissue.Simulation experiments show that compared with the traditional FCM algorithm and other image segmentation algorithms,the MICO+FCM brain segmentation algorithm proposed in this paper has improved segmentation accuracy and segmentation efficiency.