论文标题
3DPIFCM新型算法用于分割嘈杂的脑MRI图像
3DPIFCM Novel Algorithm for Segmentation of Noisy Brain MRI Images
论文作者
论文摘要
我们提出了一种名为3DPIFCM的新型算法，用于自动分割嘈杂的MRI脑图像。该算法是众所周知的IFCM（改善模糊C均值）算法的扩展。它执行模糊分割，并引入了一个适应性函数，该功能受体素邻近和3D图像中的颜色强度的影响。 3DPIFCM算法使用PSO（粒子群优化）来优化健身函数。此外，3DPIFCM使用近体素的3D特征来更好地调整噪声。在我们的实验中，我们在T1 BhainWeb数据集上评估了3DPIFCM的噪声水平范围从1％到20％，并且在3D中都具有地面真相的合成数据集上。分割结果的分析表明，与原始FCM相比，分割质量高达28％的分割质量高达28％，而与原始FCM相比（模糊C-Means）相比，分割质量高达28％。
We present a novel algorithm named 3DPIFCM, for automatic segmentation of noisy MRI Brain images. The algorithm is an extension of a well-known IFCM (Improved Fuzzy C-Means) algorithm. It performs fuzzy segmentation and introduces a fitness function that is affected by proximity of the voxels and by the color intensity in 3D images. The 3DPIFCM algorithm uses PSO (Particle Swarm Optimization) in order to optimize the fitness function. In addition, the 3DPIFCM uses 3D features of near voxels to better adjust the noisy artifacts. In our experiments, we evaluate 3DPIFCM on T1 Brainweb dataset with noise levels ranging from 1% to 20% and on a synthetic dataset with ground truth both in 3D. The analysis of the segmentation results shows a significant improvement in the segmentation quality of up to 28% compared to two generic variants in noisy images and up to 60% when compared to the original FCM (Fuzzy C-Means).
