A clustering segmentation algorithm based on an improved K-means clustering method is used to improve the efficiency and accuracy of 3d medical image segmentation.
为提高三维医学数据场的分割效率和准确率，本文利用特征聚类技术，提出了一种新的基于改进K - means聚类的三维医学数据场的体分割算法。
Firstly, RAC and K-means clustering method are combined in this algorithm by the way of searching pre-matches feature points, which are called the cluster point set, of the unknown model.
此算法首先结合RAC和K -均值聚类方法对未知模型的特征点进行预匹配，得到的匹配结果称为聚类点集。
The function model of the multiquadric's central node to choose to do an in-depth discussion, put forward the "Adaptive location" to match the characteristics of the K-means clustering method.
对函数模型法中的多面函数中心节点的选择做了深入讨论，提出了具有“位置自适应”匹配特点的K均值聚类法。
The clustering method based on partitioning is mainly included K-Means and K-Medoids; the other methods are the mutation of these two methods.
基于划分的聚类算法主要有K均值和K中心点算法，其他的方法都是这两种算法的变种。
After analyzing the traditional clustering algorithms, the paper presents a new clustering ensemble method based on K-means to cluster data.
本文在分析传统聚类算法的基础上，提出了一种聚类融合算法。
A new algorithm method of image enhancement based on K-means clustering is presented, according to the characteristics of infrared gray-image.
根据红外灰度图像的特点，提出了一种基于K -均值聚类的图像增强的新算法。
A new algorithm method of image enhancement based on K-means clustering is presented, according to the characteristics of infrared gray-image.
根据红外灰度图像的特点，提出了一种基于K -均值聚类的图像增强的新算法。
应用推荐