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| 基于树形聚类匹配的脑肿瘤自动分割方法 | |
| 引用本文： | 贾迪,杨金柱,张一飞,赵大哲,于戈. 基于树形聚类匹配的脑肿瘤自动分割方法[J]. 小型微型计算机系统, 2012, 33(2): 285-288 |
| 作者姓名： | 贾迪 杨金柱 张一飞 赵大哲 于戈 |
| 作者单位： | 1. 东北大学医学影像计算机教育部重点实验室,沈阳110179;辽宁工程技术大学电子与信息工程学院,辽宁葫芦岛123105 2. 东北大学医学影像计算机教育部重点实验室,沈阳,110179 3. 辽宁工程技术大学电子与信息工程学院,辽宁葫芦岛,123105 |
| 摘 要： | 提出一种基于树形聚类匹配的脑肿瘤自动分割方法.为了去除非脑组织对于脑肿瘤定位的影响,首先提出一种新的脑组织提取算法,这种算法无需完整的序列影像,可直接对三维影像数据进行分割.其次对分割后的脑组织影像进行中心定位,建立树形索引匹配结构,采用一种节点匹配算法完成粗分割,最后根据粗分割结果,采用形变模型完成精确分割.算法的特点是无需数据集的训练,能够较为准确的完成脑肿瘤的自动分割,实验结果验证了算法的实用性及可行性. |
| 关 键 词： | 脑肿瘤分割 聚类算法 C-V模型 形态学算法 形变模型 |
Method of Automatic Brain Tumor Segmentation Based on Clustering Tree Matching |
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| JIA Di , YANG Jin-zhu , ZHANG Yi-fei , ZHAO Da-zhe , YU GE. Method of Automatic Brain Tumor Segmentation Based on Clustering Tree Matching[J]. Mini-micro Systems, 2012, 33(2): 285-288 | |
| Authors: | JIA Di YANG Jin-zhu ZHANG Yi-fei ZHAO Da-zhe YU GE |
| Affiliation: | 1(Key Laboratory of Medical Image Computing of Ministry of Education,Northeast University,Shenyang 110179,China) 2(School of Electronic and Information Engineering,Liaoning Technical University,Huludao 123105,China) |
| Abstract: | In this paper,an automatic method of brain tumor segmentation is proposed based on clustering tree matching.Firstly,we raise a new algorithm of brain tissue extraction for the purpose of unless the impact of non-brain tissues.This algorithm is not needed complete sequence of images to segment the 3D data.Secondly,we locate the centre of the segmented brain images,and establish a matching structure of index tree.Coarse segmentation is completed using a node matching algorithm.Finally,according to the coarse result of segmentation,precision division is adopted from deformation model.The feature of algorithm is not requiring the training data set,and it can automatic complete the segmentation of brain tumors more accurate.The practicality and feasibility of the algorithm are verified by experimental results. |
| Keywords: | brain tumor segmentation clustering algorithm C-V model morphology algorithm deform model |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |