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Pattern Recognition of Head MRI Images and It's 3-D Display

头部MRI图像的模式识别及其3D显示

基本信息

批准号：
01580032
负责人：
NIKI Noboru
金额：
$ 1.09万
依托单位：
The University of Tokushima
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1989
资助国家：
日本
起止时间：
1989 至 1991
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-01580032/
关键词：
Head multichannel MR images Fuzzy clustering Connectivity of organs Integrated method of images Volume rendering Parallel processing Recognition support system 3-D fabrication of organs 頭部マルチチャンネルMR画像線形統合と非線形統合大脳基底核や視床下部脳血管網 3次元

项目摘要

We proposed a pattern recognition of multichannel MR images and it's 3-D display method for supporting diagnosis and surgical plannning. The effectiveness was shown using various types of multichannel MR images. 3-D display of MR images allows better perception of the continuity of structures and spatial relationships than a display of individual slices. But a boundary judgement of soft tissues, like tumor, is difficult even for expert doctor. Proper classification of normal or abnormal soft tissues is sought. We use multiple 3-D MR images of the same anatomical section using different pulse sequences to improve the accuracy of tissue extraction. The extraction methods proposed here consist of nonlinear combination methods of multichannel MR images a fuzzy clustering of the mapped images and a connectivity algorithm of soft tissue clusters of interest. The shading of soft tissue cluster uses a volume rendering. The shading model is improved to realize a powerfully interactive 3-D display. PD-weighted images. TI-weighted images. T2-weighted images and MRA images of human brain are numerically analyzed. Organs of interest are 3-D displayed and 3-D fabricated. A complicated discrimination of tumor, blood vessels, white matter, gray matter, CSF and spatial relationships among the human brain can be recognized for supporting diagnosis and surgical planning.

本文提出了一种多通道MR图像的模式识别及其三维显示方法，为临床诊断和手术计划提供了支持。使用各种类型的多通道MR图像显示的有效性。MR图像的3-D显示允许比单个切片的显示更好地感知结构的连续性和空间关系。但软组织的边界判断，如肿瘤，是很困难的，即使是专家医生。寻求正常或异常软组织的正确分类。我们使用同一解剖切片的多个3-D MR图像，使用不同的脉冲序列来提高组织提取的准确性。本文提出的提取方法包括多通道MR图像的非线性组合方法、映射图像的模糊聚类和感兴趣软组织簇的连通性算法。软组织簇的着色使用体积渲染。改进了光照模型，实现了强大的交互式三维显示。PD加权图像。TI加权图像。对人脑的T2加权像和MRA像进行了数值分析。感兴趣的器官是3D显示和3D制造的。可以识别肿瘤、血管、白色物质、灰质、CSF和人脑之间的空间关系的复杂区分，以支持诊断和手术计划。