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首页> 外文会议>International conference on advanced data mining and applications >3-D MRI Brain Scan Feature Classification Using an Oct-Tree Representation
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3-D MRI Brain Scan Feature Classification Using an Oct-Tree Representation

机译：使用八叉树表示的3-D MRI脑扫描特征分类

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摘要

This paper presents a procedure for the classification of specific 3-D features in Magnetic Resonance Imaging (MRI) brain scan volumes. The main contributions of the paper are: (ⅰ) a proposed Bounding Box segmentation technique to extract the 3-D features of interest from MRI volumes, (ⅱ) an oct-tree technique to represent the extracted sub-volumes and (ⅲ) a frequent sub-graph mining based feature space mechanism to support classification. The proposed process was evaluated using 210 3-D MRI brain scans of which 105 were from "healthy" people and 105 from epilepsy patients. The features of interest were the left and right ventricles. Both the process and the evaluation are fully described. The results indicate that the proposed process can be effectively used to classify 3-D MRI brain scan features.
机译：本文介绍了在磁共振成像（MRI）脑部扫描量中对特定3-D特征进行分类的过程。该论文的主要贡献是：（ⅰ）一种拟议的边界框分割技术，用于从MRI体积中提取感兴趣的3-D特征;（ⅱ）八叉树技术来代表提取的子体积;以及（ⅲ）a基于频繁子图挖掘的特征空间机制来支持分类。使用210幅3-D MRI脑部扫描评估了拟议的过程，其中105幅来自“健康”人群，而105幅来自癫痫患者。感兴趣的特征是左心室和右心室。完整描述了过程和评估。结果表明，所提出的过程可以有效地用于对3-D MRI脑部扫描特征进行分类。

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