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首页> 外文会议>IEEE Signal Processing in Medicine and Biology Symposium >Identifying mild traumatic brain injury patients from MR images using bag of visual words
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Identifying mild traumatic brain injury patients from MR images using bag of visual words

机译：使用视觉文字袋从MR图像中识别轻度颅脑损伤患者

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摘要

Mild traumatic brain injury (mTBI) is a growing public health problem with an estimated incidence of one million people annually in US. Neurocognitive tests are used to both assess the patient condition and to monitor the patient progress. This work aims to directly use MR images taken shortly after injury to detect whether a patient suffers from mTBI, by incorporating machine learning and computer vision techniques to learn features suitable discriminating between mTBI and normal patients. We focus on 3 regions in brain, and extract multiple patches from them, and use bag-of-visual-word technique to represent each subject as a histogram of representative patterns derived from patches from all training subjects. After extracting the features, we use greedy forward feature selection, to choose a subset of features which achieves highest accuracy. We show through experimental studies that BoW features perform better than the simple mean value features which were used previously.
机译：轻度创伤性脑损伤（mTBI）是一个日益严重的公共健康问题，在美国，每年估计有100万人发病。神经认知测试既可以用来评估患者的病情，也可以用来监测患者的病情。这项工作旨在通过结合机器学习和计算机视觉技术来学习适合区分mTBI和正常患者的特征，从而直接使用受伤后不久拍摄的MR图像来检测患者是否患有mTBI。我们专注于大脑的3个区域，并从它们中提取多个补丁，并使用视觉袋技术将每个主题表示为从所有训练主题的补丁中衍生出的典型模式直方图。提取特征后，我们使用贪婪前向特征选择来选择能获得最高准确度的特征子集。通过实验研究，我们显示BoW特征的性能优于以前使用的简单均值特征。

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