Voxel-Wise Displacement as Independent Features in Classification of Multiple Sclerosis.
Voxel-Wise Displacement as Independent Features in Classification of Multiple Sclerosis.
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体素位移作为多发性硬化症分类中的独立特征。
DOI：
10.1117/12.2007150
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Prince,JerryL
中科院分区：
文献类型：
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作者：
Chen,Min;Carass,Aaron;Reich,DanielS;Calabresi,PeterA;Pham,Dzung;Prince,JerryL
我们提出了一种利用配准位移场对从健康个体和诊断为多发性硬化症 (MS) 的患者获取的大脑磁共振图像 (MRI) 进行精确分类的方法。与标准方法相反，位移场中的每个体素被视为单独分类的独立特征。结果表明，当与简单的线性判别式和多数投票一起使用时，该方法优于使用位移场和单个分类器，即使与更复杂的分类方法（例如自适应增强、随机森林和支持向量机）相比也是如此。留一法交叉验证用于评估这种按疾病、MS 亚型 (Acc: 77%–88%) 和年龄 (Acc: 96%–100%) 对图像进行分类的方法。
We present a method that utilizes registration displacement fields to perform accurate classification of magnetic resonance images (MRI) of the brain acquired from healthy individuals and patients diagnosed with multiple sclerosis (MS). Contrary to standard approaches, each voxel in the displacement field is treated as an independent feature that is classified individually. Results show that when used with a simple linear discriminant and majority voting, the approach is superior to using the displacement field with a single classifier, even when compared against more sophisticated classification methods such as adaptive boosting, random forests, and support vector machines. Leave-one-out cross-validation was used to evaluate this method for classifying images by disease, MS subtype (Acc: 77%–88%), and age (Acc: 96%–100%).