Automatic Segmentation and Classification of Multiple Sclerosis in Multichannel MRI
Automatic Segmentation and Classification of Multiple Sclerosis in Multichannel MRI
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DOI：
10.1109/tbme.2008.926671
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发表时间：
2009-10-01
影响因子：
4.6
通讯作者：
Brandt, Achi
中科院分区：
文献类型：
--
作者：
Akselrod-Ballin, Ayelet;Galun, Meirav;Brandt, Achi
我们介绍了一种多尺度的方法，结合分割与分类，以检测医学图像中的异常脑结构，并证明其实用程序在自动检测多发性硬化症（MS）病变的3-D多通道磁共振（MR）图像。我们的方法使用分割来获得多通道各向异性MR扫描的分层分解。然后，它产生一组丰富的功能，描述段的强度，形状，位置，邻域关系和解剖背景。然后，这些特征被输入决策森林分类器，由专家标记的数据进行训练，从而能够检测所有尺度的病变。与使用逐体素分析的常见方法不同，我们的系统可以利用区域特性，这些特性对于表征异常的大脑结构通常很重要。我们提供了两种类型的真实的MR图像上的实验：25名MS患者的多通道质子密度、T2和T1加权数据集和16名MS患者的单通道液体衰减反转恢复（FLAIR）数据集。将我们的结果与人类专家的病变描绘和先前广泛验证的结果进行比较，显示了该方法的前景。
We introduce a multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery, and demonstrate its utility in automatically detecting multiple sclerosis (MS) lesions in 3-D multichannel magnetic resonance (MR) images. Our method uses segmentation to obtain a hierarchical decomposition of a multichannel, anisotropic MR scans. It then produces a rich set of features describing the segments in terms of intensity, shape, location, neighborhood relations, and anatomical context. These features are then fed into a decision forest classifier, trained with data labeled by experts, enabling the detection of lesions at all scales. Unlike common approaches that use voxel-by-voxel analysis, our system can utilize regional properties that are often important for characterizing abnormal brain structures. We provide experiments on two types of real MR images: a multichannel proton-density-, T2-, and T1-weighted dataset of 25 MS patients and a single-channel fluid attenuated inversion recovery (FLAIR) dataset of 16 MS patients. Comparing our results with lesion delineation by a human expert and with previously extensively validated results shows the promise of the approach.