School of Electrical and Computer Engineering, Cornell University, Phillips Hall, Ithaca, NY 14853-5401, USA.
Comput Math Methods Med. 2013;2013:619658. doi: 10.1155/2013/619658. Epub 2013 Jun 6.
Differentiating lymphomas and glioblastomas is important for proper treatment planning. A number of works have been proposed but there are still some problems. For example, many works depend on thresholding a single feature value, which is susceptible to noise. In other cases, experienced observers are required to extract the feature values or to provide some interactions with the system. Even if experts are involved, interobserver variance becomes another problem. In addition, most of the works use only one or a few slice(s) because 3D tumor segmentation is time consuming. In this paper, we propose a tumor classification system that analyzes the luminance distribution of the whole tumor region. Typical cases are classified by the luminance range thresholding and the apparent diffusion coefficients (ADC) thresholding. Nontypical cases are classified by a support vector machine (SVM). Most of the processing elements are semiautomatic. Therefore, even novice users can use the system easily and get the same results as experts. The experiments were conducted using 40 MRI datasets. The classification accuracy of the proposed method was 91.1% without the ADC thresholding and 95.4% with the ADC thresholding. On the other hand, the baseline method, the conventional ADC thresholding, yielded only 67.5% accuracy.
区分淋巴瘤和胶质母细胞瘤对于制定正确的治疗计划非常重要。已经提出了许多方法，但仍然存在一些问题。例如，许多方法依赖于对单个特征值进行阈值处理，这容易受到噪声的影响。在其他情况下，需要有经验的观察者来提取特征值或与系统进行一些交互。即使有专家参与，观察者之间的差异也会成为另一个问题。此外，大多数方法只使用一个或几个切片，因为 3D 肿瘤分割非常耗时。在本文中，我们提出了一种肿瘤分类系统，该系统分析整个肿瘤区域的亮度分布。典型病例通过亮度范围阈值和表观扩散系数（ADC）阈值进行分类。非典型病例通过支持向量机（SVM）进行分类。大多数处理元素都是半自动的。因此，即使是新手用户也可以轻松使用该系统，并获得与专家相同的结果。实验使用了 40 个 MRI 数据集。所提出方法的分类准确率在没有 ADC 阈值的情况下为 91.1%，在有 ADC 阈值的情况下为 95.4%。另一方面，基线方法，即传统的 ADC 阈值处理，仅产生 67.5%的准确率。
