Automatic tumor segmentation using knowledge-based techniques
Automatic tumor segmentation using knowledge-based techniques
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DOI：
10.1109/42.700731
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发表时间：
1998-04-01
影响因子：
10.6
通讯作者：
Silbiger, MS
中科院分区：
文献类型：
--
作者：
Clark, MC;Hall, LO;Silbiger, MS
提出了一种在人脑磁共振图像（MRI）中自动分割和标记多形性胶质母细胞瘤的系统。 MRI 由 T1 加权、质子密度和 T2 加权特征图像组成，并由集成了基于知识 (KB) 技术与多光谱分析的系统进行处理。初始分割由无监督聚类算法执行，分割后的图像以及每个类别的聚类中心被提供给基于规则的专家系统，该系统提取颅内区域。多光谱直方图分析将可疑肿瘤与颅内区域的其余部分分开，区域分析用于执行最终的肿瘤标记。该系统已经在三个体积数据集上进行了训练，并在从单个 MRI 系统获取的 13 个看不见的体积数据集上进行了测试。将 KB 肿瘤分割与监督的、放射科医生标记的“真实”肿瘤体积和监督的 k-最近邻肿瘤分割进行比较。该系统的结果通常与地面真实情况非常吻合，无论是在每个切片的基础上，还是更重要的是在随着时间的推移跟踪治疗期间的总肿瘤体积方面。
A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRI's) of the human brain is presented. The MRI's consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge-based (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering algorithm, The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intracranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intracranial region, with region analysis used in performing the final tumor labeling. This system has been trained on three volume data sets and tested on thirteen unseen volume data sets acquired from a single MRI system. The KB tumor segmentation was compared with supervised, radiologist-labeled "ground truth" tumor volumes and supervised k-nearest neighbors tumor segmentations. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.