Maiora J, Papakostas G A, Kaburlasos V G, Grana M
Electronic Technology Department-EUP, University of the Basque Country, San Sebastian, Spain, e-mail:
Human-Machines Interaction (HMI) Laboratory, Department of Computer and Informatics Engineering, Eastern Macedonia and Thrace Institute of Technology, Kavala, Greece, e-mail: {gpapak, vgkabs}@teikav.edu.gr.
Stud Health Technol Inform. 2014;207:311-20.
Our objective is to create an interactive image segmentation system of the abdominal area for quick volumetric segmentation of the aorta requiring minimal intervention of the human operator. The aforementioned goal is to be achieved by an Active Learning image segmentation system over enhanced image texture features, obtained from the standard Gray Level Co-occurrence Matrix (GLCM) and the Local Binary Patterns (LBP). The process iterates the following steps: first, image segmentation is produced by a Random Forest (RF) classifier trained on a set of image texture features for labeled voxels. The human operator is presented with the most uncertain unlabeled voxels to select some of them for inclusion in the training set, retraining the RF classifier. The approach will be applied to the segmentation of the thrombus in Computed Tomography Angiography (CTA) data of Abdominal Aortic Aneurysm (AAA) patients. A priori knowledge on the expected shape of the target structures is used to filter out undesired detections. On going preliminary experiments on datasets containing diverse number of CT slices (between 216 and 560), each one consisting a real human contrast-enhanced sample of the abdominal area, are underway. The segmentation results obtained with simple image features were promising and highlight the capacity of the used texture features to describe the local variation of the AAA thrombus and thus to provide useful information to the classifier.
我们的目标是创建一个腹部区域的交互式图像分割系统，用于快速进行主动脉的体积分割，且只需操作人员进行最少干预。上述目标将通过主动学习图像分割系统来实现，该系统基于从标准灰度共生矩阵（GLCM）和局部二值模式（LBP）获得的增强图像纹理特征。该过程迭代以下步骤：首先，由在一组用于标记体素的图像纹理特征上训练的随机森林（RF）分类器生成图像分割。向操作人员展示最不确定的未标记体素，以便他们选择其中一些纳入训练集，然后重新训练RF分类器。该方法将应用于腹主动脉瘤（AAA）患者的计算机断层血管造影（CTA）数据中的血栓分割。利用关于目标结构预期形状的先验知识来滤除不需要的检测结果。正在对包含不同数量CT切片（216至560之间）的数据集进行初步实验，每个数据集均包含腹部区域的真实人体对比增强样本。使用简单图像特征获得的分割结果很有前景，并突出了所使用的纹理特征描述AAA血栓局部变化的能力，从而为分类器提供有用信息。
