Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation
Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation
复制标题
DOI：
10.1016/j.cviu.2010.09.007
复制
发表时间：
2011-02-01
影响因子：
4.5
通讯作者：
Zhu, Yuemin
中科院分区：
文献类型：
--
作者：
Zhang, Nan;Ruan, Su;Zhu, Yuemin
本文提出了一个框架的医学图像分析系统的脑肿瘤分割和脑肿瘤的后续行动，随着时间的推移，使用多光谱MRI图像。脑肿瘤在形状和外观以及强度方面具有很大的多样性。多光谱图像的优势在于提供补充信息，以解决某些模糊性。然而，它们也可能带来沿着大量冗余信息，增加数据处理时间和分割误差。如何有效地利用多光谱图像是一个挑战。我们融合这些数据的想法是提取最有用的特征，以最小的时间成本获得最佳分割。提出了一种结合核空间特征选择的支持向量机（SVM）分类方法。选择准则由核类可分性定义。在此基础上，提出了一种跟踪脑肿瘤演变的SVM分类框架，该框架包括以下步骤：（1）学习脑肿瘤并从患者的第一次MRI检查中选择特征;（2）使用SVM自动分割新数据中的肿瘤;（3）通过区域生长技术细化肿瘤轮廓。该系统已在真实的患者图像上进行了测试，结果令人满意。通过与专家人工痕迹和其他方法的比较，证明了该方法的有效性。(C)2010年爱思唯尔公司All rights reserved.
This paper presents a framework of a medical image analysis system for the brain tumor segmentation and the brain tumor following-up over time using multi-spectral MRI images. Brain tumors have a large diversity in shape and appearance with intensities. Multi-spectral images have the advantage in providing complementary information to resolve some ambiguities. However, they may also bring along a lot of redundant information, increasing the data processing time and segmentation errors. The challenge is how to make use of the multi-spectral images effectively. Our idea of fusing these data is to extract the most useful features to obtain the best segmentation with the least cost in time. The Support Vector Machine (SVM) classification integrated with a selection of the features in a kernel space is proposed. The selection criteria are defined by the kernel class separability. Based on this SVM classification a framework to follow up the brain tumor evolution is proposed, which consists of the following steps: (1) to learn the brain tumor and select the features from the first MRI examination of the patients; (2) to automatically segment the tumor in new data using SVM; (3) to refine the tumor contour by a region growing technique. The system has been tested on real patient images with satisfying results. The quantitative evaluations by comparing with experts' manual traces and with other approaches demonstrate the effectiveness of the proposed method. (C) 2010 Elsevier Inc. All rights reserved.