SVM for density estimation and application to medical image segmentation
SVM for density estimation and application to medical image segmentation
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用于密度估计的 SVM 及其在医学图像分割中的应用
DOI：
10.1631/jzus.2006.b0365
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Yazhu Chen
中科院分区：
文献类型：
--
作者：
Zhao Zhang;Su Zhang;Chen;Yazhu Chen
提出了一种基于支持向量机密度估计的医学图像分割方法。我们使用该估计器从训练图像中构造结构的图像强度和曲率轮廓的先验模型。在分割与训练图像相似的新图像时，采用了窄水平集方法。用先验模型代替能量最小化函数来定义高维表面演化度量。这种方法有几个优点。首先，SVM密度估计是一致的，它的解决方案是稀疏的。其次，与传统的水平集方法相比，该方法在分割过程中加入了待分割对象的形状信息。在合成图像、MR图像和超声图像上进行了分割实验。
A method of medical image segmentation based on support vector machine (SVM) for density estimation is presented. We used this estimator to construct a prior model of the image intensity and curvature profile of the structure from training images. When segmenting a novel image similar to the training images, the technique of narrow level set method is used. The higher dimensional surface evolution metric is defined by the prior model instead of by energy minimization function. This method offers several advantages. First, SVM for density estimation is consistent and its solution is sparse. Second, compared to the traditional level set methods, this method incorporates shape information on the object to be segmented into the segmentation process. Segmentation results are demonstrated on synthetic images, MR images and ultrasonic images.