A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI.
A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI.
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DOI：
10.1109/tip.2011.2146190
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发表时间：
2011-07
期刊：
影响因子：
--
通讯作者：
Gore JC
中科院分区：
文献类型：
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作者：
Li C;Huang R;Ding Z;Gatenby JC;Metaxas DN;Gore JC
灰度不均匀性是图像分割中的一个难题。最广泛使用的图像分割算法是基于区域的，并且通常依赖于感兴趣区域中的图像强度的均匀性，由于强度不均匀性，其通常不能提供准确的分割结果。本文提出了一种新的基于区域的图像分割方法，它能够处理分割中的强度不均匀性。首先，基于灰度不均匀图像模型，推导出图像灰度的局部灰度聚类性质，并定义了图像灰度在每个点邻域内的局部聚类准则函数。该局部聚类准则函数被结合到邻域中心，给出一个全局的图像分割准则。在水平集公式中，该标准根据表示图像域的分区的水平集函数和考虑图像的强度不均匀性的偏置场来定义能量。因此，通过最小化该能量，我们的方法能够同时分割图像和估计偏置场，并且估计的偏置场可以用于强度不均匀性校正（或偏置校正）。我们的方法已在合成图像和各种模态的真实的图像上进行了验证，在存在强度不均匀性的情况下具有理想的性能。实验结果表明，该方法对初始化的鲁棒性更强，比著名的分段光滑模型更快，更准确。作为一个应用程序，我们的方法已被用于分割和磁共振（MR）图像的偏差校正与有前途的结果。
Intensity inhomogeneity often occurs in real-world images, which presents a considerable challenge in image segmentation. The most widely used image segmentation algorithms are region-based and typically rely on the homogeneity of the image intensities in the regions of interest, which often fail to provide accurate segmentation results due to the intensity inhomogeneity. This paper proposes a novel region-based method for image segmentation, which is able to deal with intensity inhomogeneities in the segmentation. First, based on the model of images with intensity inhomogeneities, we derive a local intensity clustering property of the image intensities, and define a local clustering criterion function for the image intensities in a neighborhood of each point. This local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. In a level set formulation, this criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, by minimizing this energy, our method is able to simultaneously segment the image and estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction (or bias correction). Our method has been validated on synthetic images and real images of various modalities, with desirable performance in the presence of intensity inhomogeneities. Experiments show that our method is more robust to initialization, faster and more accurate than the well-known piecewise smooth model. As an application, our method has been used for segmentation and bias correction of magnetic resonance (MR) images with promising results.