Deformable boundary finding in medical images by integrating gradient and region information
Deformable boundary finding in medical images by integrating gradient and region information
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DOI：
10.1109/42.544503
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发表时间：
1996-12-01
影响因子：
10.6
通讯作者：
Duncan, JS
中科院分区：
文献类型：
--
作者：
Chakraborty, A;Staib, LH;Duncan, JS
准确分割和量化结构是生物医学图像分析中的一个关键问题。传统的两种图像分割方法，基于区域的分割和边界查找，往往受到各种限制。在这里，我们提出了一种方法，它努力将这两种方法结合起来，努力形成一种对噪声和不良初始化都很健壮的统一方法。我们的方法使用格林定理来导出图像中均匀区域分类区域的边界，并将其与基于灰度梯度的边界搜索器相结合。这将边缘/形状信息的感知概念与灰度同质性结合在一起。在合成的和真实的大脑和心脏的医学图像上进行了大量的实验，以评估新的方法，结果表明，与传统的基于梯度的可变形边界搜索相比，综合方法的性能更好。此外，这种方法在几乎不增加计算开销的情况下产生了这些改进，这一优势来自于格林定理的应用。
Accurately segmenting and quantifying structures is a key issue in biomedical image analysis. The two conventional methods of image segmentation, region-based segmentation, and boundary finding, often suffer from a variety of limitations. Here we propose a method which endeavors to integrate the two approaches in an effort to form a unified approach that is robust to noise and poor initialization. Our approach uses Green's theorem to derive the boundary of a homogeneous region-classified area in the image and integrates this with a gray level gradient-based boundary finder. This combines the perceptual notions of edge/shape information with gray level homogeneity. A number of experiments were performed both on synthetic and real medical images of the brain and heart to evaluate the new approach, and it is shown that the integrated method typically performs better when compared to conventional gradient-based deformable boundary finding. Further, this method yields these improvements with little increase in computational overhead, an advantage derived from the application of the Green's theorem.