Active contour models have been widely used for image segmentation purposes. However, they may fail to delineate objects of interest depicted on images with intensity inhomogeneity. To resolve this issue, a novel image feature, termed as local edge entropy, is proposed in this study to reduce the negative impact of inhomogeneity on image segmentation. An active contour model is developed on the basis of this feature, where an edge entropy fitting (EEF) energy is defined with the combination of a redesigned regularization term. Minimizing the energy in a variational level set formulation can successfully drive the motion of an initial contour curve towards optimal object boundaries. Experiments on a number of test images demonstrate that the proposed model has the capability of handling intensity inhomogeneity with reasonable segmentation accuracy.

中文翻译：

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边缘熵拟合能量驱动的主动轮廓用于图像分割

活动轮廓模型已广泛用于图像分割目的。然而，它们可能无法描绘强度不均匀的图像上描绘的感兴趣对象。为了解决这个问题，本研究提出了一种新的图像特征，称为局部边缘熵，以减少不均匀性对图像分割的负面影响。在此特征的基础上开发了一个活动轮廓模型，其中边缘熵拟合 (EEF) 能量与重新设计的正则化项的组合一起定义。最小化变分水平集公式中的能量可以成功地驱动初始轮廓曲线向最佳对象边界运动。在大量测试图像上的实验表明，所提出的模型能够以合理的分割精度处理强度不均匀性。