Image segmentation is still an open problem especially when intensities of the objects of interest are overlapped due to the presence of intensity inhomogeneities. A bias correction embedded level set model is proposed in this paper where inhomogeneities are estimated by orthogonal primary functions. First, an inhomogeneous intensity clustering energy is defined based on global distribution characteristics of the image intensities, and membership functions of the clusters described by the level set function are then introduced to define the data term energy of the proposed model. Second, a regularization term and an arc length term are also included to regularize the level set function and smooth its zero-level set contour, respectively. Third, the proposed model is extended to multichannel and multiphase patterns to segment colorful images and images with multiple objects, respectively. Experimental results and comparison with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy on widely used synthetic and real images and the BrainWeb and the IBSR image repositories.

中文翻译：

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基于全局不均匀强度聚类（GINC）的主动轮廓模型，用于图像分割和偏差校正。

图像分割仍然是一个未解决的问题，特别是当目标对象的强度由于强度不均匀性的存在而重叠时。本文提出了一种偏差校正嵌入水平集模型，该模型通过正交基函数估计不均匀性。首先，基于图像强度的全局分布特征定义不均匀的强度聚类能量，然后引入由水平集函数描述的聚类的隶属函数，以定义所提出模型的数据项能量。其次，还包括正则项和圆弧长度项，以分别对水平集函数进行正则化并平滑其零水平集轮廓。第三，提出的模型扩展到多通道和多相模式，以分别分割彩色图像和具有多个对象的图像。实验结果以及与相关模型的比较证明了该模型在广泛使用的合成图像和真实图像以及BrainWeb和IBSR图像存储库上的偏差校正和分割精度方面的优势。