Key Laboratory of Intelligent Computing in Medical Image (MIIC), Ministry of Education, Shenyang, Liaoning 110169, China.
Key Laboratory of Medical Image Computing (MIC), Shenyang, Liaoning 110169, China.
Comput Math Methods Med. 2020 Jun 1;2020:7595174. doi: 10.1155/2020/7595174. eCollection 2020.
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.
图像分割仍然是一个开放的问题，特别是当目标物体的强度由于强度不均匀而重叠时。本文提出了一种嵌入偏置校正的水平集模型，其中不均匀性由正交基本函数估计。首先，基于图像强度的全局分布特征，定义了不均匀强度聚类能量，然后引入由水平集函数描述的聚类隶属函数，以定义所提出模型的数据项能量。其次，还包括正则化项和弧长项，以分别正则化水平集函数和平滑其零水平集轮廓。第三，将所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。实验结果和与相关模型的比较表明，在所使用的广泛的合成和真实图像以及 BrainWeb 和 IBSR 图像库上，所提出的模型在偏置校正和分割准确性方面具有优势。
