Intensity inhomogeneity and noises often occur in real medical images, which present a large degree of challenge to image segmentation. At the same time, most of the existing image segmentation algorithms are sensitive to initial conditions and model parameters. This paper presents an accurate and robust active contour model to solve the above problems. Inspired by the idea of the region-scalable fitting (RSF) model, we first define a local atlas fitting term transformed by the segmentation contour of the coherent local intensity clustering (CLIC) model. Then, we define a new energy functional by merging the atlas term into the energy functional of the RSF model. The advantage of this operation is that it makes full use of the existing segmentation features and advantages of the two models and avoids cumbersome adjustment of model parameters and initial contours. The experimental results clearly show that the improved model not only has better segmentation results than the RSF model and other active contour models such as the LINC, REGAC and SMAP models, but also solves the problem of sensitivity to initial contours, parameters adjustment and noise.

强度不均匀和噪声经常出现在真实的医学图像中，这对图像分割提出了很大的挑战。同时，大多数现有的图像分割算法对初始条件和模型参数敏感。本文提出了一种精确而鲁棒的主动轮廓模型来解决上述问题。受区域可缩放拟合（RSF）模型的启发，我们首先定义一个由相干局部强度聚类（CLIC）模型的分割轮廓转换而成的局部图谱拟合项。然后，通过将图集项合并到RSF模型的能量函数中，定义一个新的能量函数。此操作的优点是，它可以充分利用两个模型的现有分割特征和优点，并且避免了模型参数和初始轮廓的繁琐调整。实验结果清楚地表明，改进的模型不仅具有比RSF模型和其他有效轮廓模型（如LINC，REGAC和SMAP模型）更好的分割效果，而且还解决了对初始轮廓，参数调整和噪声的敏感性问题。