无需初始轮廓的图像分割模型

为了增强对初始轮廓的鲁棒性并提高对灰度不均图像、噪声图像的分割效率，提出一种基于区域的活动轮廓模型。首先分别构造全局灰度拟合力与局部灰度拟合力，然后用线性组合获得模型的拟合项，并通过调整拟合力之间的权重提高模型对初始轮廓的鲁棒性，最后利用演化曲线的长度项保持曲线的光滑性。通过实验结果可以看出：与区域可变灰度拟合（RSF）模型和选择性局部或全局分割（SLGS）模型相比，所提模型的迭代步数分别减少了约57%和31%，分割时间分别减少了约62%和14%。所提模型在无需初始轮廓的情况下，不仅可以快速、准确地分割灰度不均图像和噪声图像，而且对医学图像和红外图像等一些实际应用图像也有很好的分割效果。

Image segmentation model without initial contour

In order to enhance the robustness to initial contour as well as improve the segmentation efficiency for images with intensity inhomogeneity or noise, a region-based active contour model was proposed. First, a global intensity fitting force and a local intensity fitting force were designed separately. Then, the model's fitting term was obtained by the linear combination. And the weight between the two fitting forces were adjusted to improve the robustness of the model to the initial contour. Finally, the length term of evolution curve was employed to keep the smoothness of the curve. Experimental results show that compared with Region-Scalable Fitting(RSF) model and Selective Local or Global Segmentation(SLGS) model, the proposed model has the number of iterations reduced by about 57% and 31%, and the segmentation time reduced by about 62% and 14%. The proposed model can quickly and accurately segment noisy images and images with intensity inhomogeneity without initial contour. Besides, it has good segmentation effect on some practical images such as medical images and infrared images.