利用局部离散度活动轮廓模型的强度非均匀图像分割

强度非均匀现象在真实图像中普遍存在，采用常规基于强度的分割算法会导致严重的误分割。针对强度非均匀图像分割，提出了基于局部离散度的活动轮廓模型分割算法。首先定义基于类内类间距离的离散度，然后利用核函数提取局部区域信息，同时加入边缘指示函数加权的轮廓线长度项能量，建立基于局部离散度的活动轮廓模型。最后引入水平集函数惩罚项，避免水平集方法在演化求解时需要不断初始化的问题。合成图像和真实图像实验结果证明本文算法性能稳定，适应于强度非均匀图像的分割。 

Local Dispersion Active Contour Model for Image Segmentation with Intensity Inhomogeneity

Image segmentation is an important procedure in image processing and computer vision, active contour model methods have been widely used in image segmentation. Intensity inhomogeneities often occur in real-world images, and it may lead to serious misclassifications by intensity-based segmentation algorithms that assume a uniform intensity. In order to overcome the difficulties, a local dispersion-based active contour model for image segmentation is proposed. Firstly, the dispersion energy is defined in terms of the within-class distance and between-class distance. Secondly, with a kernel function, the dispersion information in local regions is extracted to establish the local dispersion-based active contour model, and a curve length energy term that weights by an edge indicator function is also incorporated into the novel model. Finally, a penalty term is added to avoid reinitializing periodically during the evolution of the level set method. Experimental results for both synthetic images and real images show desirable performances of the proposed method.