【摘要】 针对DRLSE水平集模型对噪声敏感、依赖初始轮廓位置以及演化速度缓慢等不足,利用小波变换和小波阈值去噪的方法,构造对噪声不敏感的边缘信息刻画矩阵,定义基于图像信息的边缘停止函数和自适应权重系数,获得了改进的DRLSE水平集图像分割模型。利用有限差分法对模型求解,并采用Jaccard相似度作为评价模型的定量分析方法,数值结果显示改进的模型及算法对图像分割的有效性,克服了DRLSE水平集模型分割含噪图像以及定义初始轮廓位置的局限性,提高了DRLSE水平集模型的计算效率和图像分割精度。更多还原

【Abstract】 Aiming at the fact that the DRLSE level set model is inadequately sensitive to noise and dependent on the initial contour and slow evolution we used wavelet transform and wavelet threshold denoising methods. A new edge stop function and adaptive weight coefficient based on image information are defined by constructing the edge characterization matrix which is not sensitive to noise. An improved DRLSE level set image segmentation model is thus obtained. The finite difference method is employed to solve the model, and Jaccard similarity is used as the quantitative analysis method of evaluation model. The numerical results show that the improved model and algorithm are effective for image segmentation, overcoming the limitation of DRLSE level set model and dividing the noisy image and defining the initial contour position, which improve the computational efficiency and image segmentation precision of the DRLSE level set model.更多还原