聚类分析和活动轮廓模型相融合的图像分割算法

针对当前图像分割算法存在的分割误差大、 分割时间长及无法进行在线图像分割的不足, 提出一种基于聚类分析和活动轮廓模型相融合的图像分割算法. 首先, 对原始图像进行去噪处理, 采用聚类分析算法对原始图像进行粗分割, 将粗分割结果作为活动轮廓模型的初始轮廓线; 其次, 将活动轮廓模型根据初始轮廓线对图像不同区域轮廓进行拟合, 实现图像的精细分割； 最后与聚类分析算法、 活动轮廓模型以及当前经典图像分割算法进行对比测试实验. 实验结果表明, 本文算法克服了当前图像分割算法存在的缺陷, 提高了图像分割效率和精度, 对噪声不敏感, 并具有较强的鲁棒性, 图像整体分割效果显著优于对比算法.

Image Segmentation Algorithm Based on Combination ofClustering Analysis and Active Contour Model

In order to overcome the shortcomings of current imagesegmentation algorithms, such as large segmentation error and long segmentationtime and inability to segment online image, we proposed an image segmentation algorithm based on combination of clustering analysis and active contour model. Firstly, the original image was denoised and roughlysegmented by clustering analysis algorithm. The roughly segmented result was taken as the initial contour line of the active contour model. Secondly,the active contour model was used to fit the contour of different regions of the image according to the initial contour line to realize the fine segmentation of the image. Finally, it wascompared with clustering analysis algorithm, active contour model and the current classical image segmentation algorithm. The experimental results show thatthe proposed algorithm overcomes the shortcomings of the current image segmentation algorithm, improves the efficiency and accuracy of imagesegmentation, it is insensitive to noise and has strong robustness. The overall image segmentation effect is significantly better than that of the contrast algorithm.