改进的二维最小卡方散度图像分割方法

Improved image segmentation based on 2-D minimum chi-square-divergence

传统的交叉熵阈值法具有抗噪性能差,计算时间长等问题。为了改进算法的性能,提出了一种二维最小卡方散度图像阈值化分割新准则,构建了基于改进中值滤波的新型二维直方图。利用对称卡方散度描述分割前后图像之间的差异程度。使用关键阈值对滤波图像进行分割,达到最佳的分割效果。实验结果表明,与二维Otsu和二维最小交叉熵法相比,提出的方法不仅大大缩短了分割时间,而且分割性能与抗噪性能更强。

Traditional cross-entropy thresholding method is sensitive to noise and with much longer computation time. In order to improve the performance of this algorithm, a novel image thresholding segmentation criteria based on 2-D minimum chi-square divergence is proposed. A novel 2-D histogram based on improved median filter is structured. The difference between the segmented image and the original one is measured by the symmetric chi-square divergence. The neighborhood image is segmented with the key threshold to obtain better segmentation effects. Experimental results demonstrate that the proposed method’s computing time is much less, and its segmentation effect and anti-noise are better than 2-D minimum cross entropy and 2-D Otsu.