基于二维直方图的中智模糊聚类分割方法

A neutrosophic c-means clustering algorithm based on two-dimension histogram

加入邻域像素均值,对中智模糊聚类分割算法加以改进,以提高其抗噪性能。将各像素与其邻域像素均值相结合,形成二元数组,统计其出现的频次,由此构造二维直方图。通过对此二维直方图进行中智模糊聚类,实现图像分割。对标准灰度图像添加椒盐噪声和高斯噪声,用以验证改进算法的性能。视觉效果及分割图像的峰值信噪比均显示,改进算法相比原中智模糊聚类分割法具有更好的抗噪能力和分割效果。

The neutrosophic c-means clustering algorithm is improved on its anti-noise performance by adding the neighbor pixels information. A two-element array is formed by combining the value of each pixel and the mean value of its neighboring pixels, and the frequency of its emergence is constructed. The image segmentation is realized by using the neutrosophic c-means clustering algorithm based on the two dimensional histogram. The performance of the improved algorithm is validated by standard image with added salt and pepper noise and Gauss noise. Both the visual effect and the peak signal to noise ratio of the segmented image show that the improved algorithm has better anti noise ability compared to the original one.