To improve the noise immunity and universality of the fuzzy C-means clustering segmentation algorithm based on a two-dimensional histogram

we propose a weighted fuzzy C-means clustering segmentation method on the basis of a dimensional histogram. The threshold parameter selection inherent in the fuzzy C-means clustering segmentation algorithm based on a two-dimensional histogram leads to poor noise immunity. This issue is addressed in this work with the introduction of weighting properties for the weighted fuzzy C-means clustering segmentation method based on a two-dimensional histogram. This approach is an effective solution for each dimension of the attributes of the poly problem class contribution. Compared with the algorithm based on a two-dimensional histogram

the proposed algorithm shows an average increase of 2 dB to 3 dB in its salt and pepper and Gaussian noise immunity. The same is true for the proposed algorithm when compared with the C-means clustering segmentation algorithm based on fuzzy local information. In the latter comparison

the proposed method reduces its anti-Gaussian noise to less than 1 dB and is 40 times slower than the C-means clustering segmentation algorithm based on fuzzy local information. The proposed method more effectively addresses noisy image segmentation requirements compared with the existing fuzzy C-means clustering algorithm based on a two-dimensional histogram. Moreover

the proposed method is more applicable in target tracking occasions and identification than the fuzzy C-means clustering algorithm based on fuzzy local information.At the same time

a large number of tests proved that the proposed algorithm is suitable for the synthetic images

intelligent traffic images and remote sensing image.