Objective

2

In existing image segmentation theories and methods

the fuzzy clustering segmentation method has been widely studied and applied in image segmentation and object detection in images with intrinsic fuzziness and uncertainty. Fuzzy C-means clustering and its related improvement is a typical unsupervised clustering learning method

which has become an important tool in solving problems in image processing

machine vision

and remote sensing image interpretation. Many relevant scholars

local and abroad

have paid high attention to this research field for a long time. The classical fuzzy C-means clustering applied in image segmentation has only considered the clustering problem of pixels and cannot overcome the influence of noise on image segmentation results and thus cannot meet the requirements for target extraction

recognition

and interpretation of industrial

medical

and high-resolution remote sensing images. Hence

a robust fuzzy C-means clustering segmentation algorithm that is embedded in pixel spatial neighborhood or local information is a popular topic in image segmentation theory in recent years. The existing robust kernel-based fuzzy C-means clustering segmentation algorithms cannot meet the requirement of fast and robust segmentation of large images with strong interferences

such as Gaussian

salt-and-pepper

and mixed noise due to the large time cost and weak ability to suppress the aforementioned noise. Hence

a fast robust kernel-based fuzzy C-means clustering segmentation algorithm

which is based on a two-dimensional histogram

is proposed in view of pixel clustering accumulation.

Method

2

The linear weighted filtering image is first obtained by combining the gray and spatial location information of neighboring pixels with other related information in the segmenting gray image. The weighted filtering image is segmented by the existing robust kernel-based fuzzy C-means clustering to obtain positive segmentation quality and strong robustness against Gaussian

salt-and-pepper

and mixed noise. Second

to improve the operational efficiency of the robust fuzzy clustering segmentation algorithm in further segmenting large images

the two-dimensional histogram between the current clustered pixel and mean value of its neighborhood pixels is introduced into the existing robust kernel-based fuzzy C-means clustering method with spatial information. The two-dimensional histogram-based fuzzy clustering optimization mathematical model of the robust kernel-based fuzzy C-means clustering method is constructed in view of pixel clustering accumulation. In summary

the iterative expression of the gray level robust fuzzy clustering segmentation algorithm to segment image is rapidly deduced by the Lagrange multiplier method to solute the new optimization model of the robust kernel-based fuzzy clustering segmentation method embedded in the two-dimensional histogram of the weighted filtered image. Meanwhile

a fast robust kernel-based fuzzy C-means clustering segmentation in the two-dimensional histogram is designed to implement image segmentation.

Result

2

Segmentation results of the noiseless standard medical and large remote sensing images that are interrupted by Gaussian

salt-and-pepper

and mixed noise

show that the proposed fast algorithm in the two-dimensional histogram can obtain better segmentation performance than those of the existing robust kernel-based fuzzy C-means and other kernel-based fuzzy clustering segmentation algorithms. In detail

the proposed fast robust kernel-based fuzzy clustering segmentation algorithm has increased the peak signal-to-noise ratio by nearly 1.5 dB

reduced at least 5% of the misclassification rate

and increased approximately 10% of the partition coefficient compared with the existing robust kernel-based fuzzy clustering segmentation algorithms with spatial neighborhood information. In the meantime

the method can reduce the computational complexity to a considerable extent

and the running speed of the proposed robust kernel-based fuzzy clustering segmentation algorithm is similar to that of the classical kernel-based fuzzy C-means clustering segmentation method based on a one-dimensional histogram. Furthermore

the result of the proposed fast robust kernel-based fuzzy C-means clustering segmentation algorithm has better segmentation effects than those of the existing kernel-based fuzzy C-means clustering segmentation algorithms from the perspective of human visual perception.

Conclusion

2

Compared with the existing robust kernel-based fuzzy clustering segmentation algorithm

which is based on spatial constraints of neighborhood information

the kernel-based fuzzy C-means and classical fuzzy clustering segmentation algorithms and the proposed algorithm with spatial neighborhood formation not only have stronger ability against noise but has also improved segmentation performance and real time. Meanwhile

it has a positive effect on rapid interpretation of images

such as large high resolution remote sensing and medical images. The proposed algorithm can meet the requirements of large remote sensing and medical image segmentation with high real-time demands.