Image-segmentation algorithm based on wavelet and data-driven neutrosophic fuzzy clustering

ABSTRACT

Aim to that Neutrosophic C-mean clustering segmentation does not consider the membership distribution of every sample point to different classes. Herein, an image-segmentation algorithm based on wavelet and data-driven neutrosophic fuzzy clustering is proposed. When the maximum membership value of a sample point is far greater than other membership values, the centre of the class with the maximum membership value is taken as the centre of the fuzzy class. Otherwise, the average value of the centre of the two classes with the highest and second-highest membership values is used as the centre of the fuzzy class. In the preprocessing stage, wavelet technology is used to remove noise from the processed image, and the improved Bayesian algorithm is employed to calculate the filter threshold. The experiment results for synthetic and natural images show that the proposed method is more accurate and effective than the existing methods.

KEYWORDS:

• Image segmentation
• wavelet transformation
• neutrosophic fuzzy clustering

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Jinyu Wen is currently pursuing his master’s degree at the Guangxi University for Nationalities, Nanning, China. His main research interests include image processing and pattern recognition.

Shibin Xuan received his PhD in computer science and technology from Sichuan University, Chengdu, China, in 2011. He is currently a full professor and master’s supervisor in the School of Information Science and Engineering, Guangxi University for Nationalities, Nanning, China. His main research interests include image processing and pattern recognition.

Yuqi Li is currently pursuing his master’s degree at the School of Information Engineering, Nanchang Hangkong University, Nanchang, China. His main research interests include adaptive inspection control system, joint simulation and magnetic sensor.

Qing Gao is currently pursuing his master’s degree at the Guangxi University for Nationalities, Nanning, China. His main research interests include image processing and pattern recognition.

Qihui Peng is currently pursuing his master’s degree at the Guangxi University for Nationalities, Nanning, China. His main research interests include image processing and pattern recognition.

Image notes

The images in figure 6 were taken from the work referred in ref [16].

The images in Figure 8 were taken from Matlab's image library.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 61866003]; the Education Innovation Program project of Guangxi University for Nationalities [grant number gxun-chxzs2017113]; the National Science Foundation Council of Guangxi [grant number 2015GXNSFAA139311].