文献类型：期刊文献

英文题名：A novel image thresholding algorithm based on neutrosophic similarity score

作者：Guo, Yanhui[1];Sengur, Abdulkadir[2];Ye, Jun[3]

机构：[1]St Thomas Univ, Sch Sci Technol & Engn Management, Miami Gardens, FL 33054 USA;[2]Firat Univ, Dept Elect & Elect Engn, Fac Technol, TR-23169 Elazig, Turkey;[3]Shaoxing Univ, Dept Elect & Informat Engn, Shaoxing 312000, Zhejiang, Peoples R China

年份：2014

卷号：58

起止页码：175

外文期刊名：MEASUREMENT

收录：SCI-EXPANDED(收录号：WOS:000344485600020)、、EI(收录号：20144200105379)、Scopus(收录号：2-s2.0-84907843613)、WOS

语种：英文

外文关键词：Image thresholding; Image segmentation; Fuzzy set; Neutrosophic set; Similarity score

外文摘要：Image thresholding is an important field in image processing. It has been employed to segment the images and extract objects. A variety of algorithms have been proposed in this field. However, these methods perform well on the images without noise, and their results on the noisy images are not good. Neutrosophic set (NS) is a new general formal framework to study the neutralities' origin, nature, and scope. It has an inherent ability to handle the indeterminant information. Noise is one kind of indeterminant information on images. Therefore, NS has been successfully applied into image processing and computer vision research fields. This paper proposed a novel algorithm based on neutrosophic similarity score to perform thresholding on image. We utilize the neutrosophic set in image processing field and define a new concept for image thresholding. At first, an image is represented in the neutrosophic set domain via three membership subsets T, I and F. Then, a neutrosophic similarity score (NSS) is defined and employed to measure the degree to the ideal object. Finally, an optimized value is selected on the NSS to complete the image thresholding task. Experiments have been conducted on a variety of artificial and real images. Several measurements are used to evaluate the proposed method's performance. The experimental results demonstrate that the proposed method selects the threshold values effectively and properly. It can process both images without noise and noisy images having different levels of noises well. It will be helpful to applications in image processing and computer vision. (C) 2014 Elsevier Ltd. All rights reserved.