A novel image thresholding algorithm based on neutrosophic similarity score

p>Image thresholding is an important field in image processing. It has been employed to segmentnbsp;the images and extract objects. A variety of algorithms have been proposed in thisnbsp;field. However, these methods perform well on the images without noise, and their resultsnbsp;on the noisy images are not good. Neutrosophic set (NS) is a new general formal frameworknbsp;to study the neutralitiesrsquo; origin, nature, and scope. It has an inherent ability to handle thenbsp;indeterminant information. Noise is one kind of indeterminant information on images.nbsp;Therefore, NS has been successfully applied into image processing and computer visionnbsp;research fields. This paper proposed a novel algorithm based on neutrosophic similaritynbsp;score to perform thresholding on image. We utilize the neutrosophic set in image processingnbsp;field and define a new concept for image thresholding. At first, an image is representednbsp;in the neutrosophic set domain via three membership subsets T, I and F. Then, a neutrosophicnbsp;similarity score (NSS) is defined and employed to measure the degree to the idealnbsp;object. Finally, an optimized value is selected on the NSS to complete the image thresholdingbr /> task. Experiments have been conducted on a variety of artificial and real images.nbsp;Several measurements are used to evaluate the proposed methodrsquo;s performance. Thenbsp;experimental results demonstrate that the proposed method selects the threshold valuesnbsp;effectively and properly. It can process both images without noise and noisy images havingnbsp;different levels of noises well. It will be helpful to applications in image processing andnbsp;computer vision./p>