A novel image thresholding algorithm based on neutrosophic similarity score

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 neutro-sophic 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 threshold-ing 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. Image thresholding, one of the simple image segmenta-tion procedures, is a crucial step for several image-processing applications such as object detection, shape recognition, and optical character recognition [1]. In the image thres-holding process, a threshold value is selected, and the pixels on the images are classified into background or objects according to their values. Image thresholding can convert the gray level images into binary ones [2]. Thresholding is quite efficient when the object pixels and background pixels have distinct gray level distributions. Furthermore, it is easy to be implemented and usually be run fast [3,4]. A variety of algorithms have been proposed. Generally, image thresholding methods are classified into two groups based on the criteria to select the threshold value: global and local methods [5]. Global methods select the threshold values according to the characteristics of the entire images, and local ones adopt threshold values using the local information on …