Segmentation is considered as an important step in image processing and computer vision applications, which divides an input image into various non-overlapping homogenous regions and helps to interpret the image more conveniently. This paper presents an efficient image segmentation algorithm using neutrosophic graph cut (NGC).

Due to more or less deviations in the imaging system, there will be noise in the image, which makes the image segmentation inaccurate. To divide a natural image into a more accurate binary image, the target and background of the image are effectively separated to achieve a more effective segmentation result. Therefore, this paper proposes an image segmentation algorithm combining a saliency map and neutrosophic set (NS) theory. First, to overcome the problem of weak edges in the image, we highlight the details and use the guided filter to filter the various channels of the natural image. Then, the initial saliency map is generated. After the weighted superposition of the initial saliency map, the local entropy map and the gray scale map, the final saliency map can be generated using the nonlinear function, and it can effectively highlight the foreground information of the image. Second, the saliency map is transformed to the NS domain and interpreted by three subsets: true (T), indeterminate (I), and false (F). According to NS theory, the indeterminacy is reduced, and the segmentation results are finally obtained by using the method of threshold. Various experiments were done and compared with other state-of-the-art approaches. These experiments demonstrate the effect of the proposed work, which is fast and effective for de-noising.

This Special Issue presents original research papers that report on state-of-the-art and recent advancements in neutrosophic sets and logic in soft computing, artificial intelligence, big and small data mining, decision making problems, and practical achievements.

Image segmentation is a commonly used technique in digital image processing with many applications in the area of computer vision and medical image analysis. The goal of image segmentation is to partition an image into multiple regions, normally based on the characteristics of pixels in a given image. Image segmentation could involve separating the foreground from background in an image, or clustering image regions based on similarities in intensity, color, or shape. In this thesis, we consider the problem of cell image segmentation and evaluate the performance of two major techniques on a dataset of cell image sequences. First, we apply a traditional segmentation algorithm based on the so-called graph cut that addresses the segmentation problem using an energy minimization scheme defined on a weighted graph. Second, we use modern techniques based on deep neural networks, namely U-Net and LSTM that have a time-consuming training and a relatively quick testing phase. Performance of each technique will be analyzed qualitatively and quantitatively based on various standard measures and will be compared statistically.

Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature and scope of neutralities, as well as their interactions with different ideational spectra. NS is a formal framework that has been recently proposed.

In order to realize themultithreshold segmentation of images, an improved segmentation algorithm based on graph cut theory using artificial bee colony is proposed. A newweight function based on gray level and the location of pixels is constructed in this paper to calculate the probability that each pixel belongs to the same region. On this basis, a new cost function is reconstructed that can use both square and nonsquare images.Then the optimal threshold of the image is obtained through searching for theminimum value of the cost function using artificial bee colony algorithm. In this paper, public dataset for segmentation and widely used images were measured separately. Experimental results show that the algorithm proposed in this paper can achieve larger Information Entropy (IE), higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), smaller Root Mean Squared Error (RMSE), and shorter time than other image segmentation algorithms.

In this thesis, we present a set of novel image segmentation algorithms that utilize high-level semantic priors available from specific application domains. These priors are incorporated into the segmentation framework to further constrain the results to a more semantically meaningful solution space. Our algorithms are formulated using Random Field models and employ combinatorial graph cuts for efficient optimization. For many instances, they guarantee the globally optimal solutions, and our experiments demonstrate that the algorithms are applicable to a wide range of segmentation tasks.