Neutrosophic image segmentation with Dice Coefficients.

• We proposed a novel approach for image segmentation using neutrosophic sets. • Min-Max normalization was used to reduce uncertain noises. • Activation functions were applied for non-linearity in the images. • Membership functions were computed on different regions to form neutrosophic sets. • The proposed method was illustrated by numerical examples. This paper explores various properties of Neutrosophic sets (NS) and proposes a novel idea on Image Segmentation using NS. A theoretical Neutrosophic model is proposed to reduce uncertainty from missing data. Besides, we also tackle the problem of image segmentation with fewer assumptions. Min-Max Normalization is used to reduce any uncertain noise in an image due to a number of factors during image capturing. Next, we apply activation functions to resolve the non-linearity in the image followed by the computed membership functions. These sets are then transformed and compared with others to find similarities and dissimilarities. Neutrosophic Sets and Dice's Coefficients are fused to ensure proper evaluation of uncertainty of the missing data and their indeterminacy for image segmentation. The proposed method is experimentally validated. [ABSTRACT FROM AUTHOR]