Nondestructive Testing Image Segmentation based on Neutrosophic Set and Bat Algorithm

Abstract

Industry uses nondestructive testing (NDT) to detect a fault in metal without damaging it. Image segmentation based technique for detecting the fault from an NDT image is a difficult task. The difficulty emerges due to uncertainties in the NDT image pattern. To segment an NDT image efficiently the uncertainties should be handled efficiently. In this paper, we present a novel technique to segment an NDT image by handling the uncertainties based on neutrosophic set(NS). The NS manages the uncertainties by representing an image into a true, false, and indeterminate subset. For proper NS value representation, two operations α – mean and β – enhancement are essential. For finding the proper values of α and β depending on the image statistics we utilize the bat algorithm(BA). The algorithm finds the optimal values of α and β for managing the uncertainties properly. We find that in terms of performance the proposed method is quite satisfying in comparison to the latest methods.