Neutrosophic fusion of multimodal brain images: Integrating neutrosophic entropy and feature extraction

Abstract

Due to the rapid growth of imaging modalities in clinical analysis and the indispensable requirement of brain images from various imaging modalities for diagnosing a disease, multi-modal brain image fusion has become an intriguing problem among researchers. Thus, the main motive of this paper is to obtain all the necessary information about the source images in a single fused image of high contrast with clear boundaries and without unnecessary noise. Accordingly, this paper proposes a new approach to eradicate the indeterministic and uncertainty present in brain images with the benefits of the neutrosophic set. Also, a novel neutrosophic entropy is developed to acquire the accurate edge details of the image. In addition, to extract requisite features from the images, Tamura features are implemented. Finally, the fusion is performed by comparing the extracted feature values from the images. Subsequently, the experiment is conducted with three different sets of brain datasets and compared with six other fusion algorithms to prove the efficiency of the proposed method. To support this, qualitative and quantitative assessments for each dataset are executed, and the results are tabulated. The results clearly show that the information is accurately represented, preserving the curves and edges. Moreover, this algorithm consistently produces the highest metric values and remains reasonably efficient in time consumption, thus balancing performance and efficiency.