8 - Neutrosophic set in medical image clustering

Abstract

Neutrosophic sets (NS) have a significant role for denoising, clustering, segmentation, and classification in numerous medical image-processing applications. For efficient development of computer-aided diagnosis systems, clustering techniques have been incorporated with NS to diminish uncertainty for efficient diagnosis. To integrate the clustering methods with NS for further clustering, the image is transformed into the NS domain, which is defined by three membership functions (MFs): true (T), indeterminacy (I), and false (F). These MFs map the medical image into the NS domain to generate an NS image. During this process, different types of filters can be applied, such as the median filter to compute T, which iteratively determines the best version of the image of T and F functions. In addition, another filter can be used to compute the indeterminacy neutrosophic subset I, such as Sobel, Prewitt, and the unsharp filter. Such filters have a significant role in the determination of MFs. In the clustering stage, the pixels whose I values are greater than a specific threshold will be excluded from the clustering process. Other pixels are clustered using the K-means. Additionally, this chapter studies the effect of the filter type and size in NS during the calculation of I MF (uncertainty MF) and the segmentation results on the skin lesion regions in dermoscopic images using the NS-based K-means (NKM) clustering. A comparative study was conducted using these different types and/or sizes of these NS-based filters on the K-means clustering process on the ISIC 2016 skin lesion dermoscopic image dataset. The results established that the characteristics of the filter in the calculations of the indeterminacy neutrosophic subset I have a significant effect on the NS performance and the clustering process as well. Additionally, the results proved the superiority of the unsharp filter with the size of 7×7 compared to the other edge detection filters with an accuracy of 95.25% accuracy.