A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation

Abstract

Abstract The human-brain is a vital and complicated organ within the body. Identifying brain-related diseases can be challenging. Typically, Magnetic Resonance Imaging (MRI) scanning methods are used to gain insights of the protected regions in the body. Brain segmentation can result in identifying region boundaries as a set of contours. However, segmenting brain images poses several challenges, including noise, bias field, and partial volume effect (PVE). Removing noise, accurately segmenting tissues and tumors are crucial for effective evaluation. To enhance tissue and tumor segmentation, a new machine learning-based method called as Gaussian-Kernelized Enhanced Intuitionistic Fuzzy-C-Means (GKEIFCM) has been proposed. Approach enhances Improved Intuitionistic Fuzzy-C-Means Algorithm (IIFCM) by utilizing Gaussian kernelized distance between pixels, resulting in uncomplicated segmentation with reduced computational times and improved efficiency. This proposed novel method proved to be expertise in tissue and tumor classification and identification respectively. The results demonstrate the effectiveness of GKEIFCM interms of Dice, Jaccard-similarity-index, Accuracy and Execution time.

Keywords

• MRI
• Brain segmentation
• Tissue classification
• Tumor identification
• Machine learning
• Intuitionistic –FCM