De-Noising and Segmentation of Medical Images using Neutrophilic Sets
C. S. Manigandaa1, V. D. Ambeth Kumar2*, G. Ragunath2 , R. Venkatesan3, N. Senthil Kumar4
1 Department of AI&DS, Panimalar Engineering College, Chennai, 600123, India
2 Department of Computer Engineering, Mizoram University, Aizawl 796004. India
3Computer Science and Engineering, Karunya University, Coimbatore 641114, India
4 Department of Biotechnology, Mizoram University, Aizawl, Mizoram, 796004, India
Emails: csmanigandaa@gmail.com; ambeth@mzu.edu.in; ragunath2004112@gmail.com; rlvenkei_2000@karunya.edu; nskmzu@gmail.com
Abstract
Medical diagnosis and prognosis are challenging tasks due to subjectivity and inherent uncertainty in medical images. Inconsistencies in expert opinions can result in incorrect diagnoses. Neutrosophic theory, a mathematical framework that deals with imprecise or incomplete data, has shown promise in addressing the challenges posed by medical image processing. A neutrosophic theory approach is explored in this paper for de-noising and segmenting medical images. Neutrosophic theory has been utilized to represent the different degrees of truth in each piece of information, resulting in better performance in de-noising and segmentation tasks. Neutosophic theory presents a promising avenue for future investigation in medical image processing as shown in this study.
Keywords: neutrophilic set; medical image; Noise; Segmentation