Review of Set Theoretic Approaches to Magnetic Resonance Brain Image Segmentation

Abstract

Image segmentation is a vital step in image processing and has attracted many researchers towards its potential applications like object recognition, pattern recognition, computer vision and artificial intelligence. The medical image segmentation is challenging and complex due to the artifacts or noise present in the image. Many segmentation techniques are proposed in the literature, using mathematical set theoretic approaches like k-means, fuzzy c-means, rough c-means, intutionistic fuzzy c-means, and hybrid clustering algorithms to extract the brain tissues from magnetic resonance brain images. The set theoretic approaches easily model the clustering techniques to extract the brain tissues without the operator intervention. The fuzzy sets, rough sets, and intutionistic sets proved to handle the vagueness, noise and uncertainty present in the medical images, whereas the soft sets can easily parameterize the rough sets for better performance. In this paper, a summary of all brain image segmentation methods based on set theoretic approach is described in detail and also categorized accordingly. Set theoretic based-image segmentation provides a better framework for medical image segmentation.

Keywords:

• Brain image
• Clustering
• Intutionistic fuzzy sets and soft sets
• Rough sets
• Segmentation
• Set theory

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Anupama Namburu

N Anupama is working as an associate professor in the Department of Computer Science and Engineering, Vellore Institute of Technology, Amaravathi, Guntur. She worked as an assistant professor in the Department of Computer Science and Engineering in Acharya Nagarjuna University, Guntur. She received her MTech from Andhra University, Visakhaptanam, Andhra Pradesh, India. She completed her PhD thesis in Jawaharlal Nehru Technological University, Kakinada. She has 12 years of experience in teaching and research. She has published more than 10 research papers in national and international journals. Her research areas include digital image processing, soft computing techniques and Fuzzy applications. Corresponding author. E-mail: [email protected]

Samayamantula Srinivas Kumar

S Srinivas Kumar is working as a professor in the Department of Electronics and Communication Engineering, JNTU College of Engineering, director (Research and Development) Jawaharlal Nehru Technological University, Kakinada, India. He received his MTech from Jawaharlal Nehru Technological University, Hyderabad, India. He received his PhD from E&ECE Department, IIT Kharagpur. He has 29 years of experience in teaching and research. He has published more than 50 research papers in national and international journals. His research interests are digital image processing, computer vision, pattern recognition and application of artificial neural networks and fuzzy logic to engineering problems. E-mail: [email protected]

Edara Srinivasa Reddy

E Srinivasa Reddy is working as a professor in the Department of Computer Science and Engineering and principal, University College of Engineering and Technology, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India. He received his MTech from Visveswaraya Technological University, Karnataka, India. He received his PhD in computer science & engineering, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India. He has 27 years of experience in teaching and research. He has published more than 70 research papers in national and international journals. His research interests include digital image processing and pattern recognition. E-mail: [email protected]