Skull stripping on multimodal brain MRI scans using thresholding and morphology

ABSTRACT

This paper introduces a novel thresholding and morphology-based skull stripping method for different MRI modalities. The proposed method is designed in a way which is easy to use and generates satisfactory results with minimal parameter adjustments. The method is evaluated on three different benchmark datasets and compared with nine state-of-the-art skull stripping methods. The experimental results suggest that the proposed method generates comparable results to some of the best methods in literature. However, unlike many other methods, it works well on different types of MRI scans. Moreover, this method generates the skull mask along with the brain mask that can be used to study various skull pathologies.

KEYWORDS:

• Medical imaging
• image processing
• skull stripping
• thresholding
• morphology
• MRI
• skull
• brain

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 https://github.com/bhatsajid/SkullStripping2023.git

Additional information

Notes on contributors

Sajid Y. Bhat

Sajid Y. Bhat has received his bachelors and masters in computer science from University of Kashmir, Hazratbal in 2006 and 2009, respectively. He received his PhD in computer science from Jamia Millia Islamia in 2014. Currently he is working as an Assistant Professor at the Department of Computer Sciences, University of Kashmir. He has published many articles in peer reviewed journals and conferences. His current areas of research include image analysis, machine learning, network analysis and business intelligence.

Afnan Naqshbandi

Afnan Naqshbandi has received her Bachelor of Engineering from North Campus, University of Kashmir in 2016 and has contributed to this work as part of her M.Tech Programme which she completed from the Department of Computer Science, University of Kashmir, Hazratbal in 2021. Her interests include image analysis and machine learning.

Muhammad Abulaish

Muhammad Abulaish (Senior Member: IEEE, ACM, and CSI) received the Ph.D. degree in Computer Science from Indian Institute of Technology (IIT) Delhi. He is a Full Professor and Chairperson of the Department of Computer Science, South Asian University (SAU), New Delhi, India, with over 24 years of experience in Academics and Research. He founded the Laboratory for Data Science and Analytics (LDSA) at SAU, which serves as a central hub of data-intensive, inter-disciplinary applied research. His research focuses on the development of innovative data mining, machine learning, and network analysis techniques to address real-world societal and industrial problems, particularly for text mining, social network analysis, figurative language detection, rumor detection, sentiment and emotion analysis, health informatics, and data-driven cybersecurity. He has published over 130 research articles in international journals, books, and conference proceedings, including seven in IEEE/ACM Transactions. He is an Associate Editor for the Social Network Analysis and Mining journal. He served as a Senior Program Committee member for CIKM'22. As a member of the Program Committee, he frequently serves prestigious international conferences such as SDM, CIKM, IJCAI-ECAI, PAKDD, Web Intelligence, and BIOKDD. He has also served as Publicity Co-chair for WI'19 and WI'20, as well as Workshop Co-chair for ASONAM'20.