Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series

Sadegh Ghaderi; Sana Mohammadi; Kayvan Ghaderi; Fereshteh Kiasat; Mahdi Mohammadi

doi:10.1097/MS9.0000000000001756

Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series

Ann Med Surg (Lond). 2024 Jan 26;86(3):1460-1475. doi: 10.1097/MS9.0000000000001756. eCollection 2024 Mar.

Authors

Sadegh Ghaderi¹, Sana Mohammadi², Kayvan Ghaderi³, Fereshteh Kiasat³, Mahdi Mohammadi⁴

Affiliations

¹ Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran.
² Department of Medical Sciences, School of Medicine, Iran University of Medical Sciences, Tehran.
³ Department of Information Technology and Computer Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj.
⁴ Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.

Abstract

Introduction and importance: Automated segmentation of glioblastoma multiforme (GBM) from MRI images is crucial for accurate diagnosis and treatment planning. This paper presents a new and innovative approach for automating the segmentation of GBM from MRI images using the marker-controlled watershed segmentation (MCWS) algorithm.

Case presentation and methods: The technique involves several image processing techniques, including adaptive thresholding, morphological filtering, gradient magnitude calculation, and regional maxima identification. The MCWS algorithm efficiently segments images based on local intensity structures using the watershed transform, and fuzzy c-means (FCM) clustering improves segmentation accuracy. The presented approach achieved improved segmentation accuracy in detecting and segmenting GBM tumours from axial T2-weighted (T2-w) MRI images, as demonstrated by the mean characteristics performance metrics for GBM segmentation (sensitivity: 0.9905, specificity: 0.9483, accuracy: 0.9508, precision: 0.5481, F_measure: 0.7052, and jaccard: 0.9340).

Clinical discussion: The results of this study underline the importance of reliable and accurate image segmentation for effective diagnosis and treatment planning of GBM tumours.

Conclusion: The MCWS technique provides an effective and efficient approach for the segmentation of challenging medical images.

Keywords: GBM; MRI; marker-controlled watershed algorithm; tumour segmentation.