Semi-automatic unsupervised MR brain tumour segmentation using a simple Bayesian Framework

ABSTRACT

Tumours, one of the pathologies of the brain, are diverse in shape and appearance and overlap with the normal brain tissues making accurate automatic segmentation a challenge. This work proposes a semi- automatic, unsupervised method for brain tumour segmentation, using magnetic resonance images in a simple Bayesian framework. Pixel is classified as a tumour class by taking into account the knowledge of different brain tissue classes, grey level of pixels and their neighbourhood. For the Bayesian frame work, the likelihood of the different brain tissue classes is assumed as Gaussian and Gaussian density weights of the pixel neighbourhood serve as the prior information for accurate tumour segmentation. Experiments conducted on the publically available BRATS database result in an overall accuracy of 98% for tumour core and 96% for oedema.

KEYWORDS:

• MRI
• Bayes theorem
• brain tumour
• segmentation
• likelihood
• conditional probability
• prior
• posterior probability

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Archana Chaudhari received the B.E. and M. Tech. degrees from Savitribai Phule Pune University, Pune, India. She is currently working as Assistant Professor in Instrumentation Engineering Department at Vishwakarma Institute of Technology, affiliated to Savitribai Phule Pune University. Her main areas of interest are Signal and Image processing, Pattern Recognition, Biomedical Image Analysis and Machine Learning.

Jayant Kulkarni received the B.E., M.E., and Ph.D. degrees from Marathwada University, India. He is currently working as Professor in Instrumentation Engineering Department at Vishwakarma Institute of Technology, affiliated to Savitribai Phule Pune University. His main areas of interest are Signal and Image processing, Pattern Recognition, Machine Learning, Image Surveillance applications.