An unsupervised learning with feature approach for brain tumor segmentation using magnetic resonance imaging

• Main Authors: Khurram, Ejaz, Mohd. Rahim, Mohd. Shafy, Ijaz, Bajwa Usama, Nadim, Rana, Amjad, Rehman
• Format: Conference or Workshop Item
• Published: 2019
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/97078/; http://dx.doi.org/10.1145/3314367.3314384

Segmentation methods are so much efficient to segment complex tumor from challenging datasets. MACCAI BRATS 2013-2017 brain tumor dataset (FLAIR, T2) had been taken for high grade glioma (HGG). This data set is challenging to segment tumor due to homogenous intensity and difficult to separate tumor...