Brain tumor classification from multi-modality MRI using wavelets and machine learning

Khalid Usman, Kashif Rajpoot

Research output: Contribution to journalArticlepeer-review

75 Citations (Scopus)
338 Downloads (Pure)


In this paper, we propose a brain tumor segmentation and classification method for multi-modality magnetic resonance imaging scans. The data from multimodal brain tumor segmentation challenge (MICCAI BraTS 2013) are utilized which are co-registered and skullstripped, and the histogram matching is performed with a reference volume of high contrast. From the preprocessed
images, the following features are then extracted: intensity, intensity differences, local neighborhood and wavelet texture. The integrated features are subsequently provided to the random forest classifier to predict five classes: background, necrosis, edema, enhancing tumor and non-enhancing
tumor, and then these class labels are used to hierarchically compute three different regions (complete tumor, active tumor and enhancing tumor). We performed a leave-one-out cross-validation and achieved 88% Dice overlap for the complete tumor region, 75% for the core tumor region and 95% for enhancing tumor region, which is higher than the Dice overlap reported from MICCAI BraTS challenge.
Original languageEnglish
Pages (from-to)871–881
Number of pages11
JournalPattern Analysis and Applications
Issue number3
Early online date15 Feb 2017
Publication statusPublished - Aug 2017


  • Multi-modality
  • MRI
  • Wavelet transform
  • Random forest
  • Brain tumor
  • Segmentation


Dive into the research topics of 'Brain tumor classification from multi-modality MRI using wavelets and machine learning'. Together they form a unique fingerprint.

Cite this