Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence
magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However,
this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images.
This similarity may lead to misclassification and could affect the treatment results. In this paper, we
propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain
tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests
(ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical
knowledge and validated using a t-test; and 3) features that are helpful for classification are used to
build an original feature vector and a support vector machine is applied to perform classification. In total,
58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation
strategy is adopted in our experiments. The global accuracy of our method was determined as
96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and
can be applied in clinical diagnosis.
Keywords: Feature extraction, glioblastoma, lymphoma, support vector machine.
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