Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine

Author(s): Zhangjing Yang , Piaopiao Feng , Tian Wen , Minghua Wan , Xunning Hong .

Journal Name: CNS & Neurological Disorders - Drug Targets

Volume 16 , Issue 2 , 2017

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Abstract:

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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Article Details

VOLUME: 16
ISSUE: 2
Year: 2017
Page: [160 - 168]
Pages: 9
DOI: 10.2174/1871527315666161018122909
Price: $58

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