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

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

Volume: 16 Issue: 2

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

Affiliation:

Keywords: Feature extraction, glioblastoma, lymphoma, support vector machine.

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.

Cite this article as:

Yang Zhangjing, Feng Piaopiao, Wen Tian, Wan Minghua and Hong Xunning, Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine, CNS & Neurological Disorders - Drug Targets 2017; 16 (2) . https://dx.doi.org/10.2174/1871527315666161018122909