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An automatic glioma grading method based on multi-feature extraction and fusion.

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conference contribution
posted on 19.08.2019, 08:39 by T Zhan, P Feng, X Hong, Z Lu, L Xiao, Y Zhang
BACKGROUND: An accurate assessment of tumor malignancy grade in the preoperative situation is important for clinical management. However, the manual grading of gliomas from MRIs is both a tiresome and time consuming task for radiologists. Thus, it is a priority to design an automatic and effective computer-aided diagnosis (CAD) tool to assist radiologists in grading gliomas. OBJECTIVE: To design an automatic computer-aided diagnosis for grading gliomas using multi-sequence magnetic resonance imaging. METHODS: The proposed method consists of two steps: (1) the features of high and low grade gliomas are extracted from multi-sequence magnetic resonance images, and (2) then, a KNN classifier is trained to grade the gliomas. In the feature extraction step, the intensity, volume, and local binary patterns (LBP) of the gliomas are extracted, and PCA is used to reduce the data dimension. RESULTS: The proposed "Intensity-Volume-LBP-PCA-KNN" method is validated on the MICCAI 2015 BraTS challenge dataset, and an average grade accuracy of 87.59% is obtained. CONCLUSIONS: The proposed method is an effective method for automatically grading gliomas and can be applied to real situations.


This work is supported by the National Nature Science Foundation of China under Grant No.61502206 and 61473334; the Nature Science Foundation of Jiangsu Province under Grant No.BK20150523, 20150983, and BY2014007-04; the Open Project Program of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education (JYB201604); the Open Project of Jiangsu Key Laboratory of Meteorological Observation and Information Processing; Nanjing University of Information Science and Technology (KDXS1404); Jiangsu Postdoctoral Science Foundation Funded Project (No.1402094C); Research Foundation for Talented Scholars, Jiangsu University (No.14JDG041); and Open Project Program of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University (No.93K172016K17).



Technology and Health Care, 2017, 25 (S1), pp. 377-385

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/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics


5th International Conference on Biomedical Engineering and Biotechnology (ICBEB2016), Hangzhou, China


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