JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (2): 86-93.doi: 10.6040/j.issn.1672-3961.1.2016.282

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MR image classification based on LBP and extreme learning machine

HE Qijia, LIU Zhenbing*, XU Tao, JIANG Shujie   

  1. School of Electrical Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China
  • Received:2016-03-01 Online:2017-04-20 Published:2016-03-01

Abstract: To solve the problem that theMR brain images are collect from different sources and the pathological fields are varied, a method combining the texture feature extractor which was based on the local binary patterns(LBP)with the extreme learning machine(ELM)classifier was proposed. Mask for region of interest(ROI)was calculated, the image was divided into some sector subareas, LBP histograms were calculatedin every subarea, all the LBP histograms were connected as feature vector and then classified through ELM.Compared with previous methods, the new method could calculate local features, and it was feasible to classify the different sources of MR images and variously lesion images. Some experiments for MR image classification were done, and the accuracy was more than 92% for all samples, the accuracy was more than 93% for positive sample, the accuracy was more than 91% for negative sample. The results showed that the method was available for the varied MR images.

Key words: MR image, image classification, local binary patterns, image block, extreme learning machine

CLC Number: 

  • TP393
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