Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process.

Journal: Medical image analysis
PMID: 27236223

Abstract

The gastrointestinal endoscopy in this study refers to conventional gastroscopy and wireless capsule endoscopy (WCE). Both of these techniques produce a large number of images in each diagnosis. The lesion detection done by hand from the images above is time consuming and inaccurate. This study designed a new computer-aided method to detect lesion images. We initially designed an algorithm named joint diagonalisation principal component analysis (JDPCA), in which there are no approximation, iteration or inverting procedures. Thus, JDPCA has a low computational complexity and is suitable for dimension reduction of the gastrointestinal endoscopic images. Then, a novel image feature extraction method was established through combining the algorithm of machine learning based on JDPCA and conventional feature extraction algorithm without learning. Finally, a new computer-aided method is proposed to identify the gastrointestinal endoscopic images containing lesions. The clinical data of gastroscopic images and WCE images containing the lesions of early upper digestive tract cancer and small intestinal bleeding, which consist of 1330 images from 291 patients totally, were used to confirm the validation of the proposed method. The experimental results shows that, for the detection of early oesophageal cancer images, early gastric cancer images and small intestinal bleeding images, the mean values of accuracy of the proposed method were 90.75%, 90.75% and 94.34%, with the standard deviations (SDs) of 0.0426, 0.0334 and 0.0235, respectively. The areas under the curves (AUCs) were 0.9471, 0.9532 and 0.9776, with the SDs of 0.0296, 0.0285 and 0.0172, respectively. Compared with the traditional related methods, our method showed a better performance. It may therefore provide worthwhile guidance for improving the efficiency and accuracy of gastrointestinal disease diagnosis and is a good prospect for clinical application.

Authors

  • Ding-Yun Liu
    Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China; Department of Biomedical Engineering, University of Electronic Science and Technology of China, Chengdu, China.
  • Tao Gan
    Department of Gastroenterology, West China Hospital, Chengdu, Sichuan 610041, China.
  • Ni-Ni Rao
    Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China; Department of Biomedical Engineering, University of Electronic Science and Technology of China, Chengdu, China. Electronic address: raonn@uestc.edu.cn.
  • Yao-Wen Xing
    Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China; Department of Biomedical Engineering, University of Electronic Science and Technology of China, Chengdu, China.
  • Jie Zheng
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
  • Sang Li
    Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China; Department of Biomedical Engineering, University of Electronic Science and Technology of China, Chengdu, China.
  • Cheng-Si Luo
    Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China; Department of Biomedical Engineering, University of Electronic Science and Technology of China, Chengdu, China.
  • Zhong-Jun Zhou
    Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China; Department of Biomedical Engineering, University of Electronic Science and Technology of China, Chengdu, China.
  • Yong-Li Wan
    Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China; Department of Biomedical Engineering, University of Electronic Science and Technology of China, Chengdu, China.

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