期刊
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
卷 69, 期 -, 页码 33-42出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compmedimag.2018.08.001
关键词
Colonoscopy; Computer-aided detection; Shape and color feature; Dictionary learning; Polyp classification; Sparse coding
资金
- European Research Consortium for Informatics and Mathematics (ERCIM) 'Alain Bensoussan' Fellowship Programme
- Research Council of Norway through the MELODY project [225885/O70]
Polyps in the colon can potentially become malignant cancer tissues where early detection and removal lead to high survival rate. Certain types of polyps can be difficult to detect even for highly trained physicians. Inspired by aforementioned problem our study aims to improve the human detection performance by developing an automatic polyp screening framework as a decision support tool. We use a small image patch based combined feature method. Features include shape and color information and are extracted using histogram of oriented gradient and hue histogram methods. Dictionary learning based training is used to learn features and final feature vector is formed using sparse coding. For classification, we use patch image classification based on linear support vector machine and whole image thresholding. The proposed framework is evaluated using three public polyp databases. Our experimental results show that the proposed scheme successfully classified polyps and normal images with over 95% of classification accuracy, sensitivity, specificity and precision. In addition, we compare performance of the proposed scheme with conventional feature based methods and the convolutional neural network (CNN) based deep learning approach which is the state of the art technique in many image classification applications. (C) 2018 Elsevier Ltd. All rights reserved.
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