期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 73, 期 -, 页码 119-130出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2016.04.010
关键词
DCE-MRI; Time-intensity curve; Curve shape pattern; Prostate cancer; Bayes classifier; Heuristic rules
This paper considers the problem of an automatic quantification of DCE-MRI curve shape patterns. In particular, the semi-quantitative approach which classifies DCE time-intensity curves into clusters representing the tree main shape patterns is proposed. The approach combines heuristic rules with the naive Bayes classifier. In particular, the descriptive parameters are firstly derived from pixel-by-pixel analysis of the DCE time intensity curves and then used to recognise the curves which without a doubt represent the three main shape patterns. These curves are next used to train the naive Bayes classifier intended to classify the remaining curves within the dataset. Results of applying the proposed approach to the DCE-MRI scans of patients with prostate cancer are presented and discussed. Additionally, the overall performance of the approach is estimated through the comparison with the ground truth results provided by the expert. (C) 2016 Elsevier Ltd. All rights reserved.
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