[1]JIANG Ting,XI Xiaoming,YUE Houguang.Classification of pulmonary nodules by semi-supervised FCM based on prior distribution[J].CAAI Transactions on Intelligent Systems,2017,12(5):729-734.[doi:10.11992/tis.201706018]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 5
Page number:
729-734
Column:
学术论文—机器感知与模式识别
Public date:
2017-10-25
- Title:
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Classification of pulmonary nodules by semi-supervised FCM based on prior distribution
- Author(s):
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JIANG Ting; XI Xiaoming; YUE Houguang
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School of Computer Science and Technology, Shandong University of Finance and Economics, Ji’nan 250014, China
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- Keywords:
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classification of pulmonary nodules; semi-supervised FCM; prior distribution; image processing; LIDC database
- CLC:
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TP399
- DOI:
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10.11992/tis.201706018
- Abstract:
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The classification of pulmonary nodules is significant for the early detection and treatment of lung cancer. However, in real clinical applications, few medical images are labeled and it is difficult to obtain these labels. Semi-supervised learning methods that utilize supervised information to label images may be employed to improve the classification performance of pulmonary nodules. Traditional semi-supervised methods ignore the use of label information when the distribution between labeled and unlabeled specimens is imbalanced. To solve this problem, we propose a semi-supervised fuzzy c-means (FCM) algorithm based on prior distribution for classifying pulmonary nodules. This algorithm first calculates the prior probability of the specimens. Based on the obtained probability, a weight is assigned to each specimen for clustering to intensify the instruction role of a few labeled specimens in the clustering process. We conducted a corresponding test for Lung Image Database Consortium (LIDC) database. The result shows that, compared with the traditional semi-supervised FCM algorithm, the proposed algorithm can obtain better classification performances of pulmonary nodules.