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An efficient sampling algorithm for uncertain abnormal data detection in biomedical image processing and disease prediction

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Liu, Feia; * | Zhang, Xib | Jia, Yana

Affiliations: [a] School of computer, National University of Defense Technology. 410073, Changsha, Hunan, China | [b] School of Biomedical Engineering, Fourth Military Medical University. 710032, Xian, Shaanxi, China

Correspondence: [*] Address for correspondence: Fei Liu, Team 7, School of Computer, National University of Defense Technology, Changsha, Hunan, 410073, China. Tel.: +86 15873192183; Fax: +86 0731 84574614; E-mail: 1986figo@163.com.

Keywords: Biomedical image, disease diagnosis, computer information processing, abnormal detection, outlier, uncertain

DOI: 10.3233/BME-151422

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1249-S1255, 2015

Published: 2015
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In this paper, we propose a computer information processing algorithm that can be used for biomedical image processing and disease prediction. A biomedical image is considered a data object in a multi-dimensional space. Each dimension is a feature that can be used for disease diagnosis. We introduce a new concept of the top ( k1,k2 ) outlier. It can be used to detect abnormal data objects in the multi-dimensional space. This technique focuses on uncertain space, where each data object has several possible instances with distinct probabilities. We design an efficient sampling algorithm for the top ( k1,k2 ) outlier in uncertain space. Some improvement techniques are used for acceleration. Experiments show our methods’ high accuracy and high efficiency.

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