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Data Fusion Techniques for Biomedica...

Guo, Peng.

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Data Fusion Techniques for Biomedical Informatics and Clinical Decision Support.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data Fusion Techniques for Biomedical Informatics and Clinical Decision Support./
作者:	Guo, Peng.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	135 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Contained By:	Dissertation Abstracts International79-11B(E).
標題:	Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10743577
ISBN:	9780438111646

Data Fusion Techniques for Biomedical Informatics and Clinical Decision Support.
Guo, Peng.

Data Fusion Techniques for Biomedical Informatics and Clinical Decision Support. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 135 p.

Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.

Thesis (Ph.D.)--Missouri University of Science and Technology, 2018.

Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visualization, analysis, detection, estimation, or classification. Data fusion can be applied at the raw-data, feature-based, and decision-based levels. Data fusion applications of different sorts have been built up in areas such as statistics, computer vision and other machine learning aspects. It has been employed in a variety of realistic scenarios such as medical diagnosis, clinical decision support, and structural health monitoring. This dissertation includes investigation and development of methods to perform data fusion for cervical cancer intraepithelial neoplasia (CIN) and a clinical decision support system. The general framework for these applications includes image processing followed by feature development and classification of the detected region of interest (ROI). Image processing methods such as k-means clustering based on color information, dilation, erosion and centroid locating methods were used for ROI detection. The features extracted include texture, color, nuclei-based and triangle features. Analysis and classification was performed using feature- and decision-level data fusion techniques such as support vector machine, statistical methods such as logistic regression, linear discriminant analysis and voting algorithms.

ISBN: 9780438111646Subjects--Topical Terms:

586835
Engineering.

Data Fusion Techniques for Biomedical Informatics and Clinical Decision Support.
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