pyHIVE, a health-related image visualization and engineering system using Python

作     者：Zhang, Ruochi Zhao, Ruixue Zhao, Xinyang Wu, Di Zheng, Weiwei Feng, Xin Zhou, Fengfeng 

作者机构：Jilin Univ BioKnow Hlth Informat Lab Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Jilin Peoples R China Jilin Univ Coll Software BioKnow Hlth Informat Lab Changchun 130012 Jilin Peoples R China 

出 版 物：《BMC BIOINFORMATICS》 (英国医学委员会:生物信息)

年 卷 期：2018年第19卷第1期

页      面：1-6页

核心收录：

学科分类：0710[理学-生物学] 0836[工学-生物工程] 10[医学] 

基　　金：Strategic Priority Research Program of the Chinese Academy of Sciences [XDB13040400] Jilin Provincial Key Laboratory of Big Data Intelligent Computing [20180622002JC] Education Department of Jilin Province [JJKH20180145KJ] Jilin University Bioknow MedAI Institute [BMCPP-2018-001] High Performance Computing Center of Jilin University, China 

主　　题：Biomedical Imaging Endoscopic Images Demonstrative Example Image Feature Extraction Algorithm Gray Level Co-occurrence Matrix (GLCM) 

摘      要：BackgroundImaging is one of the major biomedical technologies to investigate the status of a living object. But the biomedical image based data mining problem requires extensive knowledge across multiple disciplinaries, e.g. biology, mathematics and computer science, *** (a Health-related Image Visualization and Engineering system using Python) was implemented as an image processing system, providing five widely used image feature engineering algorithms. A standard binary classification pipeline was also provided to help researchers build data models immediately after the data is collected. pyHIVE may calculate five widely-used image feature engineering algorithms efficiently using multiple computing cores, and also featured the modules of Principal Component Analysis (PCA) based preprocessing and *** demonstrative example shows that the image features generated by pyHIVE achieved very good classification performances based on the gastrointestinal endoscopic images. This system pyHIVE and the demonstrative example are freely available and maintained at http://***/supp/***.