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

• Published in: BMC bioinformatics Vol. 19; no. 1; pp. 452 - 6
• Main Authors: Zhang, Ruochi, Zhao, Ruixue, Zhao, Xinyang, Wu, Di, Zheng, Weiwei, Feng, Xin, Zhou, Fengfeng
• Format: Journal Article
• Language: English
• Published: London BioMed Central 26.11.2018; BioMed Central Ltd; BMC
• Subjects: Algorithms; Analysis; Applications software; Bioinformatics; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Endoscopy, Gastrointestinal; Humans; image analysis and data visualization; Image Processing, Computer-Assisted - methods; Imaging; Life Sciences; Microarrays; Principal Component Analysis; Programming Languages; Python (Programming language); Software; Visualization (Psychology); Demonstrative Example; Endoscopic Images; Image Feature Extraction Algorithm; Gray Level Co-occurrence Matrix (GLCM); Biomedical Imaging
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2477-7

Background Imaging 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, etc. Results pyHIVE (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 normalization. Conclusions The 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://www.healthinformaticslab.org/supp/resources.php .