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

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, etc.ResultspyHIVE (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.ConclusionsThe 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.

[1]  Sushil Sharma,et al.  Translational Multimodality Neuroimaging. , 2017, Current drug targets.

[2]  R. Renuka,et al.  On Intuitionistic Fuzzy β-Almost Compactness and β-Nearly Compactness , 2015, TheScientificWorldJournal.

[3]  D Yang,et al.  SU-E-J-76: 3D Soft Tissue Boundary Detection for Automatic Verification of Deformable Image Registration. , 2013, Medical physics.

[4]  I El Naqa,et al.  WE-C-WAB-02: Joint FDG-PET/MR Imaging for the Early Prediction of Tumor Outcomes. , 2013, Medical physics.

[5]  L. Bai,et al.  A portable image-based cytometer for rapid malaria detection and quantification , 2017, PloS one.

[6]  Crnp,et al.  Use of ß-blocker therapy to prevent primary bleeding of esophageal varices , 2010 .

[7]  Jin Zhang,et al.  Selective Search and Intensity Context Based Retina Vessel Image Segmentation , 2017, Journal of Medical Systems.

[8]  A. Ercil,et al.  Robustness of Local Binary Patterns in Brain MR Image Analysis , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  F. Zhou,et al.  Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images. , 2016, Biomedical optics express.

[10]  Sardul Singh Sandhu,et al.  Prognostic and Predictive Biomarkers in Cancer. , 2014, Current cancer drug targets.

[11]  Oludayo O. Olugbara,et al.  Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features , 2015, TheScientificWorldJournal.

[12]  Loris Nanni,et al.  Local binary patterns variants as texture descriptors for medical image analysis , 2010, Artif. Intell. Medicine.

[13]  Jinming Duan,et al.  Retinal vasculature classification using novel multifractal features , 2015, Physics in medicine and biology.

[14]  Matti Pietikäinen,et al.  Outex - new framework for empirical evaluation of texture analysis algorithms , 2002, Object recognition supported by user interaction for service robots.

[15]  Veronique Kiermer Disclosures , 2016, Plastic and Reconstructive Surgery Global Open.

[16]  Li Bai,et al.  Novel Methods for Microglia Segmentation, Feature Extraction, and Classification , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[17]  Zhen Xu,et al.  Non-Invasive Liver Ablation Using Histotripsy: Preclinical Safety Study in an In Vivo Porcine Model. , 2017, Ultrasound in medicine & biology.

[18]  Guoqing Wang,et al.  McTwo: a two-step feature selection algorithm based on maximal information coefficient , 2016, BMC Bioinformatics.