Data Fusion Approach for Constructing Unsupervised Augmented Voxel-Based Statistical Anthropomorphic Phantoms

Hamidreza Khodajou-Chokami, Dmitry V. Dylov

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    6 Citations (Scopus)

    Abstract

    Approaches to constructing statistical phantoms (SPs) for the needs of data fusion with the real data have attracted a lot of attention in the recent years. SPs assist with radiation dosimetry and provide metrics for estimating image quality in the medical X-ray systems. Unlike existing SPs, we propose to consider both material electron densities and anatomical size variations models to portray realistic variations in radiologic data. We introduce a new adaptive unsupervised fusion approach for data augmentation, capable of generating a variety of medically adequate images. The model is based on a combination of continuous Poisson modelling of voxel values, Monte Carlo rejection sampling scheme, and a landmark-based warping. Unlike mere average of HU values of each organ (typical in the other state-of-art SPs), our augmented voxels depict intensity fluctuations, effectively mimicking a distribution of electron densities within each organ. In the experimental section, we evaluate the proposed method and demonstrate its superiority compared to the existing methods. This phantom generation could be instrumental for assessing dose uncertainty, unsupervised refinement of image reconstruction, image classification, and semantic segmentation tasks carried out by machine learning algorithms in the scenarios of limited available data.

    Original languageEnglish
    Title of host publicationProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
    EditorsIllhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1508-1512
    Number of pages5
    ISBN (Electronic)9781728118673
    DOIs
    Publication statusPublished - Nov 2019
    Event2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States
    Duration: 18 Nov 201921 Nov 2019

    Publication series

    NameProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

    Conference

    Conference2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
    Country/TerritoryUnited States
    CitySan Diego
    Period18/11/1921/11/19

    Keywords

    • computed tomography
    • continuous poisson
    • data augmentation
    • data fusion
    • machine learning
    • medical imaging
    • monte carlo
    • piece-wise affine warp
    • statistical phantoms

    Fingerprint

    Dive into the research topics of 'Data Fusion Approach for Constructing Unsupervised Augmented Voxel-Based Statistical Anthropomorphic Phantoms'. Together they form a unique fingerprint.

    Cite this