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ROIX-Comp: Optimizing X-ray Computed Tomography Imaging Strategy for Data Reduction and Reconstruction

Amarjit Singh, Kento Sato, Kohei Yoshida, Kentaro Uesugi, Yasumasa Joti, Takaki Hatsui, Andrès Rubio Proaño

TL;DR

This work introduces a region-of-interest (ROI)-driven extraction framework (ROIX-Comp) that intelligently compresses X-CT data by identifying and retaining only essential features, resulting in significantly improved compression ratios.

Abstract

In high-performance computing (HPC) environments, particularly in synchrotron radiation facilities, vast amounts of X-ray images are generated. Processing large-scale X-ray Computed Tomography (X-CT) datasets presents significant computational and storage challenges due to their high dimensionality and data volume. Traditional approaches often require extensive storage capacity and high transmission bandwidth, limiting real-time processing capabilities and workflow efficiency. To address these constraints, we introduce a region-of-interest (ROI)-driven extraction framework (ROIX-Comp) that intelligently compresses X-CT data by identifying and retaining only essential features. Our work reduces data volume while preserving critical information for downstream processing tasks. At pre-processing stage, we utilize error-bounded quantization to reduce the amount of data to be processed and therefore improve computational efficiencies. At the compression stage, our methodology combines object extraction with multiple state-of-the-art lossless and lossy compressors, resulting in significantly improved compression ratios. We evaluated this framework against seven X-CT datasets and observed a relative compression ratio improvement of 12.34x compared to the standard compression.

ROIX-Comp: Optimizing X-ray Computed Tomography Imaging Strategy for Data Reduction and Reconstruction

TL;DR

This work introduces a region-of-interest (ROI)-driven extraction framework (ROIX-Comp) that intelligently compresses X-CT data by identifying and retaining only essential features, resulting in significantly improved compression ratios.

Abstract

In high-performance computing (HPC) environments, particularly in synchrotron radiation facilities, vast amounts of X-ray images are generated. Processing large-scale X-ray Computed Tomography (X-CT) datasets presents significant computational and storage challenges due to their high dimensionality and data volume. Traditional approaches often require extensive storage capacity and high transmission bandwidth, limiting real-time processing capabilities and workflow efficiency. To address these constraints, we introduce a region-of-interest (ROI)-driven extraction framework (ROIX-Comp) that intelligently compresses X-CT data by identifying and retaining only essential features. Our work reduces data volume while preserving critical information for downstream processing tasks. At pre-processing stage, we utilize error-bounded quantization to reduce the amount of data to be processed and therefore improve computational efficiencies. At the compression stage, our methodology combines object extraction with multiple state-of-the-art lossless and lossy compressors, resulting in significantly improved compression ratios. We evaluated this framework against seven X-CT datasets and observed a relative compression ratio improvement of 12.34x compared to the standard compression.
Paper Structure (23 sections, 18 equations, 9 figures, 2 tables)

This paper contains 23 sections, 18 equations, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Related Work
  4. Proposed Approach
  5. Pre-processing Stage
  6. Background subtraction:
  7. Intensity normalization:
  8. Adaptive Thresholding:
  9. Binarization:
  10. Feature Extraction
  11. Contour detection:
  12. Feature extraction:
  13. Absolute Error-Bounded Quantization
  14. Integrating HPC-based compression
  15. Experimental Evaluation
  16. ...and 8 more sections

Figures (9)

  • Figure 1: Projection of X-CT Object Capturing Structural and Density
  • Figure 2: Adaptive ROIX Compression Framework: Systematic Workflow
  • Figure 3: ROI Extraction Pipeline: From Data to Targeted Feature Isolation
  • Figure 4: Spatial reduction achieved through ROI extraction across different datasets. The spatial reduction factor indicates how many times smaller the ROI is compared to the original image.
  • Figure 5: Evaluation metrics measuring different aspects of segmentation performance: overlap accuracy (DSC, IoU), detection capability (Sensitivity, Specificity), overall correctness (Accuracy), and boundary precision (AHD).
  • ...and 4 more figures