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.
Synchrotron radiation facilities such as ESRF [4], APS [2], and SPring-8 [22] specialize in the generation of high-intensity X-rays. These facilities provide advanced material analysis and imaging across various scientific disciplines. In recent years, detectors at these light source facilities have become increasingly efficient: they are now capable of generating data ranging from terabytes to petabytes per day. This exponential growth of data creates significant challenges in storage, processing, and analysis that demand highly efficient data management strategies. To effectively handle these datasets, distributed storage architectures with compression capabilities must be integrated into high performance computing (HPC) environments [8].
X-ray computed tomography (X-CT) images created by these large radiation facilities are particularly important because of their applications in various scientific, industrial, and medical domains. High-resolution imaging enables detailed investigation of internal material properties and structures [1]. The data generation rate in SPring-8 has increased significantly since the introduction of DIFRAS detectors [9]. This exponential growth of data creates challenges in storage, processing, and analysis that demand highly efficient data management strategies. Recent variants of DIFRAS detectors equipped with the IMX661 (13.9𝑘 x 9.7𝑘 pixels), 21.8 frames/sec @ 10 bit depth generate data at a rate of 10.4 GB/s maximum (899 TB/day) [21]. Therefore, data compression is critical for efficient storage and data analysis.
Traditional data compression approaches typically apply a generalpurpose compression algorithm directly to X-CT images. These techniques do not achieve optimal results because they do not account for the specialized nature of X-CT data, including its unique noise patterns, spatial correlations, and critical structural features. Moreover, these standard methods often struggle with the high dynamic range found in X-CT scans, where both high-density and low-density materials must be preserved with high fidelity.
Lossless compression strategies such as Zstd [3], Gzip [5], Huffman coding [7], Bzip2 [20], and Lz77 [28] prioritize data fidelity over compression ratio and rate, which limits their effectiveness for large image datasets. In contrast, lossy compression methods such as Sz3 [11] and Zfp [13] achieve high data reduction while preserving key characteristics of the object, such as precision, by removing less significant information.
Our approach employs region-of-interest (ROI) recognition to automatically segment significant structural regions from background elements based on density distributions. Because processing whole volumetric scans is computationally expensive and storage intensive, this domain-specific methodology extracts only the object of interest, effectively eliminating non-valuable areas entirely from further processing. By focusing exclusively on these relevant regions, we optimize both the reconstruction and segmentation steps. This reduction in working dataset size delivers dual benefits: speeding up data-intensive operations while minimizing storage and transmission costs.
Building on this foundation, we introduce a specialized preprocessing algorithm that conditions the segmented X-CT data to better align with the capabilities of standard compressors. This conditioning step enables significantly higher compression ratios compared to applying compressors directly to raw X-CT data while maintaining essential data quality throughout the process.
The key contributions of this paper include:
• The development of an adaptive thresholding and binarization framework for processing 2D X-CT images. • The Implementation of a region/object extraction strategies to isolate diagnostically relevant areas from X-CT data. • The integration of a region-of-interest recognition with errorbounded quantization to analyze the relationship between compression ratio and data preservation within specified error tolerances.
• The demonstration that pre-processing segmentation enhances data processing efficiency while reducing storage requirements for X-CT datasets. • The evaluation of the approach across multiple datasets, comparing compression ratios with state-of-the-art compressors to validate performance improvements.
This paper is organized as follows. Section 2 provides background on X-CT and reviews the fundamental techniques. Section 3 discusses related work in X-CT data compression and ROI extraction. Section 4 presents our object extraction strategy, while Section 5 evaluates our approach across multiple datasets, analyzing the compression ratio, processing time, and reconstruction quality. Finally, we conclude with a summary of the findings and directions for future research.
X-ray Computed Tomography (X-CT) is an imaging technique that provides a high-resolution 3D representation of an object’s internal structure. X-CT acquires numerous 2D X-ray p
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