Abstract
Region-based compression technique is particularly useful for radiological archiving system as it allows diagnostically important regions to be compressed with near lossless quality while the non-diagnostically important regions (NROI) to be compressed at lossy quality. In this paper, we present a region-based compression technique tailored for MRI brain scans. In the proposed technique termed as automated arbitrary PCA (AAPCA), an automatic segmentation based on brain symmetrical property is used to separate the ROI from the background. The arbitrary-shape ROI is then compressed by block-to-row PCA algorithm (BTRPCA) based on a factorization approach. The ROI is optimally compressed with lower compression rate while the NROI is compressed with higher compression rate. The proposed technique achieves satisfactory segmentation performance. The subjective and objective evaluation performed confirmed that the proposed technique achieves better performance metrics (PSNR and CoC) and higher overall compression rate. The experimental results also demonstrated that the proposed technique is more superior to various state-of-the-art compression methods.
Highlights
Since the technological advancement in medical imaging modalities, medical image processing and image analysis have become the important diagnostic aids for medical diagnostics and healthcare
The recent research in the field of medical image compression involves the wavelet transform are methods such as embedded zerotree wavelet (EZW), set partitioning in hierarchical trees (SPIHT) and embedded block coding with optimized truncation (EBCOT)
We present and compare the proposed arbitrary Principal Component Analysis (PCA) (AAPCA) with the mainstream compression methods using default parameters except as noted
Summary
Since the technological advancement in medical imaging modalities, medical image processing and image analysis have become the important diagnostic aids for medical diagnostics and healthcare. In order for any diagnostic aids to be reliable, the images acquired from imaging modalities need to be of adequate quality and requires high amount of resolution. The medical images may be required to be saved in PACS and HIS for over thirty years and an efficient compression algorithm is in need to store and archive the images. Some common lossless compression methods include Lempel-Ziv-Welch (LZW), Run-Length Encoded (RLE), JPEG Lossless Compression Standard (JPEG-LS), Arithmetic coding and Huffman coding. These methods can only achieve up to 3:1 compression ratio and it is not a feasible solution for bulk medical image storage and high speed transmission. The recent research in the field of medical image compression involves the wavelet transform are methods such as embedded zerotree wavelet (EZW), set partitioning in hierarchical trees (SPIHT) and embedded block coding with optimized truncation (EBCOT)
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