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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Volumetric medical image compression using 3D listless embedded block partitioning.

Ranjan K Senapati1, P M K Prasad2, Gandharba Swain3

  • 1Department of ECE, K L University, Vaddeswaram, Guntur, Andhra Pradesh 522502 India.

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|January 6, 2017
PubMed
Summary

A new medical image compression algorithm, 3D hierarchical listless block (3D-HLCK), offers improved performance and reduced memory usage. This method achieves better compression than 3D-SPIHT for various medical imaging modalities.

Keywords:
3D hierarchical listless embedded blockEmbedded coderPeak-signal-to-noise-ratioSet partitioning in hierarchical treesVolumetric compression

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Area of Science:

  • Medical Imaging
  • Image Compression
  • Signal Processing

Background:

  • Medical image compression is crucial for efficient storage and transmission.
  • Existing methods like 3D-SPIHT face limitations in compression efficiency and memory usage.
  • Wavelet transforms and block coding are established techniques in image compression.

Purpose of the Study:

  • To introduce a novel, listless variant of a 3D-block coding algorithm for enhanced medical image compression.
  • To improve compression performance and reduce memory requirements compared to existing algorithms.
  • To achieve rate and resolution scalability for medical imaging applications.

Main Methods:

  • A 3D hybrid transform combining wavelet transform (spatial) and Karhunen-Loueve transform (spectral) was employed.
  • Transformed coefficients were arranged in a 1D fashion, following wavelet coefficient distribution.
  • A novel listless block coding algorithm (3D-HLCK) was applied to encode coefficients in an ordered-bit-plane fashion.

Main Results:

  • The proposed 3D-HLCK algorithm demonstrated superior compression performance compared to 3D-SPIHT.
  • 3D-HLCK showed competitive results against state-of-the-art 3D wavelet coders across various bit rates.
  • Significant memory reduction was achieved due to the listless nature of the algorithm.
  • Rate and resolution scalability comparable to 3D-SPIHT and 3D-SPECK were maintained.

Conclusions:

  • 3D-HLCK offers a promising solution for efficient medical image compression.
  • The algorithm provides a favorable trade-off between compression performance, memory usage, and scalability.
  • This method is effective for compressing magnetic resonance, DICOM, and angiogram images.