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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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A region-adaptive non-local denoising algorithm for low-dose computed tomography images.

Pengcheng Zhang1, Yi Liu1, Zhiguo Gui1

  • 1State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan 030051, China.

Mathematical Biosciences and Engineering : MBE
|March 11, 2023
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Summary

This study introduces a region-adaptive non-local means (NLM) method to improve low-dose computed tomography (LDCT) image quality. The adaptive approach enhances denoising performance, reducing noise and artifacts in medical imaging.

Keywords:
edge detectionimage denoisingintuitionistic fuzzy divergencelow-dose computed tomographynon-local means

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Science

Background:

  • Low-dose computed tomography (LDCT) reduces patient radiation exposure but introduces significant noise and artifacts.
  • Existing non-local means (NLM) methods show potential for LDCT image denoising but have limitations in performance.
  • Degraded image quality in LDCT can hinder accurate diagnosis.

Purpose of the Study:

  • To develop and evaluate a novel region-adaptive non-local means (NLM) method for enhanced low-dose computed tomography (LDCT) image denoising.
  • To improve the visual quality and numerical accuracy of reconstructed LDCT images.
  • To address the limitations of traditional NLM methods in handling noise and artifacts.

Main Methods:

  • Proposed a region-adaptive NLM method classifying pixels based on image edge information.
  • Implemented adaptive adjustments for searching window size, block size, and smoothing parameter across different image regions.
  • Incorporated filtering of candidate pixels and adaptive parameter adjustment using intuitionistic fuzzy divergence (IFD).

Main Results:

  • The proposed region-adaptive NLM method demonstrated superior performance in LDCT image denoising compared to existing methods.
  • Experimental results showed significant improvements in both numerical metrics and visual quality of denoised images.
  • The adaptive strategy effectively mitigated noise and artifacts inherent in LDCT imaging.

Conclusions:

  • The region-adaptive NLM method offers a significant advancement in LDCT image denoising.
  • This approach effectively enhances image quality, potentially improving diagnostic accuracy in low-dose CT scans.
  • The method provides a robust solution for overcoming the challenges of noise and artifacts in low-dose CT imaging.