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Computed Tomography

Computed Tomography

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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Related Experiment Video

Updated: Mar 24, 2026

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Computed Tomography Images De-noising using a Novel Two Stage Adaptive Algorithm.

Mojtaba Fadaee¹, Mousa Shamsi¹, Hamidreza Saberkari²

¹Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

Journal of Medical Signals and Sensors

|March 9, 2016

Summary

This summary is machine-generated.

This study introduces an advanced algorithm for medical image de-noising, improving principal component analysis with local pixel grouping. The method enhances image quality, achieving superior results in de-noising performance.

Keywords:

Algorithms Computed Tomography Images Noise Principal Component Analysis Signal-to-Noise Ratio

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Medical Imaging
Image Processing
Computer Vision

Background:

Medical image de-noising is crucial for accurate diagnosis.
Existing methods often struggle with preserving image details while removing noise.
Principal Component Analysis (PCA) and local pixel grouping are established techniques.

Purpose of the Study:

To develop an optimal algorithm for de-noising medical images.
To improve upon existing local pixel grouping and PCA methods.
To enhance the performance metrics of medical image de-noising.

Main Methods:

An improved local pixels grouping algorithm utilizing L(2) norm for block matching.
Integration of local pixels grouping with principal component analysis (PCA).
A two-stage de-noising and cleanup process with comparative, iterative refinement based on PSNR and SSIM.

Main Results:

The proposed algorithm demonstrates significant superiority in de-noising medical images.
Achieved higher Peak Signal to Noise Ratio (PSNR) values compared to other methods.
Attained higher Structural Similarity Index Measure (SSIM) values, indicating better image fidelity.

Conclusions:

The presented algorithm offers an effective solution for medical image de-noising.
The combination of enhanced local pixel grouping and PCA yields superior de-noising performance.
The iterative cleanup stage effectively optimizes image quality based on PSNR and SSIM criteria.