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Imaging Studies III: Computed Tomography

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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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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Related Experiment Video

Updated: Jan 9, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

996

Enhancing image compression through a novel Structural Fidelity Weighted Ensemble (SFWE) model.

Priya Stella Mary I1, Rashmi Siddalingappa2, Vinay M1

  • 1Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India.

Methodsx
|December 1, 2025
PubMed
Summary

A new Structural Fidelity Weighted Ensemble (SFWE) model enhances image compression by dynamically balancing Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) outputs, improving quality and structural preservation.

Keywords:
CRPCAPSNRSFWESSIMVD

Related Experiment Videos

Last Updated: Jan 9, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • The proliferation of digital images necessitates efficient storage and transmission solutions.
  • Existing image compression methods often struggle to balance compression ratio with reconstruction quality.

Purpose of the Study:

  • To introduce a novel Structural Fidelity Weighted Ensemble (SFWE) model for superior image compression.
  • To dynamically optimize the fusion of SVD and PCA outputs for enhanced image reconstruction.

Main Methods:

  • Developed a Structural Fidelity Weighted Ensemble (SFWE) model.
  • Employed a fast bounded scalar optimization strategy for dynamic weight estimation.
  • Ensured non-negativity and simplex constraints during optimization, reducing computational overhead compared to SQP and gradient descent.

Main Results:

  • Achieved high image quality with PSNR of 40 dB and SSIM of 0.95 across diverse datasets (natural, medical, remote sensing).
  • Outperformed traditional methods like DCT, Wavelet Transform, SVD, PCA, and JPEG2000 + CNN.
  • Demonstrated a favorable compression ratio, balancing file size reduction with visual fidelity.

Conclusions:

  • The SFWE model offers significant improvements in image quality and structural preservation for diverse applications.
  • Its adaptive nature and computational efficiency make it suitable for various image-intensive sectors.
  • SFWE provides an effective balance between compression efficiency and high-fidelity image reconstruction.