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Imaging Studies III: Computed Tomography01:27

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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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
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Convolution computations can be simplified by utilizing their inherent properties.
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The important convolution properties include width, area, differentiation, and integration properties.
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Effects of JPEG Compression on Vision Transformer Image Classification for Encryption-then-Compression Images.

Genki Hamano1, Shoko Imaizumi2, Hitoshi Kiya3

  • 1Graduate School of Science and Engineering, Chiba University, 1-33 Yayoicho, Chiba 263-8522, Japan.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

JPEG compression significantly reduces encrypted image data size while preserving Vision Transformer (ViT) classification accuracy. This method allows for efficient, high-accuracy image classification in the encrypted domain.

Keywords:
JPEG compressionencrypted domainencryption-then-compression systemimage classificationvision transformer

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Area of Science:

  • Computer Vision
  • Image Processing
  • Cryptography

Background:

  • Image classification in the encrypted domain is crucial for privacy preservation.
  • Previous methods achieved high accuracy but did not consider compression effects.

Purpose of the Study:

  • To evaluate the impact of JPEG compression on encrypted image classification using Vision Transformer (ViT).
  • To investigate the trade-off between data reduction and classification accuracy for compressed encrypted images.

Main Methods:

  • An encryption-then-compression system was used for test images.
  • A Vision Transformer (ViT) model was trained on plain images.
  • Encrypted test images were compressed using JPEG lossy compression.
  • Classification accuracy was verified on compressed encrypted images.

Main Results:

  • JPEG compression significantly reduces the data size of encrypted images (over 90% reduction with quality factor 85).
  • Classification accuracy is highly preserved, maintaining over 98% accuracy.
  • JPEG compression effectiveness was confirmed through comparison with linear quantization.

Conclusions:

  • Compressed encrypted images can be classified with high accuracy, comparable to uncompressed encrypted images.
  • This study demonstrates the first successful classification of JPEG-compressed encrypted images without significant accuracy loss.
  • The findings enable efficient and private image classification through significant data reduction.