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Related Concept Videos

Computed Tomography01:10

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...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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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Using autoencoders for mammogram compression.

Chun Chet Tan1, Chikkannan Eswaran

  • 1Faculty of Information Technology, Multimedia University, 63100 Cyberjaya, Selangor, Malaysia. cctan@mmu.edu.my

Journal of Medical Systems
|August 13, 2010
PubMed
Summary
This summary is machine-generated.

Training autoencoders for medical image compression is feasible using image patches. Autoencoders without Restricted Boltzmann Machine pre-training demonstrate superior performance in mammogram compression compared to those with pre-training.

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Published on: August 30, 2013

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Biomedical Engineering

Background:

  • Medical images like mammograms are large, posing challenges for autoencoder training.
  • Whole-image training for autoencoders can be computationally intensive and difficult.
  • Image compression is crucial for efficient storage and transmission of medical data.

Purpose of the Study:

  • To investigate the effectiveness of autoencoder neural networks for medical image compression.
  • To evaluate training autoencoders using image patches as an alternative to whole-image training.
  • To compare the compression performance of different autoencoder architectures.

Main Methods:

  • Utilized autoencoder neural networks for medical image compression.
  • Implemented training strategies using image patches instead of entire mammograms.
  • Assessed compression performance using Mean Square Error (MSE) and Structural Similarity Index (SSIM).
  • Compared autoencoders with and without Restricted Boltzmann Machine (RBM) pre-training.

Main Results:

  • Autoencoders can be successfully trained using image patches for medical image compression.
  • The autoencoder architecture that omits Restricted Boltzmann Machine pre-training achieved better compression results.
  • Performance was evaluated using quantitative metrics like Mean Square Error and Structural Similarity Index.

Conclusions:

  • Training autoencoders on image patches is a viable method for compressing large medical images like mammograms.
  • Autoencoders without Restricted Boltzmann Machine pre-training offer improved compression performance for mammograms.
  • This approach facilitates more efficient medical image compression techniques.