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Related Concept Videos

X-ray Imaging01:24

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...
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...
Ultrasonography01:17

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called a...

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Related Experiment Video

Updated: May 11, 2026

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
08:30

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

Fuzzy-based Medical X-Ray Image Classification.

Fatemeh Ghofrani1, Mohammad Sadegh Helfroush, Mahmoud Rashidpour

  • 1Department of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran.

Journal of Medical Signals and Sensors
|April 30, 2013
PubMed
Summary
This summary is machine-generated.

A new fuzzy scheme improves medical X-ray image classification by analyzing subimage features. This novel approach achieves higher accuracy than traditional methods like multilayer perceptron and support vector machines (SVM).

Keywords:
Fuzzy classifiermedical X-ray imagesmultilayer perceptronshape-texture featuressupport vector machines

Related Experiment Videos

Last Updated: May 11, 2026

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
08:30

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

Area of Science:

  • Medical Imaging
  • Computer Science
  • Artificial Intelligence

Background:

  • Accurate classification of medical X-ray images is crucial for diagnosis.
  • Existing classification methods may have limitations in handling complex image features.

Purpose of the Study:

  • To introduce a novel fuzzy scheme for enhanced medical X-ray image classification.
  • To evaluate the performance of the proposed fuzzy method against established classifiers.

Main Methods:

  • Images are partitioned into overlapping subimages.
  • Shape-texture features are extracted from each subimage.
  • A fuzzy membership function based on Euclidean distance is applied for classification.
  • Final classification is achieved through a max operation on summed fuzzy memberships.

Main Results:

  • The proposed fuzzy scheme was evaluated on 2655 radiographic images from the IRMA dataset.
  • Classification accuracy rates were compared using 300 training and 2355 test samples.
  • The fuzzy classifier demonstrated superior accuracy compared to multilayer perceptron and SVM classifiers.

Conclusions:

  • The novel fuzzy scheme offers a promising approach for medical X-ray image classification.
  • The method effectively utilizes shape-texture features and fuzzy logic for improved performance.
  • This technique provides a more accurate alternative to conventional classification algorithms in medical imaging.