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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...
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and the...
Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...

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Related Experiment Video

Updated: Jul 3, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Radiomics-based mammographic abnormality identification via radiologist annotations.

Ravi Bullock1, Yiwen Xu1, Rasika Rajapakshe1

  • 1Department of Medical Physics, BC Cancer, Kelowna, BC V1Y 5L3, Canada.

BJR Artificial Intelligence
|July 2, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a radiomics pipeline to detect abnormalities on mammograms. The models showed promise in distinguishing abnormal from normal breast tissue, aiding breast cancer detection.

Keywords:
artificial intelligencebreast cancerbreast cancer risk assessmentmammographyradiomics

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Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Related Experiment Videos

Last Updated: Jul 3, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiomics

Background:

  • Screening mammography is crucial for early breast cancer detection.
  • Distinguishing subtle abnormalities from normal tissue remains a challenge.
  • Radiomics offers a quantitative approach to analyze medical images.

Purpose of the Study:

  • To develop and evaluate a radiomics-based pipeline for identifying suspicious findings on 2D screening mammograms.
  • To train machine learning models to differentiate between radiologist-annotated abnormalities and normal breast tissue.

Main Methods:

  • A retrospective study analyzed 1604 screening mammograms from 1294 participants.
  • Radiomics features were extracted from regions of interest (ROIs) with abnormalities and normal tissue.
  • Multiple machine learning classifiers were trained and evaluated using the area under the receiver operating characteristic curve (AUC).

Main Results:

  • The radiomics pipeline achieved AUC values ranging from 0.69 to 0.73.
  • No significant differences were observed between the performance of different machine learning models.
  • The highest nominal performance (AUC: 0.73) was achieved using ANOVA F-score feature selection and Discriminant Analysis (DA).

Conclusions:

  • The developed radiomics pipeline demonstrates potential in differentiating abnormalities from normal tissue on screening mammograms.
  • Radiomics shows promise for enhancing breast cancer detection and integrating advanced machine learning into screening workflows.