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Histogram01:05

Histogram

The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in the 3500–3100 cm−1 range. Even though both O−H and N−H bonds vibrate at a similar...
Probability Histograms01:17

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
Transformations of Functions III01:20

Transformations of Functions III

Transformations modify the graphical representation of a function without changing its fundamental form. One common transformation is reflection, which flips the graph across a designated axis. When the vertical coordinates of all points are multiplied by the negative one, the entire graph is mirrored over the horizontal axis. This transformation reverses the vertical orientation of peaks and troughs, akin to signal inversion in electrical systems, where a waveform is flipped, but the timing of...

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Related Experiment Video

Updated: May 9, 2026

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

CUDA implementation of histogram stretching function for improving X-ray image.

Yong H Lee1, Kwan W Kim, Soon S Kim

  • 1Department of Computer Engineering, Halla University, Wonju, Gangwon-Do, Korea.

Studies in Health Technology and Informatics
|August 8, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a faster method for enhancing digital X-ray image contrast using histogram stretching on a Graphics Processing Unit (GPU). The CUDA program efficiently processes image data for real-time visibility improvements.

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

Published on: August 30, 2013

Related Experiment Videos

Last Updated: May 9, 2026

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

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

Area of Science:

  • Medical Imaging
  • Computer Science
  • Parallel Computing

Background:

  • Digital X-ray imaging requires high contrast for accurate diagnosis.
  • Histogram equalization is a common technique for contrast enhancement.
  • Real-time image processing demands efficient computational methods.

Purpose of the Study:

  • To develop and demonstrate a method for real-time contrast enhancement of digital X-ray images.
  • To optimize the histogram stretching function for Graphics Processing Unit (GPU) implementation.
  • To address the challenges of data transfer between GPU memory and host systems.

Main Methods:

  • Implementation of a histogram stretching algorithm using CUDA programming.
  • Leveraging GPU parallel processing capabilities for accelerated image contrast enhancement.
  • Managing data transfer protocols between GPU and host memory for efficient workflow.

Main Results:

  • Achieved significant speed-up in histogram stretching execution on the GPU.
  • Demonstrated effective contrast improvement in digital X-ray images.
  • Successfully managed complex data transfer for real-time processing.

Conclusions:

  • CUDA programming enables efficient real-time histogram stretching on GPUs for X-ray images.
  • The proposed method overcomes previous limitations in GPU-based image contrast enhancement.
  • This approach offers a viable solution for improving diagnostic accuracy through enhanced image visibility.