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Related Concept Videos

Design Example: Measuring Distance Between Two Points with Obstructions01:10

Design Example: Measuring Distance Between Two Points with Obstructions

66
When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
66

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Related Experiment Video

Updated: Jul 24, 2025

Visualizing Scar Development Using SCAD Assay - An Ex-situ Skin Scarring Assay
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A deep learning-based automatic tool for measuring the lengths of linear scars: forensic applications.

Jian Zhou1, Zhilu Zhou2,3, Xinjian Chen1

  • 1School of Electronics and Information Engineering, Soochow University, Suzhou, China.

Forensic Sciences Research
|July 7, 2023
PubMed
Summary

This study introduces an automated method for measuring linear scar length using smartphone photos. Combining photogrammetry and deep learning, it offers accurate, objective scar assessment, reducing manual measurement variability.

Keywords:
deep learningimage segmentationmultiview stereophotogrammetry technologystructure from motion

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Manual scar measurement is subjective and prone to errors.
  • Digital imaging and AI offer potential for objective, automated measurement.
  • Photogrammetry is emerging in practical applications for non-contact measurement.

Purpose of the Study:

  • To develop an automatic method for measuring linear scar length.
  • To leverage multiview stereo and deep learning for scar measurement.
  • To provide an accurate and objective alternative to manual scar assessment.

Main Methods:

  • Utilized structure from motion (multiview stereo) for 3D reconstruction.
  • Employed a convolutional neural network-based image segmentation algorithm.
  • Developed an automatic scar segmentation and measurement process using smartphone images.

Main Results:

  • Simulation experiments on artificial scars showed length errors <5%.
  • Clinical scar sample measurements demonstrated high agreement with manual methods (average error 3.69%).
  • The automated method proved reliable and accurate for scar length measurement.

Conclusions:

  • Photogrammetry is an effective tool for scar measurement.
  • Deep learning enables accurate and automated scar length assessment.
  • The proposed method reduces subjectivity and improves measurement consistency.