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Related Experiment Video

Updated: Jul 10, 2026

Image Rendering Techniques in Postmortem Computed Tomography: Evaluation of Biological Health and Profile in Stranded Cetaceans

Published on: September 27, 2020

An application of image processing techniques in computed tomography image analysis.

Fintan J McEvoy¹

¹Department of Small Animal Clinical Sciences, Faculty of Life Sciences, Frederiksberg Campus, University of Copenhagen, Frederiksberg C, Denmark. fme@life.ku.dk

Veterinary Radiology & Ultrasound : the Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association

|November 21, 2007

Summary

Automated image analysis using ImageJ software accurately measures subcutaneous adipose tissue thickness in animals. This method reduces human error and enhances body composition studies.

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Area of Science:

Veterinary Imaging
Biomedical Image Analysis
Animal Body Composition

Background:

Accurate measurement of subcutaneous adipose tissue is crucial for body composition studies.
Manual measurement methods are prone to human error and are time-consuming for large datasets.
Automation is desirable for consistent and efficient data collection in veterinary imaging.

Purpose of the Study:

To develop an automated ImageJ macro for measuring subcutaneous adipose tissue thickness.
To eliminate manual marker placement errors in animal imaging studies.
To facilitate efficient data acquisition from multiple animal subjects and image slices.

Main Methods:

Utilized the open-source ImageJ program to create a macro for automated image analysis.
Implemented image processing steps including background removal and animal centering.
Automated the detection of subcutaneous adipose tissue boundaries along rotated image segments.

Main Results:

The macro successfully automated the measurement of subcutaneous adipose tissue thickness.
The automated process minimized human error associated with manual measurements.
Generated images displaying detected boundary points for visual verification of measurement sites.

Conclusions:

The developed ImageJ macro provides a reliable and efficient method for automated subcutaneous adipose tissue measurement.
This automated approach is applicable to veterinary imaging and enhances body composition analysis.
Standard image processing techniques can be effectively applied to veterinary imaging challenges.