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Automatic Identification of Dendritic Branches and their Orientation

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

Automated analysis of nerve-cell images using active contour models.

Y L Fok¹, J K Chan, R T Chin

¹Dept. of Comput. Sci., Hong Kong Univ. of Sci. & Technol., Kowloon.

IEEE Transactions on Medical Imaging

|January 1, 1996

Summary

Automating nerve fiber analysis from electron microscopy images significantly speeds up neuroanatomical studies. This new method accurately measures axon size and shape, overcoming limitations of manual counting.

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Area of Science:

Neuroscience
Computational Biology
Medical Imaging

Background:

Axon count, size, and shape are crucial neuroanatomical features.
Manual analysis of numerous nerves is time-consuming and limits research scope.

Purpose of the Study:

To develop an automated method for analyzing axons in electron-micrographic images.
To improve the efficiency and accuracy of neuroanatomical studies.

Main Methods:

Utilized an elliptical Hough transform for initial axon center identification.
Employed active contour models (snakes) for precise axon boundary extraction and sheath measurement.
Implemented a conflict resolution scheme to eliminate false detections caused by image quality or irrelevant features.

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Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Published on: November 14, 2010

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Last Updated: Jul 8, 2026

Automatic Identification of Dendritic Branches and their Orientation

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Published on: November 14, 2010

Main Results:

The automated method successfully identified and measured axons from nerve images.
The conflict resolution scheme improved the accuracy of axon detection.

Conclusions:

The developed automated method offers an efficient alternative to manual axon analysis.
This technique can facilitate large-scale neuroanatomical research by overcoming manual measurement limitations.