Expected Label Value (ELV) for Image Segmentation

Supervised image soft-segmentation using the multi-atlas based Expected Label Value (ELV) approach.

https://www.nitrc.org/projects/elv

Version 1.1.1 (15.1 KB)
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1 Dec 2023

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This is the public Matlab implementation of medical image soft segmentation using the supervised multi-atlas based Expected Label Value (ELV) approach proposed by Aganj and Fischl (IEEE TMI 2021). This approach considers the probability of all possible atlas-to-image transformations and computes the ELV, thus bypassing deformable registration and avoiding the associated computational costs. A short tutorial is included in EXAMPLE.m.
This package also includes functions for FFT-based convolution, which can be used independently.

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Cite As

Iman Aganj (2026). Expected Label Value (ELV) for Image Segmentation (https://au.mathworks.com/matlabcentral/fileexchange/81283-expected-label-value-elv-for-image-segmentation), MATLAB Central File Exchange. Retrieved .

Aganj, Iman, and Bruce Fischl. “Multi-Atlas Image Soft Segmentation via Computation of the Expected Label Value.” IEEE Transactions on Medical Imaging, vol. 40, no. 6, Institute of Electrical and Electronics Engineers (IEEE), June 2021, pp. 1702–10, doi:10.1109/tmi.2021.3064661.

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General Information

MATLAB Release Compatibility

  • Compatible with any release

Platform Compatibility

  • Windows
  • macOS
  • Linux
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Version Published Release Notes
1.1.1

Adding a missing file (sizeD.m).
1.1.0

ELV segmentation has been extended to accept multi-channel images.
1.0.4

Minor update.
1.0.3

The IBSR example has been updated.
1.0.2

Minor update.
1.0.1

Minor update.
1.0.0