Expected Label Value (ELV) for Image Segmentation

Version 1.1.1 (15.1 KB) by Iman Aganj

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

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

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Updated 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.

Cite As

Iman Aganj (2024). Expected Label Value (ELV) for Image Segmentation (https://www.mathworks.com/matlabcentral/fileexchange/81283-expected-label-value-elv-for-image-segmentation), MATLAB Central File Exchange. Retrieved April 25, 2024.

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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MATLAB Release Compatibility

Created with R2020b

Compatible with any release

Platform Compatibility

Windows macOS Linux

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Acknowledgements

Inspired by: Mid-Space-Independent and IDIR Deformable Image Registration

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Version	Published	Release Notes
1.1.1	1 Dec 2023	Adding a missing file (sizeD.m).	Download
1.1.0	1 May 2023	ELV segmentation has been extended to accept multi-channel images.	Download
1.0.4	2 Jun 2021	Minor update.	Download
1.0.3	25 Jan 2021	The IBSR example has been updated.	Download
1.0.2	22 Oct 2020	Minor update.	Download
1.0.1	16 Oct 2020	Minor update.	Download
1.0.0	16 Oct 2020		Download

MLA	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.
APA	Aganj, I., & Fischl, B. (2021). Multi-Atlas Image Soft Segmentation via Computation of the Expected Label Value. IEEE Transactions on Medical Imaging, 40(6), 1702–1710. Institute of Electrical and Electronics Engineers (IEEE). Retrieved from https://doi.org/10.1109%2Ftmi.2021.3064661
BibTeX	@article{Aganj_2021, doi = {10.1109/tmi.2021.3064661}, url = {https://doi.org/10.1109%2Ftmi.2021.3064661}, year = 2021, month = {jun}, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, volume = {40}, number = {6}, pages = {1702--1710}, author = {Iman Aganj and Bruce Fischl}, title = {Multi-Atlas Image Soft Segmentation via Computation of the Expected Label Value}, journal = {{IEEE} Transactions on Medical Imaging} }