Expected Label Value (ELV) for Image Segmentation
バージョン 1.1.1 (15.1 KB) 作成者:
Iman Aganj
Supervised image soft-segmentation using the multi-atlas based Expected Label Value (ELV) approach.
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.
引用
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. 取得済み .
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.
MATLAB リリースの互換性
作成:
R2020b
すべてのリリースと互換性あり
プラットフォームの互換性
Windows macOS Linuxタグ
謝辞
ヒントを得たファイル: Mid-Space-Independent and IDIR Deformable Image Registration
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!