• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A Variational Model For Longitudinal Brain Tissue Segmentation

Vol. 16, No. 11, November 30, 2022
10.3837/tiis.2022.11.001, Download Paper (Free):

Abstract

Longitudinal quantification of brain changes due to development, aging or disease plays an important role in the filed of personalized-medicine applications. However, due to the temporal variability in shape and different imaging equipment and parameters, estimating anatomical changes in longitudinal studies is significantly challenging. In this paper, a longitudinal Magnetic Resonance(MR) brain image segmentation algorithm proposed by combining intensity information and anisotropic smoothness term which contain a spatial smoothness constraint and longitudinal consistent constraint into a variational framework. The minimization of the proposed energy functional is strictly and effectively derived from a fast optimization algorithm. A large number of experimental results show that the proposed method can guarantee segmentation accuracy and longitudinal consistency in both simulated and real longitudinal MR brain images for analysis of anatomical changes over time.


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Cite this article

[IEEE Style]
M. Tang, R. Chen, Z. You, "A Variational Model For Longitudinal Brain Tissue Segmentation," KSII Transactions on Internet and Information Systems, vol. 16, no. 11, pp. 3479-3492, 2022. DOI: 10.3837/tiis.2022.11.001.

[ACM Style]
Mingjun Tang, Renwen Chen, and Zijuan You. 2022. A Variational Model For Longitudinal Brain Tissue Segmentation. KSII Transactions on Internet and Information Systems, 16, 11, (2022), 3479-3492. DOI: 10.3837/tiis.2022.11.001.

[BibTeX Style]
@article{tiis:37995, title="A Variational Model For Longitudinal Brain Tissue Segmentation", author="Mingjun Tang and Renwen Chen and Zijuan You and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.11.001}, volume={16}, number={11}, year="2022", month={November}, pages={3479-3492}}