A distance regularized level-set evolution model based MRI dataset segmentation of brain’s caudate nucleus

Chen, Y., Chen, G., Wang, Y., Dey, N., Sherratt, R. S. ORCID: https://orcid.org/0000-0001-7899-4445 and Shi, F. (2019) A distance regularized level-set evolution model based MRI dataset segmentation of brain’s caudate nucleus. IEEE Access, 7. pp. 124128-124140. ISSN 2169-3536 doi: 10.1109/ACCESS.2019.2937964

Abstract/Summary

The caudate nucleus of the brain is highly correlated to the emotional decision-making of pessimism, which is an important process for improving the understanding and treatment of depression; and the segmentation of the caudate nucleus is the most basic step in the process of analysis and research concerning this region. In this paper, Level Set Method (LSM) is applied for caudate nucleus segmentation. Firstly, Distance Regularized Level Set Evolution (DRLSE), Region-Scalable Fitting (RSF) and Local Image Fitting (LIF) models are proposed for segmentation of the caudate nucleus of Magnetic Resonance Imaging (MRI) images of the brain, and the segmentation results are compared by using selected evaluation indices. The average Dice Similarity Coefficient (DSC) values of the proposed three methods all exceed 85%, and the average Jaccard Similarity (JS) values are over 77%, respectively. The results indicate that all these three models can have good segmentation results for medical images with intensity inhomogeneity and meet the general segmentation requirements, while the proposed DRLSE model performs better in segmentation.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/85943
Identification Number/DOI	10.1109/ACCESS.2019.2937964
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Publisher	IEEE
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