- 期刊
- OpenAccess
Evaluation of Level Set Segmentation for Medical Images with Intensity Inhomogeinity
並列摘要
Image segmentation is an important area in the medical image guide surgery. Major advances in the field of medical imaging provide richer information in clinical applications and support the advancement in the biomedical knowledge. With the growing research on image segmentation, it has become crucial challenge for the images with inhomogeneity in intensity. Level set is the most powerful and broadly used segmentation technique in the medical image processing. This study aims at making a review on the current level set methods such as Region Scalable Fitting (RSF), Statistical and Variational Multiphase Level Set (SVMLS) and Local Clustering based Variational Level Set (LCVLS) developed for intensity inhomogeneous medical image segmentation. Experiments that apply these algorithms to segment the medical images are presented to highlight the distinct characteristics of each method. Results prove that the LCVLS method is most suitable and accurate for intensity inhomogeneous medical image segmentation.
延伸閱讀
- Chen, H., & Xu, Z. (2021). A new end-to-end network model for medical image segmentation. 淡江理工學刊, 24(2), 207-213. https://doi.org/10.6180/jase.202104_24(2).0009
- 許家群（2006）。Edge-based Region Growing and Interactive Editing Tool for Medical Image Segmentation〔碩士論文，國立臺灣大學〕。華藝線上圖書館。https://doi.org/10.6342/NTU.2006.02690
- Renugadevi, M., Varghese, D., Vaithiyanathan, V., & Raju, N. (2012). Variational Level Set Segmentation and Bias Correction of Fused Medical Images. Asian Journal of Medical Sciences, 4(2), 66-74. https://www.airitilibrary.com/Article/Detail?DocID=20408773-201204-201510290013-201510290013-66-74
- Wu, C. C., Lin, K. P., & Chung, B. T. (2007). A Study of Image Quality Analysis for a Cutaneous Capillary Imaging System. Journal of Medical and Biological Engineering, 27(3), 110-115. https://www.airitilibrary.com/Article/Detail?DocID=16090985-200709-27-3-110-115-a
- 陳鴻鈞（2022）。Study of DEFU-Net with Cross-Attention Mechanism for Medical Image Segmentation〔碩士論文，淡江大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0002-3008202206381900