Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review
By Jasjit S. Suri, Kecheng Liu, Sameer Singh, Swamy Laxminarayan, Xiaolan Zeng, Laura Reden. IEEE Transactions on Information Technology in Biomedicine, 2002
387 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/5bc72a3eca225fece325fab5ad0393b07a89b24e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Jasjit S. Suri
- Daniel Racoceanu
- Antoine Veillard
- C. Chui
- Bing Nan Li
- Ludovic Roux
- Sim Heng Ong
- Stephen K. Y. Chang
In Computer Vision and Pattern Recognition
- J. Manuel
- Luca Saba
- Mainak Biswas
- Jasjit S. Suri
- Daniel Racoceanu
- Antoine Veillard
- Roberto Frias
- Zhen Ma
Citations in the corpus
- Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation (2011, methodology): “It is appropriate to refer the work presented in this paper to those incorporating prior knowledge into deformable models [10,11,13,21].”
- The Virtual Family—development of surface-based anatomical models of two adults and two children for dosimetric simulations (2010, methodology): “…(Neufeld 2008). iSEG provides a large number of different image processing algorithms, such as Live Wire (Schenk et al 2000), Thresholding (Suri et al 2002), Region Growing (Hamerly and Elkan 2002), Fuzzy Connectedness (Udupa and Saha 2003), Interactive Watershed Transformation (Hahn and…”
- State-of-the-art review on deep learning in medical imaging. (2019, background): “In segmentation, deformable models (33) are generally used for inferring the shape of infected/abnormal region in a medical image.”
- A review of algorithms for medical image segmentation and their applications to the female pelvic cavity (2010, background): “Compared with the former segmentation results, the structure boundaries are more regular and less influenced by the noises due to the smoothing items defined in the speed function.”
- Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential (2014, background): “(21) While the emission pattern captures the tissue-dependent staining, the excitation pattern captures the illumination.”
- Edge and line oriented contour detection: State of the art (2011, neutral): “For an overview, we refer to [204,205].”