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Simultaneous segmentation and bias field estimation using local fitted images
| Content Provider | Scilit |
|---|---|
| Author | Wang, Lei Zhu, Jianbing Sheng, Mao Cribb, Adriena Zhu, Shao Cheng Pu, Jiantao |
| Copyright Year | 2018 |
| Description | Journal: Pattern Recognition Level set methods often suffer from boundary leakage and inadequate segmentation when used to segment images with inhomogeneous intensities. To handle this issue, a novel region-based level set method was developed, in which two different local fitted images are used to construct a hybrid region intensity fitting energy functional. This novel method enables simultaneous segmentation of the regions of interest and estimation of the bias fields from inhomogeneous images. Our experiments on both synthetic images and a publicly available dataset demonstrate the feasibility and reliability of the proposed method. |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5761354/pdf |
| Ending Page | 155 |
| Page Count | 11 |
| Starting Page | 145 |
| ISSN | 00313203 |
| DOI | 10.1016/j.patcog.2017.08.031 |
| Journal | Pattern Recognition |
| Volume Number | 74 |
| Language | English |
| Publisher | Elsevier BV |
| Publisher Date | 2018-02-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Pattern Recognition Radiology, Nuclear Medicine and Imaging Level Set Image Segmentation Local Fitted Images Intensity Inhomogeneity Bias Field |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Signal Processing Computer Vision and Pattern Recognition Software |
