.png){kind=icon}
.png){kind=icon}
Return
Brain tumor segmentation in MR images using a sparse constrained level set algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2020.114262.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Brain tumor segmentation using Magnetic Resonance (MR) Imaging technology plays a significant role in computer-aided brain tumor diagnosis. However, when applying classic segmentation methods, limitations such as inhomogeneous intensity, complex physiological structure and blurred tissues boundaries in brain MR images usually lead to unsatisfactory results. To address these issues, this paper proposes an automatic sparse constrained level set method to realize the brain tumor segmentation in MR images. By studying brain tumor images, this method finds out common characteristics of brain tumors' shape and constructs a sparse representation model. By considering this model as a prior constraint, an energy function based on level set method is constructed. In experiments, the proposed method can achieve an average accuracy of 96.20% for the MR images from the dataset Brats2017 and performs better than the others. With lower false positive rate and stronger robustness, the experimental results show that the proposed method can segment brain tumor from MR image accurately and stably.
Keywords:
Brain tumor
Sparse constraint
Level set
Image segmentation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W