A Novel Coarse-to-fine Level Set Framework for Ultrasound Image Segmentation
文献类型：会议论文
| 作者 | Jinze Yu; Pheng-Ann Heng; Weiming Wang; Jing Qin; |
| 出版日期 | 2015 |
| 会议名称 | The Second International Conference on Artificial Intelligence and Pattern Recognition (AIPR2015) |
| 会议地点 | Shenzhen, China |
| 英文摘要 | Ultrasound image segmentation is a fundamental but undoubtedly challenging problem in many medical applications due to various unpleasant artifacts, e.g., noise, low contrast and intensity inhomogeneity. This paper presents a coarse-to-fine framework for ultrasound image segmentation based on a preprocessing step via speckle reducing anisotropic diffusion (SRAD) and a modified version of Chan-Vese model by proposing novel evolution functional involving the Sobolev gradient. SRAD is a diffusion method tailored for ultrasound image denoising, and is adopted here to construct a despeckled image which allows us to obtain a coarse segmentation of the input image by carrying out our proposed CV model. This coarse segmentation will be further used by our level set model as a constraint to guide the fine segmentation. We compare the proposed model with some famous region-based level set methods. Experimental results in both synthetic and clinical ultrasound images validate the high accuracy and robustness of our approach, indicating its potential for practical applications in ultrasound imaging. |
| 收录类别 | 其他 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/6786]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2015 |
| 推荐引用方式 GB/T 7714 | Jinze Yu,Pheng-Ann Heng,Weiming Wang,et al. A Novel Coarse-to-fine Level Set Framework for Ultrasound Image Segmentation[C]. 见:The Second International Conference on Artificial Intelligence and Pattern Recognition (AIPR2015). Shenzhen, China. |
入库方式： OAI收割
来源：深圳先进技术研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。