CAJ | 학술논문

针对腹部器官边缘模糊、形状差异大、小样本集合难建立统计模型等问题,提出了基于多分辨率统计集成模型和曲面缺失数据恢复的混合图像分割算法.该算法根据器官模型的纹理特征,建立外观轮廓模型;并定义标志点自信度.对于自信度较高的点,使用基于主动图像搜索和模型变形的方法进行分割;将自信度较低的点视为未知点,利用统计模型和自信度高的已知点进行数据恢复.实验结果表明,该混合算法可成功地降低器官分割的平均误差.
침대복부기관변연모호、형상차이대、소양본집합난건립통계모형등문제,제출료기우다분변솔통계집성모형화곡면결실수거회복적혼합도상분할산법.해산법근거기관모형적문리특정,건립외관륜곽모형;병정의표지점자신도.대우자신도교고적점,사용기우주동도상수색화모형변형적방법진행분할;장자신도교저적점시위미지점,이용통계모형화자신도고적이지점진행수거회복.실험결과표명,해혼합산법가성공지강저기관분할적평균오차.
The segmentation of abdominal CT series is a challenging task due to problems such as blur edges, large variance among individuals and small sample sizes. In this paper, a hybrid 3D surface segmentation algorithm based on a multi-resolution inter'areal model and missing data recovery technique is proposed. The appearance models to characterize the texture features around surface points are established, and the"confidence level (CFL)"for each point is defined. For the points which have high confidence, segmentation is accomplished by active image searching and model deformation. While for the points which have low confidence, instead of using unreliable edge information, data recovery technique is applied based on a statistical deformable model and available high confidence points. The experimental results demonstrate that the Hyhrid-MISTO achieves the lowest segmentation error compared with a variety of state-of-the-art techniques such as Snake, ASM, and MISTO.