Yan Pingkun, Kassim Ashraf A
Department of Electrical and Computer Engineering, National University of Singapore, Singapore.
IEEE Trans Inf Technol Biomed. 2006 Oct;10(4):677-84. doi: 10.1109/titb.2006.874199.
This paper presents a new method for segmentation of medical images by extracting organ contours, using minimal path deformable models incorporated with statistical shape priors. In our approach, boundaries of structures are considered as minimal paths, i.e., paths associated with the minimal energy, on weighted graphs. Starting from the theory of minimal path deformable models, an intelligent "worm" algorithm is proposed for segmentation, which is used to evaluate the paths and finally find the minimal path. Prior shape knowledge is incorporated into the segmentation process to achieve more robust segmentation. The shape priors are implicitly represented and the estimated shapes of the structures can be conveniently obtained. The worm evolves under the joint influence of the image features, its internal energy, and the shape priors. The contour of the structure is then extracted as the worm trail. The proposed segmentation framework overcomes the short-comings of existing deformable models and has been successfully applied to segmenting various medical images.
本文提出了一种通过提取器官轮廓来分割医学图像的新方法，该方法使用了结合统计形状先验的最小路径可变形模型。在我们的方法中，结构的边界被视为加权图上的最小路径，即与最小能量相关的路径。从最小路径可变形模型理论出发，提出了一种智能“蠕虫”算法用于分割，该算法用于评估路径并最终找到最小路径。将先验形状知识纳入分割过程以实现更稳健的分割。形状先验以隐式方式表示，并且可以方便地获得结构的估计形状。蠕虫在图像特征、其内部能量和形状先验的共同影响下进化。然后将结构的轮廓提取为蠕虫轨迹。所提出的分割框架克服了现有可变形模型的缺点，并已成功应用于各种医学图像的分割。
