摘要
Active shape models (ASM), consisting of a shape model and a local gray-level appearance model, can be used to locate the objects in images. In original ASM scheme, the model of object′s gray-level variations is based on the assumption of one-dimensional sampling and searching method. In this work a new way to model the gray-level appearance of the objects is explored, using a two-dimensional sampling and searching technique in a rectangular area around each landmark of object shape. The ASM based on this improvement is compared with the original ASM on an identical medical image set for task of spine localization. Experiments demonstrate that the method produces significantly fast, effective, accurate results for spine localization in medical images.
主动形状模型 ( ASM)通过把物体的形状模型及其相应的局部灰度表象模型有机结合以实现图像中的物体定位。在传统 ASM中 ,对物体形状的灰度变化 (局部灰度表象 )的建模建立在一维采样与搜索方法的基础之上 ,故其定位准确性低且速度慢。本文提出了一种在物体形状每一特征点周围的矩形邻域中使用二维采样与搜索技术 ,从而改进物体形状的局部灰度表象模型的方法。针对医学图像中的物体定位问题 ,本文比较了基于这种改进的 ASM模型和传统 ASM模型对同一医学图像集中的脊柱定位能力。实验表明 ,基于这种改进的 ASM模型能快速有效地对医学图像中的脊柱 (物体 )
