基于群体和特定病人的形状统计分割序列X片上的肺部区域  

Segmenting Lung Fields in Serial Chest Radiographs Using Both Population and Patient-Specific Shape Statistics

作　　者：史勇红[1] 戚飞虎[1] 栾红霞[1] 吴国荣[1] 

机构地区：[1]上海交通大学计算机科学和工程系,上海市200240

出　　处：《中国医疗器械杂志》2006年第4期264-267,255,共5页Chinese Journal of Medical Instrumentation

摘　　要：提出一种新的可变形模型,该可变形模型在分割序列X片图像上的肺部区域时,使用了群体和特定病人的形状统计约束。首先,一个修改的尺度不变特征变换(SIFT)局部描述算子被用于表示每一像素周围的图像特征,以便模型是以寻找区域内最相似的SIFT局部描述算子的方式变形：其次,可变形模型受到基于群体和基于特定病人的形状统计的约束。最初,当序列图像的个数较少时,基于群体的形状统计起主导作用,逐渐地当同一病人的一定数量的图像被分割出来后,特定病人的形状统计开始起越来越强的约束作用。实验证明,所建的可变形模型,能自适应地分割不同病人的形状变化,可获得较为鲁棒和精确的分割结果。This paper presents a new deformable model using both population-based and patient-specific shape statistics to segment lung fields from serial chest radiographs. First, a modified scale-invariant feature transform （SIFT） local descriptor is used to characterize the image features in the vicinity of each pixel, so that the deformable model deforms in a way that seeks for the region with similar SIFT local descriptors; second, the deformable model is constrained by both population-based and patient-specific shape statistics. At first, population-based shape statistics plays an leading role when the number of serial images is small, and gradually, patient-specific shape statistics plays a more and more important role after a sufficient number of segmentation results on the same patient have been obtained. The proposed deformable model can adapt to the shape variability of different patients, and obtain more robust and accurate segmentation results.

关 键 词：可变形模型 序列胸腔X片 形状统计 图像分割 SIFT局部描述算子 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]