Automatic scoliosis detection based on local centroids evaluation on moire topographic images of human backs
Automatic scoliosis detection based on local centroids evaluation on moire topographic images of human backs
复制标题
基于人体背部云纹地形图像局部质心评估的脊柱侧弯自动检测
DOI：
10.1109/42.974926
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发表时间：
2001
影响因子：
10.6
通讯作者：
M. Viergever
中科院分区：
文献类型：
--
作者：
Hyoungseop Kim;S. Ishikawa;Y. Otsuka;H. Shimizu;Takashi Shinomiya;M. Viergever
提出了一种基于人体背部云纹图像的脊柱侧凸计算机自动检测技术。脊柱侧弯是青少年常患的一种严重疾病。为了预防，日本的学校采用云纹法进行筛查，医生从视觉上检查受试者背部的云纹图像。对学校筛查收集的大量云纹图像进行检查，使医生疲惫不堪，导致误判。因此，计算机辅助诊断脊柱侧凸已被骨科医生迫切要求。为了使检测过程自动化，与现有的三维技术不同，在本技术中，局部质心的位移在云纹图像的左侧和右侧之间进行二维评估。该技术应用于真实的云纹图像，以区分正常和异常情况。根据略去法，将全部120张图像数据（60张正常和60张异常）分离为3个数据集。在二维特征空间上定义基于Mahalanobis距离的线性判别函数，选取其中一个包含40幅云纹图像的数据集，对其余两个数据集中的80幅图像进行分类。该技术最终实现了平均分类率88.3%。
This paper presents a technique for automating human scoliosis detection by computer based on moire topographic images of human backs. Scollosis is a serious disease often suffered by teenagers. For prevention, screening is performed at schools in Japan employing a moire method in which doctors inspect moire images of subjects' backs visually. The inspection of a large number of moire images collected by the school screening causes exhaustion of doctors and leads to misjudgment. Computer-aided diagnosis of scoliosis has, therefore, been requested eagerly by orthopedists. To automate the inspection process, unlike existent three-dimensional techniques, displacement of local centroids is evaluated two-dimensionally between the left-hand side and the right-hand side of the moire images in the present technique. The technique was applied to real moire images to draw a distinction between normal and abnormal cases. According to the leave-out method, the entire 120 image data (60 normal and 60 abnormal) were separated into three data sets. The linear discriminant function based on Mahalanobis distance was defined on the two-dimensional feature space employing one of the data sets containing 40 moire images and classified 80 images in the remaining two sets. The technique finally achieved the average classification rate of 88.3%.