Automatic landmark detection in cephalometry using a modified Active Shape Model with sub image matching
Automatic landmark detection in cephalometry using a modified Active Shape Model with sub image matching
复制标题
使用带有子图像匹配的改进的主动形状模型进行头影测量中的自动标志检测
DOI：
10.1109/icmv.2007.4469276
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Saeed Sadri
中科院分区：
文献类型：
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作者：
Raheleh Kafieh;A. Mehri;Saeed Sadri
本文介绍了一种改进的主动形状模型(ASM)用于头影测量中的标志点自动检测，并结合了许多新的思想来提高其性能。在第一步，提取一些特征点来模拟头骨的大小、旋转和平移。利用学习矢量量化(LVQ)神经网络，根据图像的几何特征对图像进行分类。对于每一幅新图像，在知道新图像的类别的情况下，使用LVQ估计可能的地标坐标。然后应用改进的多分辨率ASM方法，并结合主成分分析(PCA)对每个模板进行分析，计算平均形状。然后使用局部搜索来找到与强度轮廓的最佳匹配，并且移动每个点以获得最佳位置。最后，在模板收敛后，采用基于互相关的子图像匹配过程来精确定位每个地标的确切位置。平均而言，地标的百分比在正确坐标的1毫米以内，百分比在1毫米以内，百分比在1毫米以内，这表明与其他建议的方法相比有明显的改进。
This paper introduces a modification on using active shape models (ASM) for automatic landmark detection in cephalometry and combines many new ideas to improve its performance. In first step, some feature points are extracted to model the size, rotation, and translation of skull. A learning vector quantization (LVQ) neural network is used to classify images according to their geometrical specifications. Using LVQ for every new image, the possible coordinates of landmarks are estimated, knowing the class of new image. Then a modified ASM with a multi resolution approach is applied and a principal component analysis (PCA) is incorporated to analyze each template and the mean shape is calculated. The local search to find the best match to the intensity profile is then used and every point is moved to get the best location. Finally a sub image matching procedure, based on cross correlation, is applied to pinpoint the exact location of each landmark after the template has converged. On average It percent of the landmarks are within 1 mm of correct coordinates,percent within 1 mm, and percent within 1 mm, which shows a distinct improvement on other proposed methods.