Support Vector Machine Classification Based on Correlation Prototypes Applied to Bone Age Assessment
Support Vector Machine Classification Based on Correlation Prototypes Applied to Bone Age Assessment
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DOI：
10.1109/titb.2012.2228211
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发表时间：
2013-01-01
影响因子：
7.7
通讯作者：
Deserno, Thomas M.
中科院分区：
文献类型：
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作者：
Harmsen, Markus;Fischer, Benedikt;Deserno, Thomas M.
手部X线片上的骨龄评估(BAA)是放射学中一项频繁且耗时的任务。我们提出了一种(半)自动骨痂自动分析的方法，该方法分为几个步骤：1)从X光片中提取14个骨痂区域；2)对于每个区域，利用医学应用框架中的图像检索保留图像特征；3)利用这些特征建立分类器模型(训练阶段)；4)评估交叉验证方案的性能(测试阶段)；5)对未知手部图像进行分类(应用阶段)。在本文中，我们结合一个支持向量机(支持向量机)与互相关的每一类的原型图像。这些原型是在每个类中随机选择一只手来获得的。本文对标称支持向量机、实值支持向量机和k近邻分类法在30个诊断类别(0-19年)的1097张手部X线片上进行了系统评价。5-NN和支持向量机预测年龄的平均误差分别为1.0岁和0.83岁。基于六个突出区域(原型)的标称支持向量机和实值支持向量机，在接受两年左右的年龄范围时，准确率分别为91.57%和96.16%。
Bone age assessment (BAA) on hand radiographs is a frequent and time-consuming task in radiology. We present a method for (semi) automatic BAA which is done in several steps: 1) extract 14 epiphyseal regions from the radiographs; 2) for each region, retain image features using the image retrieval in medical application framework; 3) use these features to build a classifier model (training phase); 4) evaluate performance on cross-validation schemes (testing phase); 5) classify unknown hand images (application phase). In this paper, we combine a support vector machine (SVM) with cross correlation to a prototype image for each class. These prototypes are obtained choosing one random hand per class. A systematic evaluation is presented comparing nominal-and real-valued SVM with k nearest neighbor classification on 1097 hand radiographs of 30 diagnostic classes (0-19 years). Mean error in age prediction is 1.0 and 0.83 years for 5-NN and SVM, respectively. Accuracy of nominal-and real-valued SVM based on six prominent regions (prototypes) is 91.57% and 96.16%, respectively, for accepting about two years age range.