Hu Shan, Xu Chao, Guan Weiqiao, Tang Yong, Liu Yana
Department of Biomedical Engineering, ZhongShan School of Medicine, Sun Yat-Sen University, GuangZhou, 510060, PR China.
Biomed Mater Eng. 2014;24(1):129-43. doi: 10.3233/BME-130793.
Osteosarcoma is the most common malignant bone tumor among children and adolescents. In this study, image texture analysis was made to extract texture features from bone CR images to evaluate the recognition rate of osteosarcoma. To obtain the optimal set of features, Sym4 and Db4 wavelet transforms and gray-level co-occurrence matrices were applied to the image, with statistical methods being used to maximize the feature selection. To evaluate the performance of these methods, a support vector machine algorithm was used. The experimental results demonstrated that the Sym4 wavelet had a higher classification accuracy (93.44%) than the Db4 wavelet with respect to osteosarcoma occurrence in the epiphysis, whereas the Db4 wavelet had a higher classification accuracy (96.25%) for osteosarcoma occurrence in the diaphysis. Results including accuracy, sensitivity, specificity and ROC curves obtained using the wavelets were all higher than those obtained using the features derived from the GLCM method. It is concluded that, a set of texture features can be extracted from the wavelets and used in computer-aided osteosarcoma diagnosis systems. In addition, this study also confirms that multi-resolution analysis is a useful tool for texture feature extraction during bone CR image processing.
骨肉瘤是儿童和青少年中最常见的恶性骨肿瘤。在本研究中，进行了图像纹理分析，以从骨骼CR图像中提取纹理特征，从而评估骨肉瘤的识别率。为了获得最佳特征集，将Sym4和Db4小波变换以及灰度共生矩阵应用于图像，并使用统计方法来最大化特征选择。为了评估这些方法的性能，使用了支持向量机算法。实验结果表明，就骨骺处骨肉瘤的发生而言，Sym4小波的分类准确率（93.44%）高于Db4小波；而对于骨干处骨肉瘤的发生，Db4小波具有更高的分类准确率（96.25%）。使用小波获得的包括准确率、灵敏度、特异性和ROC曲线在内的结果均高于使用灰度共生矩阵方法得出的特征所获得的结果。得出的结论是，可以从小波中提取一组纹理特征并将其用于计算机辅助骨肉瘤诊断系统。此外，本研究还证实，多分辨率分析是骨骼CR图像处理过程中纹理特征提取的有用工具。
