title: Texture feature extraction based on wavelet transform and gray-level co-occurrence matrices applied to osteosarcoma diagnosis.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, ZhongShan School of Medicine, Sun Yat-Sen University, GuangZhou, 510060, PR China.
authors.forename: Shan
authors.initials: S
authors.lastname: Hu
authors.forename: Chao
authors.initials: C
authors.lastname: Xu
authors.forename: Weiqiao
authors.initials: W
authors.lastname: Guan
authors.forename: Yong
authors.initials: Y
authors.lastname: Tang
authors.forename: Yana
authors.initials: Y
authors.lastname: Liu
mesh_terms: D000293:Adolescent; D000465:Algorithms; D000704:Analysis of Variance; D019540:Area Under Curve; D001859:Bone Neoplasms / Q000000981:diagnostic imaging*; D001842:Bone and Bones / Q000473:pathology; D002648:Child; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D012516:Osteosarcoma / Q000000981:diagnostic imaging*; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D058067:Wavelet Analysis
keywords: Feature selection; gray-level co-occurrence matrix; osteosarcoma diagnosis; texture feature; wavelet transform