IEEE J Biomed Health Inform - Support vector machine classification based on correlation prototypes applied to bone age assessment.

Tópicos

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Resumo

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: (i) extract 14 epiphyseal regions from the radiographs, (ii) for each region, retain image features using the IRMA framework, (iii) use these features to build a classifier model (training phase), (iv) evaluate performance on cross validation schemes (testing phase), (v) 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 (kNN) classification on 1,097 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 6 prominent regions (prototypes) is 91.57% and 96.16%, respectively, for accepting about two years age range.

Resumo Limpo

bone age assess baa hand radiograph frequent time consum task radiolog present method semiautomat baa done sever step extract epiphys region radiograph ii region retain imag featur use irma framework iii use featur build classifi model train phase iv evalu perform cross valid scheme test phase v classifi unknown hand imag applic phase paper combin support vector machin svm crosscorrel prototyp imag class prototyp obtain choos one random hand per class systemat evalu present compar nomin realvalu svm k nearest neighbor knn classif hand radiograph diagnost class year mean error age predict year nn svm respect accuraci nomin realvalu svm base promin region prototyp respect accept two year age rang

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