Article2012

Support Vector Machine Classification Based on Correlation Prototypes Applied to Bone Age Assessment

Markus Harmsen · H. Schramm · Thomas Seidl · Thomas Martin Deserno

60 citations0/0 files storedIEEE Journal of Biomedical and Health Informatics

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Forensic Anthropology and Bioarchaeology Studies Image Retrieval and Classification Techniques Radiomics and Machine Learning in Medical Imaging

Abstract

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.

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2002Assessment of skeletal maturity and prediction of adult height (TW3 method).2009The BoneXpert Method for Automated Determination of Skeletal Maturity 2007Bone age assessment of children using a digital hand atlas 2001Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction 2005Hand Bone Age: A Digital Atlas of Skeletal Maturity 2010Validation and Reference Values of Automated Bone Age Determination for Four Ethnicities 2000Digital hand atlas and web-based bone age assessment: system design and implementation

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2018Skeletal Maturity Recognition Using a Fully Automated System With Convolutional Neural Networks 2019A multi-scale data fusion framework for bone age assessment with convolutional neural networks 2021Fully Automatic Model Based on SE-ResNet for Bone Age Assessment 2014Support vector machines classification based on particle swarm optimization for bone age determination 2019A bone age assessment system for real-world X-ray images based on convolutional neural networks 2020

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