Abstract
Human skeletal maturity has been typically estimated from radiographic images of the non-dominant hand through a subjective analysis performed by expert radiologists. In this paper we present a semiautomatic learning approach for estimating bone age. We consider five regions of interest, shortly ROIs, located between metacarpal and phalanges, which are obtained by placing strategic landmarks. ROI images are reshaped in the form of vectors which are merged in order to generate aligned feature vectors of each hand. The method consists of two stages, training and testing, for which radiographic images of female gender were used in a range of 1 to 18 years old. The training stage focuses on structuring the feature vectors of 300 bone-age-labeled images to generate a set of prototypes for a regression classifier. The second step is to approximate the bone age of a novel testing image, by computing its respective feature vector and comparing it with the set of prototypes. The age was determined using regression through a weighted \(k-NN\) classifier. By using a set of 100 testing images, we demonstrate that it is possible to obtain an error comparable with state of the art algorithms by using only five small ROIs within the hand image.
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Banda-Escobar, J.L.T. et al. (2018). Towards an Automatic Estimation of Skeletal Age Using \(k-NN\) Regression with a Reduced Set of Tinny Aligned Regions of Interest. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Computational Intelligence. MICAI 2017. Lecture Notes in Computer Science(), vol 10633. Springer, Cham. https://doi.org/10.1007/978-3-030-02840-4_23
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