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Automated classification system for Bone Age X-ray images

Jinwoo Seok · Baro Hyun · Joséphine Z. Kasa-Vubu · Anouck Girard

21 citations0/0 files storedConference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics

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

Abstract

Bone Age (BA) determination using radiological images of left hands and wrists is important in pediatric endocrinology to correctly assess growth and pubertal maturation. In this paper, we propose a fully automated Greulich and Pyle Atlas (GP) bone age determination system using feature extraction and machine learning classifiers. The original contributions of this paper are as follows: (i) We use commercially available morphing tools to create a modified GP atlas that has images regularly spaced at three month intervals, (ii) We propose a novel Singular Value Decomposition (SVD) based feature extractor to create a feature vector. We use the Scale Invariant Feature Transform (SIFT) to extract features from the images then apply SVD to compose the feature vectors. Then, we train a Neural Network classifier using the generated feature vectors. Our preliminary results show that, even with a small number of training data sets, we obtain promising results. Future direction is discussed.

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References

1959RADIOGRAPHIC ATLAS OF SKELETAL DEVELOPMENT OF THE HAND AND WRIST 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 2001Effect of Knowledge of Chronologic Age on the Variability of Pediatric Bone Age Determined Using the Greulich and Pyle Standards 1971Comparison of Tanner‐Whitehouse and Greulich‐Pyle methods in a large scale Danish survey

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2021Report of Clinical Bone Age Assessment using Deep Learning for an Asian population in Taiwan 2023Bone age recognition based on mask R-CNN using xception regression model 2015Bone age assessment meets SIFT 2018Skeletal Bone Age Assessment Based on Deep Convolutional Neural Networks 2018End-to-End Bone Age Assessment with Residual Learning 2023Skeletal age evaluation using hand X-rays to determine growth problems 2021Features and Methods of Human Age Estimation: Opportunities and Challenges in Medical Image Processing

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