Paper
20 March 2015 Bone age assessment meets SIFT
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Abstract
Bone age assessment (BAA) is a method of determining the skeletal maturity and finding the growth disorder in the skeleton of a person. BAA is frequently used in pediatric medicine but also a time-consuming and cumbersome task for a radiologist. Conventionally, the Greulich and Pyle and the Tanner and Whitehouse methods are used for bone age assessment, which are based on visual comparison of left hand radiographs with a standard atlas. We present a novel approach for automated bone age assessment, combining scale invariant feature transform (SIFT) features and support vector machine (SVM) classification. In this approach, (i) data is grouped into 30 classes to represent the age range of 0- 18 years, (ii) 14 epiphyseal ROIs are extracted from left hand radiographs, (iii) multi-level image thresholding, using Otsu method, is applied to specify key points on bone and osseous tissues of eROIs, (iv) SIFT features are extracted for specified key points for each eROI of hand radiograph, and (v) classification is performed using a multi-class extension of SVM. A total of 1101 radiographs of University of Southern California are used in training and testing phases using 5- fold cross-validation. Evaluation is performed for two age ranges (0-18 years and 2-17 years) for comparison with previous work and the commercial product BoneXpert, respectively. Results were improved significantly, where the mean errors of 0.67 years and 0.68 years for the age ranges 0-18 years and 2-17 years, respectively, were obtained. Accuracy of 98.09 %, within the range of two years was achieved.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Muhammad Kashif, Stephan Jonas, Daniel Haak, and Thomas M. Deserno "Bone age assessment meets SIFT", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941439 (20 March 2015); https://doi.org/10.1117/12.2074572
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CITATIONS
Cited by 8 scholarly publications.
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KEYWORDS
Bone

Radiography

Feature extraction

Image segmentation

Image classification

Medicine

Tissues

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