TY - GEN
T1 - Bone age assessment meets SIFT
AU - Kashif, Muhammad
AU - Jonas, Stephan
AU - Haak, Daniel
AU - Deserno, Thomas M.
N1 - Publisher Copyright:
© 2015 SPIE.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Bone age assessment
KW - Classification.
KW - Epiphyseal region of interest (eROIs)
KW - Feature extraction
KW - Scale invariant feature transform (SIFT)
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84948808555&partnerID=8YFLogxK
U2 - 10.1117/12.2074572
DO - 10.1117/12.2074572
M3 - Conference contribution
AN - SCOPUS:84948808555
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2015
A2 - Hadjiiski, Lubomir M.
A2 - Hadjiiski, Lubomir M.
A2 - Tourassi, Georgia D.
A2 - Tourassi, Georgia D.
PB - SPIE
T2 - SPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis
Y2 - 22 February 2015 through 25 February 2015
ER -