Name
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 & Pyle and the Tanner & 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 018 years, (ii) 14 epiphyseal ROIs are extracted from left hand radiographs, (iii) multi-level image thresholding, using Otsu method, is applied to specify keypoints on bone and osseous tissues of eROIs, (iv) SIFT features are extracted for specified keypoints 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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/12.2074572 | Proceedings of SPIE |
Keywords | Field | DocType |
Bone age assessment,Epiphyseal region of interest (eROIs),Scale invariant feature transform (SIFT),Feature extraction,Support vector machine,Classification | Scale-invariant feature transform,Otsu's method,Radiography,Artificial intelligence,Thresholding,Computer vision,Visual comparison,Pattern recognition,Support vector machine,Feature extraction,Speech recognition,Skeleton (computer programming),Physics | Conference |
Volume | ISSN | Citations |
9414 | 0277-786X | 3 |
PageRank | References | Authors |
0.40 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Kashif | 1 | 34 | 8.17 |
| Stephan Jonas | 2 | 35 | 8.92 |
| Daniel Haak | 3 | 40 | 6.52 |
| Thomas M Deserno | 4 | 358 | 33.79 |