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- Title
Computer Assisted Bone Age Estimation of Children Using Middle Finger and Carpal Bones.
- Authors
Aung, Aye Aye; Win, Zin Mar
- Abstract
Bone age assessment methods have been replaced from manual to automatic evaluation in medical field. Bone age is also needed for pediatricians and endocrinologists to compare chronological age for growth disorders and endocrinological problems. Recently, bone age estimation has gained remarkable ground in academia and medicine. Automated bone age assessment (BAA) methods are needed to improve the accuracy of the bone age because manual BAA methods are time-consuming and inefficient. Hand radiograph is the most widely applied to calculate the bone age of children. There are several challenges in bone age estimation such as the less of accuracy to segment region of interest (ROI), to identify boundaries and to classify bone age. The aim of the proposed system is to improve robustness and accuracy of the BAA system. The system applies Histogram of Oriented Gradient (HOG) features for middle finger and Hu moments features for carpal bones. Then, the system uses Support Vector Machines (SVM) by applying extracted features from middle finger and carpal bones for bone age classification. The proposed system uses the dataset from University of Southern California. Combination of HOG features from Epiphysis/Metaphysis region of interests (EMROIs) and Hu moments features from carpal region of interest (CROI) achieved better results than other methods. Running time of combining HOG and Hu moments features is faster than other methods and this method reduces feature dimensions than using HOG features from EMROIs and CROI. As the experimental results, the accuracy of the system achieves 89% of Asian (ASI) and African American (BLK) female radiographs and 86% of ASI and BLK male radiographs in age range from one year to ten years old. In the experiment, this method obtains 1.2717 years of Mean Absolute Error (MAE) compared with radiologist 1 and 1.1997 years of MAE compared with radiologist 2 in testing ASI and BLK females. Then, the system also obtains 1.3681 years of MAE compared with radiologist 1 and 1.3311 years of MAE compared with radiologist 2 in testing ASI and BLK males.
- Subjects
UNIVERSITY of Southern California; SUPPORT vector machines; AGE; GROWTH disorders; CARPAL bones; FINGERS
- Publication
International Journal of Intelligent Engineering & Systems, 2021, Vol 14, Issue 3, p119
- ISSN
2185-310X
- Publication type
Article
- DOI
10.22266/ijies2021.0630.11