title: Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones.
abstract: A computer-aided-diagnosis (CAD) method has been previously developed based on features extracted from phalangeal regions of interest (ROI) in a digital hand atlas, which can assess bone age of children from ages 7 to 18 accurately. Therefore, in order to assess the bone age of children in younger ages, the inclusion of carpal bones is necessary. However, due to various factors including the uncertain number of bones appearing, non-uniformity of soft tissue, low contrast between the bony structure and soft tissue, automatic segmentation and identification of carpal bone boundaries is an extremely challenging task. Past research works on carpal bone segmentation were performed utilizing dynamic thresholding. However, due to the limitation of the segmentation algorithm, carpal bones have not been taken into consideration in the bone age assessment procedure. In this paper, we developed and implemented a knowledge-based method for fully automatic carpal bone segmentation and morphological feature analysis. Fuzzy classification was then used to assess the bone age based on the selected features. This method has been successfully applied on all cases in which carpal bones have not overlapped. CAD results of total about 205 cases from the digital hand atlas were evaluated against subject chronological age as well as readings of two radiologists. It was found that the carpal ROI provides reliable information in determining the bone age for young children from newborn to 7-year-old.
authors: Image Processing & Informatics Lab, Department of Radiology, University of Southern California, Marina Del Rey, CA 90292, USA. azhang@usc.edu
authors: Aifeng
authors: A
authors: Zhang
authors: Arkadiusz
authors: A
authors: Gertych
authors: Brent J
authors: BJ
authors: Liu
mesh_terms: D000293:Adolescent; D000365:Age Determination by Skeleton / Q000379:methods*; D016880:Anisotropy; D002140:California; D002348:Carpal Bones / Q000000981:diagnostic imaging* / Q000378:metabolism; D002648:Child; D002657:Child Development; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male