@article{MAKHILLJEAS2019142118601,
    title = {Deep Learning-based Finger Big ROIs Extraction for Bone Age Assessment in
Smart Intelligence Systems},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {21},
    pages = {7870-7876},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.7870.7876},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.7870.7876},
    author = {Chansu and},
    keywords = {ROI,TW3,Bone age assessment,image processing,deep-learning,performance},
    abstract = {Tanner-Whitehouse 3 (TW3) method assesses bone age by finding 13 Regions of Interest (ROIs)
from left hand X-ray image and evaluating each region. TW3 method has complicated evaluation process and
many assessment factors are subjective. Hence, an automated bone age assessment system that enables
objective evaluation in short period of time is required. Automatic extraction of 13 ROIs is necessary for the
implementation of the automated bone age assessment system. However, direct extraction based on
deep-learning mechanism can produce erroneous extraction of ROI in wrong region because many regions in
left hand bone have similar appearance. To prevent this problem, finding Big ROIs from the left hand X-ray
image first and extracting the corresponding ROIs from each Big ROI can increase the probability of correctly
finding ROIs. This study proposes a method of finding thumb Big ROI, middle finger Big ROI and little finger
Big ROI from left hand X-ray image and evaluates the performance of the method.}
    }