Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/15289
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSimu S.
dc.contributor.authorLal S.
dc.date.accessioned2021-05-05T10:26:51Z-
dc.date.available2021-05-05T10:26:51Z-
dc.date.issued2020
dc.identifier.citationMultimedia Tools and Applications , Vol. 79 , 21-22 , p. 15747 - 15764en_US
dc.identifier.urihttps://doi.org/10.1007/s11042-020-08816-7
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/15289-
dc.description.abstractBone age assessment (BAA) is a method or technique that helps in predicting the age of a person whose age is unavailable and can also be used to find growth disorders if any. The automated bone age assessment system (ABAA) depends heavily on the efficiency of the feature extraction stage and the accuracy of a successive classification stage of the system. This paper has presented the implementation and analysis of feature extraction methods like Bag of features (BoF), Histogram of Oriented Gradients (HOG), and Texture Feature Analysis (TFA) methods on the segmented phalangeal region of interest (PROI) images and segmented radius-ulna region of interest (RUROI) images. Artificial Neural Networks (ANN) and Random Forest classifiers are used for evaluating classification problems. The experimental results obtained by BoF method for feature extraction along with Random Forest for classification have outperformed preceding techniques available in the literature. The mean error (ME) accomplished is 0.58 years and RMSE value of 0.77 years for PROI images and mean error of 0.53 years and RMSE of 0.72 years was achieved for RUROI images. Additionally results also proved that prior knowledge of gender of the person gives better results. The dataset contains radiographs of the left hand for an age range of 0-18 years. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.en_US
dc.titleA framework for automated bone age assessment from digital hand radiographsen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.