{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:48:38Z","timestamp":1777704518188,"version":"3.51.4"},"reference-count":26,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,4,22]]},"abstract":"<jats:p>Assessing the age of an individual via bones serves as a technique in determination of individual skills. In this work, the assessment of chronological age for varying age groups of individuals is carried out using left hand wrist radiographs. The datasets employed for experimentation are preprocessed and extracted using an automated segmentation technique using bit plane level data of radiograph images. The flow of proposed work is comprised of three stages, in stage 1 preprocessing is carried out, classification of preprocessed radiographs are classified into male and female samples using convolution kernels based deep neural net. Further, distance features are extracted from the origin of carpal bones to tip of extracted phalangeal regions in the classified outcomes from stage 2 using imtool image analyzer. Finally, classification of distance features is performed using Support Vector Machines with Gaussian Kernel (SVM-GK) to label the radiographs into ages from 1 to 17. The experimentation is performed on the datasets of Pediatric Bone Age challenge of Radiological Society of North America (RSNA) of about 12000 images of 1\u201317 year age groups. The convergence between actual and clinically validated chronological age is also tested with Gaussian process regression model (GPRM) along with SVM. A very minimal loss of about 4.7% is occurred during classification using deep neural network. The classification accuracy is found to be 76.8% and 88.1% and 0.75 and 1.41 RMSE with respect to GPRM and SVM-GK.<\/jats:p>","DOI":"10.3233\/jifs-190779","type":"journal-article","created":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T12:44:48Z","timestamp":1612788288000},"page":"8651-8663","source":"Crossref","is-referenced-by-count":3,"title":["Chronological age assessment based on wrist radiograph processing \u2013 Some novel approaches"],"prefix":"10.1177","volume":"40","author":[{"given":"N.","family":"Shobha Rani","sequence":"first","affiliation":[{"name":"Department of Computer Science, Amrita School of Arts and Sciences, Mysuru, Amrita Vishwa Vidyapeetham, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C. R.","family":"Yadhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Amrita School of Arts and Sciences, Mysuru, Amrita Vishwa Vidyapeetham, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"U.","family":"Karthik","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Amrita School of Arts and Sciences, Mysuru, Amrita Vishwa Vidyapeetham, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"8","key":"10.3233\/JIFS-190779_ref1","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1109\/42.938240","article-title":"Computer-assisted bone age assessment: Image preprocessing and epiphyseal\/metaphyseal ROI extraction","volume":"20","author":"Pietka","year":"2001","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"4","key":"10.3233\/JIFS-190779_ref2","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1109\/4233.897061","article-title":"Skeletal growth estimation using radiographic image processing and analysis","volume":"4","author":"Mahmoodi","year":"2000","journal-title":"IEEE Transactions on Information Technology in Biomedicine"},{"issue":"2","key":"10.3233\/JIFS-190779_ref3","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.jbi.2004.01.002","article-title":"A computational TW3 classifier for skeletal maturity assessment. A computing with words approach","volume":"37","author":"Aja-Fern\u00e1ndez","year":"2004","journal-title":"Journal of Biomedical Informatics"},{"key":"10.3233\/JIFS-190779_ref4","unstructured":"Giordano D. , Leonardi R. , Maiorana F. , Scarciofalo G. and Spampinato C. , Epiphysis and metaphysis extraction and classification by adaptive thresholding and DoG filtering for automated skeletal bone age analysis, In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE (pp. 6551\u20136556), IEEE, 2007."},{"issue":"5","key":"10.3233\/JIFS-190779_ref5","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1109\/TBME.2008.918554","article-title":"A radius and ulna TW3 bone age assessment system","volume":"55","author":"Trist\u00e1n-Vega","year":"2008","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"8","key":"10.3233\/JIFS-190779_ref6","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1016\/j.compmedimag.2008.08.005","article-title":"Automatic bone age assessment based on intelligent algorithms and comparison with TW3 method","volume":"32","author":"Liu","year":"2008","journal-title":"Computerized Medical Imaging and Graphics"},{"issue":"10","key":"10.3233\/JIFS-190779_ref7","doi-asserted-by":"crossref","first-page":"2539","DOI":"10.1109\/TIM.2010.2058210","article-title":"An automatic system for skeletal bone age measurement by robust processing of carpal and epiphysial\/metaphysial bones","volume":"59","author":"Giordano","year":"2010","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.3233\/JIFS-190779_ref9","doi-asserted-by":"crossref","first-page":"106529","DOI":"10.1016\/j.compeleceng.2019.106529","article-title":"A bone age assessment system for real-world X-ray images based on convolutional neural networks","volume":"8","author":"Guo","year":"2020","journal-title":"Computers & Electrical Engineering"},{"key":"10.3233\/JIFS-190779_ref10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artmed.2019.04.005","article-title":"Incorporated region detection and classification using deep convolution networks for bone age assessment","volume":"97","author":"Buia","year":"2019","journal-title":"Artificial Intelligence In Medicine"},{"issue":"8","key":"10.3233\/JIFS-190779_ref11","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1016\/j.compmedimag.2008.08.005","article-title":"Automatic bone age assessment based on intelligent algorithms and comparison with TW3 method","volume":"32","author":"Liu","year":"2008","journal-title":"Computerized Medical Imaging and Graphics"},{"key":"10.3233\/JIFS-190779_ref12","doi-asserted-by":"crossref","first-page":"101538","DOI":"10.1016\/j.media.2019.101538","article-title":"Automated age estimation from MRI volumes of the hand","volume":"58","author":"Stern","year":"2019","journal-title":"Medical Image Analysis"},{"key":"10.3233\/JIFS-190779_ref13","unstructured":"Stern D. , Payer C. , Lepetit V. , Urschler M. , Automated age estimation from MRI volumes using deep learning, Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention, (2016), pp. 194\u2013202."},{"key":"10.3233\/JIFS-190779_ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.293734"},{"key":"10.3233\/JIFS-190779_ref15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/DICTA.2018.8615764","article-title":"Bone Age Assessment Based on Two-Stage Deep Neural Networks","volume":"2019","author":"Chu","year":"2019","journal-title":"Int Conf Digit Image Comput Tech Appl DICTA 2018"},{"issue":"7","key":"10.3233\/JIFS-190779_ref16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pbio.2005263","article-title":"Differential aging of growth plate cartilage underlies differences in bone length and thus helps determine skeletal proportions","volume":"16","author":"Lui","year":"2018","journal-title":"PLoS Biol"},{"issue":"1","key":"10.3233\/JIFS-190779_ref18","first-page":"211","article-title":"Bone age assessment methods: A critical review","volume":"30","author":"Mughal","year":"2014","journal-title":"Pakistan J Med Sci"},{"issue":"1","key":"10.3233\/JIFS-190779_ref19","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1109\/TITB.2012.2228211","article-title":"Support vector machine classification based on correlation prototypes applied to bone age assessment","volume":"17","author":"Harmsen","year":"2013","journal-title":"IEEE J Biomed Heal Informatics"},{"key":"10.3233\/JIFS-190779_ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.29373401"},{"key":"10.3233\/JIFS-190779_ref23","unstructured":"Chai H.Y. , Wee L.K. , Swee T.T. and Salleh S.-H. , Adaptive Crossed Reconstructed (ACR) Kmean Clustering Segmentation for Computer aided Bone Age Assessment System, International Journal of Mathematical Models and Methods in Applied Sciences 5(3) (2011)."},{"key":"10.3233\/JIFS-190779_ref24","unstructured":"Mualla N. , Houssein E.H. and Hassan M.R. , Automatic Bone Age Assessment Using Hand X-Ray Images, Journal of Theoretical and Applied Information Technology 98(02) (2020)."},{"key":"10.3233\/JIFS-190779_ref25","doi-asserted-by":"crossref","unstructured":"Li W. , Wang Y. and Zhang Z. , A hierarchical framework for image-based human age estimation by weighted and OHRanked sparse representation-based classification. In 2012 5th IAPR International Conference on Biometrics (ICB) (pp. 19\u201325). IEEE (2012).","DOI":"10.1109\/ICB.2012.6199753"},{"key":"10.3233\/JIFS-190779_ref26","doi-asserted-by":"crossref","unstructured":"Adeshina S.A. , Lindner C. and Cootes T.F. , Automatic segmentation of carpal area bones with random forest regression voting for estimating skeletal maturity in infants. In Electronics, Computer and Computation (ICECCO), 2014 11th International Conference on (pp. 1\u20134). IEEE (2014).","DOI":"10.1109\/ICECCO.2014.6997559"},{"key":"10.3233\/JIFS-190779_ref27","first-page":"23","volume-title":"Review on segmentation of computer-aided skeletal maturity assessment","author":"Hum","year":"2014"},{"key":"10.3233\/JIFS-190779_ref28","doi-asserted-by":"crossref","unstructured":"Mansourvar M. , Kareem S.A. , Ismail M.A. and Nasaruddin F.H. , Automatic method for bone age assessment based on combined method, In Computer and Information Sciences (ICCOINS), 2014 International Conference on (pp. 1\u20135). IEEE, (2014).","DOI":"10.1109\/ICCOINS.2014.6868424"},{"key":"10.3233\/JIFS-190779_ref29","doi-asserted-by":"crossref","unstructured":"Liu H.C. , Chou Y.H. , Tiu C.M. , Lin C.F. , Chen C.Y. , Hwang... C.H. and Jong T.L. , Bone age pre-estimation using partial least squares regression analysis with a priori knowledge, In 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1\u20134). IEEE, (2014).","DOI":"10.1109\/MeMeA.2014.6860050"},{"key":"10.3233\/JIFS-190779_ref30","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.forsciint.2013.10.036","article-title":"Accuracy of Cameriere\u2019s cut-off value for third molar in assessing 18 years of age","volume":"235","author":"De Luca","year":"2014","journal-title":"Forensic Science International"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-190779","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:23Z","timestamp":1777455683000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-190779"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,22]]},"references-count":26,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jifs-190779","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,22]]}}}