Literature DB >> 17387000

Bone age assessment of children using a digital hand atlas.

Arkadiusz Gertych1, Aifeng Zhang, James Sayre, Sylwia Pospiech-Kurkowska, H K Huang.   

Abstract

We have developed an automated method to assess bone age of children using a digital hand atlas. The hand atlas consists of two components. The first component is a database which is comprised of a collection of 1400 digitized left hand radiographs from evenly distributed normally developed children of Caucasian (CA), Asian (AS), African-American (AA) and Hispanic (HI) origin, male (M) and female (F), ranged from 1- to 18-year-old; and relevant patient demographic data along with pediatric radiologists' readings of each radiograph. This data is separate into eight categories: CAM, CAF, AAM, AAF, HIM, HIF, ASM, and ASF. In addition, CAM, AAM, HIM, and ASM are combined as one male category; and CAF, AAF, HIF, and ASF are combined as one female category. The male and female are further combined as the F & M category. The second component is a computer-assisted diagnosis (CAD) module to assess a child bone age based on the collected data. The CAD method is derived from features extracted from seven regions of interest (ROIs): the carpal bone ROI, and six phanlangeal PROIs. The PROIs are six areas including the distal and middle regions of three middle fingers. These features were used to train the 11 category fuzzy classifiers: one for each race and gender, one for the female, one male, and one F & M, to assess the bone age of a child. The digital hand atlas is being integrated with a PACS for validation of clinical use.

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Mesh:

Year:  2007        PMID: 17387000      PMCID: PMC1978493          DOI: 10.1016/j.compmedimag.2007.02.012

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  13 in total

1.  Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction.

Authors:  E Pietka; A Gertych; S Pospiech; F Cao; H K Huang; V Gilsanz
Journal:  IEEE Trans Med Imaging       Date:  2001-08       Impact factor: 10.048

2.  Integration of computer assisted bone age assessment with clinical PACS.

Authors:  Ewa Pietka; Sylwia Pospiech-Kurkowska; Arkadiusz Gertych; Fei Cao
Journal:  Comput Med Imaging Graph       Date:  2003       Impact factor: 4.790

3.  Computer-assisted bone age assessment: graphical user interface for image processing and comparison.

Authors:  Ewa Pietka; Arkadiusz Gertych; Sylwia Pospiechâ Euro Kurkowska; Fei Cao; H K Huang; Vincente Gilzanz
Journal:  J Digit Imaging       Date:  2004-06-04       Impact factor: 4.056

4.  Skeletal age determinations in children of European and African descent: applicability of the Greulich and Pyle standards.

Authors:  S Mora; M I Boechat; E Pietka; H K Huang; V Gilsanz
Journal:  Pediatr Res       Date:  2001-11       Impact factor: 3.756

5.  Informatics infrastructure of CAD system.

Authors:  Ewa Pietka; Arkadiusz Gertych; Krzysztof Witko
Journal:  Comput Med Imaging Graph       Date:  2005-01-12       Impact factor: 4.790

6.  Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones.

Authors:  Aifeng Zhang; Arkadiusz Gertych; Brent J Liu
Journal:  Comput Med Imaging Graph       Date:  2007-03-21       Impact factor: 4.790

7.  Feature extraction in carpal-bone analysis.

Authors:  E Pietka; L Kaabi; M L Kuo; H K Huang
Journal:  IEEE Trans Med Imaging       Date:  1993       Impact factor: 10.048

8.  Effect of training on replicability of assessments of skeletal maturity (Greulich-Pyle).

Authors:  A F Roche; C G Rohmann; N Y French; G H Dávila
Journal:  Am J Roentgenol Radium Ther Nucl Med       Date:  1970-03

9.  Reproducibility of bone ages when performed by radiology registrars: an audit of Tanner and Whitehouse II versus Greulich and Pyle methods.

Authors:  D G King; D M Steventon; M P O'Sullivan; A M Cook; V P Hornsby; I G Jefferson; P R King
Journal:  Br J Radiol       Date:  1994-09       Impact factor: 3.039

10.  Digital hand atlas and web-based bone age assessment: system design and implementation.

Authors:  F Cao; H K Huang; E Pietka; V Gilsanz
Journal:  Comput Med Imaging Graph       Date:  2000 Sep-Oct       Impact factor: 4.790

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  21 in total

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Authors:  H K Huang; Ruchi Deshpande; Jorge Documet; Anh H Le; Jasper Lee; Kevin Ma; Brent J Liu
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2.  Web-based bone age assessment by content-based image retrieval for case-based reasoning.

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Journal:  Int J Comput Assist Radiol Surg       Date:  2011-06-14       Impact factor: 2.924

3.  Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression.

Authors:  Krit Somkantha; Nipon Theera-Umpon; Sansanee Auephanwiriyakul
Journal:  J Digit Imaging       Date:  2011-12       Impact factor: 4.056

4.  Validation of automated Greulich-Pyle bone age determination in children with chronic renal failure?

Authors:  Saritha Ranabothu; Frederick J Kaskel
Journal:  Pediatr Nephrol       Date:  2015-04-11       Impact factor: 3.714

5.  Black box integration of computer-aided diagnosis into PACS deserves a second chance: results of a usability study concerning bone age assessment.

Authors:  Ina Geldermann; Christoph Grouls; Christiane Kuhl; Thomas M Deserno; Cord Spreckelsen
Journal:  J Digit Imaging       Date:  2013-08       Impact factor: 4.056

6.  A practical formula for determining growth.

Authors:  Çağrı Türkoz; Emine Kaygısız; Çağrı Ulusoy; Can Ateş
Journal:  Diagn Interv Radiol       Date:  2017 May-Jun       Impact factor: 2.630

7.  Towards fully automated third molar development staging in panoramic radiographs.

Authors:  Nikolay Banar; Jeroen Bertels; François Laurent; Rizky Merdietio Boedi; Jannick De Tobel; Patrick Thevissen; Dirk Vandermeulen
Journal:  Int J Legal Med       Date:  2020-04-01       Impact factor: 2.686

8.  Racial differences in growth patterns of children assessed on the basis of bone age.

Authors:  Aifeng Zhang; James W Sayre; Linda Vachon; Brent J Liu; H K Huang
Journal:  Radiology       Date:  2008-10-27       Impact factor: 11.105

9.  Robust anatomical correspondence detection by hierarchical sparse graph matching.

Authors:  Yanrong Guo; Guorong Wu; Jianguo Jiang; Dinggang Shen
Journal:  IEEE Trans Med Imaging       Date:  2012-10-10       Impact factor: 10.048

10.  Assessing the Bone Age of Children in an Automatic Manner Newborn to 18 Years Range.

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Journal:  J Digit Imaging       Date:  2020-04       Impact factor: 4.056

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