Literature DB >> 18230501

HANDX: a model-based system for automatic segmentation of bones from digital hand radiographs.

D J Michael1, A C Nelson.   

Abstract

The authors detail the design and implementation of HANDX, a model-based computer vision system used in the domain of medical image processing. Given a digitized hand radiograph, HANDX segments out specific bones and measures particular parameters of the bones, without requiring specific characterization of noise variations in background contrast and anatomical differences which arise from patient variation. Observer variability is reduced by the system, and the resulting measurement may be useful for detecting short-term skeletal growth abnormalities in children and may additional clinical applications. The overall system is modularized into three stages: preprocessing, segmentation, and measurement. In the preprocessing state model-based histogram modification is used to normalize the radiograph. The histogram model is based on the physics of the imaging process. The segmentation stage finds and outlines specific bones using domain-dependent and domain-independent knowledge of hand anatomy and physiology and image edges. The measurement stage obtains clinically useful quantitative parameters from the segmented image.

Entities:  

Year:  1989        PMID: 18230501     DOI: 10.1109/42.20363

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  12 in total

1.  A fuzzy-based growth model with principle component analysis selection for carpal bone-age assessment.

Authors:  Chi-Wen Hsieh; Tzu-Chiang Liu; Tai-Lang Jong; Chui-Mei Tiu
Journal:  Med Biol Eng Comput       Date:  2010-04-20       Impact factor: 2.602

2.  A Deep Automated Skeletal Bone Age Assessment Model with Heterogeneous Features Learning.

Authors:  Chao Tong; Baoyu Liang; Jun Li; Zhigao Zheng
Journal:  J Med Syst       Date:  2018-11-03       Impact factor: 4.460

3.  Impact of ensemble learning in the assessment of skeletal maturity.

Authors:  Pedro Cunha; Daniel C Moura; Miguel Angel Guevara López; Conceição Guerra; Daniela Pinto; Isabel Ramos
Journal:  J Med Syst       Date:  2014-07-11       Impact factor: 4.460

4.  Skull-stripping with machine learning deformable organisms.

Authors:  Gautam Prasad; Anand A Joshi; Albert Feng; Arthur W Toga; Paul M Thompson; Demetri Terzopoulos
Journal:  J Neurosci Methods       Date:  2014-08-12       Impact factor: 2.390

5.  Forensic bone age estimation of adolescent pelvis X-rays based on two-stage convolutional neural network.

Authors:  Li-Qin Peng; Yu-Cheng Guo; Lei Wan; Tai-Ang Liu; Peng Wang; Hu Zhao; Ya-Hui Wang
Journal:  Int J Legal Med       Date:  2022-01-18       Impact factor: 2.686

6.  A Novel Shorthand Approach to Knee Bone Age Using MRI: A Validation and Reliability Study.

Authors:  Blake C Meza; Scott M LaValva; Julien T Aoyama; Christopher J DeFrancesco; Brendan M Striano; James L Carey; Jie C Nguyen; Theodore J Ganley
Journal:  Orthop J Sports Med       Date:  2021-08-11

7.  An artifacts removal post-processing for epiphyseal region-of-interest (EROI) localization in automated bone age assessment (BAA).

Authors:  Hum Yan Chai; Lai Khin Wee; Tan Tian Swee; Sh-Hussain Salleh; Lim Yee Chea
Journal:  Biomed Eng Online       Date:  2011-09-28       Impact factor: 2.819

8.  Automated bone age assessment: motivation, taxonomies, and challenges.

Authors:  Marjan Mansourvar; Maizatul Akmar Ismail; Tutut Herawan; Ram Gopal Raj; Sameem Abdul Kareem; Fariza Hanum Nasaruddin
Journal:  Comput Math Methods Med       Date:  2013-12-16       Impact factor: 2.238

9.  Diagnostic performance of convolutional neural network-based Tanner-Whitehouse 3 bone age assessment system.

Authors:  Xue-Lian Zhou; Er-Gang Wang; Qiang Lin; Guan-Ping Dong; Wei Wu; Ke Huang; Can Lai; Gang Yu; Hai-Chun Zhou; Xiao-Hui Ma; Xuan Jia; Lei Shi; Yong-Sheng Zheng; Lan-Xuan Liu; Da Ha; Hao Ni; Jun Yang; Jun-Fen Fu
Journal:  Quant Imaging Med Surg       Date:  2020-03

10.  An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines.

Authors:  Marjan Mansourvar; Shahaboddin Shamshirband; Ram Gopal Raj; Roshan Gunalan; Iman Mazinani
Journal:  PLoS One       Date:  2015-09-24       Impact factor: 3.240

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