Bone Age Assessment of Iranian Children in an Automatic Manner

Farzaneh Dehghani, Alireza Karimian, Mehri Sirous, Javad Rasti, Ali Soleymanpour

DOI: 10.4103/jmss.JMSS_9_20

Abstract


Background: Bone age assessment (BAA) is a radiological process with the aim of identifying growth disorders in children. The objective of this study is to assess the bone age of Iranian children in an automatic manner. Methods: In this context, three computer vision techniques including histogram of oriented gradients (HOG), local binary pattern (LBP), and scale-invariant feature transform (SIFT) are applied to extract appropriate features from the carpal and epiphyseal regions of interest. Two different datasets are applied here: the University of Southern California hand atlas for training this computer-aided diagnosis (CAD) system and Iranian radiographs for evaluating the performance of this system for BAA of Iranian children. In this study, the concatenation of HOG, LBP, and dense SIFT feature vectors and background subtraction are applied to improve the performance of this approach. Support vector machine (SVM) and K-nearest neighbor are used here for classification and the better results yielded by SVM. Results: The accuracy of female radiographs is 90% and of male is 71.42%. The mean absolute error is 0.16 and 0.42 years for female and male test radiographs, respectively. Cohen’s kappa coefficients are 0.86 and 0.6, P < 0.05, for female and male radiographs, respectively. The results indicate that this proposed approach is in substantial agreement with the bone age reported by the experienced radiologist. Conclusion: This approach is easy to implement and reliable, thus qualified for CAD and automatic BAA of Iranian children.

Keywords


Bone age assessment, computer vision operators, Iranian race, K-nearest neighbors, left-hand radiographic images, support vector machine

Full Text:

PDF

References


Moradi M, Sirous M, Morovatti P. The reliability of skeletal age determination in an Iranian sample using Greulich and Pyle method. Forensic Sci Int 2012;223:e1-372. e4.

Greulich WW, Pyle SI, Todd TW. Radiographic atlas of skeletal development of the hand and wrist: Stanford University Press; 1959.

Tanner J, Healy MJ, Goldstein H, Cameron N. Assessment of Skeletal Maturity and Prediction of Adult Height. Philadelphia: TW3 Method Saunders; 2001.

De Sanctis V, Soliman AT, Di Maio S, Bedair S. Are the new automated methods for bone age estimation advantageous over the manual approaches? Pediatr Endocrinol Rev 2014;12:200-5.

Satoh M. Bone age: Assessment methods and clinical applications. Clin Pediatr Endocrinol 2015;24:143-52.

Dehghani F, Karimian A, Sirous M. Assessing the bone age of children in an automatic manner newborn to 18 years range. J Digit Imaging 2020;33:399-407.

Gertych A, Zhang A, Sayre J, Pospiech-Kurkowska S, Huang HK. Bone age assessment of children using a digital hand atlas. Comput Med Imaging Graph 2007;31:322-31.

Perona P, Malik M. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990;12:629-39.

Guraksin GE, Uguz H, Baykan OK. Bone age determination in young children (newborn to 6 years old) using support vector machines. Turk J Electr Eng Comput Sci 2016;24:1693-708.

Wang Q, Yang J. Eye detection in facial images with unconstrained background. J Pattern Recognit Res 2006;1:55-62.

Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis 2004;60:91-110.

Dalal N, Triggs B. Histograms of oriented gradients for human detection. In2005; In2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05); 2005;1:886-893.

Ojala T, Pietikainen M, Maenpaa T. Gray scale and rotation invariant texture classification with local binary patterns. In: European Conference on Computer Vision. Berlin, Heidelberg: Springer; 2000.

Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 2002;7:971-87.

Houam L, Hafiane A, Boukrouche A, Lespessailles E, Jennane R. One dimensional local binary pattern for bone texture characterization. Pattern Anal Appl 2014;17:179-93.

Lingras P, Butz C. Rough set based 1-v-1 and 1-vr approaches to support vector machine multi-classification. Inf Sci 2007;177:3782-98.

Perrizo W, Ding Q, Denton A. Lazy Classifiers Using p-Trees. CAINE; 2002.

Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Inf Proce Manage 2009;45:427-37.

Kashif M, Deserno TM, Haak D, Jonas S. Feature description with SIFT, SURF, BRIEF, BRISK, or FREAK? A general question answered for bone age assessment. Comput Biol Med 2016;68:67-75.


Refbacks

  • There are currently no refbacks.


 

  https://e-rasaneh.ir/Certificate/22728

https://e-rasaneh.ir/

ISSN : 2228-7477