Fully automatic ROI extraction and edge-based segmentation of radius and ulna bones from hand radiographs

• Autorzy: Simu S. , Lal S. , Nagarsekar P. , Naik A.

Identyfikatory

DOI

10.1016/j.bbe.2017.07.004

• Języki publikacji: EN

Abstrakty

EN

Bone age is a reliable measure of person's growth and maturation of skeleton. The difference between chronological age and bone age indicates presence of endocrinological problems. The automated bone age assessment system (ABAA) based on Tanner and Whitehouse method (TW3) requires monitoring the growth of radius, ulna and short bones (phalanges) of left hand. In this paper, a detailed analysis of two bones in the bone age assessment system namely, radius and ulna is presented. We propose an automatic extraction method for the region of interest (ROI) of radius and ulna bones from a left hand radiograph (RUROI). We also propose an improved edge-based segmentation technique for those bones. Quantitative and qualitative results of the proposed segmentation technique are evaluated and compared with other state-of-the-art segmentation techniques. Medical experts have also validated the qualitative results of proposed segmentation technique. Experimental results reveal that these proposed techniques provide better segmentation accuracy as compared to the other state-of-the-art segmentation techniques.

Słowa kluczowe

EN

automated bone age assessment; extraction of radius; ulna bone; RUROI; hand bone segmentation; mathematical morphology; edge based segmentation

PL

kość łokciowa; RUROI; segmentacja kości; morfologia matematyczna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2017
• Tom: Vol. 37, no. 4
• Strony: 718--732
• Opis fizyczny: Bibliogr. 36 poz., rys., tab.

Twórcy

autor

Simu S.

• Department of Electronics & Communication Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru, India

autor

Lal S.

• Department of Electronics & Communication Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru 575025, India

autor

Nagarsekar P.

• Department of General Surgery, Goa Medical College, Bambolim, Goa, India

autor

Naik A.

• Directorate of Health Services, Panjim, Goa, India

Bibliografia

• [1] UNICEF. The situation of children in India: a profile. New Delhi: United Nations Children's Fund/India; 2011.
• [2] Census Report of India. New Delhi: Registrar General and Census Commissioner of India; 2011, http://censusindia.gov.in/.
• [3] Gilsanz V, Ratib O. Hand bone age: a digital atlas of skeletal maturity. Springer Science & Business Media; 2005.
• [4] Tanner J, Healy M, Goldstein H, Cameron N. Assessment of skeletal maturity and prediction of adult height: TW3 method. Saunders; 2001.
• [5] Greulich WW, Pyle SI. Radiographic atlas of skeletal development of the hand and wrist. Am J Med Sci 1959;238:393.
• [6] Tanner JM, Whitehouse R, Marshall W, Healty M, Goldstein H. Assessment of skeleton maturity and maturity and prediction of adult height (TW2 method); 1975.
• [7] Pietka E, Gertych A, Pospiech S, Cao F, Huang H, Gilsanz V. Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction. IEEE Trans Med Imaging 2001;20:715–29.
• [8] Pietka E, Pospiech-Kurkowska S, Gertych A, Cao F. Integration of computer assisted bone age assessment with clinical PACS. Comput Med Imaging Graph 2003;27:217–28.
• [9] Chai HY, Wee LK, Swee TT, Hussain S. GLCM based adaptive crossed reconstructed (ACR) k-mean clustering hand bone segmentation; 2011;192–7.
• [10] Chai HY, Swee TT, Seng GH, Wee LK. Multipurpose contrast enhancement on epiphyseal plates and ossification centers for bone age assessment. Biomed Eng Online 2013;12:1–19.
• [11] Tristán A, Arribas JI. A radius and ulna skeletal age assessment system. IEEE Workshop on Machine Learning for Signal Processing. IEEE; 2005. p. 221–6.
• [12] Tristan-Vega A, Arribas JI. A radius and ulna TW3 bone age assessment system. IEEE Trans Biomed Eng 2008;55:1463–76.
• [13] Han CC, Lee CH, Peng WL. Hand radiograph image segmentation using a coarse-to-fine strategy. Pattern Recognit 2007;40:2994–3004.
• [14] Liu Z, Chen J, Liu J, Yang L. Automatic bone age assessment based on PSO. 2007 1st International Conference on Bioinformatics and Biomedical Engineering; 2007.
• [15] Güraksin GE, Uğuz H, Baykan ÖK. 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.
• [16] Giordano D, Spampinato C, Scarciofalo G, Leonardi R. An automatic system for skeletal bone age measurement by robust processing of carpal and epiphysial/metaphysial bones. IEEE Trans Instrum Meas 2010;59:2539–53.
• [17] Giordano D, Kavasidis I, Spampinato C. Modeling skeletal bone development with hidden Markov models. Comput Methods Programs Biomed 2016;124:138–47.
• [18] Seok J, Kasa-Vubu J, DiPietro M, Girard A. Expert system for automated bone age determination. Expert Syst Appl 2016;50:75–88.
• [19] Kashif M, Jonas S, Haak D, Deserno TM. Bone age assessment meets SIFT. SPIE Medical Imaging. International Society for Optics and Photonics; 2015. p. 941439.
• [20] 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.
• [21] Elazab A, Wang C, Jia F, Wu J, Li G, Hu Q. Segmentation of brain tissues from magnetic resonance images using adaptively regularized kernel-based fuzzy-means clustering. Comput Math Methods Med 2015;2015.
• [22] Simu S, Lal S. A study about evolutionary and non-evolutionary segmentation techniques on hand radiographs for bone age assessment. Biomed Signal Process Control 2017;33:220–35.
• [23] Dougherty ER, Lotufo RA, T. I. S. for Optical Engineering SPIE for Optical Engineering. Hands-on morphological image processing, vol. 71. Washington: SPIE Optical Engineering Press; 2003.
• [24] Gonzalez W, Woods RE. Eddins, digital image processing using Matlab. Third New Jersey: Prentice Hall; 2004.
• [25] Otsu N. A threshold selection method from gray-level histograms. Automatica 1975;11:23–7.
• [26] Li C, Huang R, Ding Z, Gatenby JC, Metaxas DN, Gore JC. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 2011;20:2007–16.
• [27] Theodoridis S, Pikrakis A, Koutroumbas K, Cavouras D. Introduction to pattern recognition: a Matlab approach. Academic Press; 2010.
• [28] Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process 2001;10:266–77.
• [29] Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J Comput Phys 1988;79:12–49.
• [30] Sethian JA. Numerical methods for propagating fronts. Variational methods for free surface interfaces. Springer; 1987. p. 155–64.
• [31] Hartigan JA, Wong MA. Algorithm as 136: a k-means clustering algorithm. J R Stat Soc Ser C: Appl Stat 1979;28:100–8.
• [32] Pappas TN. An adaptive clustering algorithm for image segmentation. IEEE Trans Signal Process 1992;40:901–14.
• [33] Eberhart C, Kennedy J, et al. A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science; 1995. p. 39–43. http://dx.doi.org/10.1109/MHS.1995.494215.
• [34] Tillett J, Rao T, Sahin F, Rao R. Darwinian particle swarm optimization; 2005.
• [35] Gertych A, Zhang A, Sayre J, Pospiech-Kurkowska S, Huang H. Bone age assessment of children using a digital hand atlas. Comput Med Imaging Graph 2007;31:322–31.
• [36] Orlando JI, Prokofyeva E, Blaschko MB. A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans Biomed Eng 2017;64:16–27.

Uwagi

PL

Opracowanie w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).