Abstract
In this chapter, we review the techniques of histogram equation, hand bone segmentation and image segmentation techniques. The basic concepts of those techniques will be analyzed and compared. The objective of this chapter is to show to readers that current techniques are not sufficient to address the hand bone segmentation problems due to various restrictions. Hence, this motivates the need for new technique that is capable of segmenting the hand bone so that the segmented hand bone can be established and implemented in the fully automated computer-aided skeletal age scoring system. The proposed novel framework of hand bone segmentation will then be presented in next Chap. 3.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aja-Fernández S, De Luis-García R, Martín-Fernández MÁ, Alberola-López C (2004) A computational TW3 classifier for skeletal maturity assessment. A Computing with Words approach. J Biomed Inform 37:99–107
Niemeijer M, Van Ginneken B, Maas CA, Beek FJA, and Viergever MA (2003) Assessing the skeletal age from a hand radiograph: Automating the tanner-whitehouse method. In: Sonka M and Fitzpatrick JM (eds), Proceedings of the 2003 SPIE Medical Imaging. vol 5032 II, pp 1197–205, San Diego, CA
Pietka E, Gertych A, Pospiech S, Cao F, Huang HK, Gilsanz V (2001) Computer-assisted bone age assessment: Image preprocessing and epiphyseal/metaphyseal ROI extraction. IEEE Trans Med Imaging 20:715–729
Pietka E, Kaabi L, Kuo ML, Huang HK (1993) Feature extraction in carpal-bone analysis. IEEE Trans Med Imaging 12:44–49
Somkantha K, Theera-Umpon N, Auephanwiriyakul S (2011) Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features. IEEE Trans Biomed Eng 58:567–573
Zhang J, Huang HK (1997) Automatic background recognition and removal (ABRR) in computed radiography images. IEEE Trans Med Imaging 16:762–771
Han CC, Lee CH, Peng WL (2007) Hand radiograph image segmentation using a coarse-to-fine strategy. Pattern Recogn 40:2994–3004
Hsieh CW, Jong TL, Tiu CM (2008) Carpal growth assessment based on fuzzy description. In Soft Computing in Industrial Applications, 2008. SMCia’08. IEEE Conference on 2008, Muroran, pp 355–358
Sotoca JM, Iñesta JM, Belmonte MA (2003) Hand bone segmentation in radioabsorptiometry images for computerised bone mass assessment. Comput Med Imaging Graph 27:459–467
Buie HR, Campbell GM, Klinck RJ, MacNeil JA, Boyd SK (2007) Automatic segmentation of cortical and trabecular compartments based on a dual threshold technique for in vivo micro-CT bone analysis. Bone 41:505–515
Smyth PP, Taylor CJ, Adams JE (1997) Automatic measurement of vertebral shape using active shape models. Image Vis Comput 15:575–581
Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vision 1:321–331
Mahmoodi S, Sharif BS, Chester EG, Owen JP, and Lee REJ (1997) Automated vision system for skeletal age assessment using knowledge based techniques. In: IEE international conference on image processing and its applications, 443 pt 2 (ed.), pp 809–13, IEE, Dublin, Irel
Mahmoodi S, Sharif BS, Graeme Chester E, Owen JP, Lee R (2000) Skeletal growth estimation using radiographic image processing and analysis. IEEE Trans Inf Technol Biomed 4:292–297
Sebastian TB, Tek H, Crisco JJ, Kimia BB (2003) Segmentation of carpal bones from CT images using skeletally coupled deformable models. Med Image Anal 7:21–45
Tristán-Vega A, Arribas JI (2008) A radius and ulna TW3 bone age assessment system. IEEE Trans Biomed Eng 55:1463–1476
Giordano D, Spampinato C, Scarciofalo G, Leonardi R (2010) An automatic system for skeletal bone age measurement by robust processing of carpal and epiphysial/metaphysial bones. IEEE Trans Instrum Meas 59:2539–2553
Jong-Min L, Whoi-Yul K (2008) Epiphyses extraction method using shape information for left hand radiography. In: Convergence and Hybrid Information Technology, 2008. ICHIT ‘08. International Conference on 28–30 Aug. 2008. pp 319–326
Michael DJ, Nelson AC (1989) HANDX: a model-based system for automatic segmentation of bones from digital hand radiographs. IEEE Trans Med Imaging 8:64–69
Manos G, Cairns AY, Ricketts IW, Sinclair D (1993) Automatic segmentation of hand-wrist radiographs. Image Vis Comput 11:100–111
Manos GK, Cairns AY, Rickets IW, Sinclair D (1994) Segmenting radiographs of the hand and wrist. Comput Methods Programs Biomed 43:227–237
Sharif BS, Zaroug SA, Chester EG, Owen JP, and Lee EJ (1994) Bone edge detection in hand radiographic images. In: Engineering in medicine and biology society, 1994. Engineering advances: new opportunities for biomedical engineers. Proceedings of the 16th annual international conference of the IEEE. pt 1 (ed.), vol 16, pp 514–5, Baltimore, MD, USA
Mahmoodi S, Sharif BS, Chester EG, Owen JP, and Lee REJ (1999) Bayesian estimation of growth age using shape and texture descriptors. In: Image processing and its applications, 1999. Seventh international conference on, 465 II (ed.), vol 2, pp 489–493, IEE, Manchester, UK
Pietka E, Pospiech-Kurkowska S, Gertych A, Cao F (2003) Integration of computer assisted bone age assessment with clinical PACS. Comput Med Imaging Graph 27:217–228
Pietka E, Gertych A, Pospiech-Kurkowska S, Cao F, Huang HK, Gilzanz V (2004) Computer-assisted bone age assessment: graphical user interface for image processing and comparison. J Digit Imaging 17:175–188
Hsieh CW, Jong TL, Chou YH, Tiu CM (2007) Computerized geometric features of carpal bone for bone age estimation. Chin Med J 120:767–770
Zhang A, Gertych A, Liu BJ (2007) Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones. Comput Med Imaging Graph 31:299–310
Tran Thi My Hue MGS, Kim JY, Choi SH (2011) Hand bone image segmentation using watershed transform with multistage merging. J Korean Inst Inf Technol 9:59–66
Thodberg HH (2002) Hands-on experience with active appearance models. In: Sonka M, Michael Fitzpatrick J (eds), vol 4684 I, pp 495–506, San Diego, CA
Thodberg HH, Rosholm A (2003) Application of the active shape model in a commercial medical device for bone densitometry. Image Vis Comput 21:1155–1161
Thodberg HH, Kreiborg S, Juul A, Pedersen KD (2009) The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging 28:52–66
Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13:146–168
Gonzalez R, Woods R (2007) Digital Image Processing, 3rd edn. Prentice Hall, Upper Saddle River
Bernsen J (1986) Dynamic Thresholding of Grey-Level Images. In: International conference on pattern recognition, pp. 1251–5, IEEE, Paris, France
Lee SU, Yoon Chung S, Park RH (1990) A comparative performance study of several global thresholding techniques for segmentation. Comput Vis, Graph, Image Process 52:171–190
Shapiro L and Stockman G (2001) Computer Vision. Prentice Hall, Upper Saddle River
Liyuan L, Ran G, Weinan C (1997) Gray level image thresholding based on fisher linear projection of two-dimensional histogram. Pattern Recogn 30:743–749
Baradez MO, McGuckin CP, Forraz N, Pettengell R, Hoppe A (2004) Robust and automated unimodal histogram thresholding and potential applications. Pattern Recogn 37:1131–1148
Yan F, Zhang H, Kube CR (2005) A multistage adaptive thresholding method. Pattern Recogn Lett 26:1183–1191
Tsai D-M (1995) A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recogn Lett 16:653–666
Parker JR (1991) Gray level thresholding in badly illuminated images. IEEE Trans Pattern Anal Mach Intell 13:813–819
Zhao M, Yang Y, Yan H (2000) An adaptive thresholding method for binarization of blueprint images. Pattern Recogn Lett 21:927–943
Shafait F, Keysers D, and Breuel TM (2008) Efficient implementation of local adaptive thresholding techniques using integral images. In: Document recognition and retrieval XV, vol 6815, San Jose, CA
Huang Q, Gao W, Cai W (2005) Thresholding technique with adaptive window selection for uneven lighting image. Pattern Recogn Lett 26:801–808
Niblack W (1990) An Introduction to Digital Image Processing. Prentice-Hall, Upper Saddle River
De Santis A, Sinisgalli C (1999) A Bayesian approach to edge detection in noisy images. Circuits and systems i: fundamental theory and applications, IEEE Transactions on 1999, vol 46, pp 686–699
Whatmough RJ (1991) Automatic threshold selection from a histogram using the “exponential hull”. CVGIP: Graph Models Image Process 53:592–600
Luijendijk H (1991) Automatic threshold selection using histograms based on the count of 4-connected regions. Pattern Recogn Lett 12:219–228
Guo R, Pandit SM (1998) Automatic threshold selection based on histogram modes and a discriminant criterion. Mach. Vision Appl 10:331–338
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst, Man, Cybern 9:62–66
Lu C, Zhu P, Cao Y (2010) The segmentation algorithm of improvement a two-dimensional Otsu and application research. In: Software technology and engineering (ICSTE), 2010 2nd international conference on 2010. Vol. 1, pp V176–V179, San Juan, PR
Ge Y, Yang RF, Zhang P (2012) Research on the image segmentation method based on improved two-dimension Otsu arithmetic. Hedianzixue Yu Tance Jishu/Nucl Electron Detect Technol 32:112–115
Scharcanski J, Venetsanopoulos AN (1997) Edge detection of color images using directional operators. IEEE Trans Circuits Syst Video Technol 7:397–401
LingFei L, ZiLiang P (2008) An edge detection algorithm of image based on empirical mode decomposition. In Intelligent information technology application. IITA ‘08. Second international symposium on 2008. vol 1, pp 128–132
Bakalexis SA, Boutalis YS, and Mertzios BG (2002) Edge detection and image segmentation based on nonlinear anisotropic diffusion. In: Digital signal processing. DSP 2002. 2002 14th international conference on 2002. vol 2, pp 1203–6
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8:679–698
You-yi Z, Ji-lai R, Lei W (2010) Edge detection methods in digital image processing. In: Computer science and education (ICCSE) 5th international conference on 2010. vol pp 471–473
Deriche R (1987) Using Canny’s criteria to derive a recursively implemented optimal edge detector. Int J Comput Vis 1:167–187
Ding L, Goshtasby A (2001) On the Canny edge detector. Pattern Recogn 34:721–725
Duda RO, Hart PE (1972) Use of the Hough transformation to detect lines and curves in pictures. Commun ACM 15:11–15
Hough P (1962) Method and means for recognizing complex patterns. In: United States Patent Office
Chung CH, Cheng SC, Chang CC (2010) Adaptive image segmentation for region-based object retrieval using generalized Hough transform. Pattern Recogn 43:3219–3232
Xu X, Zhou Y, Cheng X, Song E, Li G (2012) Ultrasound intima–media segmentation using Hough transform and dual snake model. Comput Med Imaging Graph 36:248–258
Kassim AA, Tan T, Tan KH (1999) A comparative study of efficient generalised Hough transform techniques. Image Vis Comput 17:737–748
Shapiro V A (1996) On the hough transform of multi-level pictures. Pattern Recogn 29:589–602
Hart PE (2009) How the Hough transform was invented [DSP History]. IEEE Signal Processing Magazine 26:18–22
Ji J, Chen G, Sun L (2011) A novel Hough transform method for line detection by enhancing accumulator array. Pattern Recogn Lett 32:1503–1510
Zheng L, Shi D (2011) Advanced Radon transform using generalized interpolated Fourier method for straight line detection. Comput Vis Image Underst 115:152–160
Illingworth J, Kittler J (1987) The Adaptive Hough Transform. IEEE Trans Pattern Anal Mach Intell 9:690–698
Nixon M (1990) Improving an extended version of the Hough transform. Signal Processing 19:321–335
Kiryati N, Eldar Y, Bruckstein AM (1991) A probabilistic Hough transform. Pattern recognition 24:303–316
Hanif T, Sandler MB (1994) A counter-based Hough transform system. Microprocess Microsyst 18:19–26
Torii A, Imiya A (2007) The randomized-Hough-transform-based method for great-circle detection on sphere. Pattern Recogn Lett 28:1186–1192
Ballard DH (1981) Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn 13:111–122
Lo RC, Tsai WH (1995) Gray-scale hough transform for thick line detection in gray-scale images. Pattern Recogn 28:647–661
Kang CC, Wang WJ, Kang CH (2012) Image segmentation with complicated background by using seeded region growing. AEU—International Journal of Electronics and Communications 66:767–771
Fan J, Zeng G, Body M, Hacid M-S (2005) Seeded region growing: an extensive and comparative study. Pattern Recogn Lett 26:1139–1156
Grinias I, Tziritas G (2001) A semi-automatic seeded region growing algorithm for video object localization and tracking. Sig Process: Image Commun 16:977–986
Lin GC, Wang WJ, Kang CC, Wang CM (2012) Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing. Magn Reson Imaging 30:230–246
Mehnert A, Jackway P (1997) An improved seeded region growing algorithm. Pattern Recogn Lett 18:1065–1071
Tremeau A, Borel N (1997) A region growing and merging algorithm to color segmentation. Pattern Recogn 30:1191–1203
Digabel H, Lantuéjou C (1978) Iterative algorithms. In: Actes du Second Symposium Europeen d’Analyse Quantitative des Microstructures en Sciences des Materiaux, Biologie et Medecine. pp 4–7 Caen
Beucher S and Lantuejoul C (1979) Use of Watersheds in Contour Detection. In: International workshop on image processing: Real-time edge and motion detection/estimation, Rennes, France
Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13:583–598
Cousty J, Bertrand G, Najman L, Couprie M (2010) Watershed cuts: thinnings, shortest path forests, and topological watersheds. IEEE Trans Pattern Anal Mach Intell 32:925–939
Haris K, Efstratiadis SN, Maglaveras N, Katsaggelos AK (1998) Hybrid image segmentation using watersheds and fast region merging. IEEE Trans Image Process 7:1684–1699
Kim JB, Kim HJ (2003) Multiresolution-based watersheds for efficient image segmentation. Pattern Recogn Lett 24:473–488
Frucci M, Ramella G, Sanniti di Baja G (2007) Using resolution pyramids for watershed image segmentation. Image Vis Comput 25:1021–1031
Jung CR, Scharcanski J (2005) Robust watershed segmentation using wavelets. Image Vis Comput 23:661–669
Jung CR (2007) Combining wavelets and watersheds for robust multiscale image segmentation. Image Vis Comput 25:24–33
Hamarneh G, Li X (2009) Watershed segmentation using prior shape and appearance knowledge. Image Vis Comput 27:59–68
Masoumi H, Behrad A, Pourmina MA, Roosta A (2012) Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network. Biomed Signal Process Control 7:429–437
Meyer F (1994) Topographic distance and watershed lines. Signal Processing 38:113–125
Tarabalka Y, Chanussot J, Benediktsson JA (2010) Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recogn 43:2367–2379
McInerney T, Terzopoulos D (2000) Deformable models. Academic Press, San Diego
Leymarie FF (1990) Tracking and Describing Deformable Objects Using Active Contour Models [Thesis]
Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active Shape Models-Their Training and Application. Comput Vis Image Underst 61:38–59
Xue Z, Li SZ, Teoh EK (2003) Bayesian shape model for facial feature extraction and recognition. Pattern Recogn 36:2819–2833
Zheng Z, Jiong J, Chunjiang D, Liu X, Yang J (2008) Facial feature localization based on an improved active shape model. Inf Sci 178:2215–2223
Sukno FM, Guerrero JJ, Frangi AF (2010) Projective active shape models for pose-variant image analysis of quasi-planar objects: Application to facial analysis. Pattern Recogn 43:835–849
Jang D-S, Choi H-I (2000) Active models for tracking moving objects. Pattern Recogn 33:1135–1146
Kim W, Lee J–J (2005) Object tracking based on the modular active shape model. Mechatronics 15:371–402
Nuevo J, Bergasa LM, Llorca DF, Ocaña M (2011) Face tracking with automatic model construction. Image Vis Comput 29:209–218
Liu Z, Shen H, Feng G, Hu D (2012) Tracking objects using shape context matching. Neurocomputing 83:47–55
Hodge AC, Fenster A, Downey DB, Ladak HM (2006) Prostate boundary segmentation from ultrasound images using 2D active shape models: optimisation and extension to 3D. Comput Methods Programs Biomed 84:99–113
Aung MSH, Goulermas JY, Stanschus S, Hamdy S, Power M (2010) Automated anatomical demarcation using an active shape model for videofluoroscopic analysis in swallowing. Med Eng Phys 32:1170–1179
Toth R, Tiwari P, Rosen M, Reed G, Kurhanewicz J, Kalyanpur A et al (2011) A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation. Med Image Anal 15:214–225
Edwards GJ, Taylor CJ, Cootes TF (1998) Interpreting face images using active appearance models. In FG ‘98: Proceedings of the 3rd international conference on face & gesture recognition. IEEE computer society
Cootes TF, Page GJ, Jackson CB, Taylor CJ (1996) Statistical grey-level models for object location and identification. Image Vis Comput 14:533–540
Cootes TF, Edwards GJ, Taylor CJ (2001) Active Appearance Models. IEEE Trans Pattern Anal Mach Intell 23:681–685
Butakoff C, Frangi AF (2010) Multi-view face segmentation using fusion of statistical shape and appearance models. Comput Vis Image Underst 114:311–321
Roberts M, Cootes T, Pacheco E, Adams J (2007) Quantitative Vertebral Fracture Detection on DXA Images Using Shape and Appearance Models. Academic Radiology 14:1166–1178
Andreopoulos A, Tsotsos JK (2008) Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI. Med Image Anal 12:335–357
Patenaude B, Smith SM, Kennedy DN, Jenkinson M (2011) A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage 56:907–922
Gao X, Su Y, Li X, Tao D (2010) A Review of Active Appearance Models. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 40:145–158
Thodberg HH, Van Rijn RR, Tanaka T, Martin DD, Kreiborg S (2010) A paediatric bone index derived by automated radiogrammetry. Osteoporos Int 21:1391–1400
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2013 The Author(s)
About this chapter
Cite this chapter
Hum, Y.C. (2013). Literature Reviews. In: Segmentation of Hand Bone for Bone Age Assessment. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-4451-66-6_2
Download citation
DOI: https://doi.org/10.1007/978-981-4451-66-6_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-4451-65-9
Online ISBN: 978-981-4451-66-6
eBook Packages: EngineeringEngineering (R0)