AM  Vol.5 No.18 , October 2014
Automated Cell Detection and Morphometry on Growth Plate Images of Mouse Bone
ABSTRACT
Microscopy imaging of mouse growth plates is extensively used in biology to understand the effect of specific molecules on various stages of normal bone development and on bone disease. Until now, such image analysis has been conducted by manual detection. In fact, when existing automated detection techniques were applied, morphological variations across the growth plate and heterogeneity of image background color, including the faint presence of cells (chondrocytes) located deeper in tissue away from the image’s plane of focus, and lack of cell-specific features, interfered with identification of cells. We propose the first method of automated detection and morphometry applicable to images of cells in the growth plate of long bone. Through ad hoc sequential application of the Retinex method, anisotropic diffusion and thresholding, our new cell detection algorithm (CDA) addresses these challenges on bright-field microscopy images of mouse growth plates. Five parameters, chosen by the user in respect of image characteristics, regulate our CDA. Our results demonstrate effectiveness of the proposed numerical method relative to manual methods. Our CDA confirms previously established results regarding chondrocytes’ number, area, orientation, height and shape of normal growth plates. Our CDA also confirms differences previously found between the genetic mutated mouse Smad1/5CKO and its control mouse on fluorescence images. The CDA aims to aid biomedical research by increasing efficiency and consistency of data collection regarding arrangement and characteristics of chondrocytes. Our results suggest that automated extraction of data from microscopy imaging of growth plates can assist in unlocking information on normal and pathological development, key to the underlying biological mechanisms of bone growth.

Cite this paper
Ascenzi, M. , Du, X. , Harding, J. , Beylerian, E. , de Silva, B. , Gross, B. , Kastein, H. , Wang, W. , Lyons, K. and Schaeffer, H. (2014) Automated Cell Detection and Morphometry on Growth Plate Images of Mouse Bone. Applied Mathematics, 5, 2866-2880. doi: 10.4236/am.2014.518273.
References
[1]   Pal, N.R. and Pal, S.K. (1993) A Review on Image Segmentation Techniques. Pattern Recognition, 26, 1277-1294.
http://dx.doi.org/10.1016/0031-3203(93)90135-J

[2]   Schaeffer, H. and Osher, S. (2013) A Low Patch-Rank Interpretation of Texture. SIAM Journal of Imaging Sciences, 6, 226-262.
http://dx.doi.org/10.1137/110854989

[3]   Lloyd, S.P. (1982) Least Squares Quantization in PCM. IEEE Transactions on Information Theory, 28, 129-137.
http://dx.doi.org/10.1109/TIT.1982.1056489

[4]   Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, I.H., Friman, O., et al. (2006) Cell Profiler: Image Analysis Software for Identifying and Quantifying Cell Phenotypes. General Biology, 7, R100.
http://genomebiology.com/2006/7/10/R100

[5]   Fenistein, D., Lenseigne, B., Christophe, T., Brodin, P. and Genovesio, A. (2008) A Fast, Fully Automated Cell Segmentation Algorithm for High-Throughput and High-Content Screening. Cytometry Part A, 73A, 958-964.
http://dx.doi.org/10.1002/cyto.a.20627

[6]   Schneider, C.A., Rasband, W.S. and Eliceiri, K.W. (2012) Nih Image to Image J: 25 Years of Image Analysis. Nature Methods, 9, 671-675.
http://dx.doi.org/10.1038/nmeth.2089

[7]   Collins, T.J. (2007) Image J for Microscopy. BioTechniques, 43, S25-S30.
http://dx.doi.org/10.2144/000112517

[8]   Helmy, I.M., Azim, A.M. (2012) Efficacy of Image J in the Assessment of Apoptosis. Diagnostic Pathology, 7, 15.
http://dx.doi.org/10.1186/1746-1596-7-15

[9]   Buggenthin, F., Marr, C., Schwarzfischer, M., Hoppe, P.S., Hilsenbeck, O., Schroeder, T., et al. (2013) An Automatic Method for Robust and Fast Cell Detection in Bright Field Images from High-Throughput Microscopy. BMC Bioinformatics, 14, 297.
http://dx.doi.org/10.1186/1471-2105-14-297

[10]   Ascenzi, M.-G., Blanco, C. Drayer, Kim, I. H., Wilson, R., Retting, K.N., Lyons, K.M. and Mohler, G. (2011) Effect of Localization, Length and Orientation of Chondrocytic Primary Cilium on Murine Growth Plate Organization. Journal of Theoretical Biology, 285, 147-155.
http://dx.doi.org/10.1016/j.jtbi.2011.06.016

[11]   Ivkovic, S., Yoon, B.S., Popoff, S.N., Safadi, F.F., Libuda, D.E., Stephenson, R.C., Daluiski, A. and Lyons, K.M. (2003) Connective Tissue Growth Factor Coordinates Chondrogenesis and Angiogenesis during Skeletal Development. Development, 130, 2779-2791.
http://dx.doi.org/10.1242/dev.00505

[12]   Kronenberg, H.M. (2003) Developmental Regulation of the Growth Plate. Nature, 423, 332-336.
http://dx.doi.org/10.1038/nature01657

[13]   Karsenty, G., Kronenberg, H.M. and Settembre, C. (2009) Genetic Control of Bone Formation. Annual Review of Cell and Developmental Biology, 25, 629-648.
http://dx.doi.org/10.1146/annurev.cellbio.042308.113308

[14]   Ascenzi, M.G., Lenox, M. and Farnum, C. (2007) Analysis of the Orientation of Primary Cilia in Growth Plate Cartilage: A Mathematical Method Based on Multiphoton Microscopical Images. Journal of Structural Biology, 158, 293-306.
http://dx.doi.org/10.1016/j.jsb.2006.11.004

[15]   Shapiro, I.M., Adams, C.S., Freeman, T. and Srinivas, V. (2005) Fate of the Hypertrophic Chondrocyte: Microenvironmental Perspectives on Apoptosis and Survival in the Epiphyseal Growth Plate. Birth Defects Research Part C, 75, 330-339.
http://dx.doi.org/10.1002/bdrc.20057

[16]   Long, F. and Ornitz, D.M. (2013) Development of the Endochondral Skeleton. Cold Spring Harbor Perspectives in Biology, 5, a008334.
http://dx.doi.org/10.1101/cshperspect.a008334

[17]   Cooper, K.L., Oh, S., Sung, Y., Dasari, R.R., Kirschner, M.W. and Tabin, C.J. (2013) Multiple Phases of Chondrocyte Enlargement Underlie Differences in Skeletal Proportions. Nature, 495, 375-378.
http://dx.doi.org/10.1038/nature11940

[18]   Retting, K.N., Song, B., Yoon, B.S. and Lyons, K.M. (2009) BMP Canonical Smad Signaling through Smad1 and Smad5 Is Required for Endochondral Bone Formation. Development, 136, 1093-1104.
http://dx.doi.org/10.1242/dev.029926

[19]   Estrada, K.D., Retting, K.N., Chin, A.M. and Lyons, K.M. (2011) Smad6 Is Essential to Limit BMP Signaling during Cartilage Development. Journal of Bone and Mineral Research, 26, 2498-2510.
http://dx.doi.org/10.1002/jbmr.443

[20]   Land, E.H. (1964) The Retinex. American Scientist, 52, 247-264.

[21]   Land, E.H. and McCann, J.J. (1971) Lightness and Retinex Theory. The Journal of the Optical Society of America, 61, 1-11.
http://dx.doi.org/10.1364/JOSA.61.000001

[22]   Kimmel, R., Elad, M., Shaked, D., Keshet, R. and Sobel, I. (2003) A Variational Framework for Retinex. International Journal of Computer Vision, 52, 7-23.
http://dx.doi.org/10.1023/A:1022314423998

[23]   Rahman, Z., Jobson, D.J. and Woodell, G.A. (2004) Retinex Processing for Automatic Image Enhancement. Journal of Electronic Imaging, 13, 100-110.
http://dx.doi.org/10.1117/1.1636183

[24]   Morel, J.M., Petro, A.B. and Sbert, C. (2010) A PDE Formalization of Retinex Theory. IEEE Transactions on Image Processing, 19, 2825-2837.
http://dx.doi.org/10.1109/TIP.2010.2049239

[25]   Aubert, G. and Kornprobst, P. (2006) Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, Vol. 147. Springer, New York.

[26]   Weickert, J. (1998) Anisotropic Diffusion in Image Processing. B.G. Teubner, Stuttgart.

[27]   Krein, M. and Smulian, V. (1940) On Regularly Convex Sets in the Space Conjugate to a Banach Space. Annals of Mathematics, 41, 556-583.
http://dx.doi.org/10.2307/1968735

[28]   Farnum, C.E., Turgai, J. and Wilsman, N.J. (1990) Visualization of Living Terminal Hypertrophic Chondrocytes of Growth Plate Cartilage in Situ by Differential Interference Contrast Microscopy and Time-Lapse Cinematography. Journal of Orthopaedic Research, 8, 750-763.
http://dx.doi.org/10.1002/jor.1100080517

[29]   Dice, L.R. (1945) Measures of the Amount of Ecologic Association between Species. Ecology, 26, 297-302.
http://dx.doi.org/10.2307/1932409

[30]   Moore, D.S. and McCabe, G.P. (2007) Introduction to the Practice of Statistics. 6th Edition, W. H. Freeman and Company, New York.

[31]   Song, B., Haycraft, C.J., Seo, H., Yoder, B.K. and Serra, R. (2007) Development of the Post-Natal Growth Plate Requires Intraflagellar Transport Proteins. Developmental Biology, 305, 202-216.
http://dx.doi.org/10.1016/j.ydbio.2007.02.003

[32]   Yin, W., Goldfarb, D. and Osher, S. (2005) Image Cartoon-Texture Decomposition and Feature Selection Using the Total Variation Regularized L1 Functional. Variational, Geometric, and Level Set Methods in Computer Vision. Lecture Notes in Computer Science, 3752, 73-84.
http://dx.doi.org/10.1007/11567646_7

[33]   Meyer, Y. (2001) Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures. University Lecture Series, 22, American Mathematical Society, Providence.

[34]   Vese, L.A. and Osher, S. (2003) Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing. Journal of Scientific Computing, 19, 553-572.
http://dx.doi.org/10.1023/A:1025384832106

[35]   Rocha, L., Velho, L. and Carvalho, P.C.P. (2002) Image Moments-Based Structuring and Tracking of Objects. Proceedings of the XV Brazilian Symposium on Computer Graphics and Image Processing, 36, 99-105.
http://dx.doi.org/10.1109/SIBGRA.2002.1167130

[36]   Haralick, R.M., Shanmugam, K. and Dinstein, I. (1973) Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics, 3, 610-621.
http://dx.doi.org/10.1109/TSMC.1973.4309314

[37]   Gabor, D. (1946) Theory of Communication. Journal of the Institution of Electrical Engineers, 93, 429-441.

[38]   Maragos, P. (1989) Pattern Spectrum and Multiscale Shape Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 701-716.
http://dx.doi.org/10.1109/34.192465

 
 
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