Mikroskop görüntüsünde otomatik embriyonik kök hücre tespiti ve sayımı

Yıl 2016, Cilt: 20 Sayı: 2, 83 - 97, 01.08.2016

Gökçen Çetinel , Ali Furkan Kamanlı

https://doi.org/10.16984/saufenbilder.23719

Öz

Mikroskop altında hücre sayımı, bir uzman tarafından mikroskop merceğine sürekli bakılarak veya otomatik hücre sayımı yöntemleri kullanılarak yapılabilmektedir. Sayım uzman tarafından yapıldığında oldukça yorucu, uzun süren ve düşük doğruluğa sahip bir işlem haline gelmektedir. Bunun dışında hücre sayımını zorlaştrıan ve doğruluğu düşüren başka etkenler de mevcuttur. Bu nedenle, hücre sayımının otomatik bir şekilde yapılması ve sunulan sayım yöntemlerinin iyileştirilmesi gerekmektedir. Bu çalışmada, flüoresans mikroskop görüntüsünde otomatik hücre tespiti ve sayımı için bir yöntem sunulmuştur. Yöntemin tüm adımları açıklanmıştır. Sunulan yöntemin etkinliği birçok farklı durum için simülasyon programları vasıtasıyla test edilmiş, yöntemin başarıya ulaştığı ve gelecek vadeden bir çalışma olduğu gösterilmiştir.

Anahtar Kelimeler

embriyonik kök hücre, otomatik kök hücre sayımı, histogram bölütleme, maksimum nokta analizi

An automatic embryonic stem cell counting method

Öz

The cell counting process can be performed by manual counting in which a specialist counts the cells with naked eye or the automatic counting that utilizes the computer-based techniques. The counting process becomes exhausting, long and incorrect when the counting performed by specialist. Therefore the cell counting process must be performed automatically.In this study, an automatic cell detecting and counting method under fluorescence microscopy is proposed. All steps of the method are given in details. Several computer simulations are performed to evaluate the effectiveness of the method andit is shown that the proposed method gives promising results.   

Anahtar Kelimeler

embriyonic stem cell, authomatic stem cell counting, histogram partitioning, maximum point analysis

Kaynakça

• KAYNAKÇA
• Ghiaur G, Gerber J, Jones RJ Concise review: Cancer stem cells and minimal residual disease. Stem Cells, 30: 89 – 93, 2012.
• J.M.D. Lamaziere, J. Lavallee, C. Zunino, J. Larrue, Semiquantitative study of the distribution of 2 cellular antigens by computer-directed color analysis, Lab. Invest. 68 248—252, 1993.
• S. Tseleni, N. Kavantzas, D. Yova, E. Alexandrou, V.Karydakis, J. Gogas, P. Davaris, Findings of computerised nuclear morphometry of papillary thyroid carcinoma in correlation with known prognostic factors, J. Exp. Clin. Cancer Res. 16 401—407, 1997.
• Aasen T, Belmonte, JCI. Isolation and cultivation of human keratinocytes from skin or plucked hair for the generation of induced pluripotent stem cells. Nature Protocols, 5[2]: 3171-382, 2010.
• Seçil Erden, Stem cells and clinical applications, Journal of New Results in Engineering and Natural Science, No: 3, pp.1-8, 2014.
• Akar AR, Arat M, Beksaç M, Can A, Çamurdanoğlu BZ, Çetinkaya DU, Elçin YM, Kansu E, Kırık D, Özçelik T, Özden İ, Şahin G Türkiye Bilimler Akademisi Raporları, 20, Ankara, 113s, 2009.
• Barry FP, Murphy JM. Mesenchymal Stem Cells: Clinical applications and biological characterization. The International Journal of Biochemistry & Cell Biology, 36: 568-584, 2004.
• Deliloğlu Gürhan Sİ, Özen MÖ, Sözer P, Lüleci İ. Kök hücreler ve doku mühendisliği, Sağlıkta Birikim, 1[5]: 143-168, 2009.
• J.M. Geusebroek et al., Segmentation of cell clusters by nearest neighbor graphs, Proceedings of the third annual conference of the Advanced School for Computing and Imaging, pp. 248–252, 1997.
• V. Meas-Yedid et al. Quantitative microscopic image analysis by active contours, in Vision Interface Annual Conference 2001 – Medical Applications, 2001.
• J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern Recognition, vol. 19, no. 1, pp. 41–47, 1986.
• N. Otsu, A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
• K. Wu, D. Gauthier, and M. Levine, Live cell image segmentation, IEEE Transactions on Biomedical Engineering, vol. 42, no. 1, pp. 1–12, 1995.
• T. Markiewicz et al. Myelogenous leukemia cell image preprocessing for feature generation, in 5th International Workshop on Computational Methods in Electrical Engineering, pp. 70–73, 2003.
• M. Colley, F. Kommoss, M. Bibbo, H.E. Dytch, W.A. Frnklin, J.A. Holt, G.L. Wied, Assessment of hormone receptors in breast carcinoma by immunocytochemistry and image analysis, Anal. Quant. Cytol. Histol. 11 307-314, 1989.
• J.M.D. Lamaziere, J. Lavallee, C. Zunino, J. Larrue, Semiquantitative study of the distribution of 2 cellular antigens by computer-directed color analysis, Lab. Invest. 68 248—252, 1993.
• E.J. Goldlust, R.P. Paczynski, Y.Y. He, C.Y. Hsu, M.P.Goldberg, Automated measurement of infract size with scanned images of triphenyltetrazolium chloride-stained rat brains, Stroke 27 1657-1662, 1996.
• S. Tseleni, N. Kavantzas, D. Yova, E. Alexandrou, V.Karydakis, J. Gogas, P. Davaris, Findings of computerised nuclear morphometry of papillary thyroid carcinoma in correlation with known prognostic factors, J. Exp. Clin. Cancer Res. 16 401—407, 1997.
• J. Uitto, J.L. Paul, K. Brockley, R.H. Pearce, J.G. Clark, Elastic fibers in human skin: quantitation of elastic fibers by computerized digital analysis and determination of elastin by radioimmunoassay of desmosine, Lab. Invest. 49 499—505, 1983.
• K. Beier, H.D. Fahimi, Application of automated image analysis for quantitative morphological studies of peroxisomes in rat liver in conjuction with cytochemical staining with 3-3_-diaminobenzidine and immunocytochemistry, Microsc. Res. Tech. 21 271—282, 1992.
• W.A. Franklin, M. Biddo, M.I. Doria, H.E. Dytch, J. Toth, E. DeSombre, G.L. Wied, Quantitation of estrogen receptor content in breast carcinoma by the MicroTICAS image analysis system, Anal. Quant. Cytol. Histol. 9 279—286, 1987.
• H.A. Lehr, K.D. Hansen, M.D. Coltera, J.C. Russ, A.M. Gown, Photoshop-based image analysis for the semiautomated assessment of Ki-67-defined proliferative activity in the routine diagnosis of bre, Appl. Immunohistochem. 4 117—127, 1996.
• E. Sharipo, V. Hartanto, H. Lepor, Quantifuing the smoothmuscle content of the prostate using double-immunoenzymatic staining and color assisted image analysis, J. Urol. 147 1167-1170, 1990.
• H.A. Lehr, D.A. Mankoff, D. Corwin, G. Santeusanio, A.M. Gown, Application of photoshop-based analysis to quantification of hormone receptor expression in breast cancer, J. Histochem. Cytochem. 45 1559-1565, 1997.
• H.A. Lehr, C.M. Van der Loos, P. Teeling, A.M. Gown, Complete cromogen separation and analysis in double immunohistochemical stains using photoshop-based image analysis, J. Histochem Cytochem. 47 199-225, 1999.
• M.H. Deverell, J.R.SalisburyW.F. Whimster, Comparisons of stains for image segmentation and measurement of nuclear parameters by computerised image analysis using IBAS 2000, Pathol. Res. Pract. 185 555-557, 1989.
• P.D. Kohlberger, A. Obermair, G. Sliutz, H. Heinzl, H.Koelbl, G. Breitenecker, G. Gitsch, G. Kainz, Quantitative immunohistochemistry of factor VIII-related antigen in breast carcinoma, Am. J. Clin. Pathol. 105 705-710, 1996.
• R.C.A. Gonzalez, R.E. Woods, Digital Image Processing, Addison-Wesley Reading, MA, USA, 1992.
• C. Garbay, G. Brugal, C. Choquet, Application of colored image analysis to bone marrow cell recognition, Anal. Quant. Cytol. 4 272-280, 1986.
• J.A.W. van der Laak, M.M.M. Pahlplatz, A.G.J.M. Hanselaar, P.C.M. de Wilde, Hue-saturation-density (HSD) model for stain recognition in digital images from transmitted light microscopy, Cytometry 39 275-284, 2000.
• C.G. Loukas, G.D. Wilson, B. Vojnovic, A. Linney, Automated segmentation of cancer cell nuclei in complex tissue sections, SPIE 4158 188—198, 2000.
• T. Markiewicz et al. Myelogenous leukemia cell image preprocessing for feature generation, in 5th International Workshop on Computational Methods in Electrical Engineering, pp. 70–73, 2003.
• M. Goto, Y. Nagatomo, K. Hasui, H. Amanaka, S. Murashima, E. Sato, Chromaticity analysis of immunostained tumor specimens, Pathol. Res. Pract. 188 433-437, 1992.
• A.C. Ruifrok, Quantification of immunohistochemical staining by color translation and automated thresholding, Anal. Quant. Cytol. Histol. 19 107—113, 1997.
• J. Smolle, Optimization of linear image combination for segmentation in red green and blue images, Anal. Quant. Cytol. Histol. 18 323-329, 1996.
• Geisa Martins Faustino et. al., Automatic embryonic stem cells detection and counting in fluorescence microscopy images, Monografias em Ciência da Computação, No. 04/09 ISSN: 0103-9741, 2009.