Journal of Biosystems Engineering

  • 제38권1호
  • /
  • Pages.55-63
  • /
  • 2013
  • /
  • 1738-1266(pISSN)
  • /
  • 2234-1862(eISSN)
한국농업기계학회 (Korean Society for Agricultural Machinery)
권호 표지
DOI QR코드

DOI QR Code

Cell Image Processing Methods for Automatic Cell Pattern Recognition and Morphological Analysis of Mesenchymal Stem Cells - An Algorithm for Cell Classification and Adaptive Brightness Correction -

  • Lim, Kitaek (Department of Biosystems and Biomaterials Science and Engineering, Seoul National University) ;
  • Park, Soo Hyun (Department of Biosystems and Biomaterials Science and Engineering, Seoul National University) ;
  • Kim, Jangho (Department of Biosystems and Biomaterials Science and Engineering, Seoul National University) ;
  • SeonWoo, Hoon (Department of Biosystems and Biomaterials Science and Engineering, Seoul National University) ;
  • Choung, Pill-Hoon (Department of Oral and Maxillofacial Surgery and Dental Research Institute, School of Dentistry, Seoul National University) ;
  • Chung, Jong Hoon (Department of Biosystems and Biomaterials Science and Engineering, Seoul National University)
  • 투고 : 2013.02.18
  • 심사 : 2013.02.28
  • 발행 : 2013.03.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Purpose: The present study aimed at image processing methods for automatic cell pattern recognition and morphological analysis for tissue engineering applications. The primary aim was to ascertain the novel algorithm of adaptive brightness correction from microscopic images for use as a potential image analysis. Methods: General microscopic image of cells has a minor problem which the central area is brighter than edge-area because of the light source. This may affect serious problems to threshold process for cell-number counting or cell pattern recognition. In order to compensate the problem, we processed to find the central point of brightness and give less weight-value as the distance to centroid. Results: The results presented that microscopic images through the brightness correction were performed clearer than those without brightness compensation. And the classification of mixed cells was performed as well, which is expected to be completed with pattern recognition later. Beside each detection ratio of hBMSCs and HeLa cells was 95% and 92%, respectively. Conclusions: Using this novel algorithm of adaptive brightness correction could control the easier approach to cell pattern recognition and counting cell numbers.

키워드

참고문헌

  1. Althoff, K., J. Degerman and T. Gustavsson. 2005. Combined segmentation and tracking of neural stem cells. In Image Analysis. pp. 282-291.
  2. Amini, S., D. Veilleux and I. Villemure. 2010. Tissue and cellular morphological changes in growth plate explants under compression. Journal of Biomechanics 43(13): 2582-2588. https://doi.org/10.1016/j.jbiomech.2010.05.010
  3. Brun, F., A. Accardo, M. Marchini, F. Ortolani, G. Turco and S. Paoletti. 2011. Texture analysis of TEM micrographs of alginate gels for cell microencapsulation. Microscopy Research and Technique 74(1):58-66. https://doi.org/10.1002/jemt.20874
  4. Chan, S. W. K., K. S. Leung and W. S. F. Wong. 1996. An expert system for the detection of cervical cancer cells using knowledge-based image analyzer. Artificial Intelligence in Medicine 8(1):67-90. https://doi.org/10.1016/0933-3657(95)00021-6
  5. Chaudry, Q., S. H. Raza, A. N. Young and M. D. Wang. 2009. Automated renal cell carcinoma subtype classification using morphological, textural and wavelets based features. Journal of Signal Processing Systems for Signal Image and Video Technology 55:15-23. https://doi.org/10.1007/s11265-008-0214-6
  6. Cheng, J. Z., Y. H. Chou, C. S. Huang, Y. C. Chang, C. M. Tiu, F. C. Yeh, K. W. Chen, C. H. Tsou and C. M. Chen. 2010. ACCOMP: Augmented cell competition algorithm for breast lesion demarcation in sonography. Medical Physics 37(12):6240-6252. https://doi.org/10.1118/1.3512799
  7. Diaz, E., G. Ayala, M. E. Diaz, L. W. Gong and D. Toomre. 2010. Automatic detection of large dense-core vesicles in secretory cells and statistical analysis of their intracellular distribution. IEEE-Acm Transactions on Computational Biology and Bioinformatics 7(1):2-11. https://doi.org/10.1109/TCBB.2008.30
  8. Duda, R. O., P. E. Hart and D. G. Stock. 2001. Pattern Classification, 2nd, New York: Wiley-Interscience.
  9. Garrido, A. and N. PeHrez de la Blanca. 2000. Applying deformable templates for cell image segmentation. Pattern Recognition 33(5):821-832. https://doi.org/10.1016/S0031-3203(99)00091-6
  10. Huang, P. W. and Y. H. Lai. 2010. Effective segmentation and classification for HCC biopsy images. Pattern Recognition 43(4):1550-1563. https://doi.org/10.1016/j.patcog.2009.10.014
  11. Kachouie, N. N., L. J. Lee and P. Fieguth. 2005. A probabilistic living cell segmentation model. In ICIP. pp. 137-140.
  12. Kachouie, N. N., P. Fieguth, J. Ramunas and E. Jervis. 2006. Probabilisticmodel-based cell tracking. Int. Journal of Biomedical Imaging. pp. 1-10.
  13. Kachouie, N. N., P. Fieguth and E. Jervis. 2007. Stem-cell localization: A deconvolution problem. In EMBS. pp. 5525-5528.
  14. Korzynska, A.. 2007. Automatic counting of neural stem cells growing in cultures. In Computer Recognition Systems. pp. 604-612.
  15. Li, F. H., X. B. Zhou, J. W. Ma and S. T. C. Wong. 2010. Multiple nuclei tracking using integer programming for quantitative cancer cell cycle analysis. IEEE Transactions on Medical Imaging 29(1):96-105. https://doi.org/10.1109/TMI.2009.2027813
  16. Lockett, S. J., D. Sudar and C. T. Thompson.1998. Efficient, interactive, and three-dimensional Segmentation of Cell Nuclei in Thick Tissue Sections. Cytometry 31: 275-286. https://doi.org/10.1002/(SICI)1097-0320(19980401)31:4<275::AID-CYTO7>3.0.CO;2-I
  17. Long, X., W. L. Cleveland and Y. L. Yao. 2005. Effective automatic recognition of cultured cells in bright field images using fisher's linear discriminant preprocessing. Image and Vision Computing 23(13):1203-1213. https://doi.org/10.1016/j.imavis.2005.07.019
  18. Markiewicz, T., S. Osowski, J. Patera and W. Kozlowski. 2006. Image processing for accurate cell recognition and count on histologic slides. Int. Academy of Cytology and American Society of Cytology 28(5):281-291.
  19. Reyes-Aldasoro, C. C., L. J. Williams, S. Akerman, C. Kanthou and G. M. Tozer. 2011. An automatic algorithm for the segmentation and morphological analysis of microvessels in immunostained histological tumour sections. Journal of Microscopy 242(3):262-278. https://doi.org/10.1111/j.1365-2818.2010.03464.x
  20. Plissiti, M. E., C. Nikou and A. Charchanti. 2011. Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images. Pattern Recognition Letters 32(6):838-853. https://doi.org/10.1016/j.patrec.2011.01.008
  21. Schildkraut, J. S., N. Prosser, A. Savakis, J. Gomez, D. Nazareth, A. K. Singh and H. K. Malhotra. 2010. Levelset segmentation of pulmonary nodules in megavolt electronic portal images using a CT prior. Medical Physics 37(11):5703-5710. https://doi.org/10.1118/1.3495538
  22. Shiotani, S., T. Fukuda, F. Arai, N. Takeuchi, K. Sasaki and T. Kinoshita. 1994. Cell recognition by image processing: (recognition of dead or living plant cells by neural network). JSME 37:202-208.
  23. Spencer, T., J. A. Olson, K. C. Mchardy, P. R. Sharp and J. V. Forrester. 1996. An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. Computers and Biomedical Researches 29:284-302. https://doi.org/10.1006/cbmr.1996.0021
  24. Tang, C. and E. Bengtsson. 2005. Segmentation and tracking of neural stem cell. In Advances in Intelligent Computing. pp. 851-859.
  25. Wu, K., D. Gauthier and M. D. Levine. 1995. Live cell image segmentation. IEEE Transactions on Biomedical Engineering 42:1-12. https://doi.org/10.1109/10.362924
  26. Xiong, Y., C. Kabacoff, J. Franca-Koh, P. N. Devreotes, D. N. Robinson and P. A. Iglesias. 2010. Automated characterization of cell shape changes during amoeboid motility by skeletonization. Bmc Systems Biology, vol. 4.
  27. Zheng, Q., B. K. Milthorpe and A. S. Jones. 2004. Direct neural network application for automated cell recognition. Cytometry A 57(1):1-9.

피인용 문헌

  1. Automated Cell Counting Method for HeLa Cells Image based on Cell Membrane Extraction and Back-tracking Algorithm vol.42, pp.10, 2015, https://doi.org/10.5626/JOK.2015.42.10.1239
  2. A new method of SC image processing for confluence estimation vol.101, 2017, https://doi.org/10.1016/j.micron.2017.07.013
  3. Neurogenic Differentiation of Human Dental Pulp Stem Cells on Graphene-Polycaprolactone Hybrid Nanofibers vol.8, pp.7, 2018, https://doi.org/10.3390/nano8070554