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Lasers in Medical Science

, Volume 34, Issue 7, pp 1503–1508 | Cite as

Texture feature extraction of gray-level co-occurrence matrix for metastatic cancer cells using scanned laser pico-projection images

  • Meng-Jia Lian
  • Chih-Ling HuangEmail author
Brief Report
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Abstract

Metastasis is responsible for 90% of all cancer-related deaths in humans, and the development of a rapid and promising solution for an early diagnosis of metastasis is required. The present study proposed a promising method combined with scanned laser pico-projection technique and typical texture feature (i.e., contrast, correlation, energy, entropy, and homogeneity) extraction of gray-level co-occurrence matrix (GLCM) image processing model to classify the low- and high-metastatic cancer cells using five common cancer adenocarcinoma cell line pairs (i.e., HeLa/HeLa-S3, CL1-0/CL1-5, OVTW59-P0/OVTW59-P4, and CE81T-FNlow/CE81T-FNhigh cell lines). Highly metastatic cancer cells essentially have the highest levels of disorder. Both contrast and entropy refer to the degree of disorder, and energy and homogeneity refer to the degree of uniformity. These four texture features can be effective evaluation indexes for disorder in cancer cells responding to metastatic ability. Texture feature extraction forms reflection images, which are recorded with scanned laser pico-projection system; they effectively bridge the gap in information derived from transmission images. The low- and high-metastatic cancer cells are statistically and effectively classified from the texture feature of GLCM through transmission and reflection images taken with scanned laser pico-projection system. In particular, it only requires several seconds after producing a confluent monolayer of cells and achieves the rapid method with a more reliable diagnostic performance for metastatic ability of cancer cells in vitro or ex vivo.

Keywords

Metastasis Cancer cell Gray-level co-occurrence matrix Scanned laser pico-projection Image analysis 

Notes

Funding information

The authors gratefully acknowledge the financial support provided for this study by the Ministry of Science and Technology (MOST) in Taiwan under Grant No. MOST-106-2221-E-037-004. This study is also supported by a grant from the Kaohsiung Medical University Research Foundation (KMU-Q107023).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

Approval by Ethical Commission is not needed.

Informed consent

Not applicable since there are no patients involved.

References

  1. 1.
    Weigelt B, Peterse JL, van't Veer LJ (2005) Breast cancer metastasis: markers and models. Nat Rev Cancer 5:591–602CrossRefGoogle Scholar
  2. 2.
    Stacker SA, Williams SP, Karnezis T, Shayan R, Fox SB, Achen MG (2014) Lymphangiogenesis and lymphatic vessel remodelling in cancer. Nat Rev Cancer 14:159–172CrossRefGoogle Scholar
  3. 3.
    Kinkel K, Lu Y, Both M, Warren RS, Thoeni RF (2002) Detection of hepatic metastases from cancers of the gastrointestinal tract by using noninvasive imaging methods (US, CT, MR imaging, PET): a meta-analysis. Radiology 224:748–756CrossRefGoogle Scholar
  4. 4.
    Wu Y, Liu ZG, Shi MQ, Yu HZ, Jiang XY, Yang AH et al (2017) Identification of ACTG2 functions as a promoter gene in hepatocellular carcinoma cells migration and tumor metastasis. Biochem Biophys Res Commun 491:537–544CrossRefGoogle Scholar
  5. 5.
    Molina D, Pérez-Beteta J, Martínez-González A, Martino J, Velásquez C, Arana E et al (2016) Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images. Comput Biol Med 78:49–57CrossRefGoogle Scholar
  6. 6.
    Zhou H, Dong D, Chen B, Fang M, Cheng Y, Gan Y et al (2018) Diagnosis of distant metastasis of lung cancer: based on clinical and radiomic features. Transl Oncol 11:31–36CrossRefGoogle Scholar
  7. 7.
    Hossain K, Parekh R (2010) Extending GLCM to include color information for texture recognition. AIP Conference Proceedings 1298:583–588CrossRefGoogle Scholar
  8. 8.
    CT Lam, MS Krieger, JE Gallagher, B Asma, LC Muasher, JW Schmitt, et al., "Design of a novel low cost point of care tampon (POCkeT) colposcope for use in resource limited settings," PLoS ONE, vol. 10, p. e0135869, 09/02Google Scholar
  9. 9.
    Chuang CH, Sung TW, Huang CL, Lo YL (2012) Relative two-dimensional nanoparticle concentration measurement based on scanned laser pico-projection. Sensors Actuators B Chem 173:281–287CrossRefGoogle Scholar
  10. 10.
    M-J Lian, C-L Huang, and T.-M. Lee, "Automation characterization for oral cancer by pathological image processing with gray-level co-occurrence matrix," presented at the 5th International Conference on Mechanics and Mechatronics Research, Tokyo, 2018Google Scholar
  11. 11.
    Lloyd K, Rosin PL, Marshall D, Moore SC (May 01 2017) Detecting violent and abnormal crowd activity using temporal analysis of grey level co-occurrence matrix (GLCM)-based texture measures. Mach Vis Appl 28:361–371CrossRefGoogle Scholar
  12. 12.
    R Haralick, K Shanmugam, and I H. Dinstein, Texture features for image classification vol. 3, 1975Google Scholar
  13. 13.
    Landry JJM, Pyl PT, Rausch T, Zichner T, Tekkedil MM, Stütz AM et al (2013) The genomic and transcriptomic landscape of a HeLa cell line. G3: Genes|Genomes|Genetics 3:1213–1224CrossRefGoogle Scholar
  14. 14.
    Shiraga K, Suzuki T, Kondo N, Tanaka K, Ogawa Y (2015) Hydration state inside HeLa cell monolayer investigated with terahertz spectroscopy. Appl Phys Lett 106:253701CrossRefGoogle Scholar
  15. 15.
    Puck TT, Marcus PI (1955) A rapid method for viable cell titration and clone production with HeLa cells in tissue culture: the use of X-irradiated cells to supply conditioning factors. Proc Natl Acad Sci U S A 41:432–437CrossRefGoogle Scholar
  16. 16.
    Chu YW, Yang PC, Yang SC, Shyu YC, Hendrix MJ, Wu R et al (1997) Selection of invasive and metastatic subpopulations from a human lung adenocarcinoma cell line. Am J Respir Cell Mol Biol 17:353–360CrossRefGoogle Scholar
  17. 17.
    Torng PL, Lee YC, Huang CY, Ye JH, Lin YS, Chu YW et al (2008) Insulin-like growth factor binding protein-3 (IGFBP-3) acts as an invasion-metastasis suppressor in ovarian endometrioid carcinoma. Oncogene 27:2137–2147CrossRefGoogle Scholar
  18. 18.
    Aslakson CJ, Miller FR (1992) Selective events in the metastatic process defined by analysis of the sequential dissemination of subpopulations of a mouse mammary tumor. Cancer Res 52:1399–1405Google Scholar
  19. 19.
    Huang L, Cheng HC, Isom R, Chen CS, Levine RA, Pauli BU (2008) Protein kinase Cepsilon mediates polymeric fibronectin assembly on the surface of blood-borne rat breast cancer cells to promote pulmonary metastasis. J Biol Chem 283:7616–7627CrossRefGoogle Scholar
  20. 20.
    Chan KS, Koh CG, Li HY (2012) Mitosis-targeted anti-cancer therapies: where they stand. Cell Death and Disease 3:e411CrossRefGoogle Scholar
  21. 21.
    Diepenbruck M, Christofori G (2016) Epithelial–mesenchymal transition (EMT) and metastasis: yes, no, maybe? Curr Opin Cell Biol 43:7–13CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of DentistryKaohsiung Medical UniversityKaohsiungTaiwan
  2. 2.Center for Fundamental ScienceKaohsiung Medical UniversityKaohsiungTaiwan

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