Distinguishing of different tissue types using K-Means clustering of color segmentation

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.242491

Keywords:

image analysis, tissue image segmentation, K-Means clustering, color-based segmentation

Abstract

Millions of lives might be saved if stained tissues could be detected quickly. Image classification algorithms may be used to detect the shape of cancerous cells, which is crucial in determining the severity of the disease. With the rapid advancement of digital technology, digital images now play a critical role in the current day, with rapid applications in the medical and visualization fields. Tissue segmentation in whole-slide photographs is a crucial task in digital pathology, as it is necessary for fast and accurate computer-aided diagnoses. When a tissue picture is stained with eosin and hematoxylin, precise tissue segmentation is especially important for a successful diagnosis. This kind of staining aids pathologists in distinguishing between different tissue types. This work offers a clustering-based color segmentation approach for medical images that can successfully find the core points of clusters through penetrating the red-green-blue (RGB) pairings without previous information. Here, the number of RGB pairs functions as a clusters’ number to increase the accuracy of current algorithms by establishing the automated initialization settings for conventional K-Means clustering algorithms. On a picture of tissue stained with eosin and hematoxylin, the developed K-Means clustering technique is used in this study (H&E). The blue items are found in Cluster 3. There are things in both light and dark blue. The results showed that the proposed technique can differentiate light blue from dark blue employing the 'L*' layer in L*a*b* Color Space (L*a*b* CS). The work recognized the cells' nuclei with a dark blue color successfully. As a result, this approach may aid in precisely diagnosing the stage of tumor invasion and guiding clinical therapies

Author Biographies

Zinah R. Hussein, University of Baghdad

Assistant Lecturer of Multimedia Sciences

College of Law

Ans Ibrahim Mahameed, University of Al-Hamdaniya

Assistant Lecturer of Information Technology Sciences

Department of Computer Science

Jawaher Abdulwahab Fadhil, University of Duhok

Assistant Lecturer of Computer Science & Engineering

Department of Computer Science

References

  1. Gorunescu, F. (2011). Introduction to Data Mining. Data Mining, 1–43. doi: https://doi.org/10.1007/978-3-642-19721-5_1
  2. Nain Chi, Y. (2020). Color-Based Forest Cover Type Image Segmentation using K-Means Clustering Approach. Journal of Forests, 7 (1), 18–31. doi: https://doi.org/10.18488/journal.101.2020.71.18.31
  3. Addanki, C. R., A, S., A, V. R. (2020). Study of the Clustering Algorithms for Hyper Spectral Remote Sensing Images. Journal of Hyperspectral Remote Sensing, 10 (2), 117. doi: https://doi.org/10.29150/jhrs.v10.2.p117-121
  4. Fu, K. S., Mui, J. K. (1981). A survey on image segmentation. Pattern Recognition, 13 (1), 3–16. doi: https://doi.org/10.1016/0031-3203(81)90028-5
  5. Rajani, S., Veena, M. N. (2019). Medicinal plants segmentation using thresholding and edge based techniques. International Journal of Innovative Technology and Exploring Engineering, 8 (6S4), 71–76. doi: https://doi.org/10.35940/ijitee.f1014.0486s419
  6. Lv, Z., Wang, L., Guan, Z., Wu, J., Du, X., Zhao, H., Guizani, M. (2019). An Optimizing and Differentially Private Clustering Algorithm for Mixed Data in SDN-Based Smart Grid. IEEE Access, 7, 45773–45782. doi: https://doi.org/10.1109/access.2019.2909048
  7. Tam, K.-M. M. (2015). Principal Stress Line Computation for Discrete Topology Design. Massachusetts Institute of Technology.
  8. Zhang, J., Xu, J., Su, J., Fu, R., Lin, J., Jiang, B. et. al. (2018). P1.11-18 A Classification-Based Machine Learning Method Reveals Exosomal miRNA Biomarkers for Patients with Pulmonary Ground Glass Nodule. Journal of Thoracic Oncology, 13 (10), S572. doi: https://doi.org/10.1016/j.jtho.2018.08.834
  9. Zhang, T. (2018). Optimized Fuzzy Clustering Algorithms for Brain MRI Image Segmentation Based on Local Gaussian Probability and Anisotropic Weight Models. International Journal of Pattern Recognition and Artificial Intelligence, 32 (09), 1857005. doi: https://doi.org/10.1142/s0218001418570057
  10. Hua, L., Xue, J., Zhou, L. (2021). An Automatic MR Brain Image Segmentation Method Using a Multitask Quadratic Regularized Clustering Algorithm. International Journal of Health Systems and Translational Medicine, 1 (2), 44–58. doi: https://doi.org/10.4018/ijhstm.2021070104
  11. Fuente-Tomas, L. de la, Arranz, B., Safont, G., Sierra, P., Sanchez-Autet, M., Garcia-Blanco, A., Garcia-Portilla, M. P. (2019). Classification of patients with bipolar disorder using k-means clustering. PLOS ONE, 14 (1), e0210314. doi: https://doi.org/10.1371/journal.pone.0210314
  12. Aukes, M. F., Laan, W., Termorshuizen, F., Buizer-Voskamp, J. E., Hennekam, E. A. M., Smeets, H. M. et. al. (2012). Familial clustering of schizophrenia, bipolar disorder, and major depressive disorder. Genetics in medicine, 14 (3), 338–341. doi: https://pubmed.ncbi.nlm.nih.gov/22241106/
  13. Saturi, R., Prem Chand, P. (2020). Implementation of Efficient Segmentation Method for Histopathological Images. 2020 International Conference on Inventive Computation Technologies (ICICT). doi: https://doi.org/10.1109/icict48043.2020.9112386
  14. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, 5.1.
  15. Hartati, T., Nurdiawan, O., Wiyandi, E. (2021). Analisis Dan Penerapan Algoritma K-Means Dalam Strategi Promosi Kampus Akademi Maritim Suaka Bahari. Jurnal Sains Teknologi Transportasi Maritim, 3 (1), 1–7. doi: https://doi.org/10.51578/j.sitektransmar.v3i1.30
  16. Wu, Y., Liu, G. (2020). Research on construction of vehicle driving cycle based on Markov chain and global K-means clustering algorithm. Vehicle Dynamics, 4 (1). doi: https://doi.org/10.18063/vd.v4i1.1135
  17. Busin, L., Vandenbroucke, N., Macaire, L. (2009). Color Spaces and Image Segmentation. Advances in Imaging and Electron Physics, 65–168. doi: https://doi.org/10.1016/s1076-5670(07)00402-8
  18. Moussa, M. (2021). An iterative algorithm for color space optimization on image segmentation. TELKOMNIKA (Telecommunication Computing Electronics and Control), 19 (1), 199. doi: https://doi.org/10.12928/telkomnika.v19i1.15122
  19. Adegun, A. A., Akande, N. O., Ogundokun, R. O., Asani, E. O. (2018). Image segmentation and classification of large scale satellite imagery for land use: A review of the state of the arts. International Journal of Civil Engineering and Technology, 9 (11), 1534–1541.
  20. Zheng, Y., Jeon, B., Xu, D., Wu, Q. M. J., Zhang, H. (2015). Image segmentation by generalized hierarchical fuzzy C-means algorithm. Journal of Intelligent & Fuzzy Systems, 28 (2), 961–973. doi: https://doi.org/10.3233/ifs-141378
  21. Badawi, A., Bilal, M. (2019). High-Level Synthesis of Online K-Means Clustering Hardware for a Real-Time Image Processing Pipeline. Journal of Imaging, 5 (3), 38. doi: https://doi.org/10.3390/jimaging5030038
  22. Zheng, C., Zhang, Y., Wang, L. (2017). Semantic Segmentation of Remote Sensing Imagery Using an Object-Based Markov Random Field Model With Auxiliary Label Fields. IEEE Transactions on Geoscience and Remote Sensing, 55 (5), 3015–3028. doi: https://doi.org/10.1109/tgrs.2017.2658731
  23. He, L., Wu, Z., Zhang, Y., Hu, Z. (2020). Semantic segmentation of remote sensing imagery using object-based markov random field based on hierarchical segmentation tree with auxiliary labels. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2020, 75–81. doi: https://doi.org/10.5194/isprs-archives-xliii-b3-2020-75-2020
  24. Fukunaga, K., Hostetler, L. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 21 (1), 32–40. doi: https://doi.org/10.1109/tit.1975.1055330
  25. Yu, Z., Au, O. C., Zou, R., Yu, W., Tian, J. (2010). An adaptive unsupervised approach toward pixel clustering and color image segmentation. Pattern Recognition, 43 (5), 1889–1906. doi: https://doi.org/10.1016/j.patcog.2009.11.015
  26. Colorni, A., Dorigo, M., Maniezzo, V. (1991). Distributed Optimization by ant colonies. Conference: Proceedings of ECAL91 - European Conference on Artificial Life.
  27. Steinbach, M., Karypis, G., Kumar, V. (2000). A Comparison of Document Clustering Techniques. In KDD Workshop on Text Mining.
  28. Pelleg, D., Moore, A. (2015). X-means: Extending K-means with Efficient Estimation of the Number of Clusters. CEUR Workshop Proc.
  29. Shi, J., Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888–905. doi: https://doi.org/10.1109/34.868688
  30. Beck, G., Duong, T., Azzag, H., Lebbah, M. (2016). Distributed mean shift clustering with approximate nearest neighbours. 2016 International Joint Conference on Neural Networks (IJCNN). doi: https://doi.org/10.1109/ijcnn.2016.7727595
  31. Khan, Z., Yang, J., Zheng, Y. (2019). Efficient clustering approach for adaptive unsupervised colour image segmentation. IET Image Processing, 13 (10), 1763–1772. doi: https://doi.org/10.1049/iet-ipr.2018.5976
  32. Cammarota, R., Bertolini, V., Pennesi, G., Bucci, E. O., Gottardi, O., Garlanda, C. et. al. (2010). The tumor microenvironment of colorectal cancer: stromal TLR-4 expression as a potential prognostic marker. Journal of Translational Medicine, 8 (1). doi: https://doi.org/10.1186/1479-5876-8-112
  33. Thanh, D. N. H., Hai, N. H., Hieu, L. M., Tiwari, P., Surya Prasath, V. B. (2021). Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation. Computer Optics, 45 (1), 122–129. doi: https://doi.org/10.18287/2412-6179-co-748
  34. Thanh, D. N. H., Sergey, D., Surya Prasath, V. B., Hai, N. H. (2019). Blood vessels segmentation method for retinal fundus images based on adaptive principal curvature and image derivative operators. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W12, 211–218. doi: https://doi.org/10.5194/isprs-archives-xlii-2-w12-211-2019

Downloads

Published

2021-10-31

How to Cite

Hussein, Z. R., Mahameed, A. I., & Fadhil, J. A. (2021). Distinguishing of different tissue types using K-Means clustering of color segmentation. Eastern-European Journal of Enterprise Technologies, 5(2 (113), 22–28. https://doi.org/10.15587/1729-4061.2021.242491