Semi-Automatic Red Blood Cells Counting in Microscopic Digital Images

Authors

  • Naveed Abbas ViCube Research Lab, Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Dzulkifli Mohamad ViCube Research Lab, Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Abdul Hanan Abdullah ViCube Research Lab, Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/jt.v73.4187

Keywords:

Clustered red blood cells, healthcare applications, complete blood test, occlusions, rouleaux splitting

Abstract

The main purpose of this study is to employ the modern technologies and techniques to semi-automate the quantification process of the Red Blood Cells in Microscopic thin blood smear digital images.  The process needs to be more accurate, efficient and universal then the currently practiced methods. The study considers the process to be semi-automated for two reasons, i.e. due to the critical aspect life and due to the diverse nature of the Red Blood Cells in cluster formation. The Methodology of this study involved interactive simple cuts and morphological operations for splitting clusters of Red Blood Cells while counting is carried out through labeling matrix. The Red Blood Cells counting is part of the complete blood count test and is frequently suggested by the Physician to know the number of Red Blood Cells in the patient’s body. The proposed method considers for counting process of the Red Blood Cells first split the clusters and then count the Red Blood Cells. The proposed method achieved an overall True Positive Rate (TPR) of 0.997%, True Negative Rate (TNR) of 0.00265%, accuracy of 0.998% and average error rate of 0.001375% tested on 50 images, data set also on the same number of images linear correlation coefficient R2 is 0.997 between manual and semi-automatic counting of Red Blood Cells.

References

National Institute of Health, N. 1997. Red Blood Cells Count. Retreived on 10-4-2014 from: http://www.nlm.nih.gov/medlineplus/ency/article/003644.htm.

Berge, H., Taylor, D., Krishnan, S. and Douglas, T. S. 2011. Improved Red Blood Cell Counting in Thin Blood Smears.Proceedings of the 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 30 March–2 April, McCromok Palace, Chicago: IEEE. 204–207.

Buttarello, M. and M. Plebani, 2008. Automated Blood Cell Counts State of the Art. American Journal of Clinical Pathology.130(1):104–116.

Shaikh, M. G. O., Bhat, N. and Shetty, R. 2013–2014. Automated Red Blood Cells Count. B.E., University of Mumbai, Bandra, Mumbai.

Nguyen, N.-T., Duong, A.-D. and Vu, H.-Q. 2011. Cell Splitting with High Degree of Overlapping in Peripheral Blood Smear. Int J Comp Theory Eng. 3(3).

Mahmood, N. H., Lim, P. C., Mazalan, S. M. and Razak, M. A. A. 2013. Blood Cells Extraction Using Color Based Segmentation Technique.Int. J. LifeSc. Bt & Pharm. Res. 2(2): 233–240.

Grietinfo. 2013. Detection of Abnormal Red Blood Cells Using Matlab. Retrieved 20 March 2013 from grietoinfo.in/projects/Main/BME2013/cd-8.

Mahmood, N. H. and Mansor, M. A. 2012. Red Blood Cells Estimation Using Hough Transform Technique.Signal & Image Processing: An International Journal (SIPIJ). 3(2): 53–64.

Ramesh, R., Salama, N., Tasdizen, M. E. and Tolga, T. 2012. Segmentation of Haematopoeitic Cells in Bone Marrow Using Circle Detection and Splitting Techniques.Proceedings of the 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI). 2–5 May, Barcelona, Spain, IEEE. 206–209.

Buggenthin, F., Marr, C., Schwarzfischer, M., Hoppe, P. S., Hilsenbeck, O., Schroeder, T. and Theis, F. J. 2013. An Automatic Method for Robust and Fast Cell Detection in Bright Field Images from High-throughput Microscopy. BMC bioinformatics. 14(1): 297.

Prasad, K., Winter, J., Bhat, U. M., Acharya, R. V. and Prabhu, G. K. 2012. Image Analysis Approach for Development of a Decision Support System for Detection of Malaria Parasites in Thin Blood Smear Images. Journal of Digital Imaging. 25(4): 542–549.

Kumar, A., Choudhary, A., Tembhare, P.U. and Pote, C. R. 2012. Enhanced Identification of Malarial Infected Objects using Otsu Algorithm from Thin Smear Digital Images. International Journal of Latest Research in Science and Technology. 1(2): 159–163.

Latorre, A., Alonso-Nanclares, L., Muelas, S., Peña, J. and DeFelipe, J. 2013. Segmentation of Neuronal Nuclei Based on Clump Splitting and A Two-step Binarization of Images. Expert Systems with Applications. 40(16): 6521–6530.

Tafavogh, S., Navarro, K. F., Catchpoole, D. R. and Kennedy, P. J. 2013. Segmenting Neuroblastoma Tumor Images and Splitting Overlapping Cells Using Shortest Paths between Cell Contour Convex Regions Artificial Intelligence in Medicine. Springer. 171–175.

Zhang, C., Sun, C., Su, R. and Pham, T. D. 2012. Segmentation of Clustered Nuclei Based on Curvature Weighting.Proceedings of the 2012 IEEE 27th Conference on Image and Vision Computing.27–29 November.New Zealand: ACM. 49–54.

Wang, H., Zhang, H. and Ray, N. 2011. Clump Splitting Via Bottleneck Detection.Proceedings of the 2011 IEEE 18th International Conference on Image Processing (ICIP),11–14 September, Burssel, Belgium: IEEE. 61–64.

Kumarasamy, S. K., Ong, S. and Tan, K. S. 2011. Robust Contour Reconstruction of Red Blood Cells and Parasites in the Automated Identification of the Stages of Malarial Infection. Machine Vision and Applications. 22(3): 461–469.

Wen, Q., Chang, H. and Parvin, B. 2009. A Delaunay Triangulation Approach for Segmenting Clumps of Nuclei.Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. ISBI.28June–1July. Boston, MA, USA: IEEE. 9–12.

Makkapati, V. V. and Naik, S. K. 2009. Clump Splitting Based on Detection of Dominant Points from Contours.Proceedings of the 2009 IEEE International Conference on Automation Science and Engineering,22–25 August, Banglore,India: IEEE. 197–201.

Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M. and Yener, B. 2009. Histopathological Image Analysis: A Review.IEEE Reviews inBiomedical Engineering.2: 147–171.

Cloppet, F. and Boucher, A. 2008. Segmentation of Overlapping/Aggregating Nuclei Cells in Biological Images.Proceedings of the 2008IEEE 19th International Conference on Pattern Recognition, ICPR, 8–11 Decmber, Florida,USA: IEEE. 1–4.

Tulsani, H. 2013. Segmentation Using Morphological Watershed Transformation for Counting Blood Cells. IJCAIT. 2(3): 28–36.

Ferro, L., Leal, P., Marques, M., Maciel, J., Oliveira, M. I., Barbosa, M. A. and Quelhas, P. 2013. Multinuclear Cell Analysis Using Laplacian of Gaussian and Delaunay Graphs, In Sanches,Joao Miguel,Micó,Luisa,Cardoso, Jaime (Eds.).Pattern Recognition and Image Analysis. Osolo Norway:Springer Berlin Heidelberg. 441–449.

Hodneland, E., Kögel, T., Frei, D. M., Gerdes, H.-H. and Lundervold, A. 2013. CellSegm-a MATLAB Toolbox for High-Throughput 3D Cell Segmentation. Source Code for Biology and Medicine. 8(1): 1–24.

Schmitt, O. and Reetz, S. 2009. On the Decomposition of Cell Clusters. Journal of Mathematical Imaging and Vision. 33(1): 85–103.

Schmitt, O. and Hasse, M. 2009. Morphological Multiscale Decomposition of Connected Regions with Emphasis on Cell Clusters. Computer Vision and Image Understanding. 113(2): 188–201

Springl, V. 2009. Automatic Malaria Diagnosis through Microscopy Imaging. Higher Diploma, Czech Technical University In Prague, Prague.

Goncalves, W. N. and Bruno, O. M. 2012. Automatic System for Counting Cells with Elliptical Shape.Cornell University Library,arXiv:1201.3109.9(1).

Kong, K., Gurcan, H., Boussaid, B. M. and Kamel, K. 2011b. Splitting Touching-cell Clusters on Histopathological Images.Proceedings of the 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 30 March–2 April, Chicago,USA: IEEE. 208–211.

Kong, K., Gurcan, H., Boussaid, B. M. and Kamel, K. 2011a. Partitioning Histopathological Images: An Integrated Framework for Supervised Color-texture Segmentation and Cell Splitting. IEEE Transactions on.Medical Imaging. 30(9): 1661–1677.

Koppen, K., Yoshida, M., Valle, P. and Pablo, A. 2007. Gestalt Theory in Image Processing: A Discussion Paper.Proceedings of the 2007 Three Rivers workshop on Soft Computing in Industrial Apllications,1-3 August,University of Passau, Germany. 1–5.

DPDx. 2002. DPDx, Laboratory Identification of Parasites,Centers of diseases Control and Prevention, Dvision of Parasitic Diseases and Malaria (DPDM) Govt of USA. Retrieved 15 March 2013, from http://www.dpd.cdc.gov/dpdx/default.htm.

Downloads

Published

2015-03-03

How to Cite

Semi-Automatic Red Blood Cells Counting in Microscopic Digital Images. (2015). Jurnal Teknologi, 73(2). https://doi.org/10.11113/jt.v73.4187