Vol 6, No 3 (2015) > Electrical, Electronics and Computer Engineering >

Clustered Red Blood Cells Splitting via Boundary Analysis in Microscopic Thin Blood Smear Digital Images

Naveed Abbas, Zulkifli Mohamad, Hanan Abdullah, Ayman Altameem



Clustered Red Blood Cells are observed very frequently
in the thin blood smear digital images. Separating clustered Red Blood Cells
from the single Red Blood Cells and splitting of clustered Red Blood Cells into
single Red Blood Cells is a challenging job in the computer-assisted diagnosis
of blood for any disorder in many diseases like Complete Blood Count Test,
Anemia, Leukemia and Malaria etc. The mentioned problems are highly laborious
in manual microscopy for the hematologists. Many techniques currently existing
for the solution suffer from both under- and over- splitting problems when
highly complex clusters of Red Blood Cells occur. In addition, the existing
techniques are not computationally efficient. In this paper, we address the
aforementioned problems, firstly by considering the boundaries of the convex
hulls of clustered Red Blood Cells and secondly, by splitting the boundaries
according to the number of Red Blood Cells in relation to distance measures.
Furthermore, we draw circles using a mid-point circle algorithm at each
boundary cleavage to give an illusion of the Red Blood Cells. The test results
of the proposed technique on a standard online dataset are presented in two
ways. Statistically first of all by achieving an average recall of 0.964 and
precision of 0.970 while their F-measure achieved is 0.962 as well as  secondly through ground truth data with
visual inspections.

Keywords: Automated microscopy; Clustered RBCs; Complete Blood Count; Counting Red Blood Cells; Medical applications

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Abbas, N., Mohamad, D., 2013. Microscopic RGB Color Images Enhancement for Blood Cells Segmentation in YCBCR Color Space for K-means Clustering. Journal of Theoretical and Applied Information Technology, Volume 55(1), pp. 117-125

Abbas, N., Mohamad, D., 2014a. Automatic Color Nuclei Segmentation of Leukocytes for Acute Leukemia, Research Journal of Applied Sciences, Engineering and Technology, Volume 7(14), pp. 2987-2993

Abbas, N., Muhamad, D., 2014b. Accurate Red Blood Cells Automatic Counting in Microscopic Thin Blood Smear Digital Images. Science International Journal, Volume 26(3), pp. 1119-24

Abbas, N., Mohamad, D., Abdullah, A.H., 2015. Semi-automatic Red Blood Cells Counting in Microscopic Digital Images. Jurnal Teknologi, Volume 73(2), pp. 19-24

Abbas, N., Mohamad, D., 2015a. Automated Erythrocytes Counting in Microscopic Thin Blood Smear Digital Images. TELKOMNIKA (Telecommunication Computing Electronics and Control), Volume 13(1)

Abbas, N., Mohamad, D., 2015b. Occluded Red Blood Cells Splitting via Boundaries Analysis and Lines Drawing in Microscopic Thin Blood Smear Digital Images. VFAST Transactions on Software Engineering, Volume 5(1), pp. 10-17

Abbas, N., Muhamad, D., Abdullah, A.H., 2014. Semi-automatic Clustered Red Blood Cells Splitting and Counting in Thin Blood Smear Images. 1st International Conference of Recent Trends in Information and Communication Technologies. University Technology Malaysia, Johar Bahru, Malaysia

Abbas, N., Muhamad, D., 2015c. Automated Clustered Erthrocytes Splitting in Microscopic Digital Images of Blood. IEEE, International Conference on Computer Vision and Image Analsyis (ICCVIA), 18-20 January, Sousse, Tunisia (In press)

Airsang, U., Ghorpade, V., Rajamani, S.T., 2013. Contour Feature-point Tagging as a Mechanism for Automated Splitting of Highly-occluded and Dissimilar-sized Cells in Blood Smear Images. Image Information Processing (ICIIP), IEEE Second International Conference

Amit Kumar, Choudhary, A., Tembhare, P.U., 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, Volume 1(2), pp. 159-163

Buggenthin, F., Marr, C., Schwarzfischer, M., Hoppe, P.S., Hilsenbeck, O., Schroeder, T., Theis, F.J., 2013. An Automatic Method for Robust and Fast Cell Detection in Bright Field Images from High-throughput Microscopy. BMC Bioinformatics, Volume 14(1), pp. 297-309

Cloppet, F., Boucher, A., 2008. Segmentation of Overlapping/aggregating Nuclei Cells in Biological Images. Pattern Recognition, ICPR 2008. 19th International Conference, IEEE

DPDx, 2002. DPDx, Laboratory Identification of Parasites, Center of Disease Control and Prevention. D. a. m. b. C. s. D. o. P. D. a. M. (DPDM). 1600 Clifton Rd. Atlanta, GA 30333, USA

Ferro, L., Leal, P., Marques, M., Maciel, J., Oliveira, M.I., Barbosa, M.A., Quelhas, P., 2013. Multinuclear Cell Analysis using Laplacian of Gaussian and Delaunay Graphs. Springer Proceedings of 6th Iberin Conference on Pattern Recognition and Image Analysis.

Gonçalves, W.N., Bruno, O.M., 2012. Automatic System for Counting Cells with Elliptical Shape. arXiv preprint arXiv:1201.3109.

grietinfo.in., 2013. grietinfo.in. Available online at: grietinfo.in/projects/MAIN/BME2013/cd-8-project%20report_1_.pdf, Accessed on 20-03-2013

Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B., 2009. Histopathological Image Analysis: A Review. Biomedical Engineering, IEEE Reviews Volume 2, pp. 147-171

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

Jiang, H., Ngo, C.-W., Tan, H.-K., 2006. Gestalt-based Feature Similarity Measure in Trademark Database. Pattern Recognition, Volume 39(5), pp. 988-1001

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

Kong, H., Gurcan, M., Belkacem-Boussaid, K., 2011b. Splitting Touching-cell Clusters on Histopathological Images. Biomedical Imaging: From Nano to Macro, IEEE International Symposium

Köppen, M., Yoshida, K., Valle, P.A., 2007. Gestalt Theory in Image Processing: A Discussion Paper. Proceedings.

Kumarasamy, S.K., Ong, S., 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, Volume 22(3), pp. 461-469

LaTorre, A., Alonso-Nanclares, L., Muelas, S., Peña, J., DeFelipe, J., 2013. Segmentation of Neuronal Nuclei based on Clump Splitting and a Two-step Binarization of Images. Expert Systems with Applications, Volume 40(16), pp. 6521-6530

Mahmood, N.H., Lim, P.C., Mazalan, S.M., Razak, A.A., 2013. Blood Cells Extraction using Color-based Segmentation Technique. International Journal of Life Sciences Biotechnology and Pharama Research, Volume 2(2), pp. 233-240

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

Makkapati, V.V., Naik, S.K., 2009. Clump Splitting based on Detection of Dominant Points from Contours. Automation Science and Engineering, CASE 2009. IEEE International Conference

Nguyen, N.-T., Duong, A.-D., Vu, H.-Q., 2011. Cell Splitting with High Degree of Overlapping in Peripheral Blood Smear. Int J Comp Theory Eng, Volume 3(3), pp. 473-478

Owais Shaikh, M.G., Bhat N., Shetty, R., 2013-2014. Automated Red Blood Cells Count. Department of Computer Engineering, Rizvi College of Engineering. Bandra(w), Mumbai - 400050, University of Mumbai. B.E.: 29

Prasad, K., Winter, J., Bhat, U.M., Acharya, R.V., 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, Volume 25(4), pp. 542-549

Rahman, M.M., Kumar, M., Uddin, M.S., 2013. Optimum Threshold Parameter Estimation of Wavelet Coefficients using Fisher Discriminant Analysis for Speckle Noise Reduction. The International Arab Journal of Information Technology, Volume 11(6), pp. 573-581

Ramesh, N., Salama, M.E., Tasdizen, T., 2012. Segmentation of Haematopoeitic Cells in Bone Marrow using Circle Detection and Splitting Techniques. Biomedical Imaging (ISBI), 9th IEEE International Symposium

Reis, L., Aguiar, L., Baptista, D., Morgado-Dias, F., 2014. A Software Tool for Automatic Generation of Neural Hardware. Neuron, Volume 1(1), pp. 229-235

Saadi, S., Guessoum, A., Bettayeb, M., Abdelhafidi, K., 2014. Blind Restoration of Radiological Images using Hybrid Swarm Optimized Model Implemented on FPGA. The International Arab Journal of Information Technology, Volume 11(5), pp. 476-486

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

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

Špringl, V., 2009. Automatic Malaria Diagnosis through Microscopy Imaging. Faculty of Electrical Engineering. Prague, Czech Technical University in Prague Higher Diploma, p. 128

Tafavogh, S., Navarro, K.F., Catchpoole, D.R., Kennedy, P.J., 2013. Segmenting Neuroblastoma Tumor Images and Splitting Overlapping Cells Using Shortest Paths between Cell Contour Convex Regions. Springer proceedings of 14th International Conference on Artificial Intelligence in Medicine

Tulsani, H., 2013. Segmentation using Morphological Watershed Transformation for Counting Blood Cells. IJCAIT, Volume 2(3), pp. 28-36

Wang, H., Zhang, H., Ray, N., 2011. Clump Splitting via Bottleneck Detection. Image Processing (ICIP), In: 18th IEEE International Conference on IEEE

Wen, Q., H. Chang and B. Parvin (2009). A Delaunay triangulation approach for segmenting clumps of nuclei. Biomedical Imaging: From Nano to Macro ISBI'09. IEEE International Symposium on, IEEE.

Zhang, C., Sun, C., Su, R., Pham, T.D., 2012. Segmentation of Clustered Nuclei based on Curvature Weighting. In: Proceedings of the 27th Conference on Image and Vision Computing, New Zealand, ACM.