DOI : https://doi.org/10.18517/ijaseit.9.6.7171

Concavity Point and Skeleton Analysis Algorithm for Detection and Quantization in Heavily Clumped Red Blood Cells

Izyani Ahmad (1), Siti Norul Huda Sheikh Abdullah (2), Raja Zahratul Azma Raja Sabudin (3)
(1) Pattern Recognition Research Group, Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia Bangi, Malaysia
(2) Pattern Recognition Research Group, Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia Bangi, Malaysia
(3) Pathology Department, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Malaysia.
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How to cite (IJASEIT) :
Ahmad, Izyani, et al. “Concavity Point and Skeleton Analysis Algorithm for Detection and Quantization in Heavily Clumped Red Blood Cells”. International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 6, Dec. 2019, pp. 1900-6, doi:10.18517/ijaseit.9.6.7171.
Citation Format :
In practice, most hospitals use light microscope to examine the smeared blood for blood quantification. This visual quantification is subjective, laborious and time-consuming. Although automating the process is a good solution, the available techniques are unable to count or ignore the clumpy red blood cells (RBC). Moreover, clumping cell can affect the whole counting process of RBC as well as their accuracy. This paper proposes a new quantization process called concavity point and skeleton analysis (CP-SA) for heavily clump RBC. The proposed methodology is based on induction approach, enhanced lime blood cell by using gamma correction to get the appropriate edges. Then, splitting the clump and single cells by calculating each object area in pixel. Later, the quantification of clumpy cells with the proposed CP-SA method is done. This algorithm has been tested on 556 clump RBC taken from thin blood smear images under light microscope. All dataset images are captured from Hematology Unit, UKM Medical Centre in Kuala Lumpur. On all tested images, the cells of interest are successfully detected and counted from those clump cells. A comparative study and analysis to evaluate the performance of the proposed algorithm in three levels of clump have been conducted. The first level was with two clumps, second level with three clumps and third level with four clumps. The counting number of clump cells has been analyzed using quantitative analysis, resulting in much better results compared to other recent algorithms. The comparison shows that the proposed method gives better precision result at all levels with respect to ground truth: two clump cells (92%), three clump cells (96%) and four clump cells (90%). The results prove that this study has successfully developed a new method to count heavily clump RBC more accurately in microscopic images. In addition, this can be considered as a low-cost solution for quantification in massive examination.

Y. M. Alomari, S. N. H. Sheikh Abdullah, R. Zaharatul Azma, and K. Omar, “Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm.,” Comput. Math. Methods Med., vol. 2014, p. 979302, 2014.

Albashish, D., Sahran, S., Abdullah, A., Alweshah, M., & A., Adam. (2018). A hierarchical classifier for multiclass prostate histopathology image gleason grading.,” vol. 2, no. 2, pp. 323-346, 2018.

J. M. Sharif, M. F. Miswan, M. A. Ngadi, M. S. H. Salam, and M. M. bin A. Jamil, “Red blood cell segmentation using masking and watershed algorithm: A preliminary study,” Biomed. Eng. (ICoBE), 2012 Int. Conf., no. February, pp. 258-262, 2012.

S. Chen, M. Zhao, G. Wu, C. Yao, and J. Zhang, “Recent Advances in Morphological Cell Image Analysis,” Comput. Math. Methods Med., vol. 2012, pp. 1-10, 2012.

S. Khan, A. Khan, F. Saleh Khattak, and A. Naseem, “An Accurate and Cost Effective Approach to Blood Cell Count,” Int. J. Comput. Appl., vol. 50, no. 1, pp. 18-24, 2012.

V. V. Panchbhai and L. B. Damahe, “RBCs and Parasites Segmentation from Thin Smear Blood Cell Images,” I.J. Image, Graph. Signal Process., vol. 10, pp. 54-60, 2012.

R. Cai, Q. Wu, R. Zhang, L. Fan, and C. Ruan, “Red Blood Cell Segmentation Using Active Appearance Model,” pp. 0-3.

M. Hamghalam and A. Ayatollahi, “Automatic counting of leukocytes in giemsa-stained images of peripheral blood smear,” Proc. - 2009 Int. Conf. Digit. Image Process. ICDIP 2009, pp. 13-16, 2009.

M. Maitra, R. Kumar Gupta, and M. Mukherjee, “Detection and Counting of Red Blood Cells in Blood Cell Images using Hough Transform,” Int. J. Comput. Appl., vol. 53, no. 16, pp. 13-17, 2012.

Zafari S., Eerola T., Sampo J., Kí¤lvií¤inen H., Haario H. (2015) Segmentation of Partially Overlapping Nanoparticles Using Concave Points. In: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science, vol 9474. Springer, Cham.

Y. M. Alomari, S. N. H. S. Abdullah, R. R. M. Zin, and K. Omar, “Iterative randomized irregular circular algorithm for proliferation rate estimation in brain tumor Ki-67 histology images,” Expert Syst. Appl., vol. 48, pp. 111-129, 2016.

N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man. Cybern., vol. 9, no. 1, pp. 62-66, 1979.

S. Madenda and E. P. Wibowo, “Object Feature Extraction of Songket Image Using Chain Code Algorithm,” vol. 7, no. 1, pp. 235-241, 2017.

R. Ruslan et al., “Extraction of Morphological Features of Malaysian Rice Seed Varieties Using Flatbed Scanner Extraction of Morphological Features of Malaysian Rice Seed Varieties Using Flatbed Scanner,” vol. 8, no. February, pp. 93-98, 2018.

W. Chen, L. Sui, Z. Xu, and Y. Lang, “Improved Zhang-Suen thinning algorithm in binary line drawing applications,” 2012 Int. Conf. Syst. Informatics, ICSAI 2012, no. Icsai, pp. 1947-1950, 2012.

W. Abu-Ain, S. N. H. Sheikh Abdullah, and K. Omar, “A simple iterative thinning algorithm for text and shape binary images,” J. Theor. Appl. Inf. Technol., vol. 63, no. 2, pp. 274-281, 2014.

W. Abu-Ain, S. N. H. S. Abdullah, B. Bataineh, T. Abu-Ain, and K. Omar, “Skeletonization Algorithm for Binary Images,” Procedia Technol., vol. 11, no. Iceei, pp. 704-709, 2013.

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