i-manager Publications

Overlapping Sickle Cells Detection and Separation Using Marker-Based Watershed Segmentation

Mary Kenneth O.*, Agushaka Jeffrey**, Oyefolahan I. O.***

* Department of Computer Science, Federal University of Technology, Minna, Nigeria.

** Department of Computer Science, Federal University of Lafia, Nigeria.

*** School of Information and Communication Technology, Federal University of Technology, Minna, Nigeria.

Periodicity:October - December'2019
DOI : https://doi.org/10.26634/jip.6.4.16752

Abstract

Sickle cells are abnormalities of the Red Blood Cells (RBCs). The focus of this research work is to detect sickle cells, normal RBCs, and to separate the overlapping cells present in a blood film image and also to identify the percentage of each detected cells. The image processing techniques used for the detection of these RBCs consist of five main steps which are, image acquisition and reading, image preprocessing, feature extraction, RBCs classification, and final result. The Marker-Based Watershed Segmentation (MBWS) method was used to separate the detected overlapping cells. To test the accuracy of this system, the system's result was compared to the result of the traditional method of detection and count of RBCs used in the hospital. The identification and separation of the overlapping cells performed by the proposed system made the system provide more accurate result as compared to the traditional method. The limitations encountered by the system includes, inadequate separation of the overlapping cells due to the under segmentation problem of the technique (Marker-Based Watershed Segmentation) used, the quality of images used, and also the cells at the boundary were eliminated together with its diagnostic features which it may possess.

Keywords

Abnormal Cells, Marker-Based Watershed Segmentation, Overlapping Cells, Red Blood Cells, Sickle Cells.

How to Cite this Article?

Kenneth, M. O., Jeffrey, A., and Oyefolahan I. O. (2019). Overlapping Sickle Cells Detection and Separation Using Marker-Based Watershed Segmentation. i-manager's Journal on Image Processing, 6(4), 1-10. https://doi.org/10.26634/jip.6.4.16752

References

[1]. Abbas, N., & Muhamad, D. (2014). Auccuarte red blood cells automatic counting in microscopic thin blood smear digital images. Science International Lahore, 26(3), 1119-1124.

[2]. Abbas, N., Abdul, A. H., Zulkifli, M., & Ayman, A. (2015). Clustered red blood cells splitting via boundary analysis in microscopic thin blood smear digital images. International Journal of Technology, 6(3), 306-317. https://doi.org/10.14716/ijtech.v6i3.522

[3]. Acharjee, S., Chakrabartty, S., Alam, M. I., Dey, N., Santhi, V., & Ashour, A. S. (2016, March). A semiautomated approach using GUI for the detection of red blood cells. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (pp. 525-529). IEEE. https://doi.org/10.1109/ ICEEOT.2016.7755669

[4]. Acharjya, P. P., Murherjee, S., & Ghoshal, D. (2014). Digital image segmentation using median filtering and morphological approach. International Journal of Advanced Research Computer Science and Software Engineer, 4(1), 552-557.

[5]. Alotaibi, K. (2016). Sickle Blood cell Detection Based on Image Segmentation. Open Public Research Access Institutional Repository and Information Exchange, 1- 90. Retrieved from http://openprairie.sdstate.edu/etd/ 1116

[6]. Aruna, N. S., & Hariharan, S. (2014). Edge detection of sickle cells in red blood cells. International Journal of Computer Science and Information Technologies (IJCSIT), 5(3), 4140-4144.

[7]. Bala, A. (2012). An improved watershed image segmentation technique using MATLAB. International Journal of Scientific and Engineering Research, 3(6), 1-4.

[8]. Bala, S., & Doegar, A. (2015). Automatic detection of sickle cell in red blood cell using watershed segmentation. International Journal of Advanced Research in Computer and Communication Engineering, 4(6), 488-491. https://doi.org/10.17148/ IJARCCE.2015.46105

[9]. Chintawar, I. A., Aishvarya, M., & Kuhikar, C. (2016). Detection of sickle cells using image processing. International Journal of Science Technology Engineering, 2(9), 335-339.

[10]. Chourasiya, S., & Rani, G. U. (2014). Automatic red blood cell counting using Watershed Segmentation. International Journal of Computer Science and Information Technologies, 5(4), 4834-4838.

[11]. Dalvi, P. T., & Vernekar, N. (2016, May). Computer aided detection of abnormal red blood cells. In 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 1741-1746). IEEE. https://doi.org/10.1109/ RTEICT.2016.7808132

[12]. Hariharan, S., & Aruna, N. S. (2016). Pre-processing of sickle cell anemia images. International Journal of Innovative Research in Computer and Communication Engineering, 4(11), 19092-19099. https://doi.org/10. 15680/IJIRCCE.2016. 0411017

[13]. Karthikeyan, K., & Brharama, K. (2015). Detection and counting of red blood cells using hough transform technique. International Journal of Advances in Engineering, 1(8), 622-625.

[14]. Kaur, A., Verma, A., & Derabassi, S. (2013). The Marker-based watershed segmentation - A review. International Journal of Engineering and Innovation Technology (IJEIT), 3(3), 171 -174.

[15]. Khawaldeh, B. A. I. (2013). Developing a computerbased information system to improve the diagnosis of blood anemia. Department of Computer Information Systems Faculty of Information Technology. Middle East University, Amman, Jordan, 116.

[16]. Mahmood, N. H., & Mansor, M. A. (2012). Red blood cells estimation using hough transform technique. Signal & Image Processing: An International Journal (SIPIJ), 3(2), 52-64. https://doi.org/10.5121/sipij.2012.3204

[17]. MathWorks. (2016a). Measure properties of image regions- MATL AB regionprops. Retrieved from https://www.mathworks.com/help/images/ref/regionprops.html

[18]. MathWork. (2016b). Remove small objects from binary image- MATLAB bwareaopen. Retrieved from https://www.mathworks.com/help/images/ref/bwareaopen.html?s_tid=srcht itle

[19]. MathWorks. (2016c). Regionprops (Image Processing Toolbox). Retrieved from https://edoras.sdsu. edu/doc/matlab/toolbox/images/regionprops.html

[20]. Mazalan, S. M., Mahmood, N. H., & Razak, M. A. A. (2013, December). Automated red blood cells counting in peripheral blood smear image using Circular Hough transform. In 2013 1^st International Conference on Artificial Intelligence, Modelling and Simulation (pp. 320- 324). IEEE. https://doi.org/10.1109/AIMS.2013.59

[21]. Patil, D. N., & Khot, U. P. (2015). Image processing based abnormal blood cells detection. International Journal of Technical Research and Applications, (31), 37- 43.

[22]. Rexcy, S. M. A., Akshaya, V. S., & Swetha, K. S. (2016). Effective use of image processing techniques for the detection of sickle cell anemia and presence of plasmodium parasites. International Journal of Advance Research and Innovative Ideas in Education, 2(2), 701- 706.

[23]. Safca, N., Popescu, D., Ichim, L., Elkhatib, H., & Chenaru, O. (2018, October). Image processing techniques to identify red blood cells. In 2018 22^nd International Conference on System Theory, Control and Computing (ICSTCC) (pp. 93-98). IEEE. https://doi.org/10. 1109/ICSTCC.2018.8540708

[24]. Sharma, P. (2017). Disease detection using blood smear analysis. International Journal of Computer Applications, 179(7), 41-44.

[25]. Sreekumar, A., & Bhattacharya, A. (2014). Identification of sickle cells from microscopic blood smear image using image processing. International Journal of Emerging Trends in Science and Technology, 1(05), 783-787.

[26]. Thanh, T. T. P., Vununu, C., Atoev, S., Lee, S. H., & Kwon, K. R. (2018). Leukemia blood cell image classification using convolutional neural network. International Journal of Computer Theory and Engineering, 10(2), 54-58. https://doi.org/10.7763/IJCTE. 2018.V10.1198

[27]. Thiruvinal, V. J., & Ram, S. P. (2017). Automated blood cell counting and classification using image processing. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 6(1), 74-82. https://doi.org/10.15662/IJAREEIE.2017.0601010

[28]. Tomari, R., Nurshzwani, W., Ngadengon, R., & Wahab, M. H. A. (2015). Red blood cell counting analysis by considering an overlapping constraint. Asian Research Publishing Network (ARPN), 10(3), 1413-1420.

[29]. University of Tartu. (2014). OpenScholar. Retrieved from https://sisu.ut.ee/imageprocessing/node/5253

[30]. Vignesh, U., & Loganathan, V. (2019). Extraction of blood cell image classification using convolution neural network. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 6(3), 167-173.

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25

Overlapping Sickle Cells Detection and Separation Using Marker-Based Watershed Segmentation

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content: