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Efficient detection and partitioning of overlapped red blood cells using image processing approach

  • S.I. : Low Resource Machine Learning Algorithms (LR-MLA)
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Abstract

Detecting the abnormality of Red Blood Cells automatically aids hematologists in diagnosing sickness and minimizes time, money. The complicated background, noise, artifact, illumination, and low contrast, as well as the varied shapes of RBC cells, make it difficult to divide and count RBC cells. The Watershed Fast Radial System (WFRS) is an unique, novel, low computational cost technology for detecting red blood cells in a timely and accurate manner. Overlapping Red Blood Cells make it harder to diagnose a problem so there is a need to discover overlapped cells, and accurately split them before evaluating red blood cell abnormalities. The innovative WFRS technique focuses on determining the centroid of all overlapped and individual red blood cells, then separates the cells accurately; detects and counts the red blood cells, and thereby addressing the overlapped red blood cell problem. The proposed WFRS replicates three datasets: kaggle, Acute Lymphoblastic Leukemia IDB, and PCB DIB, and exceeds recent state-of-the-art techniques in terms of precision, recall, \(F_1\) score, error rate, and specificity.

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References

  1. Miró-Nicolau M, Moyà-Alcover B, González-Hidalgo M, Jaume-i-Capó A (2020) Segmenting overlapped objects in images. A study to support the diagnosis of sickle cell disease. arXiv preprint arXiv:2008.00997

  2. Gharipour A, Liew AW-C (2016) Segmentation of cell nuclei in fluorescence microscopy images: an integrated framework using level set segmentation and touching-cell splitting. Pattern Recognit 58:1–11

    Article  Google Scholar 

  3. Panagiotakis C, Argyros AA (2018) Cell segmentation via region-based ellipse fitting. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 2426–2430 . IEEE

  4. Bai X, Sun C, Zhou F (2009) Splitting touching cells based on concave points and ellipse fitting. Pattern Recogn 42(11):2434–2446

    Article  Google Scholar 

  5. Naruenatthanaset K, Chalidabhongse TH, Palasuwan D, Anantrasirichai N, Palasuwan A (2020) Red blood cell segmentation with overlapping cell separation and classification on imbalanced dataset. arXiv preprint arXiv:2012.01321

  6. Ghane N, Vard A, Talebi A, Nematollahy P (2017) Segmentation of white blood cells from microscopic images using a novel combination of k-means clustering and modified watershed algorithm. J Med Signals Sens 7(2):92

    Article  Google Scholar 

  7. Tavakoli E, Ghaffari A, Kouzehkanan ZM, Hosseini R (2021) New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images. bioRxiv

  8. Shahzad M, Umar AI, Khan MA, Shirazi SH, Khan Z, Yousaf W (2020) Robust method for semantic segmentation of whole-slide blood cell microscopic images. Comput Math Methods Med. 2020

  9. Alagu S (2021) Automatic detection of acute lymphoblastic leukemia using unet based segmentation and statistical analysis of fused deep features. Appl Artif Intell. pp 1–18

  10. Romero-Rondón MF, Sanabria-Rosas LM, Bautista-Rozo LX, Mendoza-Castellanos A (2016) Algorithm for detection of overlapped red blood cells in microscopic images of blood smears. Dyna 83(198):187–194

    Article  Google Scholar 

  11. Abdüssamet Aslan M (2019) Blood cell detection dataset (WBC & RBC detection dataset from peripheral blood smears). https://www.kaggle.com/draaslan/blood-cell-detection-dataset

  12. Labati RD, Piuri V, Scotti F (2011) All-idb: The acute lymphoblastic leukemia image database for image processing. In: 2011 18th IEEE International Conference on Image Processing, pp. 2045–2048. IEEE

  13. Acevedo A, Merino A, Alférez S, Molina Á, Boldú L, Rodellar J (2020) A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data in Brief, ISSN: 23523409, Vol. 30, (2020)

  14. Das PK, Meher S, Panda R, Abraham A (2021) An efficient blood-cell segmentation for the detection of hematological disorders. IEEE Transactions on Cybernetics

  15. Zafari S, Eerola T, Sampo J, Kälviäinen H, Haario H (2015) Segmentation of overlapping elliptical objects in silhouette images. IEEE Trans Image Process 24(12):5942–5952

    Article  MathSciNet  Google Scholar 

  16. Song H, Wang W (2009) A new separation algorithm for overlapping blood cells using shape analysis. Int J Pattern Recognit Artif Intell 23(04):847–864

  17. Shakarami A, Menhaj MB, Mahdavi-Hormat A, Tarrah H (2021) A fast and yet efficient yolov3 for blood cell detection. Biomed Signal Process Control 66:102495

    Article  Google Scholar 

  18. Jiang Z, Liu X, Yan Z, Gu W, Jiang J (2021) Improved detection performance in blood cell count by an attention-guided deep learning method. OSA Continuum 4(2):323–333

    Article  Google Scholar 

  19. Tessema AW, Mohammed MA, Simegn GL, Kwa TC (2021) Quantitative analysis of blood cells from microscopic images using convolutional neural network. Med Biol Eng Comput 59(1):143–152

    Article  Google Scholar 

  20. Zou T, Pan T, Taylor M, Stern H (2021) Recognition of overlapping elliptical objects in a binary image. Pattern Anal Appl. pp 1–14

  21. Govind D, Lutnick BR, Tomaszewski JE, Sarder P (2018) Automated erythrocyte detection and classification from whole slide images. J Med Imaging 5(2):027501

    Article  Google Scholar 

  22. Alomari YM, Sheikh Abdullah SNH, Zaharatul Azma R, Omar K (2014) Automatic detection and quantification of wbcs and rbcs using iterative structured circle detection algorithm. Comput Math Methods Med. Vol. 2014

  23. Panagiotakis C, Argyros A (2020) Region-based fitting of overlapping ellipses and its application to cells segmentation. Image Vis Comput 93:103810

    Article  Google Scholar 

  24. Gamarra M, Zurek E, Escalante HJ, Hurtado L, San-Juan-Vergara H (2019) Split and merge watershed: a two-step method for cell segmentation in fluorescence microscopy images. Biomed Signal Process Control 53:101575

    Article  Google Scholar 

  25. Nguyen N-T, Duong A-D, Vu H-Q (2011) Cell splitting with high degree of overlapping in peripheral blood smear. Int J Comput Theory Eng 3(3):473

    Article  Google Scholar 

  26. Yadollahi M, Procházka A (2011) Image segmentation for object detection. In: Proceedings of the 19th International Conference Technical Computing Prague 2011, vol. 129, pp. 1–12

  27. Maji P, Mandal A, Ganguly M, Saha S (2015) An automated method for counting and characterizing red blood cells using mathematical morphology. In: 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), pp. 1–6. IEEE

  28. Loy G, Zelinsky A (2003) Fast radial symmetry for detecting points of interest. IEEE Trans Pattern Anal Mach Intell 25(8):959–973

    Article  Google Scholar 

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Acknowledgements

We thankfully acknowledge support the Medical Imaging Laboratory, Department of Computer Science and Engineering, National Institute of Technology Silchar, generously provided the NVIDIA RTX 4000 GPU for this study.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Silchar, Assam, 788010, India

    Prasenjit Dhar, K. Suganya Devi & Satish Kumar Satti

  2. Department of Physics, National Institute of Technology, Silchar, Assam, 788010, India

    P. Srinivasan

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  1. Prasenjit Dhar
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  2. K. Suganya Devi
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Correspondence to K. Suganya Devi.

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Dhar, P., Suganya Devi, K., Satti, S.K. et al. Efficient detection and partitioning of overlapped red blood cells using image processing approach. Innovations Syst Softw Eng (2022). https://doi.org/10.1007/s11334-022-00478-y

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