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Hematological image analysis for segmentation and characterization of erythrocytes using FC-TriSDR

  • Track 2: Medical Applications of Multimedia
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A Correction to this article was published on 05 October 2022

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Abstract

In medical science, the scrutiny of blood smears for the abnormality in erythrocyte, leads to decisive determination of several ailments like Thalasemia, Liver disease, Sickle cell anaemia and so on. The conventional methodology for determining the malformation of the erythrocytes is through visual inspection of the blood smear through light or compound microscope. Since, the process of such examination is manual, it might lead to discrepancies and subjectivity. It is a well-known fact that early and affordable diagnostics can make a significant impact on curative. Hence, this research study has proposed an image analysis perspective to characterize the erythrocytes based on their morphological changes. The prime objective of this research work is to enhance the preliminary screening of erythrocytes by analyzing the morphological, textural, and color features by the proposed model FC-TriSDR (Fuzzy C-Means clustering algorithm along with three ensembled classifiers- Support vector machine, Decision Tree, and Radial Basis Functional Neural Network). Automated identification and characterization of erythrocytes is accomplished by integrating the steps of image acquisition, preprocessing, sub-imaging, image segmentation, feature extraction, significant feature selection and classification into five different domains of erythrocytes as - Normal, Stomatocyte, Poikilocyte, Spherocyte, and Schistocyte in the Leishman stained microscopic blood smear images. Total 51 eminent features of erythrocyte were extracted and the performance of FC-TriSDR gained the highest accuracy of 96.7% with computational time of 1.68 sec. when compared amongst 5 other classic neural networks.

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Data Availability

The dataset generated and/or analysed during this research study will be made available from the corresponding author on reasonable request.

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References

  1. Aggarwal AK (2022) Learning texture features from glcm for classification of brain tumor mri images using random forest classifier. J WSEAS Trans Signal Process, pp 60–63

  2. Albertini MC, Teodori L, Piatti E, Piacentini MP, Accorsi A, Rocchi MB (2003) Automated analysis of morphometric parameters for accurate definition of erythrocyte cell shape. Cytom Part A:J Int Soc Anal Cytol 52(1):12–18

    Article  Google Scholar 

  3. Amjad RA, Geiger BC (2020) Learning representations for neural network-based classification using the information bottleneck principle. IEEE Trans Pattern Anal Mach Intell 42(9):2225–2239

    Article  Google Scholar 

  4. Arora K, Aggarwal KA (2018) Approaches for image database retrieval based on color, texture, and shape features. In: Handbook of research on advanced concepts in real-time image and video processing. IGI Global, pp 28–50

  5. Balasamy K, Shamia D (2021) Feature extraction-based medical image watermarking using fuzzy-based median filter. IETE J Res, pp 1–9

  6. Bhuyan HK, Chakraborty C, Shelke Y, Pani SK (2022) Covid-19 diagnosis system by deep learning approaches. Expert Syst 39(3):e12776

    Article  Google Scholar 

  7. Bhuyan HK, Kamila NK, Pani SK (2021) Individual privacy in data mining using fuzzy optimization. Eng Optim, pp 1–19

  8. Bhuyan HK, Kumar LR, Reddy KR (2019) Optimization model for sub-feature selection in data mining. In: 2019 International conference on smart systems and inventive technology (ICSSIT). IEEE, pp 1212–1216

  9. Bhuyan HK, Ravi V (2021) Analysis of subfeature for classification in data mining, IEEE Trans Eng Manag

  10. Brown KE, Anderson SM, Young NS (1993) Erythrocyte p antigen:cellular receptor for b19 parvovirus. Science 262(5130):114–117

    Article  Google Scholar 

  11. Çimen MB (2008) Free radical metabolism in human erythrocytes. Clinica chimica acta 390(1–2):1–11

    Article  Google Scholar 

  12. Clemens MR, Waller HD (1987) Lipid peroxidation in erythrocytes. Chem Phys Lipids 45(2-4):251–268

    Article  Google Scholar 

  13. Das D, Chakraborty C, Mitra B, Maiti A, Ray A (2013) Quantitative microscopy approach for shape-based erythrocytes characterization in anaemia. J Microsc 249(2):136–149

    Article  Google Scholar 

  14. Devi SS, Roy A, Singha J, Sheikh SA, Laskar RH (2018) Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear. Multimed Tools Appl 77(1):631–660

    Article  Google Scholar 

  15. dos Santos GS, Luvizotto LGJ, Mariani VC, dos Santos Coelho L (2012) Least squares support vector machines with tuning based on chaotic differential evolution approach applied to the identification of a thermal process. Expert Syst Appl 39(5):4805–4812

    Article  Google Scholar 

  16. Durant TJ, Olson EM, Schulz WL, Torres R (2017) Very deep convolutional neural networks for morphologic classification of erythrocytes. Clin Chem 63(12):1847–1855

    Article  Google Scholar 

  17. Elhoseny M, Bian G-B, Lakshmanaprabu S, Shankar K, Singh AK, Wu W (2019) Effective features to classify ovarian cancer data in internet of medical things. Comput Netw 159:147–156

    Article  Google Scholar 

  18. Foley D (1972) Considerations of sample and feature size. IEEE Trans Inf Theory 18(5):618–626

    Article  MATH  Google Scholar 

  19. Gálvez A, Iglesias A, Fister I, Fister Jr I, Otero C, Díaz JA (2021) Nurbs functional network approach for automatic image segmentation of macroscopic medical images in melanoma detection. J Comput Sci 56:101481

    Article  Google Scholar 

  20. Go T, Byeon H, Lee SJ (2018) Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning. Biosens Bioelectron 103:12–18

    Article  Google Scholar 

  21. Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst Appl 13(4):18–28

    Article  Google Scholar 

  22. Jannah N, Hadjiloucas S, Al-Malki J (2021) Arrhythmia detection using multi-lead ecg spectra and complex support vector machine classifiers. Procedia Computer Science 194:69–79

    Article  Google Scholar 

  23. Jha CK, Kolekar MH (2020) Cardiac arrhythmia classification using tunable q-wavelet transform based features and support vector machine classifier. Biomed Signal Process Control 59:101875

    Article  Google Scholar 

  24. Kihm A, Kaestner L, Wagner C, Quint S (2018) Classification of red blood cell shapes in flow using outlier tolerant machine learning, vol 14

  25. Kumar P, Thakur RS (2019) Diagnosis of liver disorder using fuzzy adaptive and neighbor weighted k-nn method for lft imbalanced data. In: 2019 International conference on smart structures and systems (ICSSS). IEEE, pp 1–5

  26. Kumar P, Thakur RS (2021) Liver disorder detection using variable-neighbor weighted fuzzy k nearest neighbor approach. Multimed Tools Appl 80 (11):16515–16535

    Article  Google Scholar 

  27. Mahto D, Yadav SC (2022) Hierarchical bi-lstm based emotion analysis of textual data, Bull Pol Acad Sci: Tech Sci, pp e141001–e141001

  28. Maity M, Mungle T, Dhane D, Maiti AK, Chakraborty C (2017) An ensemble rule learning approach for automated morphological classification of erythrocytes. J Med Syst 41(4):56

    Article  Google Scholar 

  29. Mohapatra S, Patra D, Satpathy S (2014) An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. Neural Comput Applic 24(7):1887–1904

    Article  Google Scholar 

  30. Mohapatra S et al (2016) Machine learning approach for automated coal characterization using scanned electron microscopic images. Comput Ind 75:35–45

    Article  Google Scholar 

  31. Monteiro ACB, Iano Y, França RP, Arthur R (2021) Deep learning methodology proposal for the classification of erythrocytes and leukocytes. Trends in deep learning methodologies, pp 129–156

  32. Moreno SR, Da Silva RG, Mariani VC, Dos Santos Coelho L (2020) Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network. Energy Convers Manag 213:112869

    Article  Google Scholar 

  33. Mui JK, Fu K-S (1980) Automated classification of nucleated blood cells using a binary tree classifier. IEEE Trans Pattern Anal Mach Intell,(5):429–443

  34. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  Google Scholar 

  35. Parvathy V, Pothiraj S, Sampson J (2020) Optimal deep neural network model based multimodality fused medical image classification. Phys Commun 41:101119

    Article  Google Scholar 

  36. Paz-Soto Y, Herold-Garcia S, Fernandes LA, Díaz-Matos S (2020) Automatic classification of erythrocytes using artificial neural networks and integral geometry-based functions. In: 2020 33rd SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE, pp 156–163

  37. Pillai CK, Paul W, Sharma CP (2009) Chitin and chitosan polymers:Chemistry, solubility and fiber formation. Prog Polym Sci 34(7):641–678

    Article  Google Scholar 

  38. Rani S, Rajani N, Reddy S (2012) Comparative study on content based image retrieval. Int J Future Comput Commun 1(4):366

    Article  Google Scholar 

  39. Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: A new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619–1630

    Article  Google Scholar 

  40. Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33 (1):1–39

    Article  Google Scholar 

  41. Rose HG, Oklander M (1965) Improved procedure for the extraction of lipids from human erythrocytes. J Lipid Res 6(3):428–431

    Article  Google Scholar 

  42. Shukla AK, Das S, Kumar P (2021) Wordnet based hybrid model for query expansion. In: 2021 IEEE International conference on technology, Research, and innovation for betterment of society (TRIBES).IEEE, pp 1–6

  43. Singh D, Singh B (2020) Investigating the impact of data normalization on classification performance. Appl Soft Comput 97:105524

    Article  Google Scholar 

  44. Sola J, Sevilla J (1997) Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Trans Nucl Sci 44(3):1464–1468

    Article  Google Scholar 

  45. Thiran J-P, Macq B (1996) Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Trans Biomed Eng 43 (10):1011–1020

    Article  Google Scholar 

  46. Thukral R, Arora A, Kumar A et al (2022) Denoising of thermal images using deep neural network. In: Proceedings of international conference on recent trends in computing. Springer, pp 827– 833

  47. Tyas DA, Hartati S, Harjoko A, Ratnaningsih T (2020) Morphological, texture, and color feature analysis for erythrocyte classification in thalassemia cases. IEEE Access 8:69849–69860

    Article  Google Scholar 

  48. Umbaugh SE, Wei Y-S, Zuke M (1997) Feature extraction in image analysis. a program for facilitating data reduction in medical image classification. IEEE Eng Med Biol Mag 16(4):62–73

    Article  Google Scholar 

  49. Zhao Q, Zhang L (2005) Ecg feature extraction and classification using wavelet transform and support vector machines. In: 2005 International conference on neural networks and brain, vol,2. IEEE, pp 1089–1092

  50. Zhou D-X, Jetter K (2006) Approximation with polynomial kernels and svm classifiers. Adv Comput Math 25(1):323–344

    Article  MATH  Google Scholar 

  51. Zhou Z-H, Wu J, Tang W (2002) Ensembling neural networks:many could be better than all. Artif Intell 137(1-2):239–263

    Article  MATH  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Science and Technology, Central University of Jharkhand, Ranchi, India

    Priyanka Kumar & Kanojia Sindhuben Babulal

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  1. Priyanka Kumar
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  2. Kanojia Sindhuben Babulal
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Correspondence to Kanojia Sindhuben Babulal.

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Kumar, P., Babulal, K.S. Hematological image analysis for segmentation and characterization of erythrocytes using FC-TriSDR. Multimed Tools Appl 82, 7861–7886 (2023). https://doi.org/10.1007/s11042-022-13613-5

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