Detection of Megakaryocyte Cell Structure Through Artificial Intelligence Tools

Shaima Ibraheem Jabbar; Abathar Qahtan Aladi

doi:10.29194/NJES.26040337

Authors

Shaima Ibraheem Jabbar Medical Instruments Technologies, Al-Furat Alawsat Technical University, Babylon Technical Institute-Iraq.
Abathar Qahtan Aladi Babylon Health Directory Mirjan Teaching Hospital

DOI:

https://doi.org/10.29194/NJES.26040337

Keywords:

Megakaryocyte Images, Artificial Intelligence, Fuzzy C-Means Technique, Fuzzy Inference Technique

Abstract

Recent research has focused on analysing megakaryocyte images to extract the information needed to track the progression of nervous system diseases. Segmentation is a fundamental step in describing and analysing the core contents of megakaryocytes, including the cytoplasm and nucleus. In this study, 45 megakaryocyte images were obtained. A new segmentation image technique was proposed, called the updating fuzzy c-means technique, through the intelligent selection of the centres of each cluster to separate cell components. The first step of this technique (fuzzification) was based on a knowledge analysis of the local parameters (entropy, contrast and standard deviation) that had a substantial influence on the grey-level distribution between the cytoplasm and nucleus. The second important step was the construction of fuzzy rules in terms of the variation in these local parameters to control the intelligent pick-out or update the centroid of each cluster and obtain a successful separation of the cytoplasm and nucleus. The final step was defuzzification to obtain the output images. The results revealed the superiority of the proposed method over recent technique. The accuracy of the segmented nucleus was greater than 7.46%; in the case of the cytoplasm, the accuracy was higher at 18%. These results indicated that this technique may be applied on other biomedical images.

Downloads

Download data is not yet available.

References

A. A. Fauser and H. A. Messner, "Identification of megakaryocytes, macrophages, and eosinophils in colonies of human bone marrow containing neutrophilic granulocytes and erythroblasts", Blood, 53(5), pp. 1023–1027,1979

A. Gonzalez-Villalva, P. Bizarro Nevaraes, M. Rajas-Lemus, M. Ustarroz-Cano, N. Lopez-Valdez, I. Garcia-Pelaez , J. Carlos Albarran-Alonso, F. Barbosa-Barron and T. Fortoul, "A brief review of the biology of megakaryocytes and platelets and their role in thrombosis associated with particulate air pollutio", Toxicology and Industrial Heath, 37(3), 164-172, 2021.

B. W. Smith and G. P. Murphy, "Stem cells, megakaryocytes, and platelets", Curr. Opin. Hematol, 21:430-437, 2014.

L. M. Bush, C. P. Healy, J. E. Marvin . et al. (2021) High-throughput enrichment and isolation of megakaryocyte progenitor cells from the mouse bone marrow, Sci Rep 11, 8268, 2021.https://doi.org/10.1038/s41598-021-87681-2

B. S. Wilkins, W. N. Erber, D. Bareford, et al., "Bone marrow pathology in essential thrombocythemia: interobserver reliability and utility for identifying disease subtypes". Blood, 111(1):60-70, 2008.

F. Valafar, "Pattern Recognition Techniques in Microarray Data Analysis”, Annals of the New York Academy of Sciences, 980 (1): 41–64, 2002.

K.Ohshima, M. Kikuchi and M. Takeshita، "megakaryocyte analysis of the bone marrow in patients with myelodysplastic syndrome, myeloproliferative disorder and allied disorders", Journal of Pathology, 177 (2), 181–189, 1995.

V. Franco and et al. "Megakaryocytic features useful for the diagnosis of myeloproliferative disorders can be obtained by a novel unsupervised software analysis", Histology and Histopathology, 21(8), pp. 813–821, 2006.

A. Bouzid-Daho, M. Boughazi, C. Tanougast and O. Benali Medjahed, "Textural analysis of bio-images for aid in the detection of abnormal blood cells", Int. J. Biomedical Engineering and Technology, Vol. 25, No. 1, pp.1–13, 20, 2017.

F. A. Choudry, F. O. Bagger, I. C. Macaulay, S. Farrow, F. Burden, C. Kempster, H. McKinney, L. R. Olsen, N. Huang N and et al. "Transcriptional characterization of human megakaryocyte polyploidization and lineage commitment". J Thromb Haemost. 2021 May;19(5):1236-1249. 2021, doi: 10.1111/jth.15271.. PMID: 33587817.

R. Verma and et al., "A Multi-organ Nuclei Segmentation and Classification Challenge", in IEEE Transactions on Medical Imaging, 2020. doi: 10.1109/TMI.2021.3085712.

S. I. Jabbar, H. Majdi and A. Aladi, "Techniques of musculoskeletal system imaging", International journal of online Biomedical Engineering, 7(2) 1-11, 2022.

X. Liu, K. Gao and et al., "Advances in Deep Learning-Based Medical Image Analysis," Health Data Science,14, 2021.https://doi.org/10.34133/2021/8786793

M. Zhao, H. Wang, Y. Han, X. Wang, H. Dai, X. Sun, J. Zhang, M. Pedersen, SEENS: Nuclei segmentation in Pap smear images with selective edge enhancement”, Future Generation Computer Systems, 114, 185-194, 2021.

K. Sirinukunwattana. and et al., Artificial intelligence–based morphological fingerprinting of megakaryocytes: a new tool for assessing disease in MPN patients”, Blood advances, 4(14),3284-3294, 2020.doi:10.1182/bloodadvances.

V. Kate, & P. Shukla, "Breast Cancer Image Multi-Classification Using Random Patch Aggregation and Depth-Wise Convolution based Deep-Net Model". International Journal of Online and Biomedical Engineering (iJOE), 17(01), pp. 83–100, 2021. https://doi.org/10.3991/ijoe.v17i01.18513

E. Shweikeh, J. Lu and M. Al-Rajab. "Detection of Cancer in Medical Images using Deep Learning". International Journal of Online and Biomedical Engineering (iJOE), 17(04), pp. 83–100. 2021, https://doi.org/10.3991/ijoe.v17i14.27349

S. Shinde, N. Sharma, P. Bansod, M. Singh and C.K. Singh, "Automated Nucleus Segmentation of Leukemia Blast Cells : Color Spaces Study", 2nd International Conference on Data, Engineering and Applications (IDEA), pp. 1-5, 2020, doi: 10.1109/IDEA49133.2020.9170721.

S. I. Jabbar, L. M. Al Sharifi, "Megakaryocyte Images Segmentation Using Wavelet Transform and Modified Fuzzy C-means Techniques". In: Tran, DT., Jeon, G., Nguyen, T.D.L., Lu, J., Xuan, TD. (eds) Intelligent Systems and Networks . ICISN 2021. Lecture Notes in Networks and Systems, vol 243. Springer, Singapore, 2021 https://doi.org/10.1007/978-981-16-2094-2_65

A. D. Luca & TS. ermini, "A definition of non-probabilistic entropy in setting of fuzzy set theory”, Information Control, 20, 301–312, 1971.

P. Luukka, "Feature selection using fuzzy entropy measures with similarity classifier”, Expert Systems with Applications, 38, 4600-460, 2011.

J. C. A. Dunn, "Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters", Journal of Cybernetics, 3: 32- 57, 1973.

J. C. Bezdek, "Pattern Recognition with Fuzzy Objective Function Algorithms", Plenum Press, New York, 1981.

D. M. Powers, "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation”, Journal of Machine Learning Technologies, 2 (1): 37–63, 2011.

A. F. Aanish and et al. (2018) The Use of Sensitivity and Specificity in Verification of Hadith's Chain of Transmission Centuries Ago, Sudan, Med J, 54 (3): 170–175, 2018.

S. Gupta, "Medical image registration based on fuzzy c- means clustering segmentation approach using SURF”, Int. J. Biomedical Engineering and Technology, Vol. 20, No. 1, pp.33–50, 2016.

F. Bunyak and et al. "Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets", in Software tools and algorithms for biological systems, pp. 413–424, 2011.