Detection of Cardiac Tissues using K-means Analysis Methods in Nuclear Medicine Images

Authors

  • Yousif Abdallah Department of Radiological Science and Medical Imaging, College of Applied Medical Sciences, Majmaah University, Majmaah, Saudi Arabia https://orcid.org/0000-0001-8434-2515

DOI:

https://doi.org/10.3889/oamjms.2021.7806

Keywords:

Cardiac tissues, K-means methods, Nuclear medicine

Abstract

BACKGROUND: Nuclear cardiology uses to diagnose the cardiac disorders such as ischemic and inflammation disorders. In cardiac scintigraphy, unraveling closely adjacent tissues in the image are challenging issue.

AIM: The aim of the study is to detect of cardiac tissues using K-means analysis methods in nuclear medicine images. This study also aimed to reduce the existence of fleck noise that disturbs the contrast and make its analysis more difficult.

METHODS: Thus, digital image processing uses to increase the detection rate of myocardium easily using its color-based algorithms. In this study, color-based K-means was used. The scintographs were converted into color space presentation. Then, each pixel in the image was segmented using color analysis algorithms.

RESULTS: The segmented scintograph was displayed in distinct fresh image. The proposed technique defines the myocardial tissues and borders precisely. Both exactness rate and recall reckoning were calculated. The results were 97.3 + 8.46 (p > 0.05).

CONCLUSION: The proposed technique offered recognition of the heart tissue with high exactness amount.

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References

Bala A. An improved watershed image segmentation technique using MATLAB. IJSER. 2012;3(6):51-8. Available from: https://www.ijser.org/researchpaper/an-improved-watershed-imagesegmentation-technique-using-matlab.pdf [Last accessed on 2021 Nov 01].

Math Works Inc. MATLAB User’s Guide. United States: The Math Works Inc.; 2021. p. 42-8. Available from: https://www.mathworks.com/help/matlab [Last accessed on 2021 Nov 01].

Petitjean C, Zuluaga MA, Bai W, Dacher JN, Grosgeorge D, Caudron J, et al. Right ventricle segmentation from cardiac MRI: A collation study. Med Image Anal. 2015;19(1):187-202. https://doi.org/10.1016/j.media.2014 PMid:25461337 DOI: https://doi.org/10.1016/j.media.2014.10.004

Peng P, Lekadir K, Gooya A, Shao L, Petersen SE, Frangi AF. A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. MAGMA. 2016;29(43):155-95. https://doi.org/10.1007/s10334-015-0521-4 PMid:26811173 DOI: https://doi.org/10.1007/s10334-015-0521-4

Greenspan H, van Ginneken B, Summers RM. Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans Med Imaging. 2016;35(5):1153-9. https://doi.org/10.1109/tmi.2016.2553401 DOI: https://doi.org/10.1109/TMI.2016.2553401

Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221-48. https://doi.org/10.1146/annurev-bioeng-071516-044442 PMid:28301734 DOI: https://doi.org/10.1146/annurev-bioeng-071516-044442

Litjens G, Kooi T, Bejnordi BE, Setio A, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60-88. https://doi.org/10.1016/j.media.2017.07.005 PMid:28778026 DOI: https://doi.org/10.1016/j.media.2017.07.005

Gandhi S, Mosleh W, Shen J, Chow CM. Automation, machine learning,and artificial intelligence in echocardiography: A brave new world. Echocardiography. 2018;35(9):1402-18. https://doi.org/10.1111/echo.14086 PMid:29974498 DOI: https://doi.org/10.1111/echo.14086

Mazurowski MA, Buda M, Saha A, Bashir MR. Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. J Magn Reson Imaging. 2019;49(4):939-54. https://doi.org/10.1002/jmri.26534 PMid:30575178 DOI: https://doi.org/10.1002/jmri.26534

Tobon-Gomez C, Geers AJ, Peters J, Weese J, Pinto K, Karim R, et al. Benchmark for algorithms segmenting the left atrium from 3DCT and MRI datasets. IEEE Trans Med Imaging. 2015;34(7):1460-73. https://doi.org/10.1109/tmi.2015.2398818 PMid:25667349 DOI: https://doi.org/10.1109/TMI.2015.2398818

Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng PA, et al. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved? IEEE Trans Med Imaging. 2018;37(11):2514-25. https://doi.org/10.1109/TMI.2018.2837502 PMid:29994302 DOI: https://doi.org/10.1109/TMI.2018.2837502

Kiri ̧sli HA, Schaap M, Metz CT, Dharampal AS, Meijboom WB, Papadopoulou SL, et al. Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. Med Image Anal. 2013;17(8):859-76. https://doi.org/10.1016/j.media.2013.05.007 PMid:23837963 DOI: https://doi.org/10.1016/j.media.2013.05.007

Bernard O, Bosch JG, Heyde B, Alessandrini M, Barbosa D, Camarasu-Pop S, et al. Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography. IEEE Trans Med Imaging. 2016;35(4):967-77. https://doi.org/10.1109/TMI.2015.2503890 PMid:26625409 DOI: https://doi.org/10.1109/TMI.2015.2503890

Suinesiaputra A, Cowan BR, Al-Agamy AO, Elattar MA, Ayache N, Fahmy AS. A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images. Med Image Anal. 2014;18(1):50-62. https://doi.org/10.1016/j.media.2013.09.001 PMid:24091241 DOI: https://doi.org/10.1016/j.media.2013.09.001

Karim R, Bhagirath P, Claus P, James Housden R, Chen Z, Karimaghaloo Z, et al. Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late gadolinium enhancement MR images. Med Image Anal. 2016;30:95-107. https://doi.org/10.1016/j.media.2016.01.004 PMid:26891066 DOI: https://doi.org/10.1016/j.media.2016.01.004

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Published

2021-11-29

How to Cite

1.
Abdallah Y. Detection of Cardiac Tissues using K-means Analysis Methods in Nuclear Medicine Images. Open Access Maced J Med Sci [Internet]. 2021 Nov. 29 [cited 2024 Nov. 21];9(A):1272-6. Available from: https://oamjms.eu/index.php/mjms/article/view/7806

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Section

Nuclear Medicine

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