No-Reference Blood Cells Images Quality Metrics
DOI:
https://doi.org/10.52575/2687-0932-2023-50-2-380-388Keywords:
images processing, computer vision, blood microscopic images, medical images quality assessment, blurring, leukocytes classificationAbstract
The article discusses the medical images quality assessing problem, in particular, blood cells images obtained with a digital microscope. Image quality assessment is an important step in the post-processing of digital images for their further analysis. This, during the leukocyte cells counting and classification, blood cells images may contain features or artifacts that don't allow performing a proper analysis, or don't allow analysis to be carried out at all. A preliminary assessment the blood cells images quality will allow you to identify images suitable for further analysis, or image whose quality can be improved by operations, such as sharpening, noise removal and etc. The authors present a study of a number of existing measures of image characteristics, including blur, entropy, flatness, and sharpness metrics. The main goal of the study is to get the most significant characteristics from the considered ones, which could adequately describe the analyzed image in terms of its quality as a specific characteristic perceived by an ordinary human observer. In this study, the program implementation of the considered metrics was performed, and those the developed algorithms were applied to a number of real blood cells images, which have initially different features, in order to obtain the most representative values. The obtained numerical results can be used to compile a set of functions that can be used for creating a tool for evaluating and improving the digital microscopic images quality.
Downloads
References
Батищев Д. С., Михелев В. М., Утянский А. А. 2020. Метод сегментации перекрывающихся форменных элементов крови на микроскопических медицинских изображениях. Экономика. Информатика. 47(4): 803-815.
Волкова С.А. Боровков Н.Н. 2013. Основы клинической гематологии: учебное пособие. Н. Новгород. Издательство Нижегородской гос. медицинской академии. 400 с.
Голуб Ю.И. 2021. Оценка качества цифровых изображений. Системный анализ и прикладная информатика. 4: 4-15.
Bosse S., Maniry D., Müller K. R., Wiegand T., Samek W. 2018. Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment. IEEE Transactions on image processing. 27(1): 206-219.
Chernykh E. M., Mikhelev V. M. 2021. A computer system for the leukocytes classification in medical images. In Journal of Physics: Conference Series. 1715(1): 012007
Chow L. S., Paramesran R. 2016. Review of medical image quality assessment. Biomedical signal processing and control. 27: 145-154.
Clunie D., Hosseinzadeh D., Wintell M., De Mena D., Lajara N., Garcia-Rojo M., Bueno G., Saligrama K., Stearrett A., Toomey D., Abels E., Apeldoorn F. V., Langevin S., Nichols S., Schmid J., Horchner U., Beckwith B., Parwani A., Pantanowitz L. 2018. Digital Imaging and Communications in Medicine Whole Slide Imaging Connectathon at Digital Pathology Association Pathology Visions 2017. Journal of pathology informatics, 9(1).
Crete F., Dolmiere T., Ladret P., Nicolas M. 2007. The blur effect: perception and estimation with a new no-reference perceptual blur metric. In Human vision and electronic imaging XII. 6492: 196-206.
Cui H., Liu Q., Zhang J., Kang B. 2019. An improved deng entropy and its application in pattern recognition. IEEE Access. 7: 18284-18292.
Haque A., Wang A. S. 2022. Noise2Quality: non-reference, pixel-wise assessment of low dose CT image quality. In Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment. 12035(1).
Kumar J., Chen F., Doermann D. 2012. Sharpness estimation for document and scene images. In Proceedings of the 21st International Conference on Pattern Recognition: 3292-3295).
Liu L., Gong J., Huang H., Sang Q. 2020. Blind image blur metric based on orientation-aware local patterns. Signal Processing: Image Communication. 80: 115654.
Ma J. J., Nakarmi U., Kin C. Y. S., Sandino C. M., Cheng J. Y., Syed A. B., Wei P., Pauly J. M., Vasanawala S. S. 2020. Diagnostic image quality assessment and classification in medical imaging: opportunities and challenges. Proceedings. IEEE International Symposium on Biomedical Imaging: 337–340.
Omarova G., Starovoitov V., Myrzamuratova A., Akzullakyzy L., Takudina A., Tanirbergenov A., Sadirmekova Z. 2023. No-reference quality assessment of medical images using contrast enhancement. Journal of Theoretical and Applied Information Technology, 101(1).
Pal N. R., Pal S. K. 1991. Entropy: A new definition and its applications. IEEE transactions on systems, man, and cybernetics. 21(5): 1260-1270.
Sagheer S. V. M., George S. N. 2020. A review on medical image denoising algorithms. Biomedical signal processing and control, 61.
Sara U., Akter M., Uddin M. S. 2019. Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study. Journal of Computer and Communications. 7(3): 8-18.
Woodard J. P., Carley-Spencer M. P. 2006. No-reference image quality metrics for structural MRI. Neuroinformatics. 4: 243-262.
Yang S. J., Berndl M., Michael Ando D., Barch M., Narayanaswamy A., Christiansen E., Nelson P. 2018. Assessing microscope image focus quality with deep learning. BMC bioinformatics. 19: 1-9.
Zhai G., Min X. 2020. Perceptual image quality assessment: a survey. Science China Information Sciences. 63: 1-52.
Abstract views: 75
Share
Published
How to Cite
Issue
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
