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.
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