COMBINED METRIC FOR EVALUATING THE QUALITY OF SYNTHESIZED BIOMEDICAL IMAGES
DOI:
https://doi.org/10.15588/1607-3274-2025-2-15Keywords:
metric, IS metric, FID metric, histopathological images, deep neural networks, diffusion models, Stable DiffusionAbstract
Context. This study addresses the problem of developing a new metric for evaluating the quality of synthesized images. The relevance of this problem is explained by the need for assessing the quality of artificially generated images. Additionally, the study highlights the potential of biomedical image synthesis based on diffusion models. The research results can be applied for biomedical image generation and quantitative quality assessment of synthesized images.
Objective. The aim of this study is to develop a combined metric and an algorithm for biomedical image synthesis to assess the quality of synthesized images.
Method. A combined metric MC for evaluating the quality of synthesized images is proposed. This metric is based on two existing metrics: MIS and MFID. Additionally, an algorithm for histopathological image synthesis using diffusion models has been developed.
Results. To study the MIS, MFID, and MC metrics, histopathological images available on the Zenodo platform were used. This dataset contains three classes of histopathological images G1, G2, and G3, representing pathological conditions of breast tissue. Based on the developed image synthesis algorithm, three classes of artificial histopathological images were generated. Using the MIS, MFID, and MC metrics, quality assessments of the synthesized histopathological images were obtained. The developed metric will form the basis of a software module for image quality assessment using metrics. This software module will be integrated into CAD systems.
Conclusions. A combined metric for evaluating the quality of synthesized images has been developed, along with a proposed algorithm for biomedical image synthesis. The software implementation of the combined metric and image synthesis algorithm has been integrated into an image quality assessment module.
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Copyright (c) 2025 O. M. Berezsky, M. O. Berezkyi, M. O. Dombrovskyi, P. B. Liashchynskyi, G. M. Melnyk
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