Abstract
In the field of medical image processing today, there are more and more medical image categories, such as cell images, tissue images, etc. The wide variety of images is of great help in medical diagnosis, not only for visual observation but also for precise analysis of various causes of disease. Due to the development of medicine, the requirements for images are also higher and the amount of data is becoming larger, and the images have reached tens of thousands of pixels, for the current computer, the current environment can no longer meet the needs of image loading display. In response to the above problems, this paper proposes a method for storing and displaying oversized medical images based on centralized points of interest, which achieves fast loading and displaying of oversized cell images, and has been practically applied in relevant medical institutions, achieving certain results in compressed storage and real-time display of cell images, showing the effectiveness and advancement of the method.
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Acknowledgement
This paper is supported by the National Natural Science Foundation of China (62073231, 61772357, 61902272, 61876217, 61902271), National Research Project (2020YFC2006602) and Anhui Province Key Laboratory Research Project (IBBE2018KX09).
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Yan, J., Wang, Y., Li, H., Lu, W., Wu, H. (2021). Super-Large Medical Image Storage and Display Technology Based on Concentrated Points of Interest. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_16
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DOI: https://doi.org/10.1007/978-3-030-84522-3_16
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