An approach to the parallelization of the process of calculating the quantitative characteristics of cell nuclei on biomedical images (cytological, histological, immunohistochemical) is proposed, which will speed up the process of making a diagnosis. The relevance of this task lies in the fact that there are a large number of micro-objects in the image that need to be investigated, and optimization of time and rational distribution of resources will speed up the stage of calculating the area of cell nuclei and their average brightness level. In the future, these data are stored in the database for further use as a dataset for the tasks of classification, clustering, and intellectual analysis. Modern means of data classification and intellectual analysis are used to make a diagnosis. When using convolutional neural networks, the input data to the classifier are images in the format .jpg, .png, .bmp, etc. Alternative algorithms and data processing tools in most cases require quantitative characteristics. In the case of using biomedical images, the quantitative characteristics are the area, perimeter, circumference, length, and major and lateral axes of the cell nucleus. The area and other characteristics of cell nuclei characterize the normal state or the presence of pathologies.
Calculating quantitative characteristics on immunohistochemical and cytological images is time-consuming because the number of cell nuclei in one image can be in the range of 10-20 units. To create a dataset of quantitative characteristics of cell nuclei, it is necessary to perform calculations on a large number of images, which in turn requires significant resources, at a particular time. The parallelization of calculating the biomedical image characteristics is implemented on the basis of computer vision algorithms to select the necessary objects and means of software parallelization of tasks at the thread level to speed up the process of calculating the cell nucleus characteristics. It was established that the existing systems of automated microscopy and diagnostic systems based on images do not have the presence of a large number of characteristics of cell nuclei and do not have mechanisms for parallelizing the process of their calculation. The proposed approach makes it possible to speed up the process of calculating the quantitative characteristics of cell nuclei by 25 %. The relevance of the problem of parallelization is due to the need to process a large amount of data for their further reduction and classification. Thread-level parallelization improves image processing speed and does not require specialized hardware.
[1] AlZubi, Shadi, Shehab, Mohammed, Al-Ayyoub, Mahmoud, Jararweh, Yaser, Gupta, Brij. (2020). Parallel implementation for 3D medical volume fuzzy segmentation. Pattern Recognition Letters, 130, 312-318.
https://doi.org/10.1016/j.patrec.2018.07.026
[2] Amgad, M., Elfandy, H., Hussein, H. (2019). Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics, 35(18), 3461 3467.
https://doi.org/10.1093/bioinformatics/btz083
[3] Araújo, T, Aresta, G, Castro, E, Rouco, J, Aguiar, P, Eloy, C, Polónia, A., & Campilho, A.. (2017). Classification of breast cancer histology images using Convolutional Neural Networks. PLoS ONE, 12(6), e0177544.
https://doi.org/10.1371/journal.pone.0177544
[4] Barreiros, W., C.M.A. Melo, A., Kong, J., Ferreira, R., M. Kurc, T., H. Saltz, J., Teodoro, G. (2022). Efficient microscopy image analysis on CPU-GPU systems with cost-aware irregular data partitioning. Journal of Parallel and Distributed Computing, 164, 40-54.
https://doi.org/10.1016/j.jpdc.2022.02.004
[5] Berezky, O. M. (Ed.), Batko, Yu. M., Berezka, K. M., Verbovy, S. O., Datsko, T. V., Dubchak, L. O., Ignatev, I V., Melnik, G. M., Nikoluk, V. D., & Pitsun, O. Y. (2017). Methods, algorithms and software tools for the processing of biomedical images: monograph. Ternopil: TNEU "Economic Thought", 330 p.
[6] Berezsky, O. M., Pitsun, O. Yo., Melnyk, G. M., & Datsko, T. V. (2021). Application of linear regression method for analysis of cytological images quantitative characteristics. Ukrainian Journal of Information Technology, 3(1), 73-77.
https://doi.org/10.23939/ujit2021.03.073
[7] Berezsky, O., Pitsun, O., Batryn, N., Berezska, K., Savka, N., & Dolynyuk, T. (2018). Image Segmentation Metric-Based Adaptive Method. IEEE Second International Conference on Data Stream Mining & Processing (DSMP), 554-557.
https://doi.org/10.1109/DSMP.2018.8478579
[8] Berezsky, O., Pitsun, O., Derish, B., Berezska, K., Melnyk, G., & Batko, Y. (2020). Adaptive Immunohistochemical Image Pre-processing Method. 10th International Conference on Advanced Computer Information Technologies (ACIT), 820-823.
https://doi.org/10.1109/ACIT49673.2020.9208920
[9] Berezsky, O., Pitsun, O., Dubchak, L., Berezka, K., Dolynyuk, T., & Derish, B. (2020). Cytological Images Clustering of Breast Pathologies. 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT), 62-65.
https://doi.org/10.1109/CSIT49958.2020.9321867
[10] Cao, Jianfang, Chen, Lichao, Wang, Min, & Tian, Yun (2018). Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform. Computational Intelligence and Neuroscience, 2018, Article ID 3598284.
https://doi.org/10.1155/2018/3598284
[11] Demirović, D., Skejić, E., & Šerifović - Trbalić, A. (2018). Performance of Some Image Processing Algorithms in Tensorflow. 25th International Conference on Systems, Signals and Image Processing (IWSSIP), 1-4.
https://doi.org/10.1109/IWSSIP.2018.8439714
[12] Fujima, N., Hirata, K., Shiga, T., Li, R. (2018). Integrating quantitative morphological and intratumoural textural characteristics in FDG-PET for the prediction of prognosis in pharynx squamous cell carcinoma patients. Clinical Radiology, 73(12).
https://doi.org/10.1016/j.crad.2018.08.011
[13] Gancheva, V. (2021). Parallel Multithreaded Medical Images Filtering. 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 1788-1793.
https://doi.org/10.1109/CSCI54926.2021.00338
[14] Haiyan, Gu, Han, Yanshun, Yang, Yi, Li, Haitao, Liu, Zhengjun, Soergel, Uwe, Blaschke, Thomas, & Cui, Shiyong (2018). An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery. Remote Sensing, 10(4), 590.
https://doi.org/10.3390/rs10040590
[15] Hangün, B., Eyecioğlu, Ö. (2017). Performance Comparison Between OpenCV Built-i CPU and GPU Functions on Image Processing Operations. International Journal of Engineering Science and Application, 1, 34-41.
[16] Huang, B., Xue, J., Lu, K., Tan, Y., & Zhao, Y. (2021). MPNet: Multi-scale Parallel Codec Net for Medical Image Segmentation. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (Eds). Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science, 13069. Springer, Cham.
https://doi.org/10.1007/978-3-030-93046-2_42
[17] Li, Y., Wu, J., & Wu, Q. (2019). Classification of Breast Cancer Histology Images Using Multi-Size and Discriminative Patches Based on Deep Learning. IEEE Access, 7, 21400-21408.
https://doi.org/10.1109/ACCESS.2019.2898044
[18] Peizhen, Xie, Ke, Zuo, Jie, Liu, Mingliang, Chen, & Shuang, Zhao (2021). Interpretable Diagnosis for Whole-Slide Melanoma Histology Images Using Convolutional Neural Network. Journal of Healthcare Engineering, 2021, Article ID 8396438.
https://doi.org/10.1155/2021/8396438
[19] Qaiser, T., Tsang, Y.-W., Taniyama, D., Sakamoto, N., Nakane, K., Epstein, D., & Rajpoot, N. (2019). Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features. Medical Image Analysis, 55, 1-14.
https://doi.org/10.1016/j.media.2019.03.014
[20] Ramirez-Quintana, J., Acosta-Lara, I., Ramirez-Alonso, G., Chacon-Murguia, M., & Corral-Saenz, A. (2022). A Lightweight Convolutional Neural Network for Breast Cancer Diagnosis with Histology Images. Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, 13264. Springer, Cham.
https://doi.org/10.1007/978-3-031-07750-0_30
[21] Roels, J., De Vylder, J., Saeys, Y., Goossens, B., & Philips, W. (2016). Decreasing Time Consumption of Microscopy Image Segmentation Through Parallel Processing on the GPU. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (Eds). Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science, 10016. Springer, Cham.
https://doi.org/10.1007/978-3-319-48680-2_14
[22] Vang, S., Chen, Y. Z., & Xie, X. (2018). Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification. Computer Vision and Pattern Recognition, Computer Science, Arxiv. https://doi.org/10.48550/arXiv.1802.00931
https://doi.org/10.1007/978-3-319-93000-8_104
[23] Xu, H., Liu, L., Lei, X., Mandal, M., Lu, C. (2021). An unsupervised method for histological image segmentation based on tissue cluster level graph cut. Computerized Medical Imaging and Graphics, 93.
https://doi.org/10.1016/j.compmedimag.2021.101974