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Cytological Images Clustering

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Advances in Intelligent Systems and Computing V (CSIT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1293))

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Abstract

The paper analyzes methods of data clustering. Cytological images of breast cancer were investigated, and nuclei feature of cytological images were calculated. The paper presents all the stages of computer vision, which are necessary to obtain quantitative characteristics of micro-objects and their clustering. Emphasis is placed on the stages of image loading, selection of image input parameters, pre-processing, noise reduction, segmentation, contour analysis and direct calculation of quantitative characteristics of cell nuclei (area, perimeter, circle, length of main and side diagonals, etc.). Authors developed algorithm for clustering cytological images, which includes reduction of numerical data, Elbow algorithm for determining the number of clusters, algorithm K-means, hierarchical algorithms for constructing a dendrogram of clusters, DBScan algorithm. To reduce the number of informative parameters, the principal components method was used. Computer experiments were implemented in the Java programming language environment for image processing and quantitative characteristics of micro-objects and the Python programming language for clustering and visual demonstration of its results. The authors also substantiate the use of selected quantitative characteristics for clustering, based on the results of the application of the principal components analysis. The accuracy of cytological images clustering was also evaluated.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, TNEU, Ternopil, Ukraine

    Oleh Berezsky, Oleh Pitsun, Lesia Dubchak, Taras Dolynyuk & Bohdan Derish

  2. Department of Applied Mathematics, TNEU, Ternopil, Ukraine

    Kateryna Berezka

Authors
  1. Oleh Berezsky
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  2. Oleh Pitsun
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  3. Lesia Dubchak
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  4. Kateryna Berezka
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  5. Taras Dolynyuk
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  6. Bohdan Derish
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Corresponding author

Correspondence to Oleh Berezsky .

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine

    Natalya Shakhovska

  2. Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Mykola O. Medykovskyy

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Berezsky, O., Pitsun, O., Dubchak, L., Berezka, K., Dolynyuk, T., Derish, B. (2021). Cytological Images Clustering. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_12

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