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Biocybernetics and Biomedical Engineering

Tytuł artykułu

Full-automatic computer aided system for stem cell clustering using content-based microscopic image analysis

Autorzy Li, C.  Huang, X.  Jiang, T.  Xu, N. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN Stem cells are very original cells that can differentiate into other cells, tissues and organs, which play a very important role in biomedical treatments. Because of the importance of stem cells, in this paper we propose a full-automatic computer aided clustering system to assist scientists to explore potential co-occurrence relations between the cell differentiation and their morphological information in phenotype. In this proposed system, a multi-stage Content-based Microscopic Image Analysis (CBMIA) framework is applied, including image segmentation, feature extraction, feature selection, feature fusion and clustering techni-ques. First, an Improved Supervised Normalized Cuts (ISNC) segmentation algorithm is newly introduced to partition multiple stem cells into individual regions in an original microscopic image, which is the most important contribution in this paper. Then, based on the seg-mented stem cells, 11 different feature extraction approaches are applied to represent the morphological characteristics of them. Thirdly, by analysing the robustness and stability of the extracted features, Hu and Zernike moments are selected. Fourthly, these two selected features are combined by an early fusion approach to further enhance the properties of the feature representation of stem cells. Finally, k-means clustering algorithm is chosen to classify stem cells into different categories using the fused feature. Furthermore, in order to prove the effectiveness and usefulness of this proposed system, we carry out a series of experiments to evaluate our methods. Especially, our ISNC segmentation obtains 92.4% similarity, 96.0% specificity and 107.8% ration of accuracy, showing the potential of our work.
Słowa kluczowe
PL komórka macierzysta   obraz mikroskopowy   segmentacja obrazu   klasteryzacja komórek  
EN stem cell   biomedical microscopic image   content-based microscopic image analysis   image segmentation   supervised normalized cuts   cell clustering  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2017
Tom Vol. 37, no. 3
Strony 540--558
Opis fizyczny Bibliogr. 79 poz., rys., tab., wykr.
autor Li, C.
  • Northeastern University, Shenyang, China; Johannes Gutenberg University Mainz, Mainz, Germany
autor Huang, X.
  • University of Siegen, Siegen, Germany
autor Jiang, T.
  • Chengdu University of Information Technology, Chengdu, China
autor Xu, N.
  • University of Siegen, Siegen, Germany
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PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-07e97ea2-e139-4769-a870-56b056447ed5
DOI 10.1016/j.bbe.2017.01.004