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

• Autorzy: Li C. , Huang X. , Jiang T. , Xu N.

Identyfikatory

DOI

10.1016/j.bbe.2017.01.004

• Języki publikacji: EN

Abstrakty

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

EN

stem cell; biomedical microscopic image; content-based microscopic image analysis; image segmentation; supervised normalized cuts; cell clustering

PL

komórka macierzysta; obraz mikroskopowy; segmentacja obrazu; klasteryzacja komórek

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2017
• Tom: Vol. 37, no. 3
• Strony: 540--558
• Opis fizyczny: Bibliogr. 79 poz., rys., tab., wykr.

Twórcy

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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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).