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Mitosis event recognition and detection based on evolution of feature in time domain

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Abstract

Mitosis detection and recognition in phase-contrast microscopy image sequences is a fundamental problem in many biomedical applications. Traditionally, researchers detect all mitotic cells from these image sequences with human eyes, which is tedious and time consuming. In recent years, many computer vision technologies were proposed to help humans to achieve the mitosis detection automatically. In this paper, we present an approach which utilized the evolution of feature in the time domain to represent the feature of mitosis. Firstly, the feature of each cell image is extracted by the different method (GIST, SIFT, CNN). Secondly, we construct the levels of motorists according to the steps of mitosis. The pooling method is utilized to handle the feature fusion in each dimension and in different time segments. Third, the pooling features were combined to one vector to represent the characters of this video. Finally, tradition machine learning method SVM is used to handle the mortises recognition problem. In order to demonstrate the performance of our approach, motorists event detection is made in some microscopy image sequences. In the experiment, some classic methods as comparison method are made in this paper. The corresponding experiments also demonstrate the superiority of our approach.

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Acknowledgements

National Natural Science Foundation of China (61502337, 31770904, 61772359, 61472275, 21106095), Tianjin Research Program of Application Foundation and Advanced Technology (15JCYBJC3070015JCYBJC16200). This study was funded by National High-Tech Research and Development Program of China (863 programs, 2012AA10A401), Grants of the Major State Basic Research Development Program of China (973 programs, 2012CB114405), National Natural Science Foundation of China (31770904, 21106095), National Key Technology R & D Program (2011BAD13B07, 2011BAD13B04), Tianjin Applied Basic and Advanced Technology Research Program (15JCYBJC30700), Project of introducing one thousand high level talents in three years (5KQM110003).

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

    Weizhi Nie, Chenchen Liu & Yuting Su

  2. Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China

    Tong Hao

  3. Tianjin University, Tianjin, China

    Yan Yan

Authors
  1. Weizhi Nie
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  2. Yan Yan
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  3. Tong Hao
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  4. Chenchen Liu
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  5. Yuting Su
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Corresponding authors

Correspondence to Tong Hao or Chenchen Liu.

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Nie, W., Yan, Y., Hao, T. et al. Mitosis event recognition and detection based on evolution of feature in time domain. Machine Vision and Applications 29, 1249–1256 (2018). https://doi.org/10.1007/s00138-018-0913-3

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