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Multimedia Tools and Applications

, Volume 77, Issue 9, pp 10485–10500 | Cite as

Active contour model-based segmentation algorithm for medical robots recognition

  • Yujie Li
  • Yun Li
  • Hyoungseop Kim
  • Seiichi Serikawa
Article
  • 125 Downloads

Abstract

In this paper, an identifying and classifying algorithm is proposed to solve the problem of recognizing objects accurately and effectively. First, via image preprocessing, initial images are obtained via denoising, smoothness, and image erosion. Then, we use granularity analysis and morphology methods to recognize the objects. For small objects identification and to analyze the objects, we calculate four characteristics of each cell: area, roundness, rectangle factor, and elongation. Finally, we segment the cells using the modified active contour method. In addition, we apply chromatic features to recognize the blood cancer cells. The algorithm is tested on multiple collected clinical cases of blood cell images. The results prove that the algorithm is valid and efficient when recognizing blood cancer cells and has relatively high accuracy rates for identification and classification. The experimental results also certificate the effectiveness of the proposed method for extracting precise, continuous edges with limited human intervention, especially for images with neighboring or overlapping blood cells. In addition, the results of the experiments show that this algorithm can accelerate the detection velocity.

Keywords

Active contour model Granularity detection Cancer cell recognition 

Notes

Acknowledgements

This work was supported by JSPS KAKENHI (No.15F15077), Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Research Fund of Chinese Academy of Sciences (No.MGE2015KG02), Research Fund of State Key Laboratory of Marine Geology in Tongji University (MGK1608), and Research Fund of State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University (1315; 1510).

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Yujie Li
    • 1
    • 2
  • Yun Li
    • 1
  • Hyoungseop Kim
    • 3
  • Seiichi Serikawa
    • 3
  1. 1.Yangzhou UniversityYangzhouChina
  2. 2.Chinese Academy of SciencesQingdaoChina
  3. 3.Kyushu Institute of TechnologyKitakyushuJapan

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