Biomedical sensor image segmentation algorithm based on improved fully convolutional network

Hong'an Li, Jiangwen Fan, Qiaozhi Hua*, Xinpeng Li, Zheng Wen, Meng Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Effective use of biomedical sensor image can help locate diseased tissues and tissue structures clearly presented, and clinical diagnosis and treatment can assist doctors in making appropriate treatment plans. In order to efficiently process the images acquired by biomedical sensors, we propose a biomedical sensor image segmentation method with improved fully convolutional network, which firstly extracts the local spatial and frequency domain information of the images acquired by biomedical sensors and enhances the texture information of the images. Secondly, the background interference is suppressed by increasing the target region weights to refine the processing of the image and enhance the features of the image while reducing the information redundancy. It is experimentally proved that the model in this paper can effectively reduce the phenomenon of cell adhesion after image segmentation, has better segmentation effect and segmentation accuracy, and can more effectively utilize the images acquired by biomedical sensors.

Original languageEnglish
Article number111307
JournalMeasurement: Journal of the International Measurement Confederation
Publication statusPublished - 2022 Jun 30


  • Assistive therapy
  • Attention mechanisms
  • Biomedical image segmentation
  • Biomedical imaging sensors
  • Fully convolutional networks

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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