Biomedical sensor image segmentation algorithm based on improved fully convolutional network

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

*この研究の対応する著者

研究成果: Article査読

抄録

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.

本文言語English
論文番号111307
ジャーナルMeasurement: Journal of the International Measurement Confederation
197
DOI
出版ステータスPublished - 2022 6月 30

ASJC Scopus subject areas

  • 器械工学
  • 電子工学および電気工学

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