Biomedical sensor image segmentation algorithm based on improved fully convolutional network. (30th June 2022)

• Record Type: Journal Article
• Authors: Li, Hong'an; Fan, Jiangwen; Hua, Qiaozhi; Li, Xinpeng; Wen, Zheng; Yang, Meng
• Is Part Of: Measurement. Volume 197(2022)
• Publication Date: 2022-06-30
• Subjects: Biomedical imaging sensors -- Biomedical image segmentation -- Assistive therapy -- Fully convolutional networks -- Attention mechanisms; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111307
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: 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.