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http://dx.doi.org/10.9717/kmms.2021.24.10.1326

Bio-Cell Image Segmentation based on Deep Learning using Denoising Autoencoder and Graph Cuts  

Lim, Seon-Ja (Dept. of IT Convergence and Application Eng., Pukyong National University)
Vununu, Caleb (Dept. of IT Convergence and Application Eng., Pukyong National University)
Kwon, Oh-Heum (Dept. of IT Convergence and Application Eng., Pukyong National University)
Lee, Suk-Hwan (Dept. of Computer Eng., Dong-A University)
Kwon, Ki-Ryoug (Dept. of IT Convergence and Application Eng., Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.10, 2021 , pp. 1326-1335 More about this Journal
Abstract
As part of the cell division method, we proposed a method for segmenting images generated by topography microscopes through deep learning-based feature generation and graph segmentation. Hybrid vector shapes preserve the overall shape and boundary information of cells, so most cell shapes can be captured without any post-processing burden. NIH-3T3 and Hela-S3 cells have satisfactory results in cell description preservation. Compared to other deep learning methods, the proposed cell image segmentation method does not require postprocessing. It is also effective in preserving the overall morphology of cells and has shown better results in terms of cell boundary preservation.
Keywords
Bio-cell Informatics; Bacterial Cell Segmentation; Denoising Autoencoder; Hybrid Feature; Artificial Neural Network; Deep Learning; Graph Cuts;
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