MRFU-Net: A Multiple Receptive Field U-Net for Environmental Microorganism Image Segmentation

[1]  Chen Li,et al.  A survey for the applications of content-based microscopic image analysis in microorganism classification domains , 2019, Artificial Intelligence Review.

[2]  Chen Li,et al.  Environmental microorganism classification using conditional random fields and deep convolutional neural networks , 2018, Pattern Recognit..

[3]  Ida-Maria Sintorn,et al.  Minimal annotation training for segmentation of microscopy images , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[4]  Humaira Nisar,et al.  Iterative region based Otsu thresholding of bright-field microscopic images of activated sludge , 2016, 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[5]  Tao Jiang,et al.  Environmental microorganism image retrieval using multiple colour channels fusion and particle swarm optimisation , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[6]  Riries Rulaningtyas,et al.  Multi patch approach in K-means clustering method for color image segmentation in pulmonary tuberculosis identification , 2015, 2015 4th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME).

[7]  Humaira Nisar,et al.  Local adaptive approach toward segmentation of microscopic images of activated sludge flocs , 2015, J. Electronic Imaging.

[8]  Dong Nie,et al.  A deep framework for bacterial image segmentation and classification , 2015, BCB.

[9]  M. Hatamoto,et al.  In situ DNA-hybridization chain reaction (HCR): a facilitated in situ HCR system for the detection of environmental microorganisms. , 2015, Environmental microbiology.

[10]  Ezzatollah Salari,et al.  A new method for the segmentation of algae images using retinex and support vector machine , 2015, 2015 IEEE International Conference on Electro/Information Technology (EIT).

[11]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[12]  Christian Szegedy,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[13]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  E. Rodner,et al.  Segmentation of microorganism in complex environments , 2013, Pattern Recognition and Image Analysis.

[15]  Hanchuan Peng,et al.  Visualization and Analysis of 3D Microscopic Images , 2012, PLoS Comput. Biol..

[16]  M. Y. Mashor,et al.  Performance comparison of clustering and thresholding algorithms for tuberculosis bacilli segmentation , 2012, 2012 International Conference on Computer, Information and Telecommunication Systems (CITS).

[17]  Alioune Ngom,et al.  Image segmentation of biofilm structures using optimal multi-level thresholding , 2011, Int. J. Data Min. Bioinform..

[18]  Manjunath Hiremath,et al.  Automated Identification and Classification of Rotavirus-A Particle in Digital Microscopic Images , 2010 .

[19]  P. S. Hiremath,et al.  Automatic classification of bacterial cells in digital microscopic images , 2010, International Conference on Digital Image Processing.

[20]  Ian L. Pepper,et al.  Introduction to Environmental Microbiology , 2009, Environmental Microbiology.

[21]  Bahram Javidi,et al.  Segmentation of 3D holographic images using bivariate jointly distributed region snake. , 2006, Optics express.

[22]  Gabriel Cristobal,et al.  Automatic identification techniques of tuberculosis bacteria , 2003, SPIE Optics + Photonics.

[23]  Anil K. Jain,et al.  Segmentation and classification of bacterial culture images , 1994 .

[24]  Ning Xu,et al.  A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential , 2019, IEEE Access.