Activated Sludge Microscopic Image Segmentation Method Based on Improved U-Net

Lijie Zhao; Xingkui Lu; Bin Chen

doi:10.3788/LOP202158.1210008

[1] Zhang Q Z, Xu Y T, Zhou Y et al. Wastewater treatment and water environment protection[J]. Water Purification Technology, 29, 1-4(2010).

[2] Molina M A, Pérez C A A, Leiva C A. Characterization of filamentous flocs to predict sedimentation parameters using image analysis[J]. Journal of Sensors, 2020, 1-8(2020).

[3] Mesquita D P, Amaral A L, Ferreira E C. Identifying different types of bulking in an activated sludge system through quantitative image analysis[J]. Chemosphere, 85, 643-652(2011). http://europepmc.org/abstract/MED/21840038

[4] Jenné R, Banadda E N, Philips N et al. Image analysis as a monitoring tool for activated sludge properties in lab-scale installations[J]. Journal of Environmental Science and Health, Part A, 38, 2009-2018(2003). http://europepmc.org/abstract/MED/14524657

[5] Heine W, Sekoulov I, Burkhardt H et al. Early warning-system for operation-failures in biological stages of WWTPs by on-line image analysis[J]. Water Science and Technology: a Journal of the International Association on Water Pollution Research, 46, 117-124(2002). http://europepmc.org/abstract/MED/12360998

[6] Sikora M, Smolka B. Feature analysis of activated sludge based on microscopic images[C]. //Canadian Conference on Electrical and Computer Engineering 2001 Conference Proceedings (Cat. No.01TH8555), May 13-16, 2001, Toronto, ON, Canada., 1309-1314(2001).

[7] Lee X Y, Khan M B, Nisar H et al. Morphological analysis of activated sludge flocs and filaments[C]. //2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, May 12-15, 2014, Montevideo, Uruguay., 1449-1453(2014).

[8] Yong L X, Nisar H, Ho Y K. An approach for the segmentation and quantification of activated sludge floc blobs[J]. Advanced Science Letters, 19, 1372-1376(2013). http://www.ingentaconnect.com/content/asp/asl/2013/00000019/00000005/art00028

[9] Perez Y G, Leite S G F, Coelho M A Z. Activated sludge morphology characterization through an image analysis procedure[J]. Brazilian Journal of Chemical Engineering, 23, 319-330(2006).

[10] Khan M B, Nisar H, Aun N C. Segmentation and quantification of activated sludge floes for wastewater treatment[C]. //2014 IEEE Conference on Open Systems (ICOS), October 26-28, 2014, Subang, Malaysia., 18-23(2014).

[11] Ang R B Q, Nisar H, Khan M B et al. Image segmentation of activated sludge phase contrast images using phase stretch transform[J]. Microscopy, 68, 144-158(2019). http://www.ncbi.nlm.nih.gov/pubmed/30496508

[12] Nisar H, Herng G J, Chiong T P. Image segmentation of bright field activated sludge microscopic images using Gaussian mixture model[C]. //2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), November 3-7, 2019, Abu Dhabi, United Arab Emirates., 1-7(2019).

[13] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

[14] Ronneberger O, Fischer P, Brox T. U-net:convolutional networks for biomedical image segmentation[M]. //Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[15] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 833-815(2018).

[16] Newell A, Yang K Y, Deng J. Stacked hourglass networks for human pose estimation[M]. //Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9912, 483-499(2016).

[17] Li D X, Zhang Z. Improved U-net segmentation algorithm for the retinal blood vessel images[J]. Acta Optica Sinica, 40, 1010001(2020).

[18] Ai L M, Li T D, Liao F Y et al. Magnetic resonance brain tumor image segmentation based on attention U-net[J]. Laser & Optoelectronics Progress, 57, 141030(2020).

[19] Zhang W X, Zhu Z C, Zhang Y H et al. Cell image segmentation method based on residual block and attention mechanism[J]. Acta Optica Sinica, 40, 1710001(2020).

[20] Boztoprak H, Özbay Y, Güçlü D et al. Prediction of sludge volume index bulking using image analysis and neural network at a full-scale activated sludge plant[J]. Desalination and Water Treatment, 57, 17195-17205(2016).

[21] Zhao L J, Zou S D, Zhang Y H et al. Segmentation of activated sludge phase contrast microscopy images using U-net deep learning model[J]. Sensors and Materials, 31, 2013-2028(2019). http://www.zhangqiaokeyan.com/academic-journal-foreign_other_thesis/0204113002143.html

[22] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 7132-7141(2018).

[23] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

[24] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 833-851(2018).

[25] He T, Zhang Z, Zhang H et al. Bag of tricks for image classification with convolutional neural networks[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 558-567(2019).