A Review of Clustering Methods in Microorganism Image Analysis

Clustering plays a great role in microorganism image segmentation, feature extraction and classification, in all major application areas of microorganisms (medical, environmental, industrial, science and agriculture). Clustering methods are used for many years in microorganism image processing because they are simple algorithms, easy to apply and efficient. Thus, in order to clarify the potential of different clustering techniques in different application domains of microorganisms, we survey related works from the 1990s till now, while pinning out the specific challenges on each work (area) with the corresponding suitable clustering algorithm.

[1]  W. R. Sam Emmanuel,et al.  An efficient approach to sputum image segmentation using improved fuzzy local information C means clustering algorithm for tuberculosis diagnosis , 2017, 2017 International Conference on Inventive Computing and Informatics (ICICI).

[2]  W. R. Sam Emmanuel,et al.  Segmentation and classification of mycobacterium from ziehl neelsen stained sputum images for tuberculosis diagnosis , 2017, 2017 International Conference on Communication and Signal Processing (ICCSP).

[3]  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).

[4]  Z. Saad,et al.  Segmentation of tuberculosis bacilli in ziehl-neelsen-stained tissue images based on k-mean clustering procedure , 2010, 2010 International Conference on Intelligent and Advanced Systems.

[5]  Gabriel Cristóbal,et al.  Identification of tuberculosis bacteria based on shape and color , 2004, Real Time Imaging.

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

[7]  R Psenner,et al.  Detection of subgroups from flow cytometry measurements of heterotrophic bacterioplankton by image analysis. , 2001, Cytometry.

[8]  M. Y. Mashor,et al.  A fast and accurate detection of Schizont plasmodium falciparum using channel color space segmentation method , 2017, 2017 5th International Conference on Cyber and IT Service Management (CITSM).

[9]  Chetan Gupta,et al.  Detection of malaria parasite species and life cycle stages using microscopic images of thin blood smear , 2016, 2016 International Conference on Inventive Computation Technologies (ICICT).

[10]  Polina Golland,et al.  Resolving clustered worms via probabilistic shape models , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  M. Pons,et al.  Characterization of Penicillium chrysogenum physiology in submerged cultures by color and monochrome image analysis , 1995, Biotechnology and bioengineering.

[12]  R. Manavalan,et al.  Image Segmentation by Clustering Methods: Performance Analysis , 2011 .

[13]  Raphael Sznitman,et al.  Model-Independent Phenotyping of C. elegans Locomotion Using Scale-Invariant Feature Transform , 2015, PloS one.

[14]  Maurice Charbit,et al.  Digital Signal and Image Processing using MATLAB®: Blanchet/Digital , 2006 .

[15]  Saeid Belkasim,et al.  Multi-resolution border segmentation for measuring spatial heterogeneity of mixed population biofilm bacteria , 2008, Comput. Medical Imaging Graph..

[16]  Mark Johnston,et al.  Whither Model Organism Research? , 2005, Science.

[17]  E Morgenroth,et al.  Textural fingerprints: A comprehensive descriptor for biofilm structure development , 2008, Biotechnology and bioengineering.

[18]  M.Y. Mashor,et al.  Colour Image Segmentation of Tuberculosis Bacilli in Ziehl-Neelsen-Stained Tissue Images Using Moving K-Mean Clustering Procedure , 2010, 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation.

[19]  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).

[20]  Matthew Kyan,et al.  Refining competition in the self-organising tree map for unsupervised biofilm image segmentation , 2005, Neural Networks.

[21]  M. Y. Mashor,et al.  Segmentation based approach for detection of malaria parasites using moving k-means clustering , 2012, 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences.

[22]  S E Reichenbach,et al.  Evaluation of automated threshold selection methods for accurately sizing microscopic fluorescent cells by image analysis , 1989, Applied and environmental microbiology.

[23]  Lutgarde Raskin,et al.  Automated Image Analysis for Quantitative Fluorescence In Situ Hybridization with Environmental Samples , 2007, Applied and Environmental Microbiology.

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

[25]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[26]  Hamid Amiri,et al.  Adaptive automatic segmentation of leishmaniasis parasite in indirect immunofluorescence images , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  Chandan Chakraborty,et al.  Plasmodium vivax segmentation using modified fuzzy divergence , 2011, 2011 International Conference on Image Information Processing.

[28]  M. Y. Mashor,et al.  Detection of mycobacterium tuberculosis in Ziehl-Neelsen stained tissue images using Zernike moments and hybrid multilayered perceptron network , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[29]  S. Gillespie,et al.  Medical microbiology and infection at a glance. , 2003 .

[30]  Frank B. Dazzo,et al.  Computer-Assisted Segmentation of Bacteria in Color Micrographs , 2004 .

[31]  Yee Lian Chew,et al.  Recordings of Caenorhabditis elegans locomotor behaviour following targeted ablation of single motorneurons , 2017, Scientific data.

[32]  Nduka Okafor,et al.  Modern Industrial Microbiology and Biotechnology , 2007 .

[33]  Pamela C. Cosman,et al.  Image Features and Natural Clustering of Worm Body Shapes and Motion , 2003, SIP.

[34]  Raju,et al.  Data mining, Classification and Clustering with Morphological features of Microbes , 2012 .

[35]  P. Cosman,et al.  Quantitative classification and natural clustering of Caenorhabditis elegans behavioral phenotypes. , 2003, Genetics.

[36]  Scott T. Acton,et al.  Bact-3D: A level set segmentation approach for dense multi-layered 3D bacterial biofilms , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

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