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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-07e97ea2-e139-4769-a870-56b056447ed5

Czasopismo

Biocybernetics and Biomedical Engineering

Tytuł artykułu

Full-automatic computer aided system for stem cell clustering using content-based microscopic image analysis

Autorzy Li, C.  Huang, X.  Jiang, T.  Xu, N. 
Treść / Zawartość http://www.ibib.waw.pl/pl/wydawnictwa/biocybernetics-and-biomedical-enginering-bbe/bbe-tomy http://www.journals.elsevier.com/biocybernetics-and-biomedical-engineering/
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Stem cells are very original cells that can differentiate into other cells, tissues and organs, which play a very important role in biomedical treatments. Because of the importance of stem cells, in this paper we propose a full-automatic computer aided clustering system to assist scientists to explore potential co-occurrence relations between the cell differentiation and their morphological information in phenotype. In this proposed system, a multi-stage Content-based Microscopic Image Analysis (CBMIA) framework is applied, including image segmentation, feature extraction, feature selection, feature fusion and clustering techni-ques. First, an Improved Supervised Normalized Cuts (ISNC) segmentation algorithm is newly introduced to partition multiple stem cells into individual regions in an original microscopic image, which is the most important contribution in this paper. Then, based on the seg-mented stem cells, 11 different feature extraction approaches are applied to represent the morphological characteristics of them. Thirdly, by analysing the robustness and stability of the extracted features, Hu and Zernike moments are selected. Fourthly, these two selected features are combined by an early fusion approach to further enhance the properties of the feature representation of stem cells. Finally, k-means clustering algorithm is chosen to classify stem cells into different categories using the fused feature. Furthermore, in order to prove the effectiveness and usefulness of this proposed system, we carry out a series of experiments to evaluate our methods. Especially, our ISNC segmentation obtains 92.4% similarity, 96.0% specificity and 107.8% ration of accuracy, showing the potential of our work.
Słowa kluczowe
PL komórka macierzysta   obraz mikroskopowy   segmentacja obrazu   klasteryzacja komórek  
EN stem cell   biomedical microscopic image   content-based microscopic image analysis   image segmentation   supervised normalized cuts   cell clustering  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Elsevier
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2017
Tom Vol. 37, no. 3
Strony 540--558
Opis fizyczny Bibliogr. 79 poz., rys., tab., wykr.
Twórcy
autor Li, C.
  • Northeastern University, Shenyang, China; Johannes Gutenberg University Mainz, Mainz, Germany
autor Huang, X.
  • University of Siegen, Siegen, Germany
autor Jiang, T.
  • Chengdu University of Information Technology, Chengdu, China
autor Xu, N.
  • University of Siegen, Siegen, Germany
Bibliografia
[1] Chen S, Zhao M, Wu G, Yao C, Zhang J. Recent advances in morphological cell image analysis. Comput Math Methods Med 2012;2012(101563):1–10.
[2] Yu H, Rahim NAA. Imaging in Cellular and Tissue Engineering. US: CRC Press; 2013.
[3] Li C. Content-based Microscopic Image Analysis. Germany: Logos Verlag Berlin GmbH; 2016.
[4] We T. Section of biology: the cytoanalyzer – an example of physics in medical research. Trans N Y Acad Sci 1955;17 (3):250–6.
[5] Prewitt JM, Mendelsohn ML. The analysis of cell images. Ann N Y Acad Sci 1966;128(3):1035–53.
[6] Kendall P. Computer processing of biomedical images. Computer 1976;9(5):54–68.
[7] Jens R. Characterization of biological processes through automated image analysis. Annu Rev Biomed Eng 2010;12:315–44.
[8] John J, Nair MS, Kumar PRA, Wilscy M. A novel approach for detection and delineation of cell nuclei using feature similarity index measure. Biocybern Biomed Eng 2015;36 (1):76–88.
[9] Korzynska A, Zychowicz M. A method of estimation of the cell doubling time on basis of the cell culture monitoring data. Biocybern Biomed Eng 2008;28(4):75–82.
[10] Markiewicz T, Korzynska A, Kowalski A, Swiderska-Chadaj Z, Murawski P, Grala B, Lorent M, Wdowiak M, Zak J, Roszkowiak L, Kozlowski W, Pijanowska D. MIAP – web-based platform for the computer analysis of microscopic images to support the pathological diagnosis. Biocybern Biomed Eng 2016;36(4):597–609.
[11] Zhang T, Jia W, Zhu Y, Yang J. Automatic tracking of neural stem cells in sequential digital images. Biocybern Biomed Eng 2016;36(1):66–75.
[12] Yang F, Mackey MA, Lanzini F. Cell segmentation, tracking, and mitosis detection using temporal context. Medical Image Computing and Computer-assisted Intervention. 2005. pp. 302–9.
[13] Chen X, Zhou X, Wong S. Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Trans Biomed Eng 2006;53(4):762–6.
[14] Jiang K, Liao Q, Dai S. A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering. International Conference on Machine Learning and Cybernetics. 2003. pp. 2820–5.
[15] Farnoosh S, Zainina S. A framework for white blood cell segmentation in microscopic blood images using digital image processing. Biol Proced Online 2009;11(1):196–206.
[16] Jain AK, Smith SP, Backer E. Segmentation of muscle cell pictures: a preliminary study. IEEE Trans Pattern Anal Mach Intell 1980;12(3):232–42.
[17] Liu F, Xing F, Yang L. Robust muscle cell segmentation using region selection with dynamic programming. International Symposium on Biomedical Imaging. 2014. pp. 521–4.
[18] Lin G, Adiga U, Olson K. A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytometr Part A 2003;56(1):23–36.
[19] Wienert S, Heim D, Saeger K, Stenzinger A, Beil M, Hufnagl P, Dietel M, Denkert C, Klauschena F. Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach. Sci Rep 2012;2(503):1–7.
[20] Beevi SK, Nair MS, Bindu GR. Automatic segmentation of cell nuclei using Krill Herd optimization based multi-thresholding and localized active contour model. Biocybern Biomed Eng 2016;36(4):584–96.
[21] Zheng X, Zhang Y, Yu Y, Zhang J, Shi J. Recognition of marrow cell images based on fuzzy clustering. Inf Technol Comput Sci 2012;1:40–9.
[22] Reta C, Altamirano L, Gonzalez JA, Diaz-Hernandez R, Peregrina H, Olmos I, Alonso JE, Lobato R. Segmentation and classification of bone marrow cells images using contextual information for medical diagnosis of acute leukemias. PLoS ONE 2015;10(6):1–18.
[23] Pan J, Kanade K, Chen M. Heterogeneous conditional random field: realizing joint detection and segmentation of cell regions in microscopic images. Computer Vision and Pattern Recognition. 2010. pp. 2940–7.
[24] Korzynska A, Iwanowski M. Multistage morphological segmentation of bright-field and fluorescent microscopy images. Opto-Electron Rev 2012;20(2):87–99.
[25] Shen M, Zimmer B, Leist M, Merhof D. Automated image processing to quantify cell migration. Bildverarbeitung fuer die Medizin. 2013. pp. 152–7.
[26] Huang X, Li C, Shen M, Shirahama K, Nyffeler J, Leist M, Grzegorzek M, Deussen O. Stem cell microscopic image segmentation using supervised normalised cuts. IEEE International Conference on Image Processing. 2016. pp. 4140–4.
[27] Fu K, Mui JK. A survey on image segmentation. Pattern Recognit 1981;13(1):3–16.
[28] Sobel I. History and Definition of the Sobel Operator; 2014.
[29] Arbelaez P, Maire M, Fowlkes C, Malik J. Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 2011;33(5):898–916.
[30] Fan J, Yau DKY, Elmagarmid AK, Aref WG. Automatic image segmentation by integrating colour-edge extraction and seeded region growing. IEEE Trans Image Process 2001;10:1454–66.
[31] Weszka JS. A survey of threshold selection techniques. Comput Graphics Image Process 1978;7(2):259–65.
[32] Kato Z, Pong T. A Markov random field image segmentation model using combined color and texture features. In: Skarbek W, editor. Computer Analysis of Images and Patterns. Springer; 2001. p. 547–54.
[33] Shi J, Malik J. Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 2000;22(8):888–905.
[34] Angermueller C, Paernamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol 2016;12 (878):1–16.
[35] Dong B, Shao L, Costaz MD, Bandmannz O, Frangi AF. Deep learning for automatic cell detection in wide-field microscopy zebrafish images. International Symposium on Biomedical Imaging. 2015. pp. 1–5.
[36] Valen DAV, Kudo T, Lane KM, Macklin DN, Quach NT, DeFelice MM, Maayan I, Tanouchi Y, Ashley EA, Covert MW. Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLOS Comput Biol 2016;12(11):1–24.
[37] Hengena H, Spoor S, Pandi M. Analysis of blood and bone marrow smears using digital processing techniques. SPIE 4684, Medical Imaging 2002: Image Processing. 2002. pp. 624–35.
[38] Reta C, Altamirano L, Gonzalez JA, Diaz R, Guichard JS. Segmentation of bone marrow cell images for morphological classification of acute leukemia. Twenty- Third Florida Artifical International Intelligence Research Society Conference. 2010. pp. 86–91.
[39] Martinez JM. MPEG-7 Overview (Version 10); 2004.
[40] Yang M, Kpalma K, Ronsin J. A survey of shape feature extraction techniques. In: Yin P, editor. Pattern Recognition. IN-TECH; 2008. p. 43–90.
[41] Zhang D, Lu G. A comparative study of Fourier descriptors for shape representation and retrieval. Asian Conference on Computer Vision. 2002. pp. 646–51.
[42] Zhang D, Lu G. Review of shape representation and description techniques. Pattern Recogn 2004;37(1):1–19.
[43] Zhang D, Lu G. A comparative study of curvature scale space and fourier descriptors for shape-based image retrieval. J Vis Commun Image Represent 2003;14(1): 39–57.
[44] Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 2002;24(4):509–22.
[45] Frigui H, Gader P. Detection and discrimination of land mines in ground-penetrating radar based on edge histogram descriptors and a possibilistic k-nearest neighbor classifier. IEEE Trans Fuzzy Syst 2011;17(1):185–99.
[46] Li C, Shirahama K, Grzegorzek M, Ma F, Zhou B. Classification of environmental microorganisms in microscopic images using shape features and support vector machines. IEEE International Conference on Image Processing. 2013. pp. 2435–9.
[47] Li C, Shirahama K, Grzegorzek M. Application of content-based image analysis to environmental microorganism classification. Biocybern Biomed Eng 2015;35(1):10–21.
[48] Li C, Shirahama K, Czajkowska J, Grzegorzek M, Ma F, Zhou B. A multi-stage approach for automatic classification of environmental microorganisms. International Conference on Image Processing, Computer Vision and Pattern Recognition. 2013. pp. 364–70.
[49] Hu M. Visual pattern recognition by moment invariants. IRE Trans Inf Theory 1962;8(2):179–87.
[50] Tahmasbi A, Saki F, Shokouhi SB. Classification of benign and malignant masses based on Zernike moments. Comput Biol Med 2011;41(8):726–35.
[51] Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis 2004;60(2):91–110.
[52] Beisbart C, Barbosa MS, Wagner H, Costa LDF. Extended morphometric analysis of neuronal cells with Minkowski valuations. Eur Phys J B – Condens Matter Complex Syst 2006;52(2):531–46.
[53] Long F, Peng H, Sudar D, Lelievre SA, Knowles DW. Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis. BMC Cell Biol 2007;8(Suppl. 1):1–15.
[54] Cao F, Wagner RA, Wilson KD, Xie X, Fu J, Drukker M, Lee A, Li RA, Gambhir SS, Weissman IL, Robbins RC, Wu JC. Transcriptional and functional profiling of human embryonic stem cell-derived cardiomyocytes. PLoS ONE 2008;3(10):e3474.
[55] Lowry WE, Richter L, Yachechko R, Pyle AD, Tchieu J, Sridharan R, Clark AT, Plath K. Generation of human induced pluripotent stem cells from dermal fibroblasts. Proc Natl Acad Sci U S A 2008;105(8):2883–8.
[56] Rodrigues PL, Rodrigues NF, Duque D, Granja S, Correia- Pinto J, Vilaca JL. Automated image analysis of lung branching morphogenesis from microscopic images of fetal rat explants. Comput Math Methods Med 2014. http://dx.doi.org/10.1155/2014/820214. https://www.hindawi.com/journals/cmmm/2014/820214/.
[57] Sarrafzadeh O, Rabbania H, Talebib A, Yousefi-Banaema H. Selection of the best features for leukocytes classification in blood smear microscopic images. SPIE 9041, Medical Imaging 2014: Digital Pathology. 2014. pp. 1–8.
[58] Higaki T, Kutsuna N, Akita K, Sato M, Sawaki F, Kobayashi M, Nagata N, Toyooka K, Hasezawa S. Semi-automatic organelle detection on transmission electron microscopic images. Sci Rep 2015;5:1–9.
[59] Cebron N, Berthold MR. Mining of cell assay images using active semi-supervised clustering. Workshop on Computational Intelligence in Data Mining. 2005. pp. 63–9.
[60] Sibson R. SLINK: an optimally efficient algorithm for the single-link cluster method. Comput J 1973;16(1):30–4.
[61] MacQueen JB. Some methods for classification and analysis of multivariate observations. Berkeley Symposium on Mathematical Statistics and Probability. 1967. pp. 281–97.
[62] Zhang W, Zhao D, Wang X. agglomerative clustering via maximum incremental path integral. Pattern Recogn 2013;46(11):3056–65.
[63] Dhillon IS, Mallela S, Kumar R. A divisive information-theoretic feature clustering algorithm for text classification. J Mach Learn Res 2003;3:1265–87.
[64] Shukla S, Naganna S. A review on k-means data clustering approach. Int J Inf Comput Technol 2014;4(17):1847–60.
[65] Lin D, Wu X. Phrase clustering for discriminative learning. Annual Meeting of the ACL and IJCNLP. 2009. pp. 1030–8.
[66] Dhillon IS, Modha DM. Concept decompositions for large sparse text data using clustering. Mach Learn 2001;42 (1):143–75.
[67] Bondy JA, Murty USR. Graph Theory. Germany: Springer; 2008.
[68] Wu Z, Leahy R. An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Trans Pattern Anal Mach Intell 1993;15 (11):1101–13.
[69] Boykov Y, Kolmogorov V. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 2004;26(9): 1124–37.
[70] Basu S, Banerjee A, Mooney R. Semi-supervised clustering by seeding. Int Conf Mach Learn 2002;27–34.
[71] Huang Z, Leng J. Analysis of Hu's moment invariants on image scaling and rotation. Int. Conf. Comput. Eng. Technol.. 2010. pp. 476–80.
[72] Qader HA, Ramli AR, Al-Haddad S. Fingerprint recognition using Zernike moments. Int Arab J Inf Technol 2007;4(4):372–6.
[73] Shu H, Luo L, Bao X, Yu W, Han G. An efficient method for computation of Legendre moments. Graph Models 2000;62:237–62.
[74] Li B. A new computation of geometric moments. Pattern Recogn 1993;26(1):109–13.
[75] Belz A, Gatt A. Intrinsic vs. extrinsic evaluation measures for referring expression generation. Annu. Meet. Assoc. Comput. Ling. Hum. Lang. Technol.. 2008. pp. 197–200.
[76] Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 1987;20:53–65.
[77] Nyffeler J, Karreman C, Leisner H, Kim YJ, Lee G, Waldmann T, Leist M. Design of a high-throughput human neural crest cell migration assay to indicate potential developmental toxicants. ALTEX Online first 2016;1–22.
[78] Snoek CGM, Worring M, Smeulders AWM. Early versus late fusion in semantic video analysis. ACM International Conference on Multimedia. 2005. pp. 399–402.
[79] Li C, Shirahama K, Grzegorzek M. Environmental Microbiology Aided by Content-based Image Analysis. Pattern Anal Appl 2016;19(2):531–47.
Uwagi
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-07e97ea2-e139-4769-a870-56b056447ed5
Identyfikatory
DOI 10.1016/j.bbe.2017.01.004