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Automatic Classification of Red Blood Cells using Gaussian Mixture Densities [chapter]

J. Dahmen, J. Hektor, R. Perrey, H. Ney
2000 Informatik aktuell  
Given a database of 5062 grayscale images, we model the distribution of the observations by using Gaussian mixture densities within a Bayesian framework.  ...  In this paper we present an invariant statistical approach to classifying red blood cells (RBC).  ...  Degenhardt, Department of Physiology, RWTH Aachen, for manually classifying the RBC images used in our experiments.  ... 
doi:10.1007/978-3-642-59757-2_62 fatcat:xuva4zx2v5d5fhcjsykzx3tbci

Assessing Malaria using Neutral Zone Classifiers with Mixture Discriminant Analysis on 2D Images of Red Blood Cells

Shariq Mohammed, Dipak K. Dey
2019 Journal of biostatistics and epidemiology  
and Aim: We aim to build a classifier to distinguish between malaria-infected red blood cells (RBCs) and healthy cells using the two-dimensional (2D) microscopic images of RBCs.  ...  The extracted features are used with Gaussian MDA to distinguish between healthy and malaria infected cells.  ...  Arun Anand, M S University of Baroda, India and Dr. Bahram Javidi, University of Connecticut, USA for providing us with the data. We also thank Dr.  ... 
doi:10.18502/jbe.v5i1.1901 fatcat:f7d5h64sevbslgkanvxopxocdm

Leukocyte Recognition Using EM-Algorithm [chapter]

Mario Chirinos Colunga, Oscar Sánchez Siordia, Stephen J. Maybank
2009 Lecture Notes in Computer Science  
This document describes a method for classifying images of blood cells. Three different classes of cells are used: Band Neutrophils, Eosinophils and Lymphocytes.  ...  The image pattern is projected down to a lower dimensional sub space using PCA; the probability density function for each class is modeled with a Gaussian mixture using the EM-Algorithm.  ...  The class conditional density function p(x|ω c ) for each leukocyte class ω c is modelled using a mixture of K Gaussian densities.  ... 
doi:10.1007/978-3-642-05258-3_48 fatcat:ipel6d3qhndlvacf7ioptmkqd4

Application of Machine Learning for Automatic MRD Assessment in Paediatric Acute Myeloid Leukaemia

Roxane Licandro, Michael Reiter, Markus Diem, Michael Dworzak, Angela Schumich, Martin Kampel
2018 Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods  
Acute Myeloid Leukaemia (AML) is a rare type of blood cancer in children.  ...  This disease originates from genetic alterations of hematopoetic progenitor cells, which are involved in the hematopoiesis process, and leads to the proliferation of undifferentiated (leukaemic) cells.  ...  Background Gaussian Mixture Model As the first approach a Gaussian Mixture Model (GMM) based formulation is used to cluster and automatically classify cells into leukaemic and normal cells.  ... 
doi:10.5220/0006595804010408 dblp:conf/icpram/LicandroRDDSK18 fatcat:hju4gzu52negnk5lsrxf2ah634

Statistical mixture modeling for cell subtype identification in flow cytometry

Cliburn Chan, Feng Feng, Janet Ottinger, David Foster, Mike West, Thomas B. Kepler
2008 Cytometry Part A  
The data was fitted with a mixture of multivariate Gaussians using standard Bayesian statistical approaches and Markov chain Monte Carlo computations.  ...  Results-Statistical mixture models were able to identify and purify major cell subsets in human peripheral blood, using an automated process that can be generalized to an arbitrary number of markers.  ...  We describe our studies of statistical mixture modeling using Gaussian mixtures for flow cytometric data densities.  ... 
doi:10.1002/cyto.a.20583 pmid:18496851 pmcid:PMC2840702 fatcat:3xfwv2pccffp3ngoeybch2icl4

Three-dimensional counting of morphologically normal human red blood cells via digital holographic microscopy

Faliu Yi, Inkyu Moon, Yeon H. Lee
2015 Journal of Biomedical Optics  
Counting morphologically normal cells in human red blood cells (RBCs) is extremely beneficial in the health care field.  ...  We also apply the principal component analysis algorithm to reduce the dimension number of variables and establish the Gaussian mixture densities using the projected data with the first eight principal  ...  Gaussian densities for each type of RBC.  ... 
doi:10.1117/1.jbo.20.1.016005 pmid:25567613 fatcat:epsxk5yyincqzkowzss6pw6x24

Automatic recognition of biological particles in microscopic images

M. Ranzato, P.E. Taylor, J.M. House, R.C. Flagan, Y. LeCun, P. Perona
2007 Pattern Recognition Letters  
Fourth, each sample is classified using a classifier obtained from a mixture-of-Gaussians generative model.  ...  It was subsequently trained and tested on a challenging set of images of airborne pollen grains where it achieved an 83% correct classification rate for the 3 categories found during one month of observation  ...  They are (columns from left to right): bacteria, white blood cell clumps, yeasts, crystals, hyaline casts, pathological casts, non squamous epithelial cells, red blood cells, sperm, squamous epithelial  ... 
doi:10.1016/j.patrec.2006.06.010 fatcat:edfijbu4onaptp5ynulzjedgfq

Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies

Lin Lin, Cliburn Chan, Sine R. Hadrup, Thomas M. Froesig, Quanli Wang, Mike West
2013 Statistical Applications in Genetics and Molecular Biology  
Novel uses of automated flow cytometry technology for measuring levels of protein markers on thousands to millions of cells are promoting increasing need for relevant, customized Bayesian mixture modelling  ...  The use of several colors enables the identification of, in principle, combinatorially increasingly numbers of subtypes of cells, each identified by a subset of colors.  ...  The mixture model also has the flexibility to represent non-Gaussian T-cell region densities by aggregating a subset of Gaussian densities.  ... 
doi:10.1515/sagmb-2012-0001 pmid:23629459 pmcid:PMC4155753 fatcat:w4fslqep6zaxdogcnfzlyrgz2i

Determining Leishmania Infection Levels by Automatic Analysis of Microscopy Images [article]

P. A Nogueira
2013 arXiv   pre-print
The previous vote is then taken into account as the number of mixtures to be used in a Gaussian Mixture Model to decluster the said region.  ...  Algorithm A intends to detect macrophage nuclei and consists of segmentation via adaptive multi-threshold, and classification of resulting regions using a set of collected features.  ...  red blood cell according using fuzzy logic [42] .  ... 
arXiv:1311.2621v1 fatcat:vpdwqdv5qjh37d7zbohqngtpzm

AFM-based detection of glycocalyx degradation and endothelial stiffening in the db/db mouse model of diabetes

Marta Targosz-Korecka, Magdalena Jaglarz, Katarzyna E. Malek-Zietek, Aleksandra Gregorius, Agnieszka Zakrzewska, Barbara Sitek, Zenon Rajfur, Stefan Chlopicki, Marek Szymonski
2017 Scientific Reports  
From a morphological viewpoint, the endothelium consists of a monolayer of endothelial cells that lines the internal lumen of blood vessels.  ...  To analyze highly heterogeneous aorta samples, we developed a tailored classification procedure of indentation data based on a bi-layer brush model supplemented with Hertz model for quantification of nanomechanics  ...  A bivariate Gaussian mixture was fit to the data points using a function in Matlab.  ... 
doi:10.1038/s41598-017-16179-7 pmid:29162916 pmcid:PMC5698475 fatcat:woed63p4onfrlopawr3zw6rtou

Wavelet-based multiscale texture segmentation: Application to stromal compartment characterization on virtual slides

Nicolas Signolle, Marinette Revenu, Benoît Plancoulaine, Paulette Herlin
2010 Signal Processing  
The classifier selection is based on a study of the influence of the hyper-parameters of the method used.  ...  Over the testing set, majority vote was found to be the best way of combining outputs of the selected classifiers.  ...  Acknowledgments This work is funded by a BDI grant cofinanced by the CNRS and the Regional Council of Lower Normandy.  ... 
doi:10.1016/j.sigpro.2009.11.008 fatcat:szxzakgnnrft7bixk5huurwesq

Towards life cycle identification of malaria parasites using machine learning and Riemannian geometry [article]

Arash Mehrjou
2017 arXiv   pre-print
A patch-based unsupervised statistical clustering algorithm is proposed which offers a novel method for classification of different regions within blood images.  ...  The core of the proposed algorithm is a model called Mixture of Independent Component Analysis.  ...  As theoretical contributions, we have proposed a caused by genus Plasmodium that invades Red Blood Cells (RBCs). There exist four species of malaria parasite and four life-stages.  ... 
arXiv:1708.05200v1 fatcat:jdem3tvg3bcapgq2y3acgvt7bu

Bayesian Learning of Generalized Gaussian Mixture Models on Biomedical Images [chapter]

Tarek Elguebaly, Nizar Bouguila
2010 Lecture Notes in Computer Science  
Generalized Gaussian mixture models are robust in the presence of noise and outliers and are more flexible to adapt the shape of data.  ...  Our work is motivated by the fact that biomedical and cDNA microarray images both contain non-Gaussian characteristics, impossible to model using rigid distributions like the Gaussian.  ...  Acknowledgment The completion of this research was made possible thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC), a NATEQ Nouveaux Chercheurs Grant, and a start-up grant  ... 
doi:10.1007/978-3-642-12159-3_19 fatcat:6b45n4kdcjhjxk5zg42nglvwtu

Interactive classification and content-based retrieval of tissue images

Selim Aksoy, Giovanni B. Marchisio, Carsten Tusk, Krzysztof Koperski, Andrew G. Tescher
2002 Applications of Digital Image Processing XXV  
The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions.  ...  The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback.  ...  Hossler from the Department of Anatomy and Cell Biology, East Tennessee State University for their permission to use the tissue images.  ... 
doi:10.1117/12.453862 fatcat:jen2edco4nchzl6gtxeqsapaba

Hue-saturation-density (HSD) model for stain recognition in digital images from transmitted light microscopy

Jeroen A.W.M. van der Laak, Martin M.M. Pahlplatz, Antonius G.J.M. Hanselaar, Peter C.M. de Wilde
2000 Cytometry  
Direct use of the three intensities obtained by a colour camera results in the red-green-blue (RGB) model.  ...  Results: In the RGB model, the mixture of chromatic and intensity information hampers standardisation of stain recognition.  ...  Example of a microscopic image as used to evaluate the colour models, containing red-stained blood vessels, brown-stained nuclei of proliferating cells, and blue-stained nuclei of non-proliferating cells  ... 
doi:10.1002/(sici)1097-0320(20000401)39:4<275::aid-cyto5>;2-8 pmid:10738280 fatcat:h4chbb5venfbfkldkxll5p7naa
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