Characterization and automatic screening of reactive and abnormal neoplastic B lymphoid cells from peripheral blood

The objective was to advance in the automatic, image‐based, characterization and recognition of a heterogeneous set of lymphoid cells from peripheral blood, including normal, reactive, and five groups of abnormal lymphocytes: hairy cells, mantle cells, follicular lymphoma, chronic lymphocytic leukemia, and prolymphocytes.

[1]  A. Hernandez,et al.  Peripheral blood manifestations of lymphoma and solid tumors. , 2002, Clinics in laboratory medicine.

[2]  G. Escolar,et al.  Estudio comparativo de la morfología de sangre periférica analizada mediante el microscopio y el CellaVision DM96 en enfermedades hematológicas y no hematológicas , 2011 .

[3]  Alférez Baquero,et al.  Methodology for automatic classification of atypical lymphoid cells from peripheral blood cell images , 2015 .

[4]  Arivazhagan Selvaraj,et al.  Texture classification using wavelet transform , 2003, Pattern Recognit. Lett..

[5]  H. Willems,et al.  The divergent morphological classification of variant lymphocytes in blood smears , 2006, Journal of Clinical Pathology.

[6]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[7]  Lin Yang,et al.  Classification of hematologic malignancies using texton signatures , 2007, Pattern Analysis and Applications.

[8]  B. Bain,et al.  Diagnosis from the blood smear. , 2005, The New England journal of medicine.

[9]  Jesus Angulo.  A Mathematical Morphology Approach to Cell Shape Analysis , 2008 .

[10]  C Briggs,et al.  Can automated blood film analysis replace the manual differential? An evaluation of the CellaVision DM96 automated image analysis system , 2009, International journal of laboratory hematology.

[11]  Gavin Brown,et al.  Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection , 2012, J. Mach. Learn. Res..

[12]  Luciano da Fontoura Costa,et al.  A texture approach to leukocyte recognition , 2004, Real Time Imaging.

[13]  J Rodellar,et al.  Automatic classification of atypical lymphoid B cells using digital blood image processing , 2014, International journal of laboratory hematology.

[14]  C Briggs,et al.  ICSH recommendations for the standardization of nomenclature and grading of peripheral blood cell morphological features , 2015, International journal of laboratory hematology.

[15]  Lin Yang,et al.  PathMiner: A Web-Based Tool for Computer-Assisted Diagnostics in Pathology , 2009, IEEE Transactions on Information Technology in Biomedicine.

[16]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[17]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Support vector machines applied to white blood cell recognition , 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).

[18]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[19]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[20]  B. Houwen.  The Differential Cell Count , 2001 .

[21]  Dorin Comaniciu,et al.  Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy , 2000, IEEE Transactions on Information Technology in Biomedicine.

[22]  H. Ceelie,et al.  Examination of peripheral blood films using automated microscopy; evaluation of Diffmaster Octavia and Cellavision DM96 , 2006, Journal of Clinical Pathology.

[23]  C Briggs,et al.  ICSH guidelines for the evaluation of blood cell analysers including those used for differential leucocyte and reticulocyte counting , 2014, International journal of laboratory hematology.

[24]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[25]  J M Jou,et al.  EQAS for peripheral blood morphology in Spain: a 6‐year experience , 2007, International journal of laboratory hematology.

[26]  F. Albregtsen.  Statistical Texture Measures Computed from Gray Level Coocurrence Matrices , 2008 .

[27]  S. Swerdlow.  WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues , 2017 .

[28]  Santiago Alférez,et al.  Automatic recognition of atypical lymphoid cells from peripheral blood by digital image analysis. , 2015, American journal of clinical pathology.

[29]  G Flandrin,et al.  Ontology-based lymphocyte population description using mathematical morphology on colour blood images. , 2007, Cellular and molecular biology.