title: Automatic recognition of atypical lymphoid cells from peripheral blood by digital image analysis.
abstract: OBJECTIVE
  The objective was the development of a method for the automatic recognition of different types of atypical lymphoid cells.
  
  
  METHODS
  In the method development, a training set (TS) of 1,500 lymphoid cell images from peripheral blood was used. To segment the images, we used clustering of color components and watershed transformation. In total, 113 features were extracted for lymphocyte recognition by linear discriminant analysis (LDA) with a 10-fold cross-validation over the TS. Then, a new validation set (VS) of 150 images was used, performing two steps: (1) tuning the LDA classifier using the TS and (2) classifying the VS in the different lymphoid cell types.
  
  
  RESULTS
  The segmentation algorithm was very effective in separating the cytoplasm, nucleus, and peripheral zone around the cell. From them, descriptive features were extracted and used to recognize the different lymphoid cells. The accuracy for the classification in the TS was 98.07%. The precision, sensitivity, and specificity values were above 99.7%, 97.5%, and 98.6%, respectively. The accuracy of the classification in the VS was 85.33%.
  
  
  CONCLUSIONS
  The method reaches a high precision in the recognition of five different types of lymphoid cells and could allow for the design of a diagnosis support tool in the future.
authors.affiliation: From the Universitat Politècnica de Catalunya, Barcelona, Spain, and.
authors.forename: Santiago
authors.initials: S
authors.lastname: Alférez
authors.affiliation: Department of Hemotherapy-Hemostasis, Hospital Clinic, Barcelona, Spain. amerino@clinic.ub.es.
authors.forename: Anna
authors.initials: A
authors.lastname: Merino
authors.affiliation: From the Universitat Politècnica de Catalunya, Barcelona, Spain, and Department of Hemotherapy-Hemostasis, Hospital Clinic, Barcelona, Spain.
authors.forename: Laura
authors.initials: L
authors.lastname: Bigorra
authors.affiliation: From the Universitat Politècnica de Catalunya, Barcelona, Spain, and.
authors.forename: Luis
authors.initials: L
authors.lastname: Mujica
authors.affiliation: From the Universitat Politècnica de Catalunya, Barcelona, Spain, and.
authors.forename: Magda
authors.initials: M
authors.lastname: Ruiz
authors.affiliation: From the Universitat Politècnica de Catalunya, Barcelona, Spain, and.
authors.forename: Jose
authors.initials: J
authors.lastname: Rodellar
mesh_terms: D000465:Algorithms; D003581:Cytodiagnosis / Q000379:methods*; D019337:Hematologic Neoplasms / Q000175:diagnosis*; D006405:Hematology / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008214:Lymphocytes / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Atypical lymphoid cells; Automatic cell classification; Digital image processing; Hematologic cytology; Morphologic analysis; Peripheral blood