title: Feature Analysis and Automatic Identification of Leukemic Lineage Blast Cells and Reactive Lymphoid Cells from Peripheral Blood Cell Images.
abstract: BACKGROUND
  Automated peripheral blood (PB) image analyzers usually underestimate the total number of blast cells, mixing them up with reactive or normal lymphocytes. Therefore, they are not able to discriminate between myeloid or lymphoid blast cell lineages. The objective of the proposed work is to achieve automatic discrimination of reactive lymphoid cells (RLC), lymphoid and myeloid blast cells and to obtain their morphologic patterns through feature analysis.
  
  
  METHODS
  In the training stage, a set of 696 blood cell images was selected in 32 patients (myeloid acute leukemia, lymphoid precursor neoplasms and viral or other infections). For classification, we used support vector machines, testing different combinations of feature categories and feature selection techniques. Further, a validation was implemented using the selected features over 220 images from 15 new patients (five corresponding to each category).
  
  
  RESULTS
  Best discrimination accuracy in the training was obtained with feature selection from the whole feature set (90.1%). We selected 60 features, showing significant differences (P < 0.001) in the mean values of the different cell groups. Nucleus-cytoplasm ratio was the most important feature for the cell classification, and color-texture features from the cytoplasm were also important. In the validation stage, the overall classification accuracy and the true-positive rates for RLC, myeloid and lymphoid blast cells were 80%, 85%, 82% and 74%, respectively.
  
  
  CONCLUSIONS
  The methodology appears to be able to recognize reactive lymphocytes well, especially between reactive lymphocytes and lymphoblasts.
authors.affiliation: Hemotherapy-Hemostasis, Hospital Clinic de Barcelona, CDB, Barcelona, Spain.|CoDAlab, Universitat Politecnica de Catalunya, Barcelona, Spain.
authors.forename: Laura
authors.initials: L
authors.lastname: Bigorra
authors.affiliation: Hemotherapy-Hemostasis, Hospital Clinic de Barcelona, CDB, Barcelona, Spain.
authors.forename: Anna
authors.initials: A
authors.lastname: Merino
authors.affiliation: CoDAlab, Universitat Politecnica de Catalunya, Barcelona, Spain.
authors.forename: Santiago
authors.initials: S
authors.lastname: Alférez
authors.affiliation: CoDAlab, Universitat Politecnica de Catalunya, Barcelona, Spain.
authors.forename: José
authors.initials: J
authors.lastname: Rodellar
mesh_terms: D002467:Cell Nucleus / Q000473:pathology; D003593:Cytoplasm / Q000473:pathology; D006801:Humans; D019044:Image Cytometry / Q000295:instrumentation* / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D015470:Leukemia, Myeloid, Acute / Q000000981:diagnostic imaging*; D008214:Lymphocytes / Q000145:classification / Q000473:pathology*; D022423:Myeloid Cells / Q000145:classification / Q000473:pathology*; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000000981:diagnostic imaging*; D060388:Support Vector Machine
keywords: cytology; hematology; image processing; leukemia; pathology