title: A new self-partition clustering method for robust identification of subsets with heterogeneous size and density and its clinical application to leukocyte differential counting.
abstract: BACKGROUND
  Identification of clusters in 2-dimensional scatterplots generated by hematology analyzer is a classical challenge. Conventional clustering algorithms fail to process cases with complicated mixtures of overlapping clusters and noise.
  
  
  METHODS
  A new method was developed that features an image processing algorithm for rational identification of initial clusters and a self-partition clustering (SPC) algorithm with iterative truncation-correction (ITC) method to handle overlapping and noise. All clusters are assumed to follow bivariate Gaussian distributions with specified means, SDs, and correlation coefficient. While, each data point is assumed to belong to all clusters but with different proportions according to the likelihood of belonging to each cluster (computed by the Mahalanobis distance) and the data size of the cluster. Bivariate cluster statistics are computed in consideration of a weight factor determined cluster by cluster by each data point. In the computation, the ITC method minimizes the effect of overlapping and data.
  
  
  RESULTS
  Performance of SPC/ITC method was evaluated by its application to differential leukocyte counting. It showed comparable performance with manual counting and much better performance than the commonly used expectation maximum algorithm.
  
  
  CONCLUSIONS
  The SPC/ITC method showed superior performance in situations with overlapping and low-density clusters such as leukopenia or leukocytosis.
authors.affiliation: Department of Clinical Laboratory Science, Faculty of Health Sciences, Yamaguchi University Graduate School of Medicine, Ube, Japan; Chiba Emergency Medical Center, Chiba, Japan.
authors.forename: Shouichi
authors.initials: S
authors.lastname: Sato
authors.affiliation: Department of Clinical Laboratory Science, Faculty of Health Sciences, Yamaguchi University Graduate School of Medicine, Ube, Japan. Electronic address: ichihara@yamaguchi-u.ac.jp.
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Ichihara
authors.affiliation: Department of Infection Control and Laboratory Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan.
authors.forename: Tohru
authors.initials: T
authors.lastname: Inaba
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D006801:Humans; D007958:Leukocyte Count
keywords: Clustering; EM algorithm; Image processing; Iterative truncation correction method; Mahalanobis distance; Non-hierarchical cluster method