title: Segmentation of complex cell clusters in microscopic images: application to bone marrow samples.
abstract: BACKGROUND
  Morphologic examination of bone marrow and peripheral blood samples continues to be the cornerstone in diagnostic hematology. In recent years, interest in automatic leukocyte classification using image analysis has increased rapidly. Such systems collect a series of images in which each cell must be segmented accurately to be classified correctly. Although segmentation algorithms have been developed for sparse cells in peripheral blood, the problem of segmenting the complex cell clusters characterizing bone marrow images is harder and has not been addressed previously.
  
  
  METHODS
  We present a novel algorithm for segmenting clusters of any number of densely packed cells. The algorithm first oversegments the image into cell subparts. These parts are then assembled into complete cells by solving a combinatorial optimization problem in an efficient way.
  
  
  RESULTS
  Our experimental results show that the algorithm succeeds in correctly segmenting densely clustered leukocytes in bone marrow images.
  
  
  CONCLUSIONS
  The presented algorithm enables image analysis-based analysis of bone marrow samples for the first time and may also be adopted for other digital cytometric applications where separation of complex cell clusters is required.
authors.affiliation: Institute of Laboratory Medicine, Department of Clinical Genetics, Lund University Hospital, Lund, Sweden.
authors.forename: Björn
authors.initials: B
authors.lastname: Nilsson
authors.forename: Anders
authors.initials: A
authors.lastname: Heyden
mesh_terms: D000465:Algorithms; D000818:Animals; D001854:Bone Marrow Cells / Q000166:cytology* / Q000648:ultrastructure; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007962:Leukocytes / Q000166:cytology* / Q000648:ultrastructure; D012984:Software