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Segmentation of complex cell clusters in microscopic images: Application to bone marrow samples. : application to bone marrow samples

Nilsson, Björn LU and Heyden, Anders LU (2005) In Cytometry Part A 66A(1). p.24-31
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... (More)

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.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Algorithms, Animals, Bone Marrow Cells, Humans, Image Processing, Computer-Assisted, Leukocytes, Software, Journal Article
in
Cytometry Part A
volume
66A
issue
1
pages
8 pages
publisher
John Wiley & Sons
external identifiers
  • wos:000230015300003
  • pmid:15915504
  • scopus:20944440661
ISSN
1552-4930
DOI
10.1002/cyto.a.20153http://dx.doi.org/10.1002/cyto.a.20153
language
English
LU publication?
yes
id
24d9af6f-21a3-45a7-a834-aca7f5703059 (old id 137812)
date added to LUP
2007-07-09 08:17:14
date last changed
2017-02-26 03:40:48
@article{24d9af6f-21a3-45a7-a834-aca7f5703059,
  abstract     = {<p>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.</p><p>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.</p><p>RESULTS: Our experimental results show that the algorithm succeeds in correctly segmenting densely clustered leukocytes in bone marrow images.</p><p>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.</p>},
  author       = {Nilsson, Björn and Heyden, Anders},
  issn         = {1552-4930},
  keyword      = {Algorithms,Animals,Bone Marrow Cells,Humans,Image Processing, Computer-Assisted,Leukocytes,Software,Journal Article},
  language     = {eng},
  number       = {1},
  pages        = {24--31},
  publisher    = {John Wiley & Sons},
  series       = {Cytometry Part A},
  title        = {Segmentation of complex cell clusters in microscopic images: Application to bone marrow samples. : application to bone marrow samples},
  url          = {http://dx.doi.org/10.1002/cyto.a.20153http://dx.doi.org/10.1002/cyto.a.20153},
  volume       = {66A},
  year         = {2005},
}