PMID- 9915563
OWN - NLM
STAT- MEDLINE
DCOM- 19990415
LR  - 20041117
IS  - 1059-910X (Print)
IS  - 1059-910X (Linking)
VI  - 44
IP  - 1
DP  - 1999 Jan 1
TI  - Efficient cell segmentation tool for confocal microscopy tissue images and 
      quantitative evaluation of FISH signals.
PG  - 49-68
AB  - In this paper we have presented a semi-automatic method for segmenting 3-D cell 
      nuclei from tissue images obtained using Confocal Laser Scanning Microscope. This 
      microscope can focus at different layers of the specimen and hence a stack of 
      images giving a 3D representation can be obtained. The existing methods for 
      segmenting the cells in 3-D confocal images are highly interactive and, hence, 
      time consuming. We have developed an approach, where, given one segmented 
      image-slice (optical section) of the set of confocal images, the remaining 
      image-slices in the image stack can be automatically segmented in a layered 
      approach. One of the image-slices in an image stack is considered as a 
      representative image-slice. In this image-slice, overlapping boundary pixels are 
      identified interactively while the remaining part of the cell boundary is marked 
      using Laplacian of a Gaussian operator. This interactively traced portion of the 
      boundary is considered as initial boundary for finding the overlapping boundary 
      pixels in the neighboring image-slices. Simple basic search strategy is used for 
      boundary search in the neighboring image-slices. The method minimizes the human 
      interaction and is also found to be efficient and reasonably accurate. Some 
      experimental results are presented to illustrate the usefulness of the technique. 
      We have also given the application of our segmentation method to quantitative 
      evaluation of fluorescence in situ hybridization (FISH) signals. A brief 
      comparative study of visual FISH signal evaluation and the FISH signal counting 
      by automatic image analysis is also given.
FAU - Adiga, P S
AU  - Adiga PS
AD  - Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, 
      Calcutta.
FAU - Chaudhuri, B B
AU  - Chaudhuri BB
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Microsc Res Tech
JT  - Microscopy research and technique
JID - 9203012
SB  - IM
MH  - Automation
MH  - Cell Nucleus/*ultrastructure
MH  - Humans
MH  - Image Processing, Computer-Assisted
MH  - In Situ Hybridization, Fluorescence/*methods
MH  - Male
MH  - Microscopy, Confocal/*methods
MH  - Neoplasms/*ultrastructure
MH  - Prostatic Neoplasms/ultrastructure
EDAT- 1999/01/23 19:27
MHDA- 2000/06/22 10:00
CRDT- 1999/01/23 19:27
PHST- 1999/01/23 19:27 [pubmed]
PHST- 2000/06/22 10:00 [medline]
PHST- 1999/01/23 19:27 [entrez]
AID - 10.1002/(SICI)1097-0029(19990101)44:1<49::AID-JEMT6>3.0.CO;2-6 [pii]
AID - 10.1002/(SICI)1097-0029(19990101)44:1<49::AID-JEMT6>3.0.CO;2-6 [doi]
PST - ppublish
SO  - Microsc Res Tech. 1999 Jan 1;44(1):49-68. doi: 
      10.1002/(SICI)1097-0029(19990101)44:1<49::AID-JEMT6>3.0.CO;2-6.