Sponsor:
Contract grant sponsor: ARCADIM Project; Contract grant number: CICYT TIC92-0922-C02-01 (Comisión Interministerial de Ciencia y Tecnología); Contract grant sponsor: European Concerted Action CA-AMCA; Contract grant number: BMH1-CT92-1307; Contract grant sponsor: Comunidad Autónoma de Madrid (CAM); Contract grant sponsor: Universidad Politécnica de Madrid (UPM).
Keywords:
FISH
,
Interphase nuclei
,
Fluorescence microscopy
,
Cluster division
,
Digital image analysis
,
Mathematical morphology
,
Automation
Rights:
Atribución-NoComercial-SinDerivadas 3.0 España
Abstract:
Cluster division is a critical issue in fluor escence
micr oscopy-based analytical cytology when preparation
protocols do not provide appropriate separation
of objects. Overlooking cluster ed nuclei and
analyzing only isolated nuclei may dramatically incr
Cluster division is a critical issue in fluor escence
micr oscopy-based analytical cytology when preparation
protocols do not provide appropriate separation
of objects. Overlooking cluster ed nuclei and
analyzing only isolated nuclei may dramatically incr
ease analysis time or af fect the statistical validation
of the r esults. Automatic segmentation of cluster
ed nuclei r equir es the implementation of specific
image segmentation tools. Most algorithms are inspired by one of the two following strategies: 1)
cluster division by the detection of inter nuclei gradients;
or 2) division by definition of domains of
influence (geometrical approach). Both strategies
lead to completely different implementations, and
usually algorithms based on a single view strategy
fail to corr ectly segment most cluster ed nuclei, or
per for m well just for a specific type of sample. An
algorithm based on morphological watersheds has
been implemented and tested on the segmentation
of micr oscopic nuclei clusters. This algorithm pr ovides
a tool that can be used for the implementation
of both gradient- and domain-based algorithms, and,
mor e importantly, for the implementation of mixed
(gradient- and shape-based) algorithms. Using this
algorithm, almost 90% of the test clusters wer e
corr ectly segmented in peripheral blood and bone
marr ow pr eparations. The algorithm was valid for
both types of samples, using the appr opriate markers
and transfor mations.[+][-]