PMID- 11169572
OWN - NLM
STAT- MEDLINE
DCOM- 20010419
LR  - 20191210
IS  - 0196-4763 (Print)
IS  - 0196-4763 (Linking)
VI  - 43
IP  - 2
DP  - 2001 Feb 1
TI  - Automatic signal classification in fluorescence in situ hybridization images.
PG  - 87-93
AB  - BACKGROUND: Previous systems for dot (signal) counting in fluorescence in situ 
      hybridization (FISH) images have relied on an auto-focusing method for obtaining 
      a clearly defined image. Because signals are distributed in three dimensions 
      within the nucleus and artifacts such as debris and background fluorescence can 
      attract the focusing method, valid signals can be left unfocused or unseen. This 
      leads to dot counting errors, which increase with the number of probes. METHODS: 
      The approach described here dispenses with auto-focusing, and instead relies on a 
      neural network (NN) classifier that discriminates between in and out-of-focus 
      images taken at different focal planes of the same field of view. Discrimination 
      is performed by the NN, which classifies signals of each image as valid data or 
      artifacts (due to out of focusing). The image that contains no artifacts is the 
      in-focus image selected for dot count proportion estimation. RESULTS: Using an NN 
      classifier and a set of features to represent signals improves upon previous 
      discrimination schemes that are based on nonadaptable decision boundaries and 
      single-feature signal representation. Moreover, the classifier is not limited by 
      the number of probes. Three classification strategies, two of them hierarchical, 
      have been examined and found to achieve each between 83% and 87% accuracy on 
      unseen data. Screening, while performing dot counting, of in and out-of-focus 
      images based on signal classification suggests an accurate and efficient 
      alternative to that obtained using an auto-focusing mechanism.
CI  - Copyright 2001 Wiley-Liss, Inc.
FAU - Lerner, B
AU  - Lerner B
AD  - Department of Electrical & Computer Engineering, Ben-Gurion University, 
      Beer-Sheva, Israel. boaz@ee.bgu.ac.il
FAU - Clocksin, W F
AU  - Clocksin WF
FAU - Dhanjal, S
AU  - Dhanjal S
FAU - Hulten, M A
AU  - Hulten MA
FAU - Bishop, C M
AU  - Bishop CM
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Cytometry
JT  - Cytometry
JID - 8102328
RN  - 0 (Fluorescent Dyes)
SB  - IM
MH  - Amniotic Fluid/chemistry/cytology
MH  - Artifacts
MH  - Cell Nucleus/chemistry
MH  - Fluorescent Dyes/chemistry
MH  - Humans
MH  - Image Cytometry/instrumentation/*methods
MH  - Image Enhancement/instrumentation/*methods
MH  - In Situ Hybridization, Fluorescence/classification/instrumentation/*methods
MH  - Neural Networks, Computer
EDAT- 2001/02/13 11:00
MHDA- 2001/04/21 10:01
CRDT- 2001/02/13 11:00
PHST- 2001/02/13 11:00 [pubmed]
PHST- 2001/04/21 10:01 [medline]
PHST- 2001/02/13 11:00 [entrez]
AID - 10.1002/1097-0320(20010201)43:2<87::AID-CYTO1022>3.0.CO;2-# [pii]
AID - 10.1002/1097-0320(20010201)43:2<87::aid-cyto1022>3.0.co;2-# [doi]
PST - ppublish
SO  - Cytometry. 2001 Feb 1;43(2):87-93. doi: 
      10.1002/1097-0320(20010201)43:2<87::aid-cyto1022>3.0.co;2-#.