MOTIVATION: Fluorescence in situ hybridization (FISH) is used to study the 
organization and the positioning of specific DNA sequences within the cell 
nucleus. Analyzing the data from FISH images is a tedious process that invokes 
an element of subjectivity. Automated FISH image analysis offers savings in time 
as well as gaining the benefit of objective data analysis. While several FISH 
image analysis software tools have been developed, they often use a 
threshold-based segmentation algorithm for nucleus segmentation. As fluorescence 
signal intensities can vary significantly from experiment to experiment, from 
cell to cell, and within a cell, threshold-based segmentation is inflexible and 
often insufficient for automatic image analysis, leading to additional manual 
segmentation and potential subjective bias. To overcome these problems, we 
developed a graphical software tool called FISH Finder to automatically analyze 
FISH images that vary significantly. By posing the nucleus segmentation as a 
classification problem, compound Bayesian classifier is employed so that 
contextual information is utilized, resulting in reliable classification and 
boundary extraction. This makes it possible to analyze FISH images efficiently 
and objectively without adjustment of input parameters. Additionally, FISH 
Finder was designed to analyze the distances between differentially stained FISH 
probes.
AVAILABILITY: FISH Finder is a standalone MATLAB application and platform 
independent software. The program is freely available from: 
http://code.google.com/p/fishfinder/downloads/list.