Minimum Entropy Segmentation Applied To Multi-spectral Chromosome Images

Wade Schwartzkopf, Brian L. Evans, Alan C. Bovik
Embeded Signal Processing Laboratory
Laboratory for Image and Video Engineering
Department of Electrical and Computer Engineering
The University of Texas at Austin, Austin, TX 78712-1084

Download

Introduction

In the early 1990s, the state-of-the-art in commercial chromosome image acquisition was grayscale. Automated chromosome classification was based on the grayscale image and boundary information obtained during segmentation. Multi-spectral image acquisition was developed in 1990 and commercialized in the mid-1990s. One acquisition method, multiplex fluorescence in-situ hybridization (M-FISH), uses five color dyes. We have implemented a segmentation algorithm for M-FISH images that minimizes the entropy of classified pixels within possible chromosomes. This method is shown to correctly decompose even difficult clusters of touching and overlapping chromosomes.

Use

This program accepts one image of classified MFISH pixels as input. It outputs an image of labelled connected components. Images can be in PGM, PNG, or raw formats. If input image is in PGM or PNG format, no height or width information is needed. The output image will be the same format as the input image.

The syntax for calling this program:

mfSegment inputImageFilename outputImageFilename [height width]

This code requires the libpng library (which, in turn, requires the zlib library).

Reference

W. Schwartzkopf, B. L. Evans, and A. C. Bovik, ``Minimum Entropy Segmentation Applied to Multi-Spectral Chromosome Images'', Proc. IEEE Int. Conf. on Image Processing, Oct. 7-10, 2001, vol. II, pp. 865-868, Thessaloniki, Greece.

Last updated: August 26, 2002