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Astronomical Data Analysis Software and Systems IV
ASP Conference Series, Vol. 77, 1995
R. A. Shaw, H. E. Payne, and J. J. E. Hayes, eds.
Improvements in Filter Design for Removing Galactic
``Cirrus'' from IRAS Images
J. P. Basart, L. X. He
Department of Electrical and Computer Engineering, Iowa State
University, Ames, IA 50011
P. N. Appleton
Department of Physics and Astronomy, Iowa State University, Ames,
IA 50011
J. A. Pedelty
Space Data and Computing Division, NASA Goddard Space Flight
Center, Greenbelt, MD 20771
Abstract. Design improvements continue to be made in the filter de­
veloped using mathematical morphology principles for the Infrared As­
tronomical Satellite (IRAS) images. The purpose of this filter is to elimi­
nate Galactic cirrus emission from extragalactic fields. Current improve­
ments are based on dividing the structure in the IRAS images into several
classes, and then filtering the information in the image class by class. The
current procedure gives significantly improved results compared to those
of the first version of the filter.
1. Introduction
``Cirrus'' emission in IRAS images at 60 ¯m and, especially, at 100 ¯m, has
hampered the study of extended extragalactic structures. The problem is made
more difficult by the fact that the cirrus emission can exhibit a wide spread in far­
IR color temperatures, ranging from 20 K to 35 K. Our approach for reducing
the IR cirrus in IRAS images is based upon an image processing procedure
called mathematical morphology (Serra 1982). Results from the first version
of our filter are discussed in Basart, Siqueira, & Appleton (1992), Appleton,
Siqueira, & Basart (1993), and Pedelty, Appleton, & Basart (1994). Previous
filtered results of the M81 group showed considerable improvement over the
original image. Not all undesired features were filtered out, however, so filter
design continued. The second version of the filter, presented here, produces
significantly better results than the first filter.
1

2
­0.05
0.00
0.05
0.10
0.15
0.20
Growth
Function
0 2 4 6 8 10 12 14 16
Growth Layer
­0.05
0.00
0.05
0.10
Filter
Coefficient
a
b
Figure 1. The top plot (a) shows an example of the growth functions
for various structural types as explained in the text. The bottom plot
(b) shows the filter coefficients created from the growth curve and used
to filter the image shown in Figure 2b.
2. Filter Design
We briefly review the basic procedure underlying both filters. The process starts
by performing an opening operation (Appleton et al. 1993) on the filter, with
a Gaussian shaped structuring element. This opening operation eliminates all
structural information smaller than the structuring element. We then open the
original image again with a slightly larger structuring element, and subtract the
first result from the second. This difference produces an image with a narrow
range of structural sizes: those that lie between the sizes of the two structuring
elements. We then open the original image with a third structuring element,
whose size is a little larger than the second structuring element. We subtract
this third opening from the second opening, giving another image with a different
range of structural sizes---larger sizes than those of the first differenced image.
We continue this process until we get a set of 16 images whose range of structural
sizes varies from seven pixels to thirty nine pixels. We call the resulting plot of
intensity vs. structural size at one pixel a ``growth curve.''
The growth curve contains information central to the filtering operation.
In version one of the filter, the growth curves for many cirrus pixels not con­

3
taining galaxies were averaged together, and the results used as the filter curve.
With an appropriate normalization, the filter curve was applied to each pixel
in the image to remove the Galactic cirrus. This filter had a high degree of
success because the growth information of the galaxies differed from that of the
cirrus. However, not all information averaged together to make the filter curve
was homogeneous, causing artifacts to be introduced into the final image. This
difficulty led to the development of version two of the filter. In this extended ap­
proach a classification procedure was introduced. The growth curve was treated
as feature information in 16 dimensional space\Gammaone dimension for each differ­
ence in openings. After placing all opening differences into the feature space, a
clustering operation was performed to determine groupings. Five clusters were
allowed. The growth curves for each of the five structural types are shown in
Figure 1a. Four of the curves are somewhat similar while the top curve is much
different. Viewing the central portion of the graph, the identification of the
structure from the top curve to the bottom curve is: (1) small bright objects,
(2) regions around the small bright objects, (3) small cloud structure, (4) large
cloud structure, and (5) corrupted structure caused by the boundaries of the
image. It is apparent from the curve that filter performance could be improved
over that of version one by selectively filtering by structure type.
3. Results
Version two of the filter uses a filter curve based upon type 3 structure, as
identified above. A filter curve was created by normalizing the growth curve by
the area under this curve. The resulting filter curve is shown in Figure 1b. This
curve, with appropriate re­normalization, was applied to all pixels in the image.
Figure 2a shows an example of IRAS field I363B4H0 before filtering, and
Figure 2b shows the results after filtering. The original image (Figure 2a) is
very heavily contaminated with 100 ¯m IR cirrus emission. The image shown
is a portion of an IRAS field which contains a variety of non­cirrus structure
ranging from Galactic nebulae to galaxies. Small objects, such as galaxies, are
difficult, if not possible, to detect. Even more ambiguous is the tenuous structure
on the periphery of galaxies. The purpose of the filter is to minimize the presence
of Galactic IR cirrus in the image in order to make the extragalactic IR emission
more visible.
The filtered image contains considerably less Galactic cirrus than the un­
filtered image. The remaining structure in the filtered image is primarily from
non­diffuse objects. About 30% of the point­like sources in the image are galax­
ies and the remaining objects are extended structures. Many of the extended
structures appear to be highly correlated with unusually bright and sharply de­
fined reflection nebulosity. The latter conclusion was drawn by comparing the
filtered image with an optical image.
Preliminary testing for flux integrity during the filtering process has been
completed. Throughout the image, flux retention is better than 1% in consis­
tency. In absolute terms, the filtered flux is within a few percent of what is
assumed to be a true flux of an object. Overall, the filter has been extremely ef­
fective at extracting unusual and interesting sources. We are currently following
up our results with wide­field CCD observations of these objects.

4
(a) (b)
Figure 2. The left figure (a) is a portion of the IRAS field I363B4H0
observed at 100 ¯m. The right figure (b) is the result of filtering out
much of the IR cirrus structure using a filter based upon mathematical
morphology.
References
Appleton, P. N., Siqueira, P. R., & Basart, J. P. 1993, AJ, 106, 1664
Basart, J. P., Siqueira, P. R., & Appleton, P. N. 1992, in Astronomical Data
Analysis Software and Systems I, ASP Conf. Ser., Vol. 25, eds. D. M.
Worrall, C. Biemesderfer, & J. Barnes (San Francisco, ASP), p. 283
Pedelty, J. A., Appleton, P. N., & Basart, J. P. 1994, in Astronomical Data
Analysis Software and Systems III, ASP Conf. Ser., Vol. 61, eds. D. R.
Crabtree, R. J. Hanisch, & J. Barnes (San Francisco, ASP), p. 308pedel­
tyj
Serra, J., 1982, Image Analysis and Mathematical Morphology (London, Aca­
demic Press)