Image filtering techniques for medical image post-processing:
an overview
1,2C P BEHRENBRUCH, PhD, 1S PETROUDI, MSc, 1S BOND, MA, 2J D DECLERCK, PhD,
2,3F J LEONG, MD, PhD, ARPS and 1J M BRADY, PhD, FRS, FREng
1Medical Vision Laboratory, Engineering Science, Oxford University, Parks Road, Oxford OX1 3PJ, 2Mirada Solutions
Ltd., 23–38 Hyth Bridge Street, Oxford OX1 2EP, UK and 3Department of Medical and Molecular Pharmacology,
University of California Los Angeles, Los Angeles CA, USA
Images from an ordinary consumer digital camera
convey information at a wide range of spatial (and
temporal) scales and enable the viewer to decompose the
image into regions that are uniform in some way (colour,
texture, …), recognize familiar objects, determine spatial
relationships between objects, and detect abnormalities
(e.g. textural markings on a region expected to be plain).
Though modern digital cameras are equipped with low
noise electronics and excellent lenses that minimize pin-
cushion (and similar) distortions, images also contain noise
and artefacts such as red-eye in flash images. Widely
distributed software packages such as Photoshop provide a
set of ‘‘filtering’’ operations which enable the user to
improve the image in some way: from image smoothing
(typically local averaging) that removes noise and high
frequencies, sharpening that increases high frequency
content, contrast stretching, through to specialized algo-
rithms, for example for red-eye reduction. Such image
filtering is designed to improve the appearance of an
image, relying on the human visual system to disregard
any unwanted change of content of the image.
Medical image analysis poses a far tougher challenge.
First, there is an even greater need for image filtering,
because medical images have a poorer noise-to-signal ratio
than scenes taken with a digital camera, the spatial
resolution is often frustratingly low, the contrast between
anatomically distinct structures is often too low to be
computed reliably using a standard image processing
technique, and artefacts are common (e.g. motion and bias
field in MRI). Second, changes to image content must
be done in a highly controlled and reliable way that does
not compromise clinical decision-making. For example,
whereas it is generally acceptable to filter out local bright
patches of noise, care must be taken in the case of
mammography not to remove microcalcifications.
This paper briefly explores some of the key areas of
development in the area of filtering in Medical Imaging
and how these techniques impact generally available
software packages in routine use in a diagnostic setting.
It is interesting to note that a great deal of image filtering
takes place at what is usually regarded as a ‘‘pre-
processing’’ stage in the formation of a medical image
and is relatively invisible to a radiologist. However, there
is an increasing awareness of the impact of post-processing
algorithms – particularly filtering – in diagnostic software
applications and an awareness of these types of techniques
is useful.
Noise equalization
All imaging modalities, but especially those that are
relevant for medical imaging, generate image noise,
whether due to stability of a low-flip angle MRI acqui-
sition [1], ultrasound speckle [2], quantum noise in an
X-ray [3] or out of field counts in a PET scan [4]. Virtually
all imaging systems also perform filtering on the image
acquisition data both at an electronic level prior to
reconstruction as well as during the image reconstruction
phase. Indeed, much recent advancement in reconstruction
techniques for 3D imaging focus on including noise removal
as part of the reconstruction optimization process [5].
Most initial attempts at removing image noise focus on
‘‘smoothing’’ the pixel or voxel data by performing some
sort of local averaging function. For example, Gaussian
smoothing is an easily implemented smoothing algorithm;
however it is clearly not desirable to locally smooth a data
set in all cases (effectively removing high-frequency and
highly spatially localized image components). For exam-
ple, as we noted above, a mammographic X-ray is only
diagnostically valuable if the resolution and spatial
accuracy is sufficient to capture attenuation due to micro-
calcifications. Therefore increasingly ‘‘smart filters’’ based
on techniques such as anisotropic diffusion [6], which
smoothes the image to different extents in the direction of
the intensity gradient (across a boundary) and along the
boundary, or wavelets [7] (a standard but highly mathe-
matical reference) or [8] (an easily understood introduction
to the Matlab wavelet toolkit) are very useful because
they can remove noise from an image while recognizing
that certain noise-like components need to be preserved. In
this way the entire fields of image processing and com-
puter vision open up to yield interesting techniques for
embedding knowledge of anatomy, tissue characteristics
and the physics of imaging into the filtering process [9].
By way of example, the images in Figure 1 show a small
segment of an X-ray mammogram that has been digitized
at 50 mm and which contains two small clusters of
calcifications and a vessel. It is evident that the image is
extremely noisy: the visual impact of the noise being
accentuated by visualizing the image as a surface (height
equates to brightness). By simply performing an iterative
local averaging or smoothing process [10], the overall
structure of the image fragment becomes clearer. However,
the precise locations of the features in the image are poorly
preserved, essentially because the filter cannot discriminate
between what is high-frequency noise and what is a highly
The British Journal of Radiology, 77 (2004), S126–S132 E 2004 The British Institute of Radiology
DOI: 10.1259/bjr/17464219
S126 The British Journal of Radiology, Special Issue 2004

spatially localized (and therefore also noise-like) ‘‘spike’’
of feature which is a calcification. A ‘‘smart’’ filter, based
on a suitable wavelet, and which is matched to the
expected shape of a microcalcification has the effect of
removing the noise, by some local averaging; but, when a
calcification is encountered, the image structure is better
preserved [11].
As radiology becomes overwhelmingly digital for all
modalities, including those which have traditionally been
film-based, clinicians will need to have a deeper under-
standing of the relationship between the imaging process
and the display of the image. A lot of filtering and post-
processing is performed either to enhance visual charac-
teristics of images or to make an image more quantitative.
Understanding these techniques and their impact on the
image characteristics is important for good decision-making.
Bias field correction
In the previous example of filtering for ‘‘de-noising’’ an
image, the high-frequency (‘‘spiky’’) parts of the image are
removed. This type of artefact removal is the one that is
most commonly thought of as being an image filtering
step, but it is important to recognize that ‘‘filtering’’ has
much broader applicability.
A good example of a filtering technique that is, in a
certain sense, at the opposite extreme from noise filtering is
MRI bias field correction. Small variations in the magnetic
field introduced by the radiofrequency (RF) system (the B1
field introduce slowly undulating (low frequency) inho-
mogeneities in the image which can be visually distracting),
can impact the textural significance of an area, and
because they can substantially reduce the contrast in
different image regions, is a barrier to using any kind of
segmentation or region delineation tool which is based on
thresholding (assigning all of those voxels above a fixed
intensity to a particular tissue class). In this case, filtering
aims to remove a low-frequency component to the image,
rather than predominantly high frequency noise as in the
previous section, as Figures 2 and 3.
In this method, a filtering approach is used to estimate
an intensity correction distribution, which is applied to the
(a) (b) (c) (d)
Figure 1. (a) A small section of a 50 mm mammogram with microcalcifications and a vessel visible. (b) The unprocessed image
displayed as a surface map. (c) The filtering of the image segment using diffusion (smoothing) techniques [10] with (d) showing the
benefit using a more selective filtering approach such as a wavelet [7] which has better structure preservation.
Figure 2. An example of bias field correction using ‘‘smart filters’’ that can detect inhomogeneities in the image [12, 13]. The left
image shows an MRI slice of the colon with clear bias field artefacts. The right image has been significantly improved, both for
visualization and the application of quantitative and computer-aided techniques.
Image filtering techniques
S127The British Journal of Radiology, Special Issue 2004