The use of texture analysis to identify
suspicious masses in mammography
R Gupta† and PE Undrill,
Department of Bio-Medical Physics & BioEngineering,
University of Aberdeen, Foresterhill,
Aberdeen, AB9 2ZD.
ABSTRACT
In mammography, national breast screening programmes have lead to a large increase in
the number of mammograms needing to be studied by radiologists. Lesion indicators can
be point-like as in micro-calcifications or extended as in stellate lesions or regular
masses. Texture analysis has been proposed as a promising method for studying
radiographic images in relation to the detection of extended objects. Laws has suggested
a suite of filters which may be used to segment or classify an image using textural
features and these have been reported as being of value in automatic mammographic
glandular tissue classification.
The work reported here suggests the incorporation of additional steps of image
processing in an attempt to improve the performance of the Laws filter masks in the
detection of lesions. By deriving approximate outlines which are used to identify
suspicious regions, the investigation illustrates the properties of one of the filters. After
applying the method to a small pre-diagnosed database of stellate lesions and regular
masses, the results show that the filter is able to identify the malignant masses in all cases
presented. For each non-suspicious case studied, the sum of any false positive areas is
statistically insignificant when compared with the extent of true detected regions in the
diagnosed instances.
Keywords: Texture Analysis, Mammography Image Processing, Identification of
Regular Masses and Stellate Lesions.
Introduction
Breast cancer is the most common cancer in women (Egan, 1977). Early detection of the
cancer leads to significant improvements in conservative treatment. In recent years
mammography has proved invaluable in the management of breast cancer and this has
resulted in national screening programmes for all women in high risk groups. This, in
turn, has lead to a large increase in the number of asymptomatic mammograms being
presented, the majority of which are normal. Automated methods of diagnosis therefore
need to be established with the aim of improving diagnostic performance by indicating
suspicious areas, and a variety of computer based methods have been proposed to
improve the radiologist's performance in searching for small, subtle, masked or
infrequent abnormalities (Astley 1990, Chan 1990).
In studying mammograms specific features are sought in routine examinations as
common indicators of malignancy (Juhl, 1982). These include masses of an
approximately regular spherical or stellate shape, micro-calcification clusters and
asymmetry between breasts. An early approach to regular mass identification (Lai, 1989)
used a median filter followed by template matching of candidate shapes, with the
consequence of long processing times. Chan (1987), Davies (1990), Karssemeijer (1992)
and Karssemeijer (1993), amongst many others, have focused on micro-calcifications,
and Kimme (1975), Giger (1990), and Miller (1993) have addressed asymmetry studies,
but less work has been done in identifying regular masses and stellate lesions.
In this work we have attempted to apply established methods of texture determination
within a structured sequence of image processing steps and we examine the benefits of
this systematic approach to identification by comparing the outlines of regions with
characteristic texture as presented by our method with those determined by expert
examination.
Texture Analysis
Central to the analysis of the mammogram is the need to identify specific types of regions
of interest. One way to achieve this is to develop features of the image which can be used
to classify the image data. The greatest difficulty lies in finding some property of the
image from which such features may be extracted.
In discussing this it is helpful to consider the way in which we interpret pictorial scenes.
It is generally believed that one of the main visual cues is texture and differences in
textural properties between regions. Textural features contain information about the
spatial distribution of tonal variations. The concept of tone is based on the intensity of
pixels within a defined region (shades of grey in a grey scale image). The texture of a
region describes the pattern of spatial variation of grey tones in a neighbourhood where
the neighbourhood is small compared to the region. In this study we will be applying the
criterion that one component of the manual detection of suspicious areas can be modelled
by the identification of areas of reduced texture energy (smoothness) rather than on
parameters that are dependent on original image intensity. There are several methods of
textural feature extraction described in the literature one of which is Laws Method (Laws,
1980a,b). This produces secondary, strengthened, features which can then be used to
segment or classify the image according to the texture energy.
Previous Work

Most reported work on texture is on feature analysis of whole images. For images
of non-homogeneous textures, such as mammograms, an additional step of
segmentation or classification is required. Laws applied specific filter masks to

the data to extract features and then used a statistical classification method to
evaluate the significance of the features according to some pre-defined criteria.
The Laws method can be used to detect dots, lines and edges. In mammography,
it has previously been used to discriminate between glandular and fatty regions of
breast tissue (Miller and Astley, 1992), as part of an overall strategy to
automatically detect breast asymmetries (Miller and Astley, 1993). They state that
processes using intensity thresholding are unreliable due to between-image and
within-image intensity variation. In the specific case of the detection of stellate
lesions, Kegelmeyer (1992, 1993) uses Laws masks as a mechanism for detecting
architectural distortions caused predominantly to the ductal patterns of the
mammogram, a stellate lesion perturbing the natural pattern and producing new
and characteristic centres of radiation.
Our work addresses the relative simple task of direct application of the masks to
achieve lesion outlines, applying histogram equalisation to the original image and
the results of texture processing, in an attempt to improve the robustness of the
segmentation protocols proposed. We apply the Laws mask to the task of
identification of suspicious masses and examine whether this texture based
approach indicates prospects of discrimination between stellate lesions and
regular masses.
Method Implementation
In our implementation there are several stages in the production of the final segmented
output, including pre-processing, texture analysis, segmentation and image enhancement.
These have been added to provide a framework for the automatic execution of the texture
analysis process which is then evaluated using a pre-diagnosed image data base. These
image processing steps are linked as shown in Figure 1 and will be explained in
sequence. The functions were constructed and executed within the visual programming
environment available in the KHOROS image processing software (Rasure, 1990).
The Mammogram Dataset
The images we used were taken from a pre-diagnosed database of mammograms
distributed by the University of South Florida. It holds digitised images of 100
mammograms, 50 non-suspicious (meaning that they show no malignant features) and 50
abnormal. The abnormal mammograms are categorised according to the type of
malignancy presented and include regular masses and stellate lesions. The diagnoses
were provided by a local radiologist and each was confirmed by biopsy. The lesion
boundaries were indicated by separate truth-maps. The images are of various sizes
(approximately 600 x 800 pixels) and each is 8-bit deep, approximately normalised in
intensity, giving a 0-255 grey scale. The average resolution is 0.22mm/pixel.
The images used in the analysis comprised 8 regular masses, 8 stellate lesions and 15
images of non suspicious mammograms chosen at random from the 50 images so
reported. The stellate lesion and regular mass images were chosen for this investigation
as these are particularly strong indicators of tumours in mammography and the automatic
delineation of tumour extent (and its variation with time and treatment) is seen as major
quantitative issue by radiologists. Initially 256 x 256 pixel regions (small extracts) of the
abnormal mammograms were used which were manually chosen to include the malignant
mass. The method was then applied to the full extent of the breast and surrounding tissue
image (large extracts) for all 31 of the mammograms, to establish the extent of false
positive detection.
Fig. 1 Image processing stages.
Histogram Equalisation
Gordon (1984) and Dhawan (1986) have applied regional contrast enhancement
techniques to mammograms, purely as a visualisation aid. The adaptive histogram
algorithm has been further developed and evaluated by Zimmerman (1988) but does
introduce within-image spatial variations which can be confusing to observers and to
subsequent analysis algorithms. Many of these regional techniques can be
computationally intensive although modern parallel processing hardware can
substantially reduce this problem (Pizer 1990, Undrill 1993). For this work we chose to
link together simple global histogram techniques, followed by image normalisation, with
texture and morphological processes.
When performed on original image extracts, histogram equalisation was found to be
useful in visualising the lesion and increasing the sensitivity of subsequent processing.
The effect can be seen on the small extract from the regular mass image, mam74, in
Figure 2. On the original, histogram equalisation highlights the lesion and other dense
adipose tissue from the rest of the parenchymal background. On the textured image it
enables the smooth area of the lesion to be clearly distinguished.
Fig. 2 Effects of Histogram Equalisation :
(a) Original Image; (b) Image after histogram equalisation; (c) Texture Image before
histogram equalisation; (d) Texture image after histogram equalisation
Texture Analysis
Although co-occurrence matrices are a well established measure of texture (Haralick,
1973), Laws has developed and described a method of texture analysis (Laws, 1980a,
1980b), particularly applicable to radiographic images, which seeks to classify each pixel
of an image. The Laws method uses filter masks to extract secondary features from
natural micro-structure characteristics of the image (level, edge, spot and ripple) which
can then be used for segmentation or classification.
Laws developed five labelled vectors which could be combined to form matrices. When
convolved with a textured image these matrices extract individual structural components
of the image. The five vectors are :
[ 1, 4, 6, 4, 1] = L5
[-1,-2, 0, 2, 1] = E5
[-1, 0, 2, 0,-1] =
S5
[ 1,-4, 6,-4, 1] = R5
[-1, 2, 0,-2, 1] = W5
After a preliminary study, the R5R5 (RR) mask was found to give the best performance
for mammograms, confirming earlier results of Miller and Astley (1992) in their different
diagnostic situation. Using a sample image from each of the abnormalities under
investigation, alternative L5S5 (LS) and S5R5 (SR) masks were evaluated (Gupta, 1993).
Although the differences were not great, the RR mask produced a lesion outline of 70%
of the true outline for both abnormalities, whilst the other masks dropped to 45% in the
case of the stellate lesions and 56% for the regular masses. The RR mask is shown below
:
This is an iso-directional, differentiating filter. Note that each row and column has an
individual mean of zero. The mask extracts non-vertical and non-horizontal edges
producing the Laws texture image. Edge strengths are enhanced dependent on underlying
texture. This has the effect of giving a relatively uniform response in smooth areas such
as breast masses compared with the more highly textured regions such as the adipose
tissue, as seen in Figures 2(c) and 2(d). So that these regions can be thresholded, a
derived image is generated by calculating the variance over a small window centred
about each point in the original Laws texture image. In accord with other workers we will
call this a measure of texture energy. The size of the window was found not to be critical,
similar effects being observed between 5 x 5 and 9 x 9 pixels.
Image smoothing
The texture energy images were then smoothed, to reinforce the difference between the
various types of micro structure detected by the masks by reducing the effects of noise
and local variability within dissimilar regions. A moving window average was taken over
the whole image using a unit weighted kernel of size of 9 x 9 pixels. The use of a median
filter was considered and might have better retained the edge strengths implicit in the
energy image, but its application at 9 x 9 over the full size image was very much slower.
Threshold Segmentation
Segmentation is the decomposition of an image into smaller, meaningful constituent
parts, or 'segmented objects'. Thresholding is the simplest method of segmentation which
uses intensity values to split the image domain into segmented objects and background
areas.
Two thresholding stages were needed. This is because the texture masks extracted not
only the relatively smooth regions of the masses but also the smooth film background.
The first thresholding, applied to the texture energy image, segmented it into smooth and
highly textured regions. This was achieved by applying a window defined by upper and
lower thresholds and retaining only those pixels falling within that window. The values
for these were taken from a histogram of the smoothed texture energy image which
contained a clear upper peak representing the highly textured regions and a lower peak
representing the smoothly textured region, as shown in Figure 3. The threshold limits
chosen encompassed this lower valued peak, from a near zero value to the mid-peak
minimum.
Fig. 3 Histogram of texture energy image used in threshold segmentation
For this work it was decided to estimate these directly from the graph. Using a few test
images to derive the representative parameters, these were fixed and applied to all
images. This approach has the advantage that it could be used to confirm that the
mammogram images satisfy the test conditions and, if required, be implemented as a
short automatic procedure prior to any future routine analysis.
A segmentation refinement was then applied using a logical mask derived from the
coincidence of areas of near-zero intensity values of the equalised original image and
low texture energy threshold to eliminate the uniform dark film background. This
threshold level could be set at a fixed value across all the images. The final result of the
segmentation process was thus a binary image of lesion segmentation objects against a
non-lesion background.
Morphology
The two morphological functions, opening and closing , (Haralick, 1987) were used to
reduce the spatial noise introduced by the binary thresholding, employing a 5 pixel
diameter circular structuring element. The technique smoothed the lesion contour, filled
small holes and eliminated small distinct areas leading to a more accurate representation
of the lesion.
Contour Mapping
The final stage in producing an image outline was to develop a contour map of the
segmented region. This was achieved using the Sobel filter as an edge detector. Edge
detection was found to work better on the binary images (using a binary version of the
output of the thresholding stage) than on the original images, where lesion edges are
indistinct due to the inherent irregularity of breast tissue density (compounded by the
depth effect in radiography of projecting a 3D object onto a 2D image plane) and where
there are noise effects intrinsic to the radiographic imaging and data capture processes. In
Figure 4(a) the smoothed texture related energy image (higher intensities show more
homogeneous areas) is thresholded to give a binary image, (b), which after morphological
processing and edge marking results in an outline image as in (c).
Fig. 4 Establishing a region boundary
KHOROS
This is a freely available image processing package produced by the University of New
Mexico. It consists of a library of 'C' functions which are accessed via the CANTATA
visual programming environment using a graphical user interface. With these functions
the user is able to generate or import images, perform various processing operations upon
them and display the results or save them to files. The software provides a flexible
prototyping approach to developing image processing routines, selecting modules as
required and 'plugging' them together as process building blocks to form a screen-based
pictorial flowchart. Pre-defined software modules can however introduce difficulties of
inflexibility, and one such problem, encountered in segmentation, was that the KHOROS
thresholding function imposed a lower limit of 1, causing small 'holes' in some images.
Qualitative Results
The qualitative results are the images produced using the test method. These provide an
initial, intuitive, assessment of the technique.
Outline images
Figure 5 shows a sample of images where the truth outline (green) and outline produced
by our method (red) are superimposed upon the original image. The truth outline was
obtained directly from the mammogram database while the test outline was produced by
finding the edges of the segmented objects.
Figure 5 shows two examples of detail from regions of the 256 x 256 extracts where (a) is
a stellate lesions and (b) is a regular masses. In both cases the test outline (red) roughly
follows the radiologist's delineation (green). Also in these two images the radiologist's
line includes regions of significantly different texture to the main body of the mass. As
expected, these areas have not been extracted by the test method.
Fig. 5 Diagnosed (green) and processed (red) detail from small region extracts.
In Figure 6 the method has been repeated on the whole mamma. The method has found
the true lesion as in the smaller extracts. However, in addition, other dense tissue, for
example the pectoralis major muscle, has also been detected.
Fig. 6 Diagnosed (green) and processed (red) outlines for complete large region extracts
Quantitative Results
Quantitative results for the abnormal mammograms have been illustrated by two sets of
statistics which compare the results given by the test method with those of the radiologist.
The first is area based and represents the absolute difference between the detected areas
in the test lesion, as determined by the texture mask and allied processing, and the true
lesion as determined from the radiologist's marked boundary. The second is taken from
the mean intensities and standard deviations of the test and true lesions. When applied to
the non-suspicious mammograms the first of these statistics seeks to measure how much
false area is extracted by the method on the whole mamma images, showing the number
of false positive pixels as a percentage of the total number of pixels.
FPR and TPR statistic
The rationale behind this representation was to find a way of numerically comparing the
shape and extent of the area (in pixels) of the test and true lesions. It was decided to
measure the truth-and-test area, called the true positive region (denoted TPR) and the
test-not-truth area, called the false positive region (denoted FPR). To make these
independent of lesion size and to relate them to the true mass size these areas were
calculated as a percentage of the true lesion area (in some cases the number of false
postive pixels exceeded the number of pixels in the true lesion giving an FPR exceeding
100%). While this data does give some indication of the accuracy of the test lesion it does
not distinguish between those test-not-truth areas connected to the mass and those distinct
from it.
Summary
TPR = true positive region
= overlap of truth and test regions
FPR = false positive region
= test region outside of truth region
W = whole true lesion area
TPR% = TPR x 100/W
FPR% = FPR x 100/W
Fig. 7 Definition of the TPR and FPR Statistic.
This data is depicted on scatter graphs plotting FPR % against TPR % for each lesion (see
Figures 8 and 9). The ideal situation would be represented by a clustering of points close
to 0% FPR and 100% TPR. [It will be noticed that in Figures 8(b), 9(a) & 9(b) there are a
few points extended by an arrow. These points represent unusually large FPR % values,
and will be discussed later.]
(a)
(b)
Fig. 8 Test lesion regions as percentages of true lesion area for
small region extracts.
Fig. 9
(a)
(b)
Test lesion regions as percentages of true lesion area for
large region extracts.
Referring to Figure 8 it is seen that for the small extract graphs the FPR % values range
(with one exception) from 0-50%. This means that, in general, the areas of normal tissue
incorrectly labelled as outside the denoted boundary were less than half the area of the
true lesion. The exception to this generalisation is the mammogram which caused the
very high value of FPR % shown by the arrow. High FPR % values can arise from areas
of lighter tissue around the mass being detected, as is beginning to happen in Figure 5(a).
Another, less common, reason is that in addition to the correct mass the test method has
also highlighted a relatively large area of tissue positionally distinct from the reported
true lesion and not included in the truth map. This region has therefore to be taken as a
false positive detection. In Figure 5(b) in addition to a common outlined area, the truth
image suggests two satellite regions, at a much lower intensity which the method has
failed to identify (apart from a very small island on the image boundary which might be
an artefact).
The overall specificity for the large extracts was less successful (see Figure 9). Here the
FPR % values have been raised by the detection of pectoral muscle area with similar
texture characteristics (Figure 6(a)), and the TPR % values have been further depressed
by a relative underestimate using large extracts, when compared with the 256 x 256 pixel
small extracts. If we exclude those points where the method has detected areas outside
the normal breast area the FPR / TPR statistic can be summarised as in Table 1,
indicating a more consistent performance when small image extracts containing the
lesion are examined, compared with the large image extracts.
Small Extracts
Large Extracts
Lower
Upper
Lower
Upper
Bound
Bound
Bound
Bound
Stellate Lesions
FPR %
TPR %
8
23
50
88
10
21
150
72
Regular Masses
FPR %
TPR %
0
47
12
89
2
34
155
93
Table 1 Variation of FPR% and TPR% with lesion type and image extract size
The TPR figures are better for regular masses than stellate lesions reflecting the fact that
the radiologist's outline for stellates includes background parenchyma containing the
stellate rays whereas the automated method focuses towards the centre of the lesion.
Nevertheless in all cases the lesion areas have been identified.
Intensity-based Statistics
This statistic attempted to give a measure of the quality of the area extracted by the test
method rather than its size. Here, quality was defined in terms of the type of tissue
extracted by the test method. The intention was to find a simple statistic characteristic of
this tissue region and thus assess whether or not it was similar to that of the true lesion.
The normalised mean intensity of the pixels within the true lesion area and the test lesion
area were compared for large extracts (whole mamma) of the original images. These
values depend upon the type of tissue found within the mask boundary and therefore
should be less dependent on the size of the area. If much non-tumorous dense tissue has
been extracted, the test mean intensity is significantly higher than the true mean intensity.
If on the other hand dark adipose tissue has been incorrectly extracted the test mean is
lower than the true mean. The test mean intensities were plotted against the truth mean
intensities for each image, for each mask. These are shown in Figure 11.
The ideal situation is shown by a straight line passing through the origin (truth value
equals test value). The degree of scatter about the line indicates the deviation of the test
mean from the true mean. Both the stellate lesion graph and the regular mass graph show
that the test lesions were in general lighter (higher intensity values) than the true lesion.
This reflects the fact that usually the test region is more closely confined to the centre of
the mass than the radiologist's outline, although the effect is more strongly evident in the
case of the stellate lesions (Figure 11(a)). It is clear that the intensity ranges do not
suggest a means of differentiating between lesion types, confirming one of the
presumptions in our earlier stated observer model.
Fig. 11 Truth area mean intensity versus test lesion area mean intensity for large region
extracts.
The standard deviations of regional intensity for the large extracts are shown in Figure
12. As we have seen, this parameter can be considered a measure of roughness or texture
energy. It can be seen that the test method gives a more consistent result for both stellate
lesions and regular masses, that is they exhibit lower standard deviations
Fig. 12 Standard deviation of intensity for large region extracts
For the regular masses and the stellate lesions, the pixel intensity standard deviations
using our method are less (more uniform intensity region delineated) than those of the
diagnosed area by average factors of 4.9 and 2.2 respectively, with each containing two
instances where the diagnosed and test region have equal standard deviations. However,
if we examine the distribution between lesion types using Student's t-test, comparing the
test results and truth results separately, 't' values of 0.83 and 1.06 are returned, giving a
probability of being representative of different populations of only 57% and 69%. If we
propose the hypothesis that following our processing and outlining method the stellates
have a higher standard deviation of texture energy than the regular lesions ( data in Fig 12
(a) greater than Fig 12 (b)) then these values increase to 78% and 85%, but none of these
results suggest differences at statistically significant levels, reinforcing our view that the
processing protocols are more successful at identification than differentiation.
Non Suspicious Mammograms
Finally the technique was evaluated on the mammograms randomly selected from the
non-suspicious section of the data base. The method outlined small regions in 5 out of the
15 non suspicious mammograms. The areas identified were generally very small
compared to those in abnormal mammograms, as shown in Figure 13.
Fig. 13 Lesion Areas detected for Abnormal and Non-Suspicious cases (large image
extracts). [Figures (a) and (b) are presented at different ordinate scales]
Fig 13(a) shows the extracted areas for the abnormal lesions. (Image sequence 54 - 87 are
stellate lesions and 74 to 100 are regular masses). We first establish whether these
represent two different populations, by applying a Mann-Whitney (Wilcoxon rank sum)
non-parametric test, since it is unrealistic to presume any specific underlying distribution.
Median values are 450 and 1450 pixels respectively which produce a confidence level of
85% that the two data sequences emanate from distinct populations. Since this is not
significant at normally acceptable levels we can compare the abnormals as a single
distribution against the non-suspicious set, Fig 13(b). Using the same test, median values
of 5500 and 10 pixels for the two distributions are established, giving a confidence level
of greater than 97.5% that the two distributions are different, suggesting that our
protocols are an effective method of area detection.
Acknowledgements
The authors wish to acknowledge the efforts of Kevin Bowyer of the University of
Southern Florida Medical School for the pre-diagnosed digital mammogram data base,
the UK Science and Engineering Research Council (SERC) for financial support to Miss
Gupta on the MSc in Information Technology (Medical Physics) at Aberdeen University,
during which part of this project was carried out and to Mr. George Cameron and Dr.
Philip Ross for their invaluable technical assistance with computers and software.
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