Automated detection of breast masses on digital mammograms using
adaptive density-weighted contrast enhancement filtering
Nicholas Petrick, Heang-Ping Chan, Berkman Sahiner, Datong Wei,
Mark A. Helvie, Mitchell M. Goodsitt and Dorit D Adler
The University of Michigan
Department of Radiology
Ann Arbor, MI 48109-0030
ABSTRACT
This paper presents a novel approach for segmentation of suspicious mass regions in digitized mammograms
using a new adaptive Density-Weighted Contrast Enhancement (DWCE) filter in conjunction with LaplacianGaussian (LG) edge detection. The new algorithm processes a mammogram in two stages. In the first stage the
entire mammogram is filtered globally using a DWCE adaptive filter which enhances the local contrast of the
image based on its local mean pixel values. The enhanced image is then segmented with an LG edge detector
into isolated objects. In the second stage of processing, the DWCE adaptive filter and the edge detector are
applied locally to each of the segmented object regions detected in the first stage. The number of objects is then
reduced based on morphological features. ROIs are selected from the remaining object set based on the centroid
locations of the individual objects. The selected ROTs are then input to either a linear discriminant analysis
( LDA) classifier or a convolution neural network (CNN) to further differentiate true-positives and false-positives.
In this study ROIs obtained from a set of 84 images were used to train the LDA and CNN classifiers. The DWCE
algorithm was then used to extract ROTs from a set of 84 test images. The trained LDA and CNN classifiers
were subsequently applied to the extracted ROTs, and the dependence of the detection system's accuracy on the
feature extraction and classification techniques was analyzed.
Keywords: Computer-Aided Diagnosis, Density-Weighted Contrast Enhancement, Adaptive Filter, Mammography, Edge Detection, Classification
1. DENSITY-WEIGHTED CONTRAST ENHANCEMENT
SEGMENTATION
We have developed a new algorithm using DWCE filtering with Laplacian-Gaussian (LG) edge detection
for segmentation of low contrast objects in digital mammograms. The DWCE algorithm is used to enhance
objects in the original image so that a simple edge detector can define the object boundaries. Once the object
borders are known, morphological features are extracted from each object and used by a classification algorithm
to differentiate mass and non-mass regions within the image.
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Figure 1: A typical mammogram from our image database (a) and the corresponding DWCE filtered image (b).
1.1 DWCE Preprocessing Filter
Edge detection applied to the original digitized mammograms has not proven effective in detecting breast
masses within the image because of the low signal-to-noise ratio of the edges and the presence of complicated
structured background. Fig. 1(a) shows a typical mammogram from our image database. It contains a single
breast mass indicated by the arrow. This mammogram also contains dense fibroglandular tissue in the breast
parenchyma. Although the mass is relatively obvious, the partially overlapping tissue makes the detection difficult.
In order to detect masses of varying shapes and intensities, we propose using an aggressive adaptive filtering
technique to suppress the background structures and enhance any potential signals. Fig. 1(b) shows the processed
mammogram of Fig. 1(a). The background in this image is clearly reduced which allows a simple edge detector
to locate the different objects within the image.
The block diagram for the DWCE preprocessing filter is depicted in Fig. 2. It is an expansion of the local
contrast and mean adaptive filter of Peli and Lim' designed for enhancing images degraded by cloud cover. The
original image, F(x, y), is initially passed through a map rescaler. The map rescaler first determines the breast
boundary (referred to as the breast map). The pixel values in the breast region are then rescaled based on the
maximum and minimum values within the breast map, producing the normalized image, FN(X, y). FN(X, y) is
then split into a density and a contrast image, FD(X, y) and F(x, y), respectively. The density image is produced
l)y filtering FN(X, y) using a zero mean Gaussian with standard deviation, 0D. FD(X, y) thus directly correlates
to a weighted average of the local optical density of the original film. The contrast image,
y), is also created
by filtering FN(X, y), but the low pass filter is replaced with a band pass or high-pass filter. Each pixel in the
density image is then used to define a multiplication factor which modifies the corresponding pixel in the contrast
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Figure 2: The block diagram of the DWCE preprocessing filter used for image enhancement.
image:
FKC(X,Y) = KM(FD(X,y)) x
Fc(x,y).
(1)
is the essence of the DWCE algorithm. It allows the local density value of each pixel to weight its local
contrast. Fig. 3(a)-(c) show the density, contrast and weighted contrast images, respectively, for the digitized
mammogram of Fig. 1(a).
This
The output of the DWCE filter is a nonlinear rescaled version of the weighted contrast image,
FE(X, y)
=
KNL(FKC(X, y))
x FKC(X, y).
(2)
nonlinear rescaling is used to help equalize the higher intensities and to de-emphasize very low ones in
FKC(X, y). This rescaling improves the definition of the object borders by eliminating many of the low intensity
The
edges that cause region merging and reduces the effect of extremely large intensities on the edge detection.
Fig. 1(b) shows the final enhanced image after nonlinear rescaling produced by the complete DWCE filter.
1.2 Edge Detection
In this study, object edges were detected from the DWCE prefiltered images using a Laplacian-Gaussian (LG)
edge detector. For a given image, I(x, y), the LG edge detector defines edges as simply the zero crossing locations
of:
2G(x,y)*I(x,y),
(3)
where G(x, y) is a two dimensional Gaussian smoothing function.2 The degree of smoothing is controlled by a
single parameter, 0E, the standard deviation of the smoothing function. This edge detector tends to produce
closed regions which facilitates detection of isolated objects within the image.
Once the object edges have been defined in the image, each enclosed object is filled to remove any holes that
may have formed. Each region produced by the filling is defined by its edge pixels, thus forming a set of detected
objects. This set of objects defines all the detected structures within the original breast image.
1.3 Morphological Object Classification
Since the DWCE prefilter enhances both breast masses and normal tissue, a large number of detected objects
are usually found. In order to reduce the number of objects to a manageable size, morphological features were
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Figure 3: The density (a), contrast (b) and weighted contrast (c) images produced by the DWCE filter applied
to the mammogram of Fig. 1(a) along with the corresponding LG edge map (d).
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extracted from each object and used in conjunction with a threshold classifier to screen breast masses from
normal tissue.3 The morphological features used in this classification include the number of edge pixels, area,
shape, contrast and a set of normalized radial length (NRL) features. Three features, circularity, rectangularity
and the ratio of the number of edge pixels (P) to the total object area (A) were used to characterize the shape
of an object. Circularity is defined as the ratio of the overlapping area between an equivalent circle of radius,
r=
with the object to the object's total area. Rectangularity is defined as the ratio of the object area to
the area of the enclosing bounding box. The radial length is defined as the Euclidean distance from the object
centroid to each of the object's edge pixels and is normalized by dividing by the maximum radial length.4 The
NRL features selected were the NRL mean, standard deviation, entropy, area ratio and zero crossing count as
defined by Kilday ei. al.4 Using these 11 morphological features object classification was performed.
The morphological classification was not meant to be the final classification of the detected objects. Instead,
it was used to reduce the number of objects so that further detailed analysis could be performed on each of the
detected regions. To perform this analysis ROIs of fixed sizes were extracted from the original mammograms
based on the centroid locations of the objects.
2. ROl CLASSIFICATION
Two different ROT classification algorithms and their combination were evaluated in their ability to reduce
the number of false positive detections. The two methods were linear discriminant analysis (LDA) applied to
multi-resolution texture features and a convolution neural network (CNN) applied directly to a set of texture
feature images and to the original spatial domain ROT images.
The multi-resolution texture features were calculated from the spatial gray level dependence (SGLD) matrices
of both the original image and the corresponding low-pass wavelet images at different scales. They include eight
features: correlation, energy, entropy, inertia, inverse difference moment, sum average, sum entropy and difference
entropy. The definitions for each texture feature can be found in the literature.5 LDA was then used to form
linear combination of these multi-resolution features by maximizing the group mean separation of the mass and the
non-mass ROIs. If the features follow a multivariate normal distribution with an identical covariance matrix for
both groups, then the LDA yields the optimal classification. This linear combination forms a single discriminant
score for each ROT.6 Therefore, by using a single threshold the ROIs can be separated into mass and non-mass
regions. A detailed description of this ROT classification method can be found in the literature.7
A CNN is a back-propagation neural network with two-dimensional weight kernels that operate on images.
The input images to the CNN are derived from the original ROTs. In this application a spatial image obtained by
smoothing and sub-sampling the original ROIs and texture images obtained by applying texture feature methods
to small sub-regions inside the ROTs were used. The small sub-regions were defined so that the texture images had
the same size as the smoothed and sub-sampled ROIs. The output of the CNN was again a single discriminant
score for each ROT. The CNN classification method used in this paper was described by Sahiner et. al.8
The final classification scheme evaluated in this paper is the combination of the LDA and CNN score using a
simple two input and one output back-propagation neural network (BPN).
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Stage 1
L
Filter
Dection
Figure 4: The block diagram of the complete DWCE segmentation method used for breast mass detection.
3. METHODS
3.1 Database
The mammograms used in this study were randomly selected from the files of patients who had undergone
biopsy in the Department of Radiology at the University of Michigan. The mammograms were acquired using a
Kodak MinR/MRE screen/film system with extended cycle processing. The system has a 0.3mm focal spot, a
molybdenum anode, 0.03mm thick molybdenum filter and a 5:1 reciprocating grid. Our selection criterion was
simply that a biopsy-proven mass could be seen on the mammogram. Our data set in this preliminary study was
composed of 168 mammograms. The size of the masses ranged from 5mm to 26mm with a mean size of 12.2mm,
and included 85 malignant and 83 benign masses.
The mammograms were digitized with a LUMISYS DIS-1000 laser film scanner with a pixel size of 100pm x
100pm and 4096 gray levels. The light transmitted through the mammographic film was logarithmically amplified
before digitization, making the gray levels linearly proportional to the optical density in the range of 0.1 to 2.8
optical density units (O.D.). The O.D. range of the scanner was 0 to 3.5. The digitized images are approximately
2000 x 2000 pixels in size. Before the DWCE segmentation was applied the images were smoothed using local
averaging and subsampled by a factor of 8. This resulted in images of approximately 256 x 256 pixels for processing.
3.2 DWCE Implementation
Fig. 4 shows the block diagram for the DWCE implementation used to detect breast masses in the digitized
mammograms. It was performed using two DWCE stages. The first stage applied the DWCE prefiltering, edge
detection and threshold classification to each subsampled mammogram to identify potential mass objects. For
each potential mass object, an ROl was extracted from the corresponding subsampled mammogram using the
object's bounding box to define the region. Each of the object ROIs were then passed through a second DWCE
stage and the final set of detected objects was defined. The parameters used in the DWCE prefiltering, edge
detection and object reduction steps were identical to those of their first stage counterparts. However, object
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extraction was more effective and accurate because it was performed in local regions. ROIs were then extracted
from the full resolution mammograms centered on the centroid location of each of the remaining objects. These
ROIs were fixed at 256 x 256 pixels.
3.3 ROl Classification
The database was then randomly divided into 84 training and 84 test images. Multi-resolution texture features
were extracted from each of the training ROTs and used to train the LDA classifier. Similarly, texture feature
images along with the smoothed and subsampled ROIs were extracted and used as input to the CNN classifier.
The discriminant scores obtained from both classifiers were then used to train the combined BPN network. After
the classifiers were trained the corresponding inputs were extracted from the test ROTs and used to evaluate the
performance of the three classifiers.
4. RESULTS
Using the 84 test images, the DWCE algorithm identified 83 of the 84 true mass ROIs. The LDA and
CNN classifiers were then applied to the extracted ROIs reducing the FPs to 2.5 per image and 4.6 per image,
respectively, at a TP rate of 80%. When the LDA and CNN were combined the FPs were reduced to 2.4 per
image at a TP rate of 80%.
5. CONCLUSIONS
The results demonstrated the feasibility of using the DWCE adaptive filter with LG edge detection to automatically extract ROTs from a mammogram. This ROT extraction method in combination with a classification
algorithm is a promising new approach to computer-aided detection of masses on mammograms.
6. ACKNOWLEDGMENTS
This work is supported by a USPHS Grant CA 48129 and U.S. Army Grant DAMD 17-93-J-007 (through
sub-grant GU RX 4300-803UM from Georgetown University). The content of this publication does not necessarily
reflect the position of Georgetown University or the government, and no official endorsement of any equipment
or product of any company mentioned in the publication should be inferred.
7. REFERENCES
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