A precise segmentation of breast tissue is often required for computer-aided diagnosis (CAD) of breast MRI.
Only a few methods have been proposed to automatically segment breast in MRI. Authors reported satisfactory
performance, but a fair comparison has not been done yet as all breast segmentation methods were evaluated on
their own data sets with different manual annotations. Moreover, breast volume overlap measures, which were
commonly used for evaluations, do not seem to be adequate to accurately quantify the segmentation qualities.
Breast volume overlap measures are not sensitive to small errors, such as local misalignments, because the
breast appears to be much larger than other structures. In this work, two atlas-based approaches and a breast
segmentation method based on Hessian sheetness filter are exhaustively evaluated and benchmarked on a data
set of 52 manually annotated breast MR images. Three quantitative measures including dense tissue error,
pectoral muscle error and pectoral surface distance are defined to objectively reflect the practical use of breast
segmentation in CAD methods. The evaluation measures provide important evidence to conclude that the three
evaluated techniques perform accurate breast segmentations. More specifically, the atlas-based methods appear
to be more precise, but require larger computation time than the sheetness-based breast segmentation approach.
The reduction of false positive marks in breast mass CAD is an active area of research. Typically, the problem
can be approached either by developing more discriminative features or by employing different classifier designs.
Usually one intends to find an optimal combination of classifier configuration and small number of features to
ensure high classification performance and a robust model with good generalization capabilities.
In this paper, we investigate the potential benefit of relying on a support vector machine (SVM) classifier
for the detection of masses. The evaluation is based on a 10-fold cross validation over a large database of screen
film mammograms (10397 images). The purpose of this study is twofold: first, we assess the SVM performance
compared to neural networks (NNet), k-nearest neighbor classification (k-NN) and linear discriminant analysis
(LDA). Second, we study the classifiers' performances when using a set of 30 and a set of 73 region-based
features. The CAD performance is quantified by the mean sensitivity in 0.05 to 1 false positives per exam on
the free-response receiver operating characteristic curve.
The best mean exam sensitivities found were 0.545, 0.636, 0.648, 0.675 for LDA, k-NN, NNet and SVM.
K-NN and NNet proved to be stable against variation of the featuresets. Conversely, LDA and SVM exhibited
an increase in performance when adding more features. It is concluded that with an SVM a more pronounced
reduction of false positives is possible, given that a large number of cases and features are available.
Mammographic breast density has been found to be a strong risk factor for breast cancer. In most studies it is
assessed with a user assisted threshold method, which is time consuming and subjective. In this study we develop
a breast density segmentation method that is fully automatic. The method is based on pixel classification in
which different approaches known in literature to segment breast density are integrated and extended. In addition
the method incorporates knowledge of a trained observer, by using segmentations obtained by the user assisted
threshold method as training data. The method is trained and tested using 1300 digitised film mammographic
images acquired with a variety of systems. Results show a high correspondence between the automated method
and the user assisted threshold method. The Spearman's rank correlation coefficient between our method and the
user assisted method was R = 0.914 for percent density, which is substantially higher than the best correlation
found in literature (R=0.70). The AUC obtained when discriminating between fatty and dense pixels was 0.985.
A combination of segmentation strategies outperformed the application of a single segmentation technique. The
method was shown to be robust for differences in mammography systems, image acquisition techniques and
image quality.
With the introduction of Full Field Digital Mammography (FFDM) accurate automatic volumetric breast density
(VBD) estimation has become possible. As VBD enables the design of features that incorporate 3D properties,
these methods offer opportunities for computer aided detection schemes. In this study we use VBD to develop
features that represent how well a segmented region resembles the projection of a spherical object. The idea
behind this is that due to compression of the breast, glandular tissue is likely to be compressed to a disc like
shape, whereas cancerous tissue, being more difficult to compress, will retain its uncompressed shape. For each
pixel in a segmented region we calculate the predicted dense tissue thickness assuming that the lesion has a
spherical shape. The predicted thickness is then compared to the observed thickness by calculating the slope of
a linear function relating the two. In addition we calculate the variance of the error of the fit. To evaluate the
contribution of the developed VBD features to our CAD system we use an FFDM dataset consisting of 266 cases,
of which 103 were biopsy proven malignant masses and 163 normals. It was found that compared to the false
positives, a large fraction of the true positives has a slope close to 1.0 indicating that the true positives fit the
modeled spheres best. When the VBD based features were added to our CAD system, aimed at the detection
and classification of malignant masses, a small but significant increase in performance was achieved.
The development of CAD systems that can handle Full Field Digital Mammography (FFDM) images is needed,
as FFDM is getting more important. In order to develop a CAD system a large database containing training
samples is of major importance. However, as FFDM is not yet as widely used as Screen Film Mammography
(SFM) it is difficult to collect a sufficient amount of exams with malignant abnormalities. Therefore it would
be of great value if the available databases of SFM images can be used to train a FFDM CAD system. In this
paper we investigate this possibility.
As we trained our system with SFM images we developed a method that converts the FFDM test images into a
SFM-like representation. Key point in this conversion method is the implementation of the characteristic curve
which describes the relationship between exposure and optical density for a SFM image. As exposure values
can be extracted from the raw FFDM images, the SFM-like representation can be obtained by applying a fitted
characteristic curve. Parameters of the curve were computed by simulating the Automatic Exposure Control
procedure as implemented in clinical practice.
We found that our FFDM CAD system, aimed at detection and classification of masses into normal and malignant,
achieved a case based sensitivity of 70%, 80%, 90%, at 0.06, 0.20, 0.60 FP/image when using SFM-training
with 552 abnormal and 810 normal cases, compared to 0.06, 0.17, 0.72 FP/image with FFDM-training with 80
abnormal and 131 normal cases. These results demonstrate that digitized film databases can still be used as part
of a FFDM CAD system.
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