The development and application of multi-class BANN classifiers in computer-aided diagnosis methods motivated this
study in which we compared estimates produced by two-class and three-class BANN classifiers to true observations
drawn from simulated distributions. Observations were drawn from three Gaussian bivariate distributions with distinct
means and variances to generate G1, G2, and G3 simulated datasets. A two-class BANN was trained on each training
dataset for a total of ten different trained BANNs. The same testing dataset was run on each trained BANN. The average
and standard deviation of the resulting ten sets of BANN outputs were then calculated. This process was repeated with
three-class BANNS. Different sample numbers and values of a priori probabilities were investigated. The relationship
between the average BANN output and true distribution was measured using Pearson and Spearman coefficients, R-squared
and mean square error for two-class and three-class BANNs. There was significantly high correlation between
the average BANN output and true distribution for two-class and three-class BANNs; however, subtle non-linearities and
spread were found in comparing the true and estimated distributions. The standard deviations of two-class and three-class
BANNs were comparable, demonstrating that three-class BANNs can perform as reliably as two-class BANN
classifiers in estimating true distributions and that the observed non-linearities and spread were not simply due to
statistical uncertainty but were valid characteristics of the BANN classifiers. In summary, three-class BANN decision
variables were similar in performance to those of two-class BANNs in estimating true observations drawn from
simulated bivariate normal distributions.
Previous research has shown that a fuzzy C-means (FCM) approach to computerized lesion analysis has
the potential to aid radiologists in the interpretation of dynamic contrast-enhanced MRI (DCE-MRI) breast
exams. 1, 2 Our purpose in this study was to optimize the performance of the FCM approach with respect
to binary (benign/malignant) breast lesion classification in DCE-MRI. We used both raw (calculated from
kinetic data points) and empirically fitted3 kinetic features for this study. FCM was used to automatically
select a characteristic kinetic curve (CKC) based on intensity-time point data of voxels within each lesion,
using four different kinetic criteria: (1) maximum initial enhancement, (2) minimum shape index, (3) maximum
washout, and (4) minimum time to peak. We extracted kinetic features from these CKCs, which were
merged using linear discriminant analysis (LDA), and evaluated with receiver operating characteristic (ROC)
analysis. There was comparable performance for methods 1, 2, and 4, while method 3 was inferior. Next,
we modified use of the FCM method by calculating a feature vector for every voxel in each lesion and using
FCM to select a characteristic feature vector (CFV) for each lesion. Using this method, we achieved performance
similar to the four CKC methods. Finally, we generated lesion color maps using FCM membership
matrices, which facilitated the visualization of enhancing voxels in a given lesion.
Computerized texture analysis of mammographic images has emerged as a means to characterize breast parenchyma
and estimate breast percentage density, and thus, to ultimately assess the risk of developing breast cancer. However,
during the digitization process, mammographic images may be modified and optimized for viewing purposes, or
mammograms may be digitized with different scanners. It is important to demonstrate how computerized texture
analysis will be affected by differences in the digital image acquisition. In this study, mammograms from 172
subjects, 30 women with the BRCA1/2 gene-mutation and 142 low-risk women, were retrospectively collected and
digitized. Contrast enhancement based on a look-up table that simulates the histogram of a mixed-density breast
was applied on very dense and very fatty breasts. Computerized texture analysis was performed on these
transformed images, and the effect of variable gain on computerized texture analysis on mammograms was
investigated. Area under the receiver operating characteristic curve (AUC) was used as a figure of merit to assess
the individual texture feature performance in the task of distinguishing between the high-risk and the low-risk
women for developing breast cancer. For those features based on coarseness measures and fractal measures, the
histogram transformation (contrast enhancement) showed little effect on the classification performance of these
features. However, as expected, for those features based on gray-scale histogram analysis, such as balance and
skewnesss, and contrast measures, large variations were observed in terms of AUC values for those features.
Understanding this effect will allow us to better assess breast cancer risk using computerized texture analysis.
The purpose of this study is to investigate three-class Bayesian artificial neural networks (BANN) in dynamic contrastenhanced
MRI (DCE-MRI) CAD in distinguishing different types of breast lesions including ductal carcinoma in situ
(DCIS), invasive ductal carcinoma (IDC), and benign. The database contains 72 DCIS lesions, 124 IDC lesions, and 131
benign breast lesions (no cysts). Breast MR images were obtained with a clinical DCE-MRI scanning protocol. In 3D,
we automatically segmented each lesion and calculated its characteristic kinetic curve using the fuzzy c-means method.
Morphological and kinetic features were automatically extracted, and stepwise linear discriminant analysis was utilized
for feature selection in four subcategories: DCIS vs. IDC, DCIS vs. benign, IDC vs. benign, and malignant (DCIS +
IDC) vs. benign. Classification was automatically performed with the selected features for each subcategory using
round-robin-by-lesion two-class BANN and three-class BANN. The performances of the classifiers were assessed with
two-class ROC analysis. We failed to show any statistically significant differences between the two-class BANN and
three-class BANN for all four classification tasks, demonstrating that the three-class BANN performed similarly to the
two-class BANN. A three-class BANN is expected to be more desirable in the clinical arena for both diagnosis and
patient management.
KEYWORDS: Breast, Breast cancer, Magnetic resonance imaging, Image segmentation, Fuzzy logic, Lithium, Cancer, Medical imaging, 3D image processing, Mammography
Breast density has been shown to be associated with the risk of developing breast cancer, and MRI has been
recommended for high-risk women screening, however, it is still unknown how the breast parenchymal
enhancement on DCE-MRI is associated with breast density and breast cancer risk. Ninety-two DCE-MRI
exams of asymptomatic women with normal MR findings were included in this study. The 3D breast volume
was automatically segmented using a volume-growing based algorithm. The extracted breast volume was
classified into fibroglandular and fatty regions based on the discriminant analysis method. The parenchymal
kinetic curves within the breast fibroglandular region were extracted and categorized by use of fuzzy c-means
clustering, and various parenchymal kinetic characteristics were extracted from the most enhancing voxels.
Correlation analysis between the computer-extracted percent dense measures and radiologist-noted BIRADS
density ratings yielded a correlation coefficient of 0.76 (p<0.0001). From kinetic analyses, 70% (64/92) of
most enhancing curves showed persistent curve type and reached peak parenchymal intensity at the last postcontrast
time point; with 89% (82/92) of most enhancing curves reaching peak intensity at either 4th or 5th
post-contrast time points. Women with dense breast (BIRADS 3 and 4) were found to have more
parenchymal enhancement at their peak time point (Ep) with an average Ep of 116.5% while those women
with fatty breasts (BIRADS 1 and 2) demonstrated an average Ep of 62.0%. In conclusion, breast
parenchymal enhancement may be associated with breast density and may be potential useful as an additional
characteristic for assessing breast cancer risk.
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