Proceedings Article | 17 March 2008
KEYWORDS: Magnetic resonance imaging, Breast, Image classification, Breast cancer, Principal component analysis, Computer aided diagnosis and therapy, Mammography, Cancer, Image segmentation, Tissues
Dynamic contrast enhanced (DCE) MRI has emerged as a promising new imaging modality for breast cancer
screening. Currently, radiologists evaluate breast lesions based on qualitative description of lesion morphology
and contrast uptake profiles. However, the subjectivity associated with qualitative description of breast lesions
on DCE-MRI introduces a high degree of inter-observer variability. In addition, the high sensitivity of MRI
results in poor specificity and thus a high rate of biopsies on benign lesions. Computer aided diagnosis (CAD)
methods have been previously proposed for breast MRI, but research in the field is far from comprehensive. Most
previous work has focused on either quantifying morphological attributes used by radiologists, characterizing
lesion intensity profiles which reflect uptake of contrast dye, or characterizing lesion texture. While there has
been much debate on the relative importance of the different classes of features (e.g., morphological, textural,
and kinetic), comprehensive quantitative comparisons between the different lesion attributes have been rare.
In addition, although kinetic signal enhancement curves may give insight into the underlying physiology of the
lesion, signal intensity is susceptible to MRI acquisition artifacts such as bias field and intensity non-standardness.
In this paper, we introduce a novel lesion feature that we call the kinetic texture feature, which we demonstrate
to be superior compared to the lesion intensity profile dynamics. Our hypothesis is that since lesion intensity is
susceptible to artifacts, lesion texture changes better reflect lesion class (benign or malignant). In this paper,
we quantitatively demonstrate the superiority of kinetic texture features for lesion classification on 18 breast
DCE-MRI studies compared to over 500 different morphological, kinetic intensity, and lesion texture features.
In conjunction with linear and non-linear dimensionality reduction methods, a support vector machine (SVM)
classifier yielded classification accuracy and positive predictive values of 78% and 86% with kinetic texture
features compared to 78% and 73% with morphological features and 72% and 83% with textural features,
respectively.
