Michael Khazen

A Pilot Study of Compositional Analysis of the Breast and Estimation of Breast Mammographic Density Using Three-Dimensional T1-Weighted Magnetic Resonance Imaging

Purpose: A method and computer tool to estimate percentage magnetic resonance (MR) imaging (MRI) breast density using three-dimensional T1-weighted MRI is introduced, and compared with mammographic percentage density [X-ray mammography (XRM)]. Materials and Methods: Ethical approval and informed consent were obtained. A method to assess MRI breast density as percentage volume occupied by water-containing tissue on three-dimensional T1-weighted MR images is described and applied in a pilot study to 138 subjects who were imaged by both MRI and XRM during the Magnetic Resonance Imaging in Breast Screening study. For comparison, percentage mammographic density was measured from matching XRMs as a ratio of dense to total projection areas scored visually using a 21-point score and measured by applying a two-dimensional interactive program (CUMULUS). The MRI and XRM percent methods were compared, including assessment of left-right and interreader consistency. Results: Percent MRI density correlated strongly (r = 0.78; P < 0.0001) with percent mammographic density estimated using Cumulus. Comparison with visual assessment also showed a strong correlation. The mammographic methods overestimate density compared with MRI volumetric assessment by a factor approaching 2. Discussion: MRI provides direct three-dimensional measurement of the proportion of water-based tissue in the breast. It correlates well with visual and computerized percent mammographic density measurements. This method may have direct application in women having breast cancer screening by breast MRI and may aid in determination of risk.(Cancer Epidemiol Biomarkers Prev 2008;17(9):2268–74)

Assessing the usefulness of a novel MRI-based breast density estimation algorithm in a cohort of women at high genetic risk of breast cancer: the UK MARIBS study

IntroductionMammographic breast density is one of the strongest known risk factors for breast cancer. We present a novel technique for estimating breast density based on 3D T1-weighted Magnetic Resonance Imaging (MRI) and evaluate its performance, including for breast cancer risk prediction, relative to two standard mammographic density-estimation methods.MethodsThe analyses were based on MRI (n = 655) and mammography (n = 607) images obtained in the course of the UK multicentre magnetic resonance imaging breast screening (MARIBS) study of asymptomatic women aged 31 to 49 years who were at high genetic risk of breast cancer. The MRI percent and absolute dense volumes were estimated using our novel algorithm (MRIBview) while mammographic percent and absolute dense area were estimated using the Cumulus thresholding algorithm and also using a 21-point Visual Assessment scale for one medio-lateral oblique image per woman. We assessed the relationships of the MRI and mammographic measures to one another, to standard anthropometric and hormonal factors, to BRCA1/2 genetic status, and to breast cancer risk (60 cases) using linear and Poisson regression.ResultsMRI percent dense volume is well correlated with mammographic percent dense area (R = 0.76) but overall gives estimates 8.1 percentage points lower (P < 0.0001). Both show strong associations with established anthropometric and hormonal factors. Mammographic percent dense area, and to a lesser extent MRI percent dense volume were lower in BRCA1 carriers (P = 0.001, P = 0.010 respectively) but there was no association with BRCA2 carrier status. The study was underpowered to detect expected associations between percent density and breast cancer, but women with absolute MRI dense volume in the upper half of the distribution had double the risk of those in the lower half (P = 0.009).ConclusionsThe MRIBview estimates of volumetric breast density are highly correlated with mammographic dense area but are not equivalent measures; the MRI absolute dense volume shows potential as a predictor of breast cancer risk that merits further investigation.

Does registration improve the performance of a computer aided diagnosis system for dynamic contrast-enhanced MR mammography?

This study investigated whether image registration improves the classification performance of a computer aided diagnosis (CAD) system for dynamic contrast-enhanced (DCE) MR mammography The CAD system that we developed included image registration, semi-automatic lesion segmentation, 3D image features extraction, and feature selection and combination by logistic regression analysis. The CAD system achieved a leave-one-out area under the ROC curve of 0.86, which is within the range of reported classification performances. This performance was not the artifact of the feature selection process or the leave-one-out test procedure. Worse results were obtained without segmentation refinement and image registration. Rigid image registration led to a statistically significant increase of the area under the ROC curve from 0.81 to 0.86

Classification Improvement by Segmentation Refinement: Application to Contrast-Enhanced MR-Mammography

In this study we investigated whether automatic refinement of manually segmented MR breast lesions improves the discrimination of benign and malignant breast lesions. A constrained maximum a-posteriori scheme was employed to extract the most probable lesion for a user-provided coarse manual segmentation. Standard shape, texture and contrast enhancement features were derived from both the manual and the refined segmentations for 10 benign and 16 malignant lesions and their discrimination ability was compared. The refined segmentations were more consistent than the manual segmentations from a radiologist and a non-expert. The automatic refinement was robust to inaccuracies of the manual segmentation. Classification accuracy improved on average from 69% to 82% after segmentation refinement.

SOM-Based wavelet filtering for the exploration of medical images

In medical image analysis there are many applications that require the definition of characteristic image features. Especially computationally generated characteristic image features have potential for the exploration of large datasets. In this work, we propose a method for investigating time series of medical images using a combination of the Discrete Wavelet Transform and the Self Organizing Map. Our approach allows relevant image information to be identified in wavelet space. This enables us to develop a filter algorithm suitable to find and extract the characteristic image features and to suppress interfering non-relevant image information.

Content Based Image Retrieval for Dynamic Time Series Data

Content based image retrieval (CBIR) systems in the field of medical image analysis are an active field of research. They allow the user to compare a given case with others in order to assist in the diagnostic process. In this work a CBIR system is described working on datasets which are both time- and space-dependent. Different possible feature sets are investigated, in order to explore how these datasets are optimally represented in the corresponding database.

Visualization of multivariate image data using image fusion and perceptually optimized color scales based on sRGB

Due to the rapid progress in medical imaging technology, analysis of multivariate image data is receiving increased interest. However, their visual exploration is a challenging task since it requires the integration of information from many different sources which usually cannot be perceived at once by an observer. Image fusion techniques are commonly used to obtain information from multivariate image data, while psychophysical aspects of data visualization are usually not considered. Visualization is typically achieved by means of device derived color scales. With respect to psychophysical aspects of visualization, more sophisticated color mapping techniques based on device independent (and perceptually uniform) color spaces like CIELUV have been proposed. Nevertheless, the benefit of these techniques is limited by the fact that they require complex color space transformations to account for device characteristics and viewing conditions. In this paper we present a new framework for the visualization of multivariate image data using image fusion and color mapping techniques. In order to overcome problems of consistent image presentations and color space transformations, we propose perceptually optimized color scales based on CIELUV in combination with sRGB (IEC 61966-2-1) color specification. In contrast to color definitions based purely on CIELUV, sRGB data can be used directly under reasonable conditions, without complex transformations and additional information. In the experimental section we demonstrate the advantages of our approach in an application of these techniques to the visualization of DCE-MRI images from breast cancer research.

Multiscale entropy analysis in dynamic contrast-enhanced MRI

In this paper we apply multiscale entropy (MSE) analysis to data obtained from magnetic resonance imaging of the female breast. All cases include lesions that were histologically proven as malignant tumors. Our results indicate that multiscale entropy analysis can play an important role in the detection of tumor tissue when applied to single datasets, but does not allow to calculate universal morphological features. The performance of MSE was examined with respect to traditional features such as difference imaging.