Preprocessing of Image Data

Depending on the protocol utilised for the acquisition of DCE-MRI sequences, the recorded images display different field-of-views, i.e. different subregions of the body. Most setups for MRI based examination of the breast allow for simultaneous imaging of both breasts. Thereby, the cuboid shaped field-of-view typically encloses a significant fraction of background such as non-body regions (air) or regions of the body which are outside the scope of investigation. Before applying the adaptive lesion detector, this background is separated from breast tissue to limit the random sampling of normal tissue signals used for adaptation of the classifiers to the region of the breast and to accelerate the evaluation of unseen cases.

Preprocessing of Munich Images

The DCE-MRI setup for breast examination employed at the City Centre Hospital of the University of Munich assumes patients to be placed in a prone position with the chest resting on the dedicated surface coil. The image volumes display 32 to 34 parallel axial slices with a mutual distance of 4mm recorded using a multi-slice protocol. Each slice displays a section of the breast and the entire thorax with a field-of-view 350mm × 350mm and a matrix size of 256 × 256 regular shaped pixels. Therewith, each voxel of the image represents a 1.37mm × 1.37mm × 4.0mm tissue volume.

Figure 5.2 (left) shows an example for an axial slice of a precontrast image of one case from the Munich group. Good signal-to-noise ratios are obtained for the tissue adjacent to the surface coil surrounding the breasts. The homogeneity and magnitude of the magnetic field decreases with the distance to the surface coil causing tissue deeper in the body to be displayed with signal intensities which are lower than those of the tissue at the body surface next to the coil. As

0 100 200 300 400 500 600 0 2 4 6 8 10 12 14 Intensity log(Number of voxels) Otsu threshold 0 100 200 300 400 500 600 0 2 4 6 8 10 12 14 Intensity log(Number of voxels) Otsu threshold

Figure 5.3:Intensity histogram of the median filtered precontrast image of case M6 (left) and B6 (right). Both histograms show a clear peak at low intensity values predominately caused by the large fraction of background. The black line illustrates the thresholds computed using the algorithm proposed by Otsu, 1979.

a consequence, the precontrast image displays the breast tissue with higher intensities than the tissue of the thorax. In particular the heart, which appears with high intensities in the postcontrast images due to the presence of large amounts of contrast agent molecules and blood in the heart cavities, is displayed by voxels of the same low intensity as the surrounding organs.

For separating the breast region from background, a binary mask for the breast is calculated using an adaptive threshold algorithm. First, the precontrast image is filtered using a 3×3×3 median filter. Afterwards, the adaptive threshold algorithm proposed by Otsu, 1979 is applied to the histogram of the filtered precontrast image (Fig. 5.3). The algorithm calculates a threshold value dividing the histogram into two classes in such a way that the ratio of inter- and intra- class variance is maximised. The major fraction of voxels corresponding to breast tissue expose intensities above the computed threshold. Binarisation of the filtered precontrast image using the determined threshold yields a binary mask covering the entire breast. Small gaps may appear in the centre of each breast where the fibro-glandular discs are located, which are typically displayed with low gray values. These gaps are closed by the application of a 5×5×5 morphological closing operator, i.e. a successive application of a morphological dilatation and erosion operator, to the binary breast mask. Figure 5.2 (right) visualises an axial slice of a precontrast image with the corresponding breast mask, which generously covers the breast tissue. The final binary mask, referred to as breast mask, determined for case m is formally described by the set PBreast_m containing all positions p of kinetic patterns xp which are to be considered during the application of the

Figure 5.4:Image slices in coronal orientation of the precontrast images (left) and the corresponding breast masks (right) of two cases of the MARIBS group.

Preprocessing of MARIBS Images

Image volumes acquired from participants of the MARIBS screening study consist of 256×128×64 voxels. The voxels have a square shape of 1.33mm × 1.33mm in the coronal plane and an extent of 2.5mm in the perpendicular direction. In contrast to the Munich images, the field-of-view is limited to both breast and displays only a small fraction of the thorax. Thus, the aim of the preprocessing step is to separate the breast from the background which, for this pool, mainly consists of voxels displaying air.

Similar to the preprocessing of the Munich images, a binary breast mask is calculated based on the application of the adaptive threshold algorithm proposed by Otsu, 1979. To this end, the three-dimensional image volumes of each sequence are filtered using a three-dimensional median filter of size 3×3×3. Subsequently, a single scalar value is determined for each voxel by adding the temporal intensity gradients between two subsequent time points to the intensity value in the precontrast image: s(xp) = xp1+ nt X i=2 |xpi− xpi−1|.

From the bimodal shaped histogram of the new image, a threshold is automatically determined using the adaptive threshold algorithm. Application of a 5×5×5 morphological closing operator to the binarised image closes small gaps in the regions of the glandular discs which are displayed larger than in the Munich images due to a higher spatial resolution of the images. The final breast mask completely covers the regions of the breast and, for several cases, small parts of the heart (Fig.5.4).