Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast has become an invaluable tool in the clinical work-up of patients suspected of having breast carcinoma. The purpose of this study is to introduce novel features extracted from the kinetics of contrast agent uptake imaged by a short (100 s) view-sharing MRI protocol, and to investigate how these features measure up to commonly used features for regular DCE-MRI of the breast. Performance is measured with a computer aided diagnosis (CADx) system aimed at distinguishing benign from malignant lesions. A bi-temporal breast MRI protocol was used. This protocol produces five regular, high spatial-resolution T1-weighted acquisitions interleaved with a series of 20 ultrafast view-sharing acquisitions during contrast agent uptake. We measure and compare the performances of morphological and kinetic features derived from both the regular DCE-MRI sequence and the ultrafast view-sharing sequence with four different classifiers. The classification performance of kinetics derived from the short (100 s) ultrafast acquisition starting with contrast agent administration, is significantly higher than the performance of kinetics derived from a much lengthier (510 s), commonly used 3-D gradient echo acquisition. When combined with morphology information all classifiers show a higher performance for the ultrafast acquisition (two out of four results are significantly better).

Automated 3-D breast ultrasound (ABUS) has gained a lot of interest and may become widely used in screening of dense breasts, where sensitivity of mammography is poor. However, reading ABUS images is time consuming, and subtle abnormalities may be missed. Therefore, we are developing a computer aided detection (CAD) system to help reduce reading time and prevent errors. In the multi-stage system we propose, segmentations of the breast, the nipple and the chestwall are performed, providing landmarks for the detection algorithm. Subsequently, voxel features characterizing coronal spiculation patterns, blobness, contrast, and depth are extracted. Using an ensemble of neural-network classifiers, a likelihood map indicating potential abnormality is computed. Local maxima in the likelihood map are determined and form a set of candidates in each image. These candidates are further processed in a second detection stage, which includes region segmentation, feature extraction and a final classification. On region level, classification experiments were performed using different classifiers including an ensemble of neural networks, a support vector machine, a k-nearest neighbors, a linear discriminant, and a gentle boost classifier. Performance was determined using a dataset of 238 patients with 348 images (views), including 169 malignant and 154 benign lesions. Using free response receiver operating characteristic (FROC) analysis, the system obtains a view-based sensitivity of 64% at 1 false positives per image using an ensemble of neural-network classifiers.

Diffusion Tensor Imaging (DTI) and fiber tracking provide unique insight into the 3D structure of fibrous tissues in the brain. However, the output of fiber tracking contains a significant amount of uncertainty accumulated in the various steps of the processing pipeline. Existing DTI visualization methods do not present these uncertainties to the end-user. This creates a false impression of precision and accuracy that can have serious consequences in applications that rely heavily on risk assessment and decision-making, such as neurosurgery. On the other hand, adding uncertainty to an already complex visualization can easily lead to information overload and visual clutter. In this work, we propose Illustrative Confidence Intervals to reduce the complexity of the visualization and present only those aspects of uncertainty that are of interest to the user. We look specifically at the uncertainty in fiber shape due to noise and modeling errors. To demonstrate the flexibility of our framework, we compute this uncertainty in two different ways, based on (1) fiber distance and (2) the probability of a fiber connection between two brain regions. We provide the user with interactive tools to define multiple confidence intervals, specify visual styles and explore the uncertainty with a Focus+Context approach. Finally, we have conducted a user evaluation with three neurosurgeons to evaluate the added value of our visualization.

A computer-aided diagnosis (CAD) system for the classification of lesions as malignant or benign in automated 3-D breast ultrasound (ABUS) images, is presented. Lesions are automatically segmented when a seed point is provided, using dynamic programming in combination with a spiral scanning technique. A novel aspect of ABUS imaging is the presence of spiculation patterns in coronal planes perpendicular to the transducer. Spiculation patterns are characteristic for malignant lesions. Therefore, we compute spiculation features and combine them with features related to echotexture, echogenicity, shape, posterior acoustic behavior and margins. Classification experiments were performed using a support vector machine classifier and evaluation was done with leave-one-patient-out cross-validation. Receiver operator characteristic (ROC) analysis was used to determine performance of the system on a dataset of 201 lesions. We found that spiculation was among the most discriminative features. Using all features, the area under the ROC curve (A(z)) was 0.93, which was significantly higher than the performance without spiculation features (A(z)=0.90, p=0.02). On a subset of 88 cases, classification performance of CAD (A(z)=0.90) was comparable to the average performance of 10 readers (A(z)=0.87).

Fiber tracking of Diffusion Tensor Imaging (DTI) data offers a unique insight into the three-dimensional organisation of white matter structures in the living brain. However, fiber tracking algorithms require a number of user-defined input parameters that strongly affect the output results. Usually the fiber tracking parameters are set once and are then re-used for several patient datasets. However, the stability of the chosen parameters is not evaluated and a small change in the parameter values can give very different results. The user remains completely unaware of such effects. Furthermore, it is difficult to reproduce output results between different users. We propose a visualization tool that allows the user to visually explore how small variations in parameter values affect the output of fiber tracking. With this knowledge the user cannot only assess the stability of commonly used parameter values but also evaluate in a more reliable way the output results between different patients. Existing tools do not provide such information. A small user evaluation of our tool has been done to show the potential of the technique.

In this paper we address the problem of object retrieval based on scale-space interest points, namely top-points. The original retrieval algorithm can only cope with scale-Euclidean transformations. We extend the algorithm to the case of non-rigid transformations like affine and perspective transformations and investigate its robustness. The proposed algorithm is proven to be highly robust under various degrading factors, such as noise, occlusion, rendering artifacts, etc. and can deal with multiple occurrences of the object.

We discuss the feasibility of using singular points in a scale space representation (referred to as top points) for image matching purposes. These points are easily extracted from the scale space of an image and they form a compact description of the image. The image matching problem thus becomes a point cloud matching problem. This is related to the transportation problem known from linear optimization and we solve it by using an earth mover's distance algorithm. To match points in scale space, a distance measure is needed, as Euclidean distance no longer applies. We suggest a metric that can be used in scale space and show that it indeed performs better than a Euclidean distance measure. To distinguish between stable and unstable top points, we derive a stability norm based on the total variation norm which only depends on the second order derivatives at the top point. To improve matching results further, we show that other features at the top points can also increase the accuracy of matching.