Statistical shape and appearance models are often based on the accurate identification of one-to-one correspondences in a training data set. At the same time, the determination of these corresponding landmarks is the most challenging part of such methods. Hufnagel et al.1 developed an alternative method using correspondence probabilities for a statistical shape model. In Krüuger et al.2, 3 we propose the use of probabilistic correspondences for statistical appearance models by incorporating appearance information into the framework. We employ a point-based representation of image data combining position and appearance information. The model is optimized and adapted by a maximum a-posteriori (MAP) approach deriving a single global optimization criterion with respect to model parameters and observation dependent parameters that directly affects shape and appearance information of the considered structures. Because initially unknown correspondence probabilities are used and a higher number of degrees of freedom is introduced to the model a regularization of the model generation process is advantageous. For this purpose we extend the derived global criterion by a regularization term which penalizes implausible topological changes. Furthermore, we propose a multi-level approach for the optimization, to increase the robustness of the model generation process.
Mammography is a standard tool for breast cancer diagnosis. In current clinical practice, typically two mammograms of each breast are taken from different angles. A fundamental step when using ipsilateral mammograms for the diagnosis of breast cancer, is the identification of corresponding locations/structures in both views, which is a very challenging task due to the projective nature of the images and the different compression parameters used for each view. In this contribution, four different approaches for the estimation of corresponding locations in ipsilateral mammograms are systematically compared using 46 mammogram pairs (50 point-to-point correspondences). The evaluation includes simple heuristic methods (annular bands and straight strips) as well as methods based on geometric and physically motivated breast compression models, which aim to simulate the mammogram acquisition process. The evaluation results show that on average no significant differences exist between the estimation accuracies obtained using the simple heuristic methods and the more involved compression models. However, the results of this study indicate the potential of a method that optimally combines the different approaches.
One-to-one correspondences are fundamental for the creation of classical statistical shape and appearance models. At the same time, the identification of these correspondences is the weak point of such model-based methods. Hufnagel et al.1 proposed an alternative method using correspondence probabilities instead of exact one-to- one correspondences for a statistical shape model. In this work, we extended the approach by incorporating appearance information into the model. For this purpose, we introduce a point-based representation of image data combining position and appearance information. Then, we pursue the concept of probabilistic correspondences and use a maximum a-posteriori (MAP) approach to derive a statistical shape and appearance model. The model generation as well as the model fitting can be expressed as a single global optimization criterion with respect to model parameters. In a first evaluation, we show the feasibility of the proposed approach and evaluate the model generation and model-based segmentation using 2D lung CT slices.
Mammography is the most commonly used imaging modality in breast cancer screening and diagnosis. The analysis of 2D mammographic images can be difficult due to the projective nature of the imaging technique and poor contrast between tumorous and healthy fibro-glandular tissue. Contrast-enhanced magnetic resonance imaging (MRI) can overcome these disadvantages by providing a 3D dataset of the breast. The detection of corresponding image structures is challenging due to large breast deformations during the image acquisition. We present a method for analyzing 2D/3D intra-individual correspondences between mammography and MRI datasets. Therefore, an ICP-based B-spline registration is used to approximate the breast deformation differences. The resulting deformed MR image is projected onto the 2D plane to enable a comparison with the 2D mammogram. A first evaluation based on six mammograms revealed an average accuracy of 4.87 mm. In contrast to previous FEM-based approaches, we propose a fast and easy to implement 3D/3D-registration, for simulating the mammographic breast compression.
Two-dimensional mammography is the major imaging modality in breast cancer detection. A disadvantage of
mammography is the projective nature of this imaging technique. Tomosynthesis is an attractive modality with
the potential to combine the high contrast and high resolution of digital mammography with the advantages of
3D imaging. In order to facilitate diagnostics and treatment in the current clinical work-flow, correspondences
between tomosynthesis images and previous mammographic exams of the same women have to be determined.
In this paper, we propose a method to detect correspondences in 2D mammograms and 3D tomosynthesis
images automatically. In general, this 2D/3D correspondence problem is ill-posed, because a point in the 2D
mammogram corresponds to a line in the 3D tomosynthesis image. The goal of our method is to detect the "most
probable" 3D position in the tomosynthesis images corresponding to a selected point in the 2D mammogram.
We present two alternative approaches to solve this 2D/3D correspondence problem: a 2D/3D registration
method and a 2D/2D mapping between mammogram and tomosynthesis projection images with a following
back projection. The advantages and limitations of both approaches are discussed and the performance of the
methods is evaluated qualitatively and quantitatively using a software phantom and clinical breast image data.
Although the proposed 2D/3D registration method can compensate for moderate breast deformations caused
by different breast compressions, this approach is not suitable for clinical tomosynthesis data due to the limited
resolution and blurring effects perpendicular to the direction of projection. The quantitative results show that
the proposed 2D/2D mapping method is capable of detecting corresponding positions in mammograms and
tomosynthesis images automatically for 61 out of 65 landmarks. The proposed method can facilitate diagnosis,
visual inspection and comparison of 2D mammograms and 3D tomosynthesis images for the physician.