Konstantin Ens

Validation and comparison of registration methods for free-breathing 4D lung CT

We have compared and validated image registration methods with respect to the clinically relevant use-case of lung CT max-inhale to max-exhale registration. Four fundamentally different algorithms representing main approaches for image registration were compared using clinical images. Each algorithm was assigned to a different person with extensive working knowledge of its usage. Quantitative and qualitative evaluation is performed. Whereas the methods achieve similar results in target registration error, characteristic differences come to show by closer analysis of the displacement fields.

A Communication Term for the Combined Registration and Segmentation

Accurate image registration is a necessary prerequisite for many diagnostic and therapy planning procedures where complementary information from different images has to be combined. The design of robust and reliable non-parametric registration schemes is currently a very active research area. Modern approaches combine the pure registration scheme with other image processing routines such that both ingredients may benefit from each other. One of the new approaches is the combination of segmentation and registration (“segistration”). Here, the segmentation part guides the registration to its desired configuration, whereas on the other hand the registration leads to an automatic segmentation. By joining these image processing methods it is possible to overcome some of the pitfalls of the individual methods. Here, we focus on the benefits for the registration task.

Improving an affine and non-linear image registration and/or segmentation task by incorporating characteristics of the displacement field

Image registration is an important and active area of medical image processing. Given two images, the idea is to compute a reasonable displacement field which deforms one image such that it becomes similar to the other image. The design of an automatic registration scheme is a tricky task and often the computed displacement field has to be discarded, when the outcome is not satisfactory. On the other hand, however, any displacement field does contain useful information on the underlying images. It is the idea of this note, to utilize this information and to benefit from an even unsuccessful attempt for the subsequent treatment of the images. Here, we make use of typical vector analysis operators like the divergence and curl operator to identify meaningful portions of the displacement field to be used in a follow-up run. The idea is illustrated with the help of academic as well as a real life medical example. It is demonstrated on how the novel methodology may be used to substantially improve a registration result and to solve a difficult segmentation problem.

Design of a synthetic database for the validation of non-linear registration and segmentation of magnetic resonance brain images

Image registration and segmentation are two important tasks in medical image analysis. However, the validation of algorithms for non-linear registration in particular often poses significant challenges:1, 2 Anatomical labeling based on scans for the validation of segmentation algorithms is often not available, and is tedious to obtain. One possibility to obtain suitable ground truth is to use anatomically labelled atlas images. Such atlas images are, however, generally limited to single subjects, and the displacement field of the registration between the template and an arbitrary data set is unknown. Therefore, the precise registration error cannot be determined, and approximations of a performance measure like the consistency error must be adapted. Thus, validation requires that some form of ground truth is available. In this work, an approach to generate a synthetic ground truth database for the validation of image registration and segmentation is proposed. Its application is illustrated using the example of the validation of a registration procedure, using 50 magnetic resonance images from different patients and two atlases. Three different non-linear image registration methods were tested to obtain a synthetic validation database consisting of 50 anatomically labelled brain scans.

Improved elastic medical image registration using mutual information

One of the future-oriented areas of medical image processing is to develop fast and exact algorithms for image registration. By joining multi-modal images we are able to compensate the disadvantages of one imaging modality with the advantages of another modality. For instance, a Computed Tomography (CT) image containing the anatomy can be combined with metabolic information of a Positron Emission Tomography (PET) image. It is quite conceivable that a patient will not have the same position in both imaging systems. Furthermore some regions for instance in the abdomen can vary in shape and position due to different filling of the rectum. So a multi-modal image registration is needed to calculate a deformation field for one image in order to maximize the similarity between the two images, described by a so-called distance measure. In this work, we present a method to adapt a multi-modal distance measure, here mutual information (MI), with weighting masks. These masks are used to enhance relevant image structures and suppress image regions which otherwise would disturb the registration process. The performance of our method is tested on phantom data and real medical images.

Wahl eines gewichteten Distanzmaßes für monomodale Bilder in der nicht-parametrischen Registrierung

In der Bildregistrierung ist es haufig notwendig, die Methoden den speziellen Problemanforderungen der zu bearbeitenden Bilder anzupassen. Ein Weg, um zusatzliches Wissen in eine Registrierung einzubringen, ist die Nutzung gewichteter Distanzmase, um damit die Bedeutung ausgewahlter Bildbereiche zu verstarken, abzuschwachen oder auszublenden. Im Fall der parameterfreien Registrierung sind zwei gewichtete Distanzmase, SSDmix und MIadd, bekannt. Diese beiden Distanzmase werden hier gegenubergestellt und ihre Wirkung auf monomodalen Bildern verglichen. Zusatzlich wird SSDmix mit ungewichtetem MI verglichen. Die Ergebnisse verdeutlichen, dass SSDmix und MIadd bessere Ergebnisse als SSD und MI liefern. Weiterhin zeigt sich, dass SSDmix und MIadd fur monomodale Bilder gleichmachtig sind.