Survey on liver CT image segmentation methods <s> Atlas matching semi-automatic methods <s> In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, the authors have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms. <s> BIB001 </s> Survey on liver CT image segmentation methods <s> Atlas matching semi-automatic methods <s> Liver segmentation is an important step for the therapeutic decision making in liver surgery. However, manual segmentation is timeconsuming and tedious and so the need for accurate and robust automatic segmentation methods for clinical data arises. In this work an atlas in combination with nonrigid registration is used to segment the liver in actual clinical CT images. First, the atlas is built on twenty training images using nonrigid registration with a novel surface distance penalty. Next, this atlas is nonrigidly registered to ten test images. Currently, the user interaction is limited to the initialization of a rigid registration and to the definition of a region of interest for the nonrigid registration. Future work will focus on replacing the remaining user interaction with fully automatic procedures. Results are promising with an average overlap error of 10.4% and an average RMS distance of 5.0mm for the ten test images. Errors occur mainly at sites where the atlas is ill-defined such as the border between the heart and the liver. <s> BIB002
Probabilistic atlases are constructed by a manual segmentation from a large number of anatomical images. These images are registered into a standard space via affine transformations. The images and corresponding segmentations are then averaged to produce a probabilistic atlas, and it is employed into a Bayesian frame. The probability to belong to a specific organ is calculated for each pixel/voxel. Finally, a simple thresholding or any conditional mode algorithm is used to extract the desired organ based on the posterior probability. The problem of this method is that the probabilistic atlas generation demands a lot of training data to be collected and manually segmented. BIB002 employ a non-rigid atlas matching to segment the liver. The atlas is built from 20 provided training images. To register two images for atlas construction, the first step is an affine transform using the mutual information (MI) metric proposed by  . Subsequently, a B-spline non-rigid registration suggested by BIB001 is conducted using a combination of MI, regularizing costs, and surface distance penalty between the corresponding segmentations. Each training image is registered to all others, and the resulting deformation maps are averaged to transform each image into a common coordinate system. Averaging all images and segmentations transformed with this method yields the final atlas. To segment a test image, the user has to define an affine transform that roughly matches the atlas to the image. After the optimization of the affine transform, the user has to define a region of interest around the liver in which the subsequent non-rigid registration is conducted. This step employs the same method as during atlas construction, but without using the surface distance penalty. The resulting deformation field is used to transform the probabilistic atlas segmentation to the test image. Thresholding this segmentation at 50%, performing a morphological opening suggested by  and subsequent removal of unconnected components yields the final result.