Temporal subtraction is a visual enhancement technique to improve the detection of pathological changes from
medical images acquired at different times. Prior to subtracting a previous image from a current image, a nonrigid
warping of the two images might be necessary. As the nonrigid warping may change the size of pathological
lesions, the subtraction image can be misleading. In this paper we present an alternative subtraction technique to
avoid this problem. Instead of subtracting the intensities of corresponding voxels, a convolution filter is applied
to both images prior to subtraction. The technique is demonstrated for computed tomography images of the
lungs. It is shown that this method results in an improved visual enhancement of changing nodules compared
with the conventional subtraction technique.
In this paper, we evaluate different non-rigid image registration methodologies in the context of atlas-based brain image segmentation. Three non-rigid voxel-based registration regularization schemes (viscous fluid, elastic and curvature-based registration) combined with the mutual information similarity measure are compared. We conduct large-scale atlas-based segmentation experiments on a set of 20 anatomically labelled MR brain images in order to find the optimal parameter settings for each scheme. The performance of the optimal registration schemes is evaluated in their capability of accurately segmenting 49 different brain sub-structures of varying size and shape.
A new generic model-based segmentation scheme is presented, which
can be trained from examples akin to the Active Shape Model (ASM)
approach in order to acquire knowledge about the shape to be
segmented and about the gray-level appearance of the object in the
image. Because in the ASM approach the intensity and shape models
are typically applied alternately during optimizing as first an
optimal target location is selected for each landmark separately
based on local gray-level appearance information only to which the
shape model is fitted subsequently, the ASM may be misled in case
of wrongly selected landmark locations. Instead, the proposed
approach optimizes for shape and intensity characteristics
simultaneously. Local gray-level appearance information at the
landmark points extracted from feature images is used to
automatically detect a number of plausible candidate locations for
each landmark. The shape information is described by multiple
landmark-specific statistical models that capture local
dependencies between adjacent landmarks on the shape. The shape
and intensity models are combined in a single cost function that
is optimized non-iteratively using dynamic programming which
allows to find the optimal landmark positions using combined shape
and intensity information, without the need for initialization.
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