In this paper we present a noise reduction filter for video processing. It is based on the recently proposed two
dimensional steering kernel, extended to three dimensions and further augmented to suit the spatial-temporal
domain of video processing. Two alternative fillters are proposed - the time symmetric kernel and the time
asymmetric kernel. The first reduces the noise on single sequences, but to handle the problems at scene shift
the asymmetric kernel is introduced. The performance of both kernels is tested on simulated data and on a
real video sequence together with the original steering kernel. The proposed kernels improves the Rooted Mean
Squared Error (RMSE) compared to the original steering kernel method on video material significantly.
This article describes a method for automatic segmentation of the abdomen into three anatomical regions: subcutaneous,
retroperitoneal and visceral. For the last two regions the amount of adipose tissue (fat) is quantified.
According to recent medical research, the distinction between retroperitoneal and visceral fat is important for
studying metabolic syndrome, which is closely related to diabetes. However previous work has neglected to
address this point, treating the two types of fat together.
We use T1-weighted three-dimensional magnetic resonance data of the abdomen of obese minipigs. The
pigs were manually dissected right after the scan, to produce the "ground truth" segmentation. We perform
automatic segmentation on a representative slice, which on humans has been shown to correlate with the amount
of adipose tissue in the abdomen. The process of automatic fat estimation consists of three steps. First,
the subcutaneous fat is removed with a modified active contour approach. The energy formulation of the
active contour exploits the homogeneous nature of the subcutaneous fat and the smoothness of the boundary.
Subsequently the retroperitoneal fat located around the abdominal cavity is separated from the visceral fat. For
this, we formulate a cost function on a contour, based on intensities, edges, distance to center and smoothness,
so as to exploit the properties of the retroperitoneal fat. We then globally optimize this function using dynamic
Finally, the fat content of the retroperitoneal and visceral regions is quantified based on a fuzzy c-means clustering
of the intensities within the segmented regions. The segmentation proved satisfactory by visual inspection,
and closely correlated with the manual dissection data. The correlation was 0.89 for the retroperitoneal fat, and
0.74 for the visceral fat.