Cardiac computed tomography (CT) is widely used in clinics for diagnosing heart diseases and assessing functionality of the heart. It is therefore desirable to achieve fully automatic whole heart segmentation for the
clinical applications, since manual work can be labor-intensive and subject to bias. However, automating this
segmentation is challenging due to the large shape variability of the heart and the poor contrast between sub-
structures such as those in the right ventricle and right atrium region in CT angiography images. In this work,
we develop a fully automatic whole heart segmentation framework for CT volumes. This framework is based on
image registration and atlas propagation techniques. Also, we investigate and compare the segmentation performance using single and multiple atlas propagation and segmentation strategies. In multiple atlas segmentation,
a ranking-and-selection scheme is used to identify the best atlas(es) from an atlas pool for an unseen image. The
segmentation methods are evaluated using fifteen clinical data. The results show that the proposed multiple
atlas segmentation method can achieve a mean Dice score of 0:889±0:023 and a mean surface distance error of
1:17±1:39 mm for the automatic whole heart segmentation of seven substructures.
Detecting and tracking space objects in video sequences is a challenging task of wide interest. In this paper, a
comprehensive framework for detecting and tracking space objects is presented. Unlike the traditional linear structure of
tracking after detection, this framework also allows detection after tracking. What is more, the combination of the level
set and the frame subtraction algorithms in the tracking subsystem makes detection and tracking of a space object during
an entire video sequence a reality. Experimental results on 15 videos generated by STK show robust tracking under both
star background and earth background.
Convective storms are dangerous atmosphere hazards often accompanied by heavy rain and strong wind. Despite their
short life time and small spatial scale, Doppler weather radars can provide a 3-D high temporal and spatial resolution of
continuous measurement. Thus, a combination of identification, tracking and nowcasting of convective storms based on
radar data is one of the most important meteorological methodologies. However, most forecasting algorithms only use
extrapolating techniques and do not reflect storm shape changes, making forecasting result unreliable. Thus, this paper
presents a new method, namely level set, to forecast storms with storm cell shape changes concerned. As a result,
forecasting precision is improved.