Appropriate validation of the segmentation algorithms is important for clinical acceptance of those methods. Receiver
operating characteristic (ROC) analysis provides the most comprehensive description of the accuracy performance of
image segmentation. Total area under an ROC curve (AUC) is widely used as an index of ROC analysis of performance
test. However, a large part of the ROC curve is in the clinically irrelevant range. The total area can be misleading in
some clinical situation. In this paper, we proposed a partial area index of ROC curves, which measures the segmentation
performance in a clinically relevant range decided by learning from subjective ratings. The boundary of the range is
defined by a linear cost function of false positive fraction (FPF) and true positive fraction (TPF). The cost factors of FPF
and TPF are learned by maximizing the Kendall's coefficient of concordance (KCC) between the partial areas and the
subjective ratings. Experiment results show that our method gives a large cost factor on FPF and a small cost factor on
TPF on a tumor data set. This is consistent with the fact that a large FPF is generally more difficult to be accepted in
tumor segmentation. Our method is able to determine the optimal range for partial area index of ROC analysis, and this
partial area index is more appropriate than AUC for evaluating segmentation performance.
Object tracking is an essential problem in the field of video and image processing. Although tracking algorithms working
on gray video are convenient in actual applications, they are more difficult to be developed than those using color
features, since less information is taken into account. Few researches have been dedicated to tracking object using edge
information. In this paper, we proposed a novel video tracking algorithm based on edge information for gray videos. This
method adopts the combination of a condensation particle filter and an improved chamfer matching. The improved chamfer matching is rotation invariant and capable of estimating the shift between an observed image patch and a template by an orientation distance transform. A modified discriminative likelihood measurement method that focuses on the difference is adopted. These values are normalized and used as the weights of particles which predict and track the object. Experiment results show that our modifications to chamfer matching improve its performance in video tracking problem. And the algorithm is stable, robust, and can effectively handle rotation distortion. Further work can be done on updating the template to adapt to significant viewpoint and scale changes of the appearance of the object during the tracking process.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.