Liya Chen

Learn More
This paper presents a new deformable model using both population-based and patient-specific shape statistics to segment lung fields from serial chest radiographs. There are two novelties in the proposed deformable model. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than the general intensity and(More)
Groupwise registration has recently been proposed for simultaneous and consistent registration of all images in a group. Since many deformation parameters need to be optimized for each image under registration, the number of images that can be effectively handled by conventional groupwise registration methods is limited. Moreover, the robustness of(More)
This paper presents a novel image similarity measure, referred to as quantitative–qualitative measure of mutual information (Q-MI), for multimodality image registration. Conventional information measures, e.g., Shannon's entropy and mutual information (MI), reflect quantitative aspects of information because they only consider probabilities of events. In(More)
To overcome the problem that the histogram equalization can fail for discrete images, a local-mean based strict pixel ordering method has been proposed recently, although it is unpractical for 3D medical image enhancement due to its complex computation. In this paper, a novel histogram mapping method is proposed. It uses a fast local feature generation(More)
  • 1