Because of its robustness and accuracy for a variety of applications, either monomodal or multimodal, mutual information (MI) is a very popular similarity measure for (medical) image registration. Calculation of MI is based on the joint histogram of the two images to be registered, expressing the statistical relationship between image intensities at corresponding positions. However, the calcula...

In medical imaging, low signal-to-noise ratio (SNR) and/or contrast-to-noise ratio (CNR) often cause many image processing algorithms to perform poorly. Postacquisition image filtering is an important off-line image processing approach widely employed to enhance the SNR and CNR. A major drawback of many filtering techniques is image degradation by diffusing/blurring edges and/or fine structures...

Non-rigid image registration is a challenging task in medical image analysis. In recent years, there are two essential issues. First, intensity similarity is not necessarily equivalent to anatomical similarity when the anatomical correspondences between subject and template images are established. Second, the registration algorithm should be robust against monotonic gray-level transformation wh...

BACKGROUND Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), w...

In this paper, based on the SαS distributions, we design SαS filters and use the filters as a new feature extraction method for non-rigid medical image registration. In brain MR images, the energy distributions of different frequency bands often exhibit heavy-tailed behavior. Such non-Gaussian behavior is essential for non-rigid image registration but cannot be satisfactorily modeled by the con...

In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the tool...

Introduction Brain region extraction is to segment brain region from nonbrain regions in magnetic resonance (MR) images. The segmented brain is usually used for measuring individual cortex thickness and brain volume, monitoring the development of the brain, and surface-based analysis. Brain region extraction is, therefore, an important step for various applications above mentioned. We present a...

Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutua...