A novel tracking algorithm via feature points matching.

Visual target tracking is a primary task in many computer vision applications and has been widely studied in recent years. Among all the tracking methods, the mean shift algorithm has attracted extraordinary interest and been well developed in the past decade due to its excellent performance. However, it is still challenging for the color histogram based algorithms to deal with the complex target tracking.

Iterative nonlocal total variation regularization method for image restoration.

In this paper, a Bregman iteration based total variation image restoration algorithm is proposed. Based on the Bregman iteration, the algorithm splits the original total variation problem into sub-problems that are easy to solve. Moreover, non-local regularization is introduced into the proposed algorithm, and a method to choose the non-local filter parameter locally and adaptively is proposed.

Serial slice image segmentation of digital human based on adaptive geometric active contour tracking.

Segmentation is one of the crucial problems for the digital human research, as currently digital human datasets are manually segmented by experts with anatomy knowledge. Due to the thin slice thickness of digital human data, the static slices can be regarded as a sequence of temporal deformation of the same slice. This gives light to the method of object contour tracking for the segmentation task for the digital human data.

Fuzzy local Gaussian mixture model for brain MR image segmentation.

Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation.

Generalized rough fuzzy c-means algorithm for brain MR image segmentation.

Fuzzy sets and rough sets have been widely used in many clustering algorithms for medical image segmentation, and have recently been combined together to better deal with the uncertainty implied in observed image data. Despite of their wide spread applications, traditional hybrid approaches are sensitive to the empirical weighting parameters and random initialization, and hence may produce less accurate results. In this paper, a novel hybrid clustering approach, namely the generalized rough fuzzy c-means (GRFCM) algorithm is proposed for brain MR image segmentation.

A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image.

A modified possibilistic fuzzy c-means clustering algorithm is presented for fuzzy segmentation of magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities and noise. By introducing a novel adaptive method to compute the weights of local spatial in the objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus allowing the suppression of noise and helping to resolve classification ambiguity. To estimate the intensity inhomogeneity, the global intensity is introduced into the coherent local intensity clustering algorithm and takes the local and global intensity information into account.

Level set segmentation of brain magnetic resonance images based on local Gaussian distribution fitting energy.

This paper presents a variational level set approach in a multi-phase formulation to segmentation of brain magnetic resonance (MR) images with intensity inhomogeneity. In our model, the local image intensities are characterized by Gaussian distributions with different means and variances. We define a local Gaussian distribution fitting energy with level set functions and local means and variances as variables.

Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation.

In this paper, we propose an improved region-based active contour model in a variational level set formulation. We define an energy functional with a local intensity fitting term, which induces a local force to attract the contour and stops it at object boundaries, and an auxiliary global intensity fitting term, which drives the motion of the contour far away from object boundaries. Therefore, the combination of these two forces allows for flexible initialization of the contours.

Brain MR image segmentation using local and global intensity fitting active contours/surfaces.

In this paper, we present an improved region-based active contour/surface model for 2D/3D brain MR image segmentation. Our model combines the advantages of both local and global intensity information, which enable the model to cope with intensity inhomogeneity. We define an energy functional with a local intensity fitting term and an auxiliary global intensity fitting term.

Shape statistics variational approach for the outer contour segmentation of left ventricle MR images.

Segmentation of left ventricles is one of the important research topics in cardiac magnetic resonance (MR) imaging. The segmentation precision influences the authenticity of ventricular motion reconstruction. In left ventricle MR images, the weak and broken boundary increases the difficulty of segmenting the outer contour precisely.