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.

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.

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.