Fast and robust image segmentation by small-world neural oscillator networks

Inspired by the temporal correlation theory of brain functions, researchers have presented a number of neural oscillator networks to implement visual scene segmentation problems. Recently, it is shown that many biological neural networks are typical small-world networks. In this paper, we propose and investigate two small-world models derived from the well-known LEGION (locally excitatory and globally inhibitory oscillator network) model. To form a small-world network, we add a proper proportion of unidirectional shortcuts (random long-range connections) to the original LEGION model. With local connections and shortcuts, the neural oscillators can not only communicate with neighbors but also exchange phase information with remote partners. Model 1 introduces excitatory shortcuts to enhance the synchronization within an oscillator group representing the same object. Model 2 goes further to replace the global inhibitor with a sparse set of inhibitory shortcuts. Simulation results indicate that the proposed small-world models could achieve synchronization faster than the original LEGION model and are more likely to bind disconnected image regions belonging together. In addition, we argue that these two models are more biologically plausible.     

JUST (Java User Segmentation Tool) for semi-automatic segmentation of tomographic maps

We are presenting a program for interactive segmentation of tomographic maps, based on objective criteria so as to yield reproducible results. The strategy starts with the automatic segmentation of the entire volume with the watershed algorithm in 3D. The watershed regions are clustered successively by supervised classification, allowing the segmentation of known organelles, such as membranes, vesicles and microtubules. These organelles are processed with topological models and input parameters manually derived from the tomograms. After known organelles are extracted from the volume, all other watershed regions can be organized into homogeneous assemblies on the basis of their densities. To complete the process, all voxels in the volume are assigned either to the background or individual structures, which can then be extracted for visualization with any rendering technique. The user interface of the program is written in Java, and computational routines are written in C. For some operations, involving the visualization of the tomogram, we refer to existing software, either open or commercial. While the program runs, a history file is created, that allows all parameters and other data to be saved for the purposes of comparison or exchange. Initially, the program was developed for the segmentation of synapses, and organelles belonging to these structures have thus far been the principal targets modeled with JUST. Since each organelle is clustered independently from the rest of the volume, however, the program can accommodate new models of different organelles as well as tomograms of other types of preparations of tissue, such as citoskeletal components in vitreous ice.     

A multi-step directional generalized gradient vector flow snake for target tumor segmentation in US-guided high-intensity focused ultrasound ablation

Getting precise locations of target tumors can help to ensure ablation of cancerous tissues and avoid unwanted destruction of healthy tissues in high-intensity focused ultrasound (HIFU) treatment system. Because of speckle noise and spurious boundaries in ultrasound images, traditional image segmentation methods are not suitable for achieving the precise locations of target tumors in HIFU ablation. In this paper, a multi-step directional generalized gradient vector flow snake model is introduced for target tumor segmentation. In the first step, the traditional generalized gradient vector flow (GGVF) snake is used to obtain an approximate contour of the tumor. According to the approximate contour, a new distance map is generated. Subsequently, a new directional edge map is created by calculating a scalar product of the gradients of the distance map and the initial image. In this process, the gradient directional information and the magnitude information of the distance map are used to attenuate unwanted edges and highlight the real edges in the new directional edge map. Finally, a refined GGVF field is derived from a diffusion operation of the gradient vectors of the directional edge map. The GGVF field is used to refine the tumor's contour, by directing the approximate contour to edges with the desired gradient directionality. Based on the newly developed snake model, the influences of the spurious boundaries and the speckle noise are significantly reduced in the ultrasound image segmentation. Experimental results indicate that this technique is greatly useful for target tumor segmentation in HIFU treatment system     

Clonal splitting in desert shrubs

Axis splitting is a widespread phenomenon in desert shrubs, and has been reported for shrubs from several plant families, both in old- and new-world deserts. It is so common in dwarf shrubs of arid environments as to be a defining characteristic of this growth form. Although anatomists described this phenomenon several decades ago, there has been only one ecological study of one species, Ambrosia dumosa. The anatomical nature of the various splitting mechanisms that have been found suggests axis splitting to be an extreme form of hydraulic segmentation. The adaptive advantage of clonal splitting in desert shrubs has yet to be determined, but it appears to be largely a risk-spreading mechanism that enables independent mortality of integrated hydraulic units (IHUs) or ramets. This should be especially advantageous in heterogeneous, water-limited environments, where soil water occurs in pockets too small to support a large shrub-genet. Clonal splitting may cause an increase in intraclonal competition among ramets, but there are also indications that at least some species possess mechanisms to reduce competition by minimizing root system overlap among ramets. Many desert shrub species that undergo clonal splitting maintain a dense clump growth form, possibly because such a growth form has positive effects on water and nutrient status of the soil and long-term effects on other soil properties.