Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Time-dependent hierarchical organization of spatial working memory: a transcranial magnetic stimulation study

The performance of memory-guided saccades with two different delays (3 and 30 s of memorization) was studied in seven healthy subjects. Double-pulse transcranial magnetic stimulation (dTMS) with an interstimulus interval of 100 ms was applied over the right dorsolateral prefrontal cortex (DLPFC) early (1 s after target presentation) and late (28 s after target presentation). Early stimulation significantly increased in both delays the percentage of error in amplitude (PEA) of contralateral memory-guided saccades compared to the control experiment without stimulation. dTMS applied late in the delay had no significant effect on PEA. Furthermore, we found a significantly smaller effect of early stimulation in the long-delay paradigm. These results suggest a time-dependent hierarchical organization of the spatial working memory with a functional dominance of DLPFC during the early memorization, independent from the memorization delay. For a long memorization delay, however, working memory seems to have an additional, DLPFC-independent component.

Spatial and temporal variation in population genetic structure of wild Nile tilapia (Oreochromis niloticus) across Africa

Background: Reconstructing the evolutionary history of a species is challenging. It often depends not only on the past biogeographic and climatic events but also the contemporary and ecological factors, such as current connectivity and habitat heterogeneity. In fact, these factors might interact with each other and shape the current species distribution. However, to what extent the current population genetic structure reflects the past and the contemporary factors is largely unknown. Here we investigated spatio-temporal genetic structures of Nile tilapia (Oreochromis niloticus) populations, across their natural distribution in Africa. While its large biogeographic distribution can cause genetic differentiation at the paleo-biogeographic scales, its restricted dispersal capacity might induce a strong genetic structure at micro-geographic scales. Results: Using nine microsatellite loci and 350 samples from ten natural populations, we found the highest genetic differentiation among the three ichthyofaunal provinces and regions (Ethiopian, Nilotic and Sudano-Sahelian) (R(ST) = 0.38 - 0.69). This result suggests the predominant effect of paleo-geographic events at macro-geographic scale. In addition, intermediate divergences were found between rivers and lakes within the regions, presumably reflecting relatively recent interruptions of gene flow between hydrographic basins (R(ST) = 0.24 - 0.32). The lowest differentiations were observed among connected populations within a basin (R(ST) = 0.015 in the Volta basin). Comparison of temporal sample series revealed subtle changes in the gene pools in a few generations (F = 0 - 0.053). The estimated effective population sizes were 23 - 143 and the estimated migration rate was moderate (m similar to 0.094 - 0.097) in the Volta populations. Conclusions: This study revealed clear hierarchical patterns of the population genetic structuring of O. niloticus in Africa. The effects of paleo-geographic and climatic events were predominant at macro-geographic scale, and the significant effect of geographic connectivity was detected at micro-geographic scale. The estimated effective population size, the moderate level of dispersal and the rapid temporal change in genetic composition might reflect a potential effect of life history strategy on population dynamics. This hypothesis deserves further investigation. The dynamic pattern revealed at micro-geographic and temporal scales appears important from a genetic resource management as well as from a biodiversity conservation point of view.

GIS based Watershed Classification in Lao PDR

Every inclined land surface has a potential for soil and water degradation, the seriousness depends on a multitude of parameters such as slope, soil type, geomorphology, rainfall, land use and natural vegetation cover. In Laos this intensified land use leads to reduced vegetation cover, to increased soil erosion, decreasing yield, and finally is likely to influence the hydrological regime. Against this background the Mekong River Commission (MRC) elaborated a spatial explicit Watershed Classification (WSC) for the Lower Mekong Basin. Based on topographic factors derived from a high-resolution Digital Terrain Model, five watershed classes are calculated, giving indication about the sensitivity to resource degradation by soil erosion. The WSC allows spatial priority setting for watershed management and generally supports informed decision making on reconnaissance level. In the conclusions focus is laid on general considerations when GIS techniques are used for spatial decision support in a development context.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

Assessment of use of microsatellite polymorphism analysis for improving spatial distribution tracking of echinococcus multilocularis

Alveolar echinococcosis (AE)--caused by the cestode Echinococcus multilocularis--is a severe zoonotic disease found in temperate and arctic regions of the northern hemisphere. Even though the transmission patterns observed in different geographical areas are heterogeneous, the nuclear and mitochondrial targets usually used for the genotyping of E. multilocularis have shown only a marked genetic homogeneity in this species. We used microsatellite sequences, because of their high typing resolution, to explore the genetic diversity of E. multilocularis. Four microsatellite targets (EmsJ, EmsK, and EmsB, which were designed in our laboratory, and NAK1, selected from the literature) were tested on a panel of 76 E. multilocularis samples (larval and adult stages) obtained from Alaska, Canada, Europe, and Asia. Genetic diversity for each target was assessed by size polymorphism analysis. With the EmsJ and EmsK targets, two alleles were found for each locus, yielding two and three genotypes, respectively, discriminating European isolates from the other groups. With NAK1, five alleles were found, yielding seven genotypes, including those specific to Tibetan and Alaskan isolates. The EmsB target, a tandem repeated multilocus microsatellite, found 17 alleles showing a complex pattern. Hierarchical clustering analyses were performed with the EmsB findings, and 29 genotypes were identified. Due to its higher genetic polymorphism, EmsB exhibited a higher discriminatory power than the other targets. The complex EmsB pattern was able to discriminate isolates on a regional and sectoral level, while avoiding overdistinction. EmsB will be used to assess the putative emergence of E. multilocularis in Europe.

A fuzzy risk attitude classification based on prospect theory

Traditional methods do not actually measure peoples’ risk attitude naturally and precisely. Therefore, a fuzzy risk attitude classification method is developed. Since the prospect theory is usually considered as an effective model of decision making, the personalized parameters in prospect theory are firstly fuzzified to distinguish people with different risk attitudes, and then a fuzzy classification database schema is applied to calculate the exact value of risk value attitude and risk be- havior attitude. Finally, by applying a two-hierarchical clas- sification model, the precise value of synthetical risk attitude can be acquired.

Hierarchical segmentation-assisted multimodal registration for MR brain images

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 mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.

Watershed classification in the Lower Mekong Basin: Rapid economic growth - a source of increasing potential for conflict over resource use

In the Lower Mekon Basin the extraordinary pace of economic development and growth contradicts with environmental protection. On base of the Watershed Classification Project (WSCP) and the inclusion of a DTM for the entire LMB the potential degradation risk was derived for each land unit. The risks were grouped into five classes, where classes one and two are considered critical with regard to soil erosion when the land is cleared of natural resources. For practical use the database has an enormous potential for further spatial analysis in combination with other datasets, as for example the NCCR North-South uses the WSCP within two research projects.