An important goal of software development in the medical field is the design of methods which are able to integrate information obtained from various imaging and nonimaging modalities into a cohesive framework in order to understand the results of qualitatively different measurements in a larger context. Moreover, it is essential to assess the various features of the data quantitatively so that relationships in anatomical and functional domains between complementing modalities can be expressed mathematically. This paper presents a clinically feasible software environment for the quantitative assessment of the relationship among biochemical functions as assessed by PET imaging and electrophysiological parameters derived from intracranial EEG. Based on the developed software tools, quantitative results obtained from individual modalities can be merged into a data structure allowing a consistent framework for advanced data mining techniques and 3D visualization. Moreover, an effort was made to derive quantitative variables (such as the spatial proximity index, SPI) characterizing the relationship between complementing modalities on a more generic level as a prerequisite for efficient data mining strategies. We describe the implementation of this software environment in twelve children (mean age 5.2 ± 4.3 years) with medically intractable partial epilepsy who underwent both high-resolution structural MR and functional PET imaging. Our experiments demonstrate that our approach will lead to a better understanding of the mechanisms of epileptogenesis and might ultimately have an impact on treatment. Moreover, our software environment holds promise to be useful in many other neurological disorders, where integration of multimodality data is crucial for a better understanding of the underlying disease mechanisms.

This paper develops a new salient keypoints-based shape description which extracts the salient surface keypoints with detected scales. Salient geometric features can then be defined collectively on all the detected scale normalized local patches to form a shape descriptor for surface matching purpose. The saliency-driven keypoints are computed as local extrema of the difference of Gaussian function defined over a curved surface in geodesic scale space. This method can properly function on either manifold or non-manifold surface without resorting to any surface mapping or parameterization procedures. Therefore, it has a wide utility in many applications such as shape matching, classification, and recognition. Our experiments on 3D shapes demonstrate that the salient keypoints and local feature descriptors are robust and stable to noisy input and insensitive to resolution change. We have applied our technique to the tasks of 3D shape matching, and the experimental results showed good performance and the effectiveness of this new method.

Abstract. Accurate registration of cortical structures plays a fundamental role in statistical analysis of brain images across population. This paper presents a novel framework for the non-rigid intersubject brain surface registration, using conformal structure and spherical thin-plate splines. By resorting to the conformal structure, complete characteristics regarding the intrinsic cortical geometry can be retained as a mean curvature function and a conformal factor function defined on a canonical, spherical domain. In this transformed space, spherical thin-plate splines are firstly used to explicitly match a few prominent homologous landmarks, and in the meanwhile, interpolate a global deformation field. A post-optimization procedure is then employed to further refine the alignment of minor cortical features based on the geometric parameters preserved on the domain. Our experiments demonstrate that the proposed framework is highly competitive with others for brain surface registration and population-based statistical analysis. We have applied our method in the identification of cortical abnormalities in PET imaging of patients with neurological disorders and accurate results are obtained.

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Recent advances in imaging technologies, such as Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) have accelerated brain research in many aspects. In order to better understand the synergy of the many processes involved in normal brain function, integrated modeling and analysis of MRI, PET, and DTI across subjects is highly desirable. The current state-of-art computational tools fall short in offering an analytic approach for intersubject brain registration and analysis. In this paper we present an approach which is based on landmark constrained conformal parameterization of a brain surface from high-resolution structural MRI data to a canonical spherical domain. This model allows natural integration of information from co-registered PET as well as DTI data and lays a foundation for the quantitative analysis of the relationship among diverse datasets across subjects. Consequently, the approach can be extended to provide a software environment able to facilitate detection of abnormal functional brain patterns in patients with neurological disorder.

PurposeThe purpose of this paper is to explore the operation strategies of a manufacturer who produces brown and green product simultaneously.Design/methodology/approachThe authors establish three models to examine the joint decisions of pricing and advertising. Three advertising strategies are: non-advertising investment (NA), advertising investment for brown product (BA) and advertising investment for green product (GA).FindingsThe theoretical analysis shows that advertising investment can substantially increase the product greening level and manufacturer's profit. More importantly, we find that the GA strategy is more likely to be the best strategy as the advertising investment efficiency increases. The BA strategy is more likely to be preferred as the R&D cost increases. Finally, the modeling results are verified by numerical experiments, and more insights are obtained.Research limitations/implicationsThis paper considers the case in which a single manufacturer produces the brown and green product simultaneously. In fact, many manufacturers in the market produce brown and green product at the same time. Furthermore, in addition to advertising investment for brown product and green product, manufacturers can also invest in advertising for brands.Originality/valueThe paper contributes to the investigations on green production and advertising decisions of a manufacturer who produces brown and green products simultaneously.

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We attempted to detect the five tastes in four different commercially available brands of beer using an electronic tongue and conducted a statistical analysis on their alcohol contents, original wort concentrations, and pH values. Statistical methods, including principal component analysis (PCA), linear discriminant analysis (LDA), and a backpropagation (BP) neural network, were used to identify and classify the four beer brands. According to PCA, in the five taste indicators of the four brands of beer, the contribution rates of the first and second principal components were 56.73 and 34.46% respectively; the beer was oxidized to a certain extent with increasing detection time. The results of LDA confirmed the high sensitivity of the electronic tongue sensors to beer tastes as the four brands were effectively identified by distinguishing the taste differences among them. The results of the BP neural network suggested that its predictive accuracy for the five tastes in the four brands can achieve 100% subject to the conformity between the measured and predicted values. The stepwise regression model established in our study could be effective for accurately predicting the original wort concentration of beer. The determination coefficients of the original wort concentration modeling set and the validation set were 0.99 and 0.96, and the root-mean-square errors were 0.06 and 0.41, respectively. As demonstrated by its high sensitivity in analyzing the tastes of four different beer brands, the electronic tongue can effectively distinguish the taste differences among different beers.