An improved lesion detection approach based on similarity measurement between fuzzy intensity segmentation and spatial probability maps

The application of automatic segmentation methods in lesion detection is desirable. However, such methods are restricted by intensity similarities between lesioned and healthy brain tissue. Using multi-spectral magnetic resonance imaging (MRI) modalities may overcome this problem but it is not always practicable. In this article, a lesion detection approach requiring a single MRI modality is presented, which is an improved method based on a recent publication. This new method assumes that a low similarity should be found in the regions of lesions when the likeness between an intensity based fuzzy segmentation and a location based tissue probabilities is measured. The usage of a normalized similarity measurement enables the current method to fine-tune the threshold for lesion detection, thus maximizing the possibility of reaching high detection accuracy. Importantly, an extra cleaning step is included in the current approach which removes enlarged ventricles from detected lesions. The performance investigation using simulated lesions demonstrated that not only the majority of lesions were well detected but also normal tissues were identified effectively. Tests on images acquired in stroke patients further confirmed the strength of the method in lesion detection. When compared with the previous version, the current approach showed a higher sensitivity in detecting small lesions and had less false positives around the ventricle and the edge of the brain

Evaluating mid-infrared spectroscopy as a new technique for predicting sensory texture attributes of processed cheese

The objective of this study was to investigate the potential application of mid-infrared spectroscopy for determination of selected sensory attributes in a range of experimentally manufactured processed cheese samples. This study also evaluates mid-infrared spectroscopy against other recently proposed techniques for predicting sensory texture attributes. Processed cheeses (n = 32) of varying compositions were manufactured on a pilot scale. After 2 and 4 wk of storage at 4 degrees C, mid-infrared spectra ( 640 to 4,000 cm(-1)) were recorded and samples were scored on a scale of 0 to 100 for 9 attributes using descriptive sensory analysis. Models were developed by partial least squares regression using raw and pretreated spectra. The mouth-coating and mass-forming models were improved by using a reduced spectral range ( 930 to 1,767 cm(-1)). The remaining attributes were most successfully modeled using a combined range ( 930 to 1,767 cm(-1) and 2,839 to 4,000 cm(-1)). The root mean square errors of cross-validation for the models were 7.4(firmness; range 65.3), 4.6 ( rubbery; range 41.7), 7.1 ( creamy; range 60.9), 5.1(chewy; range 43.3), 5.2(mouth-coating; range 37.4), 5.3 (fragmentable; range 51.0), 7.4 ( melting; range 69.3), and 3.1 (mass-forming; range 23.6). These models had a good practical utility. Model accuracy ranged from approximate quantitative predictions to excellent predictions ( range error ratio = 9.6). In general, the models compared favorably with previously reported instrumental texture models and near-infrared models, although the creamy, chewy, and melting models were slightly weaker than the previously reported near-infrared models. We concluded that mid-infrared spectroscopy could be successfully used for the nondestructive and objective assessment of processed cheese sensory quality..

Prediction of processed cheese instrumental texture and meltability by mid-infrared spectroscopy coupled with chemometric tools

The objective of this study was to determine the potential of mid-infrared spectroscopy coupled with multidimensional statistical analysis for the prediction of processed cheese instrumental texture and meltability attributes. Processed cheeses (n = 32) of varying composition were manufactured in a pilot plant. Following two and four weeks storage at 4 degrees C samples were analysed using texture profile analysis, two meltability tests (computer vision, Olson and Price) and mid-infrared spectroscopy (4000-640 cm(-1)). Partial least squares regression was used to develop predictive models for all measured attributes. Five attributes were successfully modelled with varying degrees of accuracy. The computer vision meltability model allowed for discrimination between high and low melt values (R-2 = 0.64). The hardness and springiness models gave approximate quantitative results (R-2 = 0.77) and the cohesiveness (R-2 = 0.81) and Olson and Price meltability (R-2 = 0.88) models gave good prediction results. (c) 2006 Elsevier Ltd. All rights reserved..

Application of mid-infrared spectroscopy to the prediction of maturity and sensory texture attributes of cheddar cheese

The objective of this study was to determine the potential of mid-infrared spectroscopy in conjunction with partial least squares (PLS) regression to predict various quality parameters in cheddar cheese. Cheddar cheeses (n = 24) were manufactured and stored at 8 degrees C for 12 mo. Mid-infrared spectra (640 to 4000/cm) were recorded after 4, 6, 9, and 12 mo storage. At 4, 6, and 9 mo, the water-soluble nitrogen (WSN) content of the samples was determined and the samples were also evaluated for 11 sensory texture attributes using descriptive sensory analysis. The mid-infrared spectra were subjected to a number of pretreatments, and predictive models were developed for all parameters. Age was predicted using scatter-corrected, 1st derivative spectra with a root mean square error of cross-validation (RMSECV) of 1 mo, while WSN was predicted using 1st derivative spectra (RMSECV = 2.6%). The sensory texture attributes most successfully predicted were rubbery, crumbly, chewy, and massforming. These attributes were modeled using 2nd derivative spectra and had, corresponding RMSECV values in the range of 2.5 to 4.2 on a scale of 0 to 100. It was concluded that mid-infrared spectroscopy has the potential to predict age, WSN, and several sensory texture attributes of cheddar cheese..

Integration and segmentation in European investment services markets: assessing the implications for international real estate investment

This paper analyses developments in the growth and configuration of the institutional savings markets within the European Union. The paper discusses the changing socio-economic context in which investment services within the EU are being delivered. The is followed by an examination of drivers of market integration such as the growth and consolidation of the fund management industry, the demographic and fiscal pressures for reform of pensions markets and the process and effects of the deregulation of investment services markets. There is a review of outstanding sources of market segmentation. The projections for future growth in pensions are outlined and implications for real estate investment assessed. It is concluded that, although numerous imponderables render reliable quantitative projections problematic, growth and restructuring of the institutional savings market is likely to increase cross-border capital flows to real estate markets.

An eye for an eye: robust motion segmentation by applying the latest in human vision research

Automatically extracting interesting objects from videos is a very challenging task and is applicable to many research areas such robotics, medical imaging, content based indexing and visual surveillance. Automated visual surveillance is a major research area in computational vision and a commonly applied technique in an attempt to extract objects of interest is that of motion segmentation. Motion segmentation relies on the temporal changes that occur in video sequences to detect objects, but as a technique it presents many challenges that researchers have yet to surmount. Changes in real-time video sequences not only include interesting objects, environmental conditions such as wind, cloud cover, rain and snow may be present, in addition to rapid lighting changes, poor footage quality, moving shadows and reflections. The list provides only a sample of the challenges present. This thesis explores the use of motion segmentation as part of a computational vision system and provides solutions for a practical, generic approach with robust performance, using current neuro-biological, physiological and psychological research in primate vision as inspiration.

Emerging segmentation practices in the age of the social customer

This paper reports on an exploratory study of segmentation practices of organisations with a social media presence. It investigates whether traditional segmentation approaches are still relevant in this new socio-technical environment and identifies emerging practices. The study found that social media are particularly promising in terms of targeting influencers, enabling the cost-effective delivery of personalised messages and engaging with numerous customer segments in a differentiated way. However, some problems previously identified in the segmentation literature still occur in the social media environment, such as the technical challenge of integrating databases, the preference for pragmatic rather than complex solutions and the lack of relevant analytical skills. Overall, a gap has emerged between marketing theory and practice. While segmentation is far from obsolete in the age of the social customer, it needs to adapt to reflect the characteristics of the new media.

Polar cap patch segmentation of the tongue of ionization in the morning convection cell

Two types of poleward moving plasma concentration enhancements (PMPCEs) were observed during a sequence of pulsed reconnection events, both in the morning convection cell: Type L (low density) was associated with a cusp flow channel and seems likely to have been produced by ionization associated with particle precipitation, while Type H (high density) appeared to originate from the segmentation of the tongue of ionization by the processes which produced the Type L events. As a result, the Type L and Type H PMPCEs were interspersed, producing a complex density structure which underlines the importance of cusp flow channels as a mechanism for segmenting and structuring electron density in the cusp and shows the necessity of differentiating between at least two classes of electron density patches.

Bridging the segmentation theory/practice divide

More than thirty years ago, Wind's seminal review of research in market segmentation culminated with a research agenda for the subject area. In the intervening period, research has focused on the development of segmentation bases and models, segmentation research techniques and the identification of statistically sound solutions. Practical questions about implementation and the integration of segmentation into marketing strategy have received less attention, even though practitioners are known to struggle with the actual practice of segmentation. This special issue is motivated by this tension between theory and practice, which has shaped and continues to influence the research priorities for the field. Although many years may have elapsed since Wind's original research agenda, pressing questions about effectiveness and productivity apparently remain; namely: (i) concerns about the link between segmentation and performance, and its measurement; and (ii) the notion that productivity improvements arising from segmentation are only achievable if the segmentation process is effectively implemented. There were central themes to the call for papers for this special issue, which aims to develop our understanding of segmentation value, productivity and strategies, and managerial issues and implementation.

Achieving market segmentation from B2B sectorisation

Purpose – The creation of a target market strategy is integral to developing an effective business strategy. The concept of market segmentation is often cited as pivotal to establishing a target market strategy, yet all too often business-to-business marketers utilise little more than trade sectors or product groups as the basis for their groupings of customers, rather than customers' characteristics and buying behaviour. The purpose of this paper is to offer a solution for managers, focusing on customer purchasing behaviour, which evolves from the organisation's existing criteria used for grouping its customers. Design/methodology/approach – One of the underlying reasons managers fail to embrace best practice market segmentation is their inability to manage the transition from how target markets in an organisation are currently described to how they might look when based on customer characteristics, needs, purchasing behaviour and decision-making. Any attempt to develop market segments should reflect the inability of organisations to ignore their existing customer group classification schemes and associated customer-facing operational practices, such as distribution channels and sales force allocations. Findings – A straightforward process has been derived and applied, enabling organisations to practice market segmentation in an evolutionary manner, facilitating the transition to customer-led target market segments. This process also ensures commitment from the managers responsible for implementing the eventual segmentation scheme. This paper outlines the six stages of this process and presents an illustrative example from the agrichemicals sector, supported by other cases. Research implications – The process presented in this paper for embarking on market segmentation focuses on customer purchasing behaviour rather than business sectors or product group classifications - which is true to the concept of market segmentation - but in a manner that participating managers find non-threatening. The resulting market segments have their basis in the organisation's existing customer classification schemes and are an iteration to which most managers readily buy-in. Originality/value – Despite the size of the market segmentation literature, very few papers offer step-by-step guidance for developing customer-focused market segments in business-to-business marketing. The analytical tool for assessing customer purchasing deployed in this paper originally was created to assist in marketing planning programmes, but has since proved its worth as the foundation for creating segmentation schemes in business marketing, as described in this paper.

Implementation rules to bridge the theory/practice divide in market segmentation

Despite an extensive market segmentation literature, applied academic studies which bridge segmentation theory and practice remain a priority for researchers. The need for studies which examine the segmentation implementation barriers faced by organisations is particularly acute. We explore segmentation implementation through the eyes of a European utilities business, by following its progress through a major segmentation project. The study reveals the character and impact of implementation barriers occurring at different stages in the segmentation process. By classifying the barriers, we develop implementation "rules" for practitioners which are designed to minimise their occurrence and impact. We further contribute to the literature by developing a deeper understanding of the mechanisms through which these implementation rules can be applied.

Biologically-inspired robust motion segmentation using mutual information

This paper presents a neuroscience inspired information theoretic approach to motion segmentation. Robust motion segmentation represents a fundamental first stage in many surveillance tasks. As an alternative to widely adopted individual segmentation approaches, which are challenged in different ways by imagery exhibiting a wide range of environmental variation and irrelevant motion, this paper presents a new biologically-inspired approach which computes the multivariate mutual information between multiple complementary motion segmentation outputs. Performance evaluation across a range of datasets and against competing segmentation methods demonstrates robust performance.