Characterizing and reaching high-risk drinkers using audience segmentation.

PubMed

Moss, Howard B; Kirby, Susan D; Donodeo, Fred

2009-08-01

Market or audience segmentation is widely used in social marketing efforts to help planners identify segments of a population to target for tailored program interventions. Market-based segments are typically defined by behaviors, attitudes, knowledge, opinions, or lifestyles. They are more helpful to health communication and marketing planning than epidemiologically defined groups because market-based segments are similar in respect to how they behave or might react to marketing and communication efforts. However, market segmentation has rarely been used in alcohol research. As an illustration of its utility, we employed commercial data that describes the sociodemographic characteristics of high-risk drinkers as an audience segment, including where they tend to live, lifestyles, interests, consumer behaviors, alcohol consumption behaviors, other health-related behaviors, and cultural values. Such information can be extremely valuable in targeting and planning public health campaigns, targeted mailings, prevention interventions, and research efforts. We described the results of a segmentation analysis of those individuals who self-reported to consume 5 or more drinks per drinking episode at least twice in the last 30 days. The study used the proprietary PRIZM (Claritas, Inc., San Diego, CA) audience segmentation database merged with the Center for Disease Control and Prevention's (CDC) Behavioral Risk Factor Surveillance System (BRFSS) database. The top 10 of the 66 PRIZM audience segments for this risky drinking pattern are described. For five of these segments we provided additional in-depth details about consumer behavior and the estimates of the market areas where these risky drinkers resided. The top 10 audience segments (PRIZM clusters) most likely to engage in high-risk drinking are described. The cluster with the highest concentration of binge-drinking behavior is referred to as the "Cyber Millenials." This cluster is characterized as "the nation's tech-savvy singles and couples living in fashionable neighborhoods on the urban fringe." Almost 65% of Cyber Millenials households are found in the Pacific and Middle Atlantic regions of the United States. Additional consumer behaviors of the Cyber Millenials and other segments are also described. Audience segmentation can assist in identifying and describing target audience segments, as well as identifying places where segments congregate on- or offline. This information can be helpful for recruiting subjects for alcohol prevention research as well as planning health promotion campaigns. Through commercial data about high-risk drinkers as "consumers," planners can develop interventions that have heightened salience in terms of opportunities, perceptions, and motivations, and have better media channel identification.

Characterizing and Reaching High-Risk Drinkers Using Audience Segmentation

PubMed Central

Moss, Howard B.; Kirby, Susan D.; Donodeo, Fred

2010-01-01

Background Market or audience segmentation is widely used in social marketing efforts to help planners identify segments of a population to target for tailored program interventions. Market-based segments are typically defined by behaviors, attitudes, knowledge, opinions, or lifestyles. They are more helpful to health communication and marketing planning than epidemiologically-defined groups because market-based segments are similar in respect to how they behave or might react to marketing and communication efforts. However, market segmentation has rarely been used in alcohol research. As an illustration of its utility, we employed commercial data that describes the sociodemographic characteristics of high-risk drinkers as an audience segment; where they tend to live, lifestyles, interests, consumer behaviors, alcohol consumption behaviors, other health-related behaviors, and cultural values. Such information can be extremely valuable in targeting and planning public health campaigns, targeted mailings, prevention interventions and research efforts. Methods We describe the results of a segmentation analysis of those individuals who self-report consuming five or more drinks per drinking episode at least twice in the last 30-days. The study used the proprietary PRIZM™ audience segmentation database merged with Center for Disease Control and Prevention's (CDC) Behavioral Risk Factor Surveillance System (BRFSS) database. The top ten of the 66 PRIZM™ audience segments for this risky drinking pattern are described. For five of these segments we provide additional in-depth details about consumer behavior and the estimates of the market areas where these risky drinkers reside. Results The top ten audience segments (PRIZM clusters) most likely to engage in high-risk drinking are described. The cluster with the highest concentration of binge drinking behavior is referred to as the “Cyber Millenials.” This cluster is characterized as “the nation's tech-savvy singles and couples living in fashionable neighborhoods on the urban fringe. Almost 65% of Cyber Millenials households are found in the Pacific and Middle Atlantic regions of the U.S. Additional consumer behaviors of the Cyber Millenials and other segments are also described. Conclusions Audience segmentation can assist in identifying and describing target audience segments, as well as identifying places where segments congregate on- or offline. This information can be helpful for recruiting subjects for alcohol prevention research, as well as planning health promotion campaigns. Through commercial data about high-risk drinkers as “consumers,” planners can develop interventions that have heightened salience in terms of opportunities, perceptions, and motivations, and have better media channel identification. PMID:19413650

The Importance of Take-Out Food Packaging Attributes: Conjoint Analysis and Quality Function Deployment Approach

NASA Astrophysics Data System (ADS)

Lestari Widaningrum, Dyah

2014-03-01

This research aims to investigate the importance of take-out food packaging attributes, using conjoint analysis and QFD approach among consumers of take-out food products in Jakarta, Indonesia. The conjoint results indicate that perception about packaging material (such as paper, plastic, and polystyrene foam) plays the most important role overall in consumer perception. The clustering results that there is strong segmentation in which take-out food packaging material consumer consider most important. Some consumers are mostly oriented toward the colour of packaging, while another segment of customers concerns on packaging shape and packaging information. Segmentation variables based on packaging response can provide very useful information to maximize image of products through the package's impact. The results of House of Quality development described that Conjoint Analysis - QFD is a useful combination of the two methodologies in product development, market segmentation, and the trade off between customers' requirements in the early stages of HOQ process

Motives of consumers following a vegan diet and their attitudes towards animal agriculture.

PubMed

Janssen, Meike; Busch, Claudia; Rödiger, Manika; Hamm, Ulrich

2016-10-01

The number of consumers following a vegan diet has notably increased in many industrialised countries and it is likely that their influence on the food sector will continue to grow. The aim of the present study was to identify different segments of consumers according to their motivation for following a vegan diet. Another objective was to analyse the attitudes of these consumers towards animal agriculture. The main focus was to determine whether all consumers following a vegan diet oppose animal agriculture in general or if some of these consumers accept certain forms of animal agriculture. The 2014 study, conducted at seven vegan supermarkets in Germany, was based on face-to-face interviews with 329 consumers following a vegan diet. The open question on consumer motivations for adopting a vegan diet revealed three main motives: Animal-related motives (mentioned by 89.7% of the respondents), motives related to personal well-being and/or health (69.3%), and environment-related motives (46.8%). The two-step cluster analysis identified five consumer segments with different motivations for following a vegan diet. The vast majority of respondents (81.8%) mentioned more than one motive. We conclude that making a dichotomous segmentation into ethical versus self-oriented consumers, as previous authors have done, disregards the fact that many consumers following a vegan diet are driven by more than one motive. The consumer segments had significantly different attitudes towards animal agriculture. We identified consumers following a vegan diet (about one third of the sample) who might be open to forms of animal agriculture guaranteeing animal welfare standards going beyond current practices. The present study has interesting implications for the food sector and the agricultural sector. Copyright © 2016 Elsevier Ltd. All rights reserved.

Shopping Effort Classification: Implications for Segmenting the College Student Market

ERIC Educational Resources Information Center

Wright, Robert E.; Palmer, John C.; Eidson, Vicky; Griswold, Melissa

2011-01-01

Market segmentation strategies based on levels of consumer shopping effort have long been utilized by marketing professionals. Such strategies can be beneficial in assisting marketers with development of appropriate marketing mix variables for segments. However, these types of strategies have not been assessed by researchers examining segmentation…

Consumer behaviors towards ready-to-eat foods based on food-related lifestyles in Korea

PubMed Central

Bae, Hyun-Joo; Chae, Mi-Jin

2010-01-01

The purpose of this study was to examine consumers' behaviors toward ready-to-eat foods and to develop ready-to-eat food market segmentation in Korea. The food-related lifestyle and purchase behaviors of ready-to-eat foods were evaluated using 410 ready-to-eat food consumers in the Republic of Korea. Four factors were extracted by exploratory factor analysis (health-orientation, taste-orientation, convenience-orientation, and tradition-orientation) to explain the ready-to eat food consumers' food-related lifestyles. The results of cluster analysis indicated that "tradition seekers" and "convenience seekers" should be regarded as the target segments. Chi-square tests and t-tests of the subdivided groups showed there were significant differences across marital status, education level, family type, eating-out expenditure, place of purchase, and reason for purchase. In conclusion, the tradition seekers consumed more ready-to-eat foods from discount marts or specialty stores and ate them between meals more often than the convenience seekers. In contrast, the convenience seekers purchased more ready-to-eat foods at convenience stores and ate them as meals more often than the tradition seekers. These findings suggest that ready-to-eat food market segmentation based on food-related lifestyles can be applied to develop proper marketing strategies. PMID:20827350

Consumer behaviors towards ready-to-eat foods based on food-related lifestyles in Korea.

PubMed

Bae, Hyun-Joo; Chae, Mi-Jin; Ryu, Kisang

2010-08-01

The purpose of this study was to examine consumers' behaviors toward ready-to-eat foods and to develop ready-to-eat food market segmentation in Korea. The food-related lifestyle and purchase behaviors of ready-to-eat foods were evaluated using 410 ready-to-eat food consumers in the Republic of Korea. Four factors were extracted by exploratory factor analysis (health-orientation, taste-orientation, convenience-orientation, and tradition-orientation) to explain the ready-to eat food consumers' food-related lifestyles. The results of cluster analysis indicated that "tradition seekers" and "convenience seekers" should be regarded as the target segments. Chi-square tests and t-tests of the subdivided groups showed there were significant differences across marital status, education level, family type, eating-out expenditure, place of purchase, and reason for purchase. In conclusion, the tradition seekers consumed more ready-to-eat foods from discount marts or specialty stores and ate them between meals more often than the convenience seekers. In contrast, the convenience seekers purchased more ready-to-eat foods at convenience stores and ate them as meals more often than the tradition seekers. These findings suggest that ready-to-eat food market segmentation based on food-related lifestyles can be applied to develop proper marketing strategies.

One size does not fit all: how the tobacco industry has altered cigarette design to target consumer groups with specific psychological and psychosocial needs.

PubMed

Cook, Benjamin Lê; Wayne, Geoffrey Ferris; Keithly, Lois; Connolly, Gregory

2003-11-01

To identify whether the tobacco industry has targeted cigarette product design towards individuals with varying psychological/psychosocial needs. Internal industry documents were identified through searches of an online archival document research tool database using relevancy criteria of consumer segmentation and needs assessment. The industry segmented consumer markets based on psychological needs (stress relief, behavioral arousal, performance enhancement, obesity reduction) and psychosocial needs (social acceptance, personal image). Associations between these segments and smoking behaviors, brand and design preferences were used to create cigarette brands targeting individuals with these needs. Cigarette brands created to address the psychological/psychosocial needs of smokers may increase the likelihood of smoking initiation and addiction. Awareness of targeted product development will improve smoking cessation and prevention efforts.

Analysis of consumers' preferences and behavior with regard to horse meat using a structured survey questionnaire.

PubMed

Oh, Woon Yong; Lee, Ji Woong; Lee, Chong Eon; Ko, Moon Seok; Jeong, Jae Hong

2009-12-01

In this study, a structured survey questionnaire was used to determine consumers' preferences and behavior with regard to horse meat at a horse meat restaurant located in Jeju, Korea, from October 1 to December 24, 2005. The questionnaire employed in this study consisted of 20 questions designed to characterize six general attributes: horse meat sensory property, physical appearance, health condition, origin, price, and other attributes. Of the 1370 questionnaires distributed, 1126 completed questionnaires were retained based on the completeness of the answers, representing an 82.2% response rate. Two issues were investigated that might facilitate the search for ways to improve horse meat production and marketing programs in Korea. The first step was to determine certain important factors, called principal components, which enabled the researchers to understand the needs of horse meat consumers via principal component analysis. The second step was to define consumer segments with regard to their preferences for horse meat, which was accomplished via cluster analysis. The results of the current study showed that health condition, price, origin, and leanness were the most critical physical attributes affecting the preferences of horse meat consumers. Four segments of consumers, with different demands for horse meat attributes, were identified: origin-sensitive consumers, price-sensitive consumers, quality and safety-sensitive consumers, and non-specific consumers. Significant differences existed among segments of consumers in terms of age, nature of work, frequency of consumption, and general level of acceptability of horse meat.

Shot boundary detection and label propagation for spatio-temporal video segmentation

NASA Astrophysics Data System (ADS)

Piramanayagam, Sankaranaryanan; Saber, Eli; Cahill, Nathan D.; Messinger, David

2015-02-01

This paper proposes a two stage algorithm for streaming video segmentation. In the first stage, shot boundaries are detected within a window of frames by comparing dissimilarity between 2-D segmentations of each frame. In the second stage, the 2-D segments are propagated across the window of frames in both spatial and temporal direction. The window is moved across the video to find all shot transitions and obtain spatio-temporal segments simultaneously. As opposed to techniques that operate on entire video, the proposed approach consumes significantly less memory and enables segmentation of lengthy videos. We tested our segmentation based shot detection method on the TRECVID 2007 video dataset and compared it with block-based technique. Cut detection results on the TRECVID 2007 dataset indicate that our algorithm has comparable results to the best of the block-based methods. The streaming video segmentation routine also achieves promising results on a challenging video segmentation benchmark database.

Rediscovering market segmentation.

PubMed

Yankelovich, Daniel; Meer, David

2006-02-01

In 1964, Daniel Yankelovich introduced in the pages of HBR the concept of nondemographic segmentation, by which he meant the classification of consumers according to criteria other than age, residence, income, and such. The predictive power of marketing studies based on demographics was no longer strong enough to serve as a basis for marketing strategy, he argued. Buying patterns had become far better guides to consumers' future purchases. In addition, properly constructed nondemographic segmentations could help companies determine which products to develop, which distribution channels to sell them in, how much to charge for them, and how to advertise them. But more than 40 years later, nondemographic segmentation has become just as unenlightening as demographic segmentation had been. Today, the technique is used almost exclusively to fulfill the needs of advertising, which it serves mainly by populating commercials with characters that viewers can identify with. It is true that psychographic types like "High-Tech Harry" and "Joe Six-Pack" may capture some truth about real people's lifestyles, attitudes, self-image, and aspirations. But they are no better than demographics at predicting purchase behavior. Thus they give corporate decision makers very little idea of how to keep customers or capture new ones. Now, Daniel Yankelovich returns to these pages, with consultant David Meer, to argue the case for a broad view of nondemographic segmentation. They describe the elements of a smart segmentation strategy, explaining how segmentations meant to strengthen brand identity differ from those capable of telling a company which markets it should enter and what goods to make. And they introduce their "gravity of decision spectrum", a tool that focuses on the form of consumer behavior that should be of the greatest interest to marketers--the importance that consumers place on a product or product category.

A clustering approach to segmenting users of internet-based risk calculators.

PubMed

Harle, C A; Downs, J S; Padman, R

2011-01-01

Risk calculators are widely available Internet applications that deliver quantitative health risk estimates to consumers. Although these tools are known to have varying effects on risk perceptions, little is known about who will be more likely to accept objective risk estimates. To identify clusters of online health consumers that help explain variation in individual improvement in risk perceptions from web-based quantitative disease risk information. A secondary analysis was performed on data collected in a field experiment that measured people's pre-diabetes risk perceptions before and after visiting a realistic health promotion website that provided quantitative risk information. K-means clustering was performed on numerous candidate variable sets, and the different segmentations were evaluated based on between-cluster variation in risk perception improvement. Variation in responses to risk information was best explained by clustering on pre-intervention absolute pre-diabetes risk perceptions and an objective estimate of personal risk. Members of a high-risk overestimater cluster showed large improvements in their risk perceptions, but clusters of both moderate-risk and high-risk underestimaters were much more muted in improving their optimistically biased perceptions. Cluster analysis provided a unique approach for segmenting health consumers and predicting their acceptance of quantitative disease risk information. These clusters suggest that health consumers were very responsive to good news, but tended not to incorporate bad news into their self-perceptions much. These findings help to quantify variation among online health consumers and may inform the targeted marketing of and improvements to risk communication tools on the Internet.

A spatiotemporal-based scheme for efficient registration-based segmentation of thoracic 4-D MRI.

PubMed

Yang, Y; Van Reeth, E; Poh, C L; Tan, C H; Tham, I W K

2014-05-01

Dynamic three-dimensional (3-D) (four-dimensional, 4-D) magnetic resonance (MR) imaging is gaining importance in the study of pulmonary motion for respiratory diseases and pulmonary tumor motion for radiotherapy. To perform quantitative analysis using 4-D MR images, segmentation of anatomical structures such as the lung and pulmonary tumor is required. Manual segmentation of entire thoracic 4-D MRI data that typically contains many 3-D volumes acquired over several breathing cycles is extremely tedious, time consuming, and suffers high user variability. This requires the development of new automated segmentation schemes for 4-D MRI data segmentation. Registration-based segmentation technique that uses automatic registration methods for segmentation has been shown to be an accurate method to segment structures for 4-D data series. However, directly applying registration-based segmentation to segment 4-D MRI series lacks efficiency. Here we propose an automated 4-D registration-based segmentation scheme that is based on spatiotemporal information for the segmentation of thoracic 4-D MR lung images. The proposed scheme saved up to 95% of computation amount while achieving comparable accurate segmentations compared to directly applying registration-based segmentation to 4-D dataset. The scheme facilitates rapid 3-D/4-D visualization of the lung and tumor motion and potentially the tracking of tumor during radiation delivery.

How consumers choose health insurance.

PubMed

Chakraborty, G; Ettenson, R; Gaeth, G

1994-01-01

The authors used choice-based conjoint analysis to model consumers' decision processes when evaluating and selecting health insurance in a multiplan environment. Results indicate that consumer choice is affected by as many as 19 attributes, some of which have received little attention in previous studies. Moreover, the importance of the attributes varies across different demographic segments, giving marketers several targeting opportunities.

Flemish consumer attitudes towards more sustainable food choices.

PubMed

Vanhonacker, Filiep; Van Loo, Ellen J; Gellynck, Xavier; Verbeke, Wim

2013-03-01

Intensive agricultural practices and current western consumption patterns are associated with increased ecological pressure. One way to reduce the ecological impact could be a shift to more sustainable food choices. This study investigates consumer opinions towards a series of food choices with a lower ecological impact. The investigated food choices range from well-known meat substitutes to alternatives which are more radical or innovative and that require an adaptation of food habits and cultural patterns. Results are obtained through a survey among 221 Flemish respondents in Spring 2011. Many consumers underestimate the ecological impact of animal production. Well-known alternatives such as organic meat, moderation of meat consumption and sustainable fish are accepted, although willingness to pay is clearly lower than willingness to consume. Consumers are more reluctant to alternatives that (partly) ban or replace meat in the meal. Opportunities of introducing insects currently appear to be non-existent. Five consumer segments were identified based on self-evaluated ecological footprint and personal relevance of the ecological footprint. The segments were termed Conscious, Active, Unwilling, Ignorant and Uncertain. A profile in terms of demographics, attitudinal and behavioral characteristics is developed for each segments, and conclusions with respect to opportunities for sustainable food choices are discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

European citizen and consumer attitudes and preferences regarding beef and pork.

PubMed

Verbeke, Wim; Pérez-Cueto, Federico J A; Barcellos, Marcia D de; Krystallis, Athanasios; Grunert, Klaus G

2010-02-01

This paper presents the combined mid-term findings of the consumer research components of two EU Sixth Framework Programme integrated projects concerning meat, ProSafeBeef and Q-PorkChains. The consumer pillar of ProSafeBeef carried out eight focus group discussions in May 2008, in France, Germany, Spain and the UK. Q-PorkChains conducted a large-scale, web-based, consumer survey in January 2008 in Belgium, Denmark, Germany, Greece and Poland. The first project provides a set of qualitative data from a small cohort of focus groups and the second a set of quantitative data from a larger consumer sample. This paper draws together the main findings of both projects and provides a comprehensive overview of European citizens' and consumers' attitudes towards and preferences regarding beef and pork. In general, consumers consider meat to be a healthy and important component of the diet. Consumers support the development of technologies that can improve the health attributes of meat products and guarantee eating quality, but they have a negative view of what they see to be excessive manipulation and lack of naturalness in the production and processing of beef products. In the Q-PorkChains study consumer and citizen segments are identified and profiled. Consumer segments were built upon the frequency and variety of pork consumption. The citizen segments were built upon their attitudes towards pig production systems. Overall, the relationship between individuals' views as citizens and their behaviour as consumers was found to be quite weak and did not appear to greatly or systematically influence meat-buying habits. Future studies in both projects will concentrate on consumers' acceptance of innovative meat product concepts and products, with the aim of boosting consumer trust and invigorating the European beef and pork industries.

Beef consumer segment profiles based on information source usage in Poland.

PubMed

Żakowska-Biemans, Sylwia; Pieniak, Zuzanna; Gutkowska, Krystyna; Wierzbicki, Jerzy; Cieszyńska, Katarzyna; Sajdakowska, Marta; Kosicka-Gębska, Małgorzata

2017-02-01

The main aim of this study was to identify market segments based on consumers' usage of information sources about beef and to investigate whether the use of information sources was associated with the type of information consumers were searching for, factors guiding their decision processes to buy beef and motives related to beef consumption. Data were collected in 2014 through a self-administered survey of 501 regular beef consumers. Three distinct clusters were identified: Enthusiast (38.5%), Conservative (43.1%) and Ultra Conservative (18.4%). This study revealed that culinary and personal sources of information on beef were the most frequently used. Taste, perceived healthiness and suitability to prepare many dishes were reported as primary motives to eat beef. These results show that communication channels such as culinary programs and opportunities provided by the development of labelling systems to guarantee beef quality should be considered when developing policies and strategies to increase beef consumption in Poland. Copyright © 2016 Elsevier Ltd. All rights reserved.

Conjoint analysis on the purchase intent for traditional fermented soy product (natto) among Japanese housewives.

PubMed

Kimura, Atsushi; Kuwazawa, Shigetaka; Wada, Yuji; Kyutoku, Yasushi; Okamoto, Masako; Yamaguchi, Yui; Masuda, Tomohiro; Dan, Ippeita

2011-04-01

The effect of sensory and extrinsic attributes on consumer intentions to purchase the Japanese traditional fermented soybean product natto was evaluated using conjoint analysis. Six attributes with 2 levels each were chosen and manipulated: price (high compared with low), the country of origin of the soybeans (domestic compared with imported), stickiness (strong compared with moderate), smell (rich compared with moderate), attached seasonings (attached compared with no attached seasonings), and the environmental friendliness of the packaging (high compared with low). A fractional factorial design was applied and 8 hypothetical product labels were produced. A sample of 479 Japanese housewives ranked these product labels based on their purchase intentions. Overall purchase intention was affected by country of origin, attached seasonings, and price; those attributes accounted for 81.0%, while the sensory attributes of the product accounted for 19.0% of purchase intents. In order to estimate market segments for the natto products based on consumer preference, a cluster analysis was performed. It identified 4 segments of consumers: 1 oriented to attached seasonings, another conscious of the price, and the other 2 oriented to origins. The behavioral and demographic characteristics of the respondents had a limited influence on segment membership.   This research was conducted to understand how consumers valuate various sensory and nonsensory product attributes based on their assessment of the overall product in the case of Japanese fermented soy product (natto). The data of this research would be of great importance both in understanding consumer behavior and in designing strategies for product development.

It is not just a meal, it is an emotional experience - a segmentation of older persons based on the emotions that they associate with mealtimes.

PubMed

den Uijl, Louise C; Jager, Gerry; de Graaf, Cees; Waddell, Jason; Kremer, Stefanie

2014-12-01

Worldwide, the group of older persons is growing fast. To aid this important group in their food and meal requirements, a deeper insight into the expectations and experiences of these persons regarding their mealtimes and snack times is needed. In the current study, we aim to identify consumer segments within the group of vital community-dwelling older persons on the basis of the emotions they associate with their mealtimes and snack times (from now on referred to as mealtimes). Participants (n = 392, mean age 65.8 (years) ± 5.9 (SD)) completed an online survey. The survey consisted of three questionnaires: emotions associated with mealtimes, functionality of mealtimes, and psychographic characteristics (health and taste attitudes, food fussiness, and food neophobia). Consumer segments were identified and characterised based on the emotions that the respondents reported to experience at mealtimes, using a hierarchical cluster analysis. Clusters were described using variables previously not included in the cluster analysis, such as functionality of mealtimes and psychographic characteristics. Four consumer segments were identified: Pleasurable averages, Adventurous arousals, Convivial indulgers, and Indifferent restrictives. These segments differed significantly in their emotional associations with mealtimes both in valence and level of arousal. The present study provides actionable insights for the development of products and communication strategies tailored to the needs of vital community-dwelling older persons. Copyright © 2014 Elsevier Ltd. All rights reserved.

Using lifestyle analysis to develop wellness marketing strategies for IT professionals in India.

PubMed

Suresh, Sathya; Ravichandran, Swathi

2010-01-01

Revenues for the information technology (IT) industry have grown 10 times over the past decade in India. Although this growth has resulted in increased job opportunities, heavy workloads, unhealthy eating habits, and reduced family time are significant downfalls. To understand lifestyle choices of IT professionals, this study segmented and profiled wellness clients based on lifestyle. Data were collected from clients of five wellness centers. Cluster and discriminant analyses revealed four wellness consumer segments based on lifestyle. Results indicated a need for varying positioning approaches, segmentation, and marketing strategies suited for identified segments. To assist managers of wellness centers, four distinct packages were created that can be marketed to clients in the four segments.

HealthStyles: a new psychographic segmentation system for health care marketers.

PubMed

Endresen, K W; Wintz, J C

1988-01-01

HealthStyles is a new psychographic segmentation system specifically designed for the health care industry. This segmentation system goes beyond traditional geographic and demographic analysis and examines health-related consumer attitudes and behaviors. Four statistically distinct "styles" of consumer health care preferences have been identified. The profiles of the four groups have substantial marketing implications in terms of design and promotion of products and services. Each segment of consumers also has differing expectations of physician behavior.

Does race matters in consumers' stated preferences for water and carbon footprints labelled food products? Insights from black and white South Africans

NASA Astrophysics Data System (ADS)

Owusu-Sekyere, Enoch; Jordaan, Henry

2017-04-01

In recent years, governments, policy-makers, and managers of private food companies and agribusinesses are interested in understanding how consumers will react to environmentally sustainable attributes and information on food product labels. This study examines consumers' stated preferences for water and carbon footprints labelled food products from the viewpoint of black and white South Africans. Discrete choice experimental data was collected from black and white consumers to possibly assess cross-ethnic variations in preferences for environmentally sustainable products. Two widely purchased livestock products were chosen for the choice experiment. We found that consumers' preferences for environmentally sustainable attributes vary significantly between black and white South Africans. Our findings revealed that there are profound heterogeneous consumer segments within black and white respondents. The heterogeneity within both sub-samples is better explained at the segment level, rather than at individual level. For both product categories, the findings revealed that there are more distinct consumer segments among black respondents, relative to white respondents. The black respondents consist of water sustainability advocates, carbon reduction advocates, keen environmentalist and environmental neutrals. The white respondents entail keen environmentalist, environmental cynics, and environmental neutrals. The inherent significant variations in preferences for environmentally sustainable attributes across segments and racial groups would help in formulating feasible, and segment-specific environmental sustainability policies and marketing strategies aimed at changing consumers' attitude towards environmentally sustainable products. Demographic targeting of consumer segments, sustainability awareness and segment-specific educational campaigns meant to enhance subjective and objective knowledge on environmental sustainability are important tools for food companies and agribusinesses to promote and market environmentally sustainable food products.

Brain Tumor Image Segmentation in MRI Image

NASA Astrophysics Data System (ADS)

Peni Agustin Tjahyaningtijas, Hapsari

2018-04-01

Brain tumor segmentation plays an important role in medical image processing. Treatment of patients with brain tumors is highly dependent on early detection of these tumors. Early detection of brain tumors will improve the patient’s life chances. Diagnosis of brain tumors by experts usually use a manual segmentation that is difficult and time consuming because of the necessary automatic segmentation. Nowadays automatic segmentation is very populer and can be a solution to the problem of tumor brain segmentation with better performance. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. this paper, we focus on the recent trend of automatic segmentation in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of full automatic segmentaion are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.

Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance images.

PubMed

Jian, Junming; Xiong, Fei; Xia, Wei; Zhang, Rui; Gu, Jinhui; Wu, Xiaodong; Meng, Xiaochun; Gao, Xin

2018-06-01

Segmentation of colorectal tumors is the basis of preoperative prediction, staging, and therapeutic response evaluation. Due to the blurred boundary between lesions and normal colorectal tissue, it is hard to realize accurate segmentation. Routinely manual or semi-manual segmentation methods are extremely tedious, time-consuming, and highly operator-dependent. In the framework of FCNs, a segmentation method for colorectal tumor was presented. Normalization was applied to reduce the differences among images. Borrowing from transfer learning, VGG-16 was employed to extract features from normalized images. We conducted five side-output blocks from the last convolutional layer of each block of VGG-16 along the network, these side-output blocks can deep dive multiscale features, and produced corresponding predictions. Finally, all of the predictions from side-output blocks were fused to determine the final boundaries of the tumors. A quantitative comparison of 2772 colorectal tumor manual segmentation results from T2-weighted magnetic resonance images shows that the average Dice similarity coefficient, positive predictive value, specificity, sensitivity, Hammoude distance, and Hausdorff distance were 83.56, 82.67, 96.75, 87.85%, 0.2694, and 8.20, respectively. The proposed method is superior to U-net in colorectal tumor segmentation (P < 0.05). There is no difference between cross-entropy loss and Dice-based loss in colorectal tumor segmentation (P > 0.05). The results indicate that the introduction of FCNs contributed to accurate segmentation of colorectal tumors. This method has the potential to replace the present time-consuming and nonreproducible manual segmentation method.

Using multimodal information for the segmentation of fluorescent micrographs with application to virology and microbiology.

PubMed

Held, Christian; Wenzel, Jens; Webel, Rike; Marschall, Manfred; Lang, Roland; Palmisano, Ralf; Wittenberg, Thomas

2011-01-01

In order to improve reproducibility and objectivity of fluorescence microscopy based experiments and to enable the evaluation of large datasets, flexible segmentation methods are required which are able to adapt to different stainings and cell types. This adaption is usually achieved by the manual adjustment of the segmentation methods parameters, which is time consuming and challenging for biologists with no knowledge on image processing. To avoid this, parameters of the presented methods automatically adapt to user generated ground truth to determine the best method and the optimal parameter setup. These settings can then be used for segmentation of the remaining images. As robust segmentation methods form the core of such a system, the currently used watershed transform based segmentation routine is replaced by a fast marching level set based segmentation routine which incorporates knowledge on the cell nuclei. Our evaluations reveal that incorporation of multimodal information improves segmentation quality for the presented fluorescent datasets.

Techniques for efficient, real-time, 3D visualization of multi-modality cardiac data using consumer graphics hardware.

PubMed

Levin, David; Aladl, Usaf; Germano, Guido; Slomka, Piotr

2005-09-01

We exploit consumer graphics hardware to perform real-time processing and visualization of high-resolution, 4D cardiac data. We have implemented real-time, realistic volume rendering, interactive 4D motion segmentation of cardiac data, visualization of multi-modality cardiac data and 3D display of multiple series cardiac MRI. We show that an ATI Radeon 9700 Pro can render a 512x512x128 cardiac Computed Tomography (CT) study at 0.9 to 60 frames per second (fps) depending on rendering parameters and that 4D motion based segmentation can be performed in real-time. We conclude that real-time rendering and processing of cardiac data can be implemented on consumer graphics cards.

Differences among consumer segments with regard to perceptions of comparative effectiveness research.

PubMed

Williams, Sunyna S; Frost, Sloane L

2014-11-01

To examine differences among health-related decision-making consumer segments with regard to knowledge, skills, attitudes, and behaviors pertinent to comparative effectiveness research. Data were collected via an online survey from 603 adults with chronic conditions. Consumer segment was determined using a two-item tool. Active consumers (high skills and motivation) reported the highest levels of engagement in various behaviors. Passive consumers (low skills and motivation) reported the lowest levels of engagement in various behaviors. High-effort consumers (low skills, high motivation) reported more positive attitudes and opinions and more engagement in various behaviors than did complacent consumers (high skills, low motivation). Effective translation and dissemination of comparative effectiveness research will require the development of approaches tailored to consumers with varying levels of skills and motivation.

Consumer profile analysis for different types of meat in Spain.

PubMed

Escriba-Perez, Carmen; Baviera-Puig, Amparo; Buitrago-Vera, Juan; Montero-Vicente, Luis

2017-07-01

It is important to analyse the consumer profile of each type of meat to better adapt the marketing mix to each one. To this end, we examined the average consumption frequency of different types of meat based on two methodologies: consumer segmentation using the food-related lifestyle (FRL) framework, giving rise to 4 segments, and analysis of socio-demographic profiles. The variables used were: sex, age, educational level, social class, number of people in the household, presence of children younger than 18 in the home, geographical area and habitual residence. Beef was the only meat type significant in both analyses. Turkey meat only appeared as significant in the FRL analysis. The other meats (chicken, pork, rabbit and lamb) were only significant in the sociodemographic variables analysis. From the outcomes we may conclude that there is no single consumer profile, which rather depends on the type of meat. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Consequences of the Lack of Critical Thinking-Based Education in the African-American Community.

ERIC Educational Resources Information Center

Webster, Angela F.

Large segments of the African-American population lack the educational and financial resources to participate fully in building a high-technology economy and in consuming its products. Maintaining large undereducated and unproductive segments of society is a recipe for collective social unrest. The United States today requires a highly educated…

Tailored Educational Approaches for Consumer Health: A Model to Address Health Promotion in an Era of Personalized Medicine.

PubMed

Cohn, Wendy F; Lyman, Jason; Broshek, Donna K; Guterbock, Thomas M; Hartman, David; Kinzie, Mable; Mick, David; Pannone, Aaron; Sturz, Vanessa; Schubart, Jane; Garson, Arthur T

2018-01-01

To develop a model, based on market segmentation, to improve the quality and efficiency of health promotion materials and programs. Market segmentation to create segments (groups) based on a cross-sectional questionnaire measuring individual characteristics and preferences for health information. Educational and delivery recommendations developed for each group. General population of adults in Virginia. Random sample of 1201 Virginia residents. Respondents are representative of the general population with the exception of older age. Multiple factors known to impact health promotion including health status, health system utilization, health literacy, Internet use, learning styles, and preferences. Cluster analysis and discriminate analysis to create and validate segments. Common sized means to compare factors across segments. Developed educational and delivery recommendations matched to the 8 distinct segments. For example, the "health challenged and hard to reach" are older, lower literacy, and not likely to seek out health information. Their educational and delivery recommendations include a sixth-grade reading level, delivery through a provider, and using a "push" strategy. This model addresses a need to improve the efficiency and quality of health promotion efforts in an era of personalized medicine. It demonstrates that there are distinct groups with clearly defined educational and delivery recommendations. Health promotion professionals can consider Tailored Educational Approaches for Consumer Health to develop and deliver tailored materials to encourage behavior change.

Constraint factor graph cut-based active contour method for automated cellular image segmentation in RNAi screening.

PubMed

Chen, C; Li, H; Zhou, X; Wong, S T C

2008-05-01

Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Automated screening of such experiments generates a large number of images with great variations in image quality, which makes manual analysis unreasonably time-consuming. Therefore, effective techniques for automatic image analysis are urgently needed, in which segmentation is one of the most important steps. This paper proposes a fully automatic method for cells segmentation in genome-wide RNAi screening images. The method consists of two steps: nuclei and cytoplasm segmentation. Nuclei are extracted and labelled to initialize cytoplasm segmentation. Since the quality of RNAi image is rather poor, a novel scale-adaptive steerable filter is designed to enhance the image in order to extract long and thin protrusions on the spiky cells. Then, constraint factor GCBAC method and morphological algorithms are combined to be an integrated method to segment tight clustered cells. Compared with the results obtained by using seeded watershed and the ground truth, that is, manual labelling results by experts in RNAi screening data, our method achieves higher accuracy. Compared with active contour methods, our method consumes much less time. The positive results indicate that the proposed method can be applied in automatic image analysis of multi-channel image screening data.

Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks.

PubMed

Liu, Xiaoming; Guo, Shuxu; Yang, Bingtao; Ma, Shuzhi; Zhang, Huimao; Li, Jing; Sun, Changjian; Jin, Lanyi; Li, Xueyan; Yang, Qi; Fu, Yu

2018-04-20

Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ segmentation framework is presented. The framework utilized convolution neural networks (CNN) to classify pixels. To reduce the redundant inputs, the simple linear iterative clustering (SLIC) of super-pixels and the support vector machine (SVM) classifier are introduced. To establish the perfect boundary of organs in one-pixel-level, the pixels need to be classified step-by-step. First, the SLIC is used to cut an image into grids and extract respective digital signatures. Next, the signature is classified by the SVM, and the rough edges are acquired. Finally, a precise boundary is obtained by the CNN, which is based on patches around each pixel-point. The framework is applied to abdominal CT scans of livers and high-resolution computed tomography (HRCT) scans of lungs. The experimental CT scans are derived from two public datasets (Sliver 07 and a Chinese local dataset). Experimental results show that the proposed method can precisely and efficiently detect the organs. This method consumes 38 s/slice for liver segmentation. The Dice coefficient of the liver segmentation results reaches to 97.43%. For lung segmentation, the Dice coefficient is 97.93%. This finding demonstrates that the proposed framework is a favorable method for lung segmentation of HRCT scans.

Consumer choice of pork chops in Taiwan.

PubMed

Chen, M T; Guo, H L; Tseng, T F; Roan, S W; Ngapo, T M

2010-07-01

Digital photographs of pork chops varying systematically in appearance were presented to 716 Taiwanese consumers in a study that aimed to identify the most important characteristics of fresh pork which determine consumer choice in Taiwan. Relationships between consumer segmentation in choice and socio-demographic and cultural differences were also investigated. Colour and fat cover were the most frequently chosen of the four characteristics studied. Dark red colour was preferred by 64% of consumers and lean fat cover by 44%. Marbling and drip were less important in the decision making process being used by less than a half of consumers. The four preference-based clusters of consumers showed no correlation with socio-demographic-based consumer clusters, but did show significant links with possession of a refrigerator, age at which schooling was completed, liking pork for its price and gender of consumer. Crown Copyright 2010. Published by Elsevier Ltd. All rights reserved.

Explaining Fruit and Vegetable Intake Using a Consumer Marketing Tool

ERIC Educational Resources Information Center

Della, Lindsay J.; DeJoy, David M.; Lance, Charles E.

2009-01-01

In response to calls to reinvent the 5 A Day fruit and vegetable campaign, this study assesses the utility of VALS[TM], a consumer-based audience segmentation tool that divides the U.S. population into groups leading similar lifestyles. The study examines whether the impact of theory of planned behavior (TPB) constructs varies across VALS groups…

Acceptability of Dry Dog Food Visual Characteristics by Consumer Segments Based on Overall Liking: a Case Study in Poland.

PubMed

Gomez Baquero, David; Koppel, Kadri; Chambers, Delores; Hołda, Karolina; Głogowski, Robert; Chambers, Edgar

2018-05-23

Sensory analysis of pet foods has been emerging as an important field of study for the pet food industry over the last few decades. Few studies have been conducted on understanding the pet owners’ perception of pet foods. The objective of this study is to gain a deeper understanding on the perception of the visual characteristics of dry dog foods by dog owners in different consumer segments. A total of 120 consumers evaluated the appearance of 30 dry dog food samples with varying visual characteristics. The consumers rated the acceptance of the samples and associated each one with a list of positive and negative beliefs. Cluster Analysis, ANOVA and Correspondence Analysis were used to analyze the consumer responses. The acceptability of the appearance of dry dog foods was affected by the number of different kibbles present, color(s), shape(s), and size(s) of the kibbles in the product. Three consumer clusters were identified. Consumers rated highest single-kibble samples of medium sizes, traditional shapes, and brown colors. Participants disliked extra-small or extra-large kibble sizes, shapes with high-dimensional contrast, and kibbles of light brown color. These findings can help dry dog food manufacturers to meet consumers’ needs with increasing benefits to the pet food and commodity industries.

Preference mapping of lemon lime carbonated beverages with regular and diet beverage consumers.

PubMed

Leksrisompong, P P; Lopetcharat, K; Guthrie, B; Drake, M A

2013-02-01

The drivers of liking of lemon-lime carbonated beverages were investigated with regular and diet beverage consumers. Ten beverages were selected from a category survey of commercial beverages using a D-optimal procedure. Beverages were subjected to consumer testing (n = 101 regular beverage consumers, n = 100 diet beverage consumers). Segmentation of consumers was performed on overall liking scores followed by external preference mapping of selected samples. Diet beverage consumers liked 2 diet beverages more than regular beverage consumers. There were no differences in the overall liking scores between diet and regular beverage consumers for other products except for a sparkling beverage sweetened with juice which was more liked by regular beverage consumers. Three subtle but distinct consumer preference clusters were identified. Two segments had evenly distributed diet and regular beverage consumers but one segment had a greater percentage of regular beverage consumers (P < 0.05). The 3 preference segments were named: cluster 1 (C1) sweet taste and carbonation mouthfeel lovers, cluster 2 (C2) carbonation mouthfeel lovers, sweet and bitter taste acceptors, and cluster 3 (C3) bitter taste avoiders, mouthfeel and sweet taste lovers. User status (diet or regular beverage consumers) did not have a large impact on carbonated beverage liking. Instead, mouthfeel attributes were major drivers of liking when these beverages were tested in a blind tasting. Preference mapping of lemon-lime carbonated beverage with diet and regular beverage consumers allowed the determination of drivers of liking of both populations. The understanding of how mouthfeel attributes, aromatics, and basic tastes impact liking or disliking of products was achieved. Preference drivers established in this study provide product developers of carbonated lemon-lime beverages with additional information to develop beverages that may be suitable for different groups of consumers. © 2013 Institute of Food Technologists®

Optimal reinforcement of training datasets in semi-supervised landmark-based segmentation

NASA Astrophysics Data System (ADS)

Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

2015-03-01

During the last couple of decades, the development of computerized image segmentation shifted from unsupervised to supervised methods, which made segmentation results more accurate and robust. However, the main disadvantage of supervised segmentation is a need for manual image annotation that is time-consuming and subjected to human error. To reduce the need for manual annotation, we propose a novel learning approach for training dataset reinforcement in the area of landmark-based segmentation, where newly detected landmarks are optimally combined with reference landmarks from the training dataset and therefore enriches the training process. The approach is formulated as a nonlinear optimization problem, where the solution is a vector of weighting factors that measures how reliable are the detected landmarks. The detected landmarks that are found to be more reliable are included into the training procedure with higher weighting factors, whereas the detected landmarks that are found to be less reliable are included with lower weighting factors. The approach is integrated into the landmark-based game-theoretic segmentation framework and validated against the problem of lung field segmentation from chest radiographs.

The influence of lifestyle on health behavior and preference for functional foods.

PubMed

Szakály, Zoltán; Szente, Viktória; Kövér, György; Polereczki, Zsolt; Szigeti, Orsolya

2012-02-01

The main objective of this survey is to reveal the relationship between lifestyle, health behavior, and the consumption of functional foods on the basis of Grunert's food-related lifestyle model. In order to achieve this objective, a nationwide representative questionnaire-based survey was launched with 1000 participants in Hungary. The results indicate that a Hungarian consumer makes rational decisions, he or she seeks bargains, and he wants to know whether or not he gets good value for his money. Further on, various lifestyle segments are defined by the authors: the rational, uninvolved, conservative, careless, and adventurous consumer segments. Among these, consumers with a rational approach provide the primary target group for the functional food market, where health consciousness and moderate price sensitivity can be observed together. Adventurous food consumers stand out because they search for novelty; this makes them an equally important target group. Conservative consumers are another, one characterized by positive health behavior. According to the findings of the research, there is a significant relationship between lifestyle, health behavior, and the preference for functional food products. Copyright © 2011 Elsevier Ltd. All rights reserved.

Consumer Attitudes Toward Animal Welfare-Friendly Products and Willingness to Pay: Exploration of Mexican Market Segments.

PubMed

Miranda-de la Lama, Genaro C; Estévez-Moreno, Laura X; Villarroel, Morris; Rayas-Amor, Adolfo A; María, Gustavo A; Sepúlveda, Wilmer S

2018-04-03

The study aim was to identify consumer segmentation based on nonhuman animal welfare (AW) attitudes and their relationship with demographic features and willingness to pay (WTP) for welfare-friendly products (WFP) in Mexico. Personal interviews were conducted with 843 Mexican consumers who stated they purchased most of the animal products in their home. Respondents were selected using a quota sampling method with age, gender, education, and origin as quota control variables. The multivariate analysis suggested there were three clusters or consumer profiles labeled "skeptical," "concerned," and "ethical," which helped explain the association between AW attitudes, some demographic variables, and WTP for WFP. This study is one of the first to address consumer profiling in Latin America, and the findings could have implications for the commercialization of WFP. Hence, customers should receive information to consider welfare innovations when deciding to purchase animal products. The growth of the WFP food market establishes an element of a far more multifaceted phenomenon of sustainable consumption and support of a new paradigm called responsible marketing in emerging markets such as Mexico.

Russian consumers' motives for food choice.

PubMed

Honkanen, Pirjo; Frewer, Lynn

2009-04-01

Knowledge about food choice motives which have potential to influence consumer consumption decisions is important when designing food and health policies, as well as marketing strategies. Russian consumers' food choice motives were studied in a survey (1081 respondents across four cities), with the purpose of identifying consumer segments based on these motives. These segments were then profiled using consumption, attitudinal and demographic variables. Face-to-face interviews were used to sample the data, which were analysed with two-step cluster analysis (SPSS). Three clusters emerged, representing 21.5%, 45.8% and 32.7% of the sample. The clusters were similar in terms of the order of motivations, but differed in motivational level. Sensory factors and availability were the most important motives for food choice in all three clusters, followed by price. This may reflect the turbulence which Russia has recently experienced politically and economically. Cluster profiles differed in relation to socio-demographic factors, consumption patterns and attitudes towards health and healthy food.

Consumers' health-related motive orientations and ready meal consumption behaviour.

PubMed

Geeroms, Nele; Verbeke, Wim; Van Kenhove, Patrick

2008-11-01

Based on a multidimensional perspective on the meaning of health, this study explores associations between consumers' health-related motive orientations (HRMO) and ready meal consumption behaviour. Cross-sectional data were collected from a sample of 1934 Flemish consumers through an on-line survey. The respondents rated 45 health statements referring to people's motives for pursuing health. The survey also assessed information on several aspects of ready meal consumption, i.e. consumption frequency, beliefs and attitudes toward ready meals and ready meal buying criteria. Based on a two-step cluster analysis, we identified five distinct subgroups in the sample, according to their HRMO: health is about energy (Energetic Experimenters), emotional well-being/enjoying life (Harmonious Enjoyers), social responsibility/physical well-being (Normative Carers), achievement/outward appearance (Conscious Experts) and autonomy (Rationalists). Ready meal consumption patterns differed between these segments, with Energetic Experimenters and Conscious Experts showing significantly more positive attitudes, stronger beliefs and both higher penetration and consumption frequency related to ready meals, compared to Harmonious Enjoyers, Normative Carers and Rationalists. These findings may relate to the individualistic versus altruistic health orientation perspective of the identified segments, and are valuable in the context of improving consumer-oriented product development, positioning and marketing of ready meals.

Sustainable food consumption. Product choice or curtailment?

PubMed

Verain, Muriel C D; Dagevos, Hans; Antonides, Gerrit

2015-08-01

Food consumption is an important factor in shaping the sustainability of our food supply. The present paper empirically explores different types of sustainable food behaviors. A distinction between sustainable product choices and curtailment behavior has been investigated empirically and predictors of the two types of behavior have been identified. Respondents were classified into four segments based on their sustainable food behaviors: unsustainers, curtailers, product-oriented consumers, and sustainers. Significant differences between the segments were found with regard to food choice motives, personal and social norms, food involvement, subjective knowledge on sustainable food, ability to judge how sustainably a product has been produced and socio-demographics. It is concluded that distinguishing between behavioral strategies toward sustainable food consumption is important as consumer segments can be identified that differ both in their level of sustainable food consumption and in the type of behavior they employ. Copyright © 2015 Elsevier Ltd. All rights reserved.

Risk segmentation: goal or problem?

PubMed

Feldman, R; Dowd, B

2000-07-01

This paper traces the evolution of economists' views about risk segmentation in health insurance markets. Originally seen as a desirable goal, risk segmentation has come to be viewed as leading to abnormal profits, wasted resources, and inefficient limitations on coverage and services. We suggest that risk segmentation may be efficient if one takes an ex post view (i.e., after consumers' risks are known). From this perspective, managed care may be a much better method for achieving risk segmentation than limitations on coverage. The most serious objection to risk segmentation is the ex ante concern that it undermines long-term insurance contracts that would protect consumers against changes in lifetime risk.

Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries

PubMed Central

Davuluri, Pavani; Wu, Jie; Tang, Yang; Cockrell, Charles H.; Ward, Kevin R.; Najarian, Kayvan; Hargraves, Rosalyn H.

2012-01-01

Automated hemorrhage detection and segmentation in traumatic pelvic injuries is vital for fast and accurate treatment decision making. Hemorrhage is the main cause of deaths in patients within first 24 hours after the injury. It is very time consuming for physicians to analyze all Computed Tomography (CT) images manually. As time is crucial in emergence medicine, analyzing medical images manually delays the decision-making process. Automated hemorrhage detection and segmentation can significantly help physicians to analyze these images and make fast and accurate decisions. Hemorrhage segmentation is a crucial step in the accurate diagnosis and treatment decision-making process. This paper presents a novel rule-based hemorrhage segmentation technique that utilizes pelvic anatomical information to segment hemorrhage accurately. An evaluation measure is used to quantify the accuracy of hemorrhage segmentation. The results show that the proposed method is able to segment hemorrhage very well, and the results are promising. PMID:22919433

Community pharmacy customer segmentation based on factors influencing their selection of pharmacy and over-the-counter medicines.

PubMed

Kevrekidis, Dimitrios Phaedon; Minarikova, Daniela; Markos, Angelos; Malovecka, Ivona; Minarik, Peter

2018-01-01

Within the competitive pharmacy market environment, community pharmacies are required to develop efficient marketing strategies based on contemporary information about consumer behavior in order to attract clients and develop customer loyalty. This study aimed to investigate the consumers' preferences concerning the selection of pharmacy and over-the-counter (OTC) medicines, and to identify customer segments in relation to these preferences. A cross-sectional study was conducted between February and March 2016 on a convenient quota sample of 300 participants recruited in the metropolitan area of Thessaloniki, Greece. The main instrument used for data collection was a structured questionnaire with close-ended, multiple choice questions. To identify customer segments, Two-Step cluster analysis was conducted. Three distinct pharmacy customer clusters emerged. Customers of the largest cluster (49%; 'convenience customers') were mostly younger consumers. They gave moderate to positive ratings to factors affecting the selection of pharmacy and OTCs; convenience, and previous experience and the pharmacist's opinion, received the highest ratings. Customers of the second cluster (35%; 'loyal customers') were mainly retired; most of them reported visiting a single pharmacy. They gave high ratings to all factors that influence pharmacy selection, especially the pharmacy's staff, and factors influencing the purchase of OTCs, particularly previous experience and the pharmacist's opinion. Customers of the smallest cluster (16%; 'convenience and price-sensitive customers') were mainly retired or unemployed with low to moderate education, and low personal income. They gave the lowest ratings to most of the examined factors; convenience among factors influencing pharmacy selection, whereas previous experience, the pharmacist's opinion and product price among those affecting the purchase of OTCs, received the highest ratings. The community pharmacy market comprised of distinct customer segments that varied in the consumer preferences concerning the selection of pharmacy and OTCs, the evaluation of pharmaceutical services and products, and demographic characteristics.

Model-based segmentation of hand radiographs

NASA Astrophysics Data System (ADS)

Weiler, Frank; Vogelsang, Frank

1998-06-01

An important procedure in pediatrics is to determine the skeletal maturity of a patient from radiographs of the hand. There is great interest in the automation of this tedious and time-consuming task. We present a new method for the segmentation of the bones of the hand, which allows the assessment of the skeletal maturity with an appropriate database of reference bones, similar to the atlas based methods. The proposed algorithm uses an extended active contour model for the segmentation of the hand bones, which incorporates a-priori knowledge of shape and topology of the bones in an additional energy term. This `scene knowledge' is integrated in a complex hierarchical image model, that is used for the image analysis task.

TuMore: generation of synthetic brain tumor MRI data for deep learning based segmentation approaches

NASA Astrophysics Data System (ADS)

Lindner, Lydia; Pfarrkirchner, Birgit; Gsaxner, Christina; Schmalstieg, Dieter; Egger, Jan

2018-03-01

Accurate segmentation and measurement of brain tumors plays an important role in clinical practice and research, as it is critical for treatment planning and monitoring of tumor growth. However, brain tumor segmentation is one of the most challenging tasks in medical image analysis. Since manual segmentations are subjective, time consuming and neither accurate nor reliable, there exists a need for objective, robust and fast automated segmentation methods that provide competitive performance. Therefore, deep learning based approaches are gaining interest in the field of medical image segmentation. When the training data set is large enough, deep learning approaches can be extremely effective, but in domains like medicine, only limited data is available in the majority of cases. Due to this reason, we propose a method that allows to create a large dataset of brain MRI (Magnetic Resonance Imaging) images containing synthetic brain tumors - glioblastomas more specifically - and the corresponding ground truth, that can be subsequently used to train deep neural networks.

Consumer segmentation as a means to investigate emotional associations to meals.

PubMed

Piqueras-Fiszman, Betina; Jaeger, Sara R

2016-10-01

Consumers naturally associate emotions to meal occasions and understanding these can advance knowledge of food-related behaviours and attitudes. The present study used an online survey to investigate the emotional associations that people have with recalled meals: 'memorable' (MM) and 'routine' evening (RM). Heterogeneity in the studied consumer population (UK adults, n = 576 and 571, respectively) was accounted for using a data-driven approach to establish emotion-based segments. Two groups of people were identified with very different emotional response patterns to recalled meals. For 'memorable' and 'routine' meals the majority of people (Cluster 1) held strong positive and weak negative emotional associations. In Cluster 2, positive emotions remained more strongly associated than negative emotions, but much less so. In accordance with findings based on other response variables (e.g., preference, attitudes), psychographic variables accounted better for the heterogeneity found in the emotion associations than socio-demographic variables. Participants' level of meal engagement and difficulty in describing feelings (DDF scale) were the two most important predictors of cluster membership. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data

NASA Astrophysics Data System (ADS)

Gloger, Oliver; Tönnies, Klaus; Mensel, Birger; Völzke, Henry

2015-11-01

In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches.

Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data.

PubMed

Gloger, Oliver; Tönnies, Klaus; Mensel, Birger; Völzke, Henry

2015-11-21

In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches.

Target marketing strategies for occupational therapy entrepreneurs.

PubMed

Kautzmann, L N; Kautzmann, F N; Navarro, F H

1989-01-01

Understanding marketing techniques is one of the skills needed by successful entre renews. Target marketing is an effective method for occupational therapy entrepreneurs to use in determining when and where to enter the marketplace. The two components of target marketing, market segmentation and the development of marketing mix strategies for each identified market segment, are described. The Profife of Attitudes Toward Health Care (PATH) method of psychographic market segmentation of health care consumers is presented. Occupational therapy marketing mix strategies for each PATH consumer group are delineated and compatible groupings of market segments are suggested.

A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier.

PubMed

Nanthagopal, A Padma; Rajamony, R Sukanesh

2012-07-01

The proposed system provides new textural information for segmenting tumours, efficiently and accurately and with less computational time, from benign and malignant tumour images, especially in smaller dimensions of tumour regions of computed tomography (CT) images. Region-based segmentation of tumour from brain CT image data is an important but time-consuming task performed manually by medical experts. The objective of this work is to segment brain tumour from CT images using combined grey and texture features with new edge features and nonlinear support vector machine (SVM) classifier. The selected optimal features are used to model and train the nonlinear SVM classifier to segment the tumour from computed tomography images and the segmentation accuracies are evaluated for each slice of the tumour image. The method is applied on real data of 80 benign, malignant tumour images. The results are compared with the radiologist labelled ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and the overlap similarity measure dice metric. From the analysis and performance measures such as segmentation accuracy and dice metric, it is inferred that better segmentation accuracy and higher dice metric are achieved with the normalized cut segmentation method than with the fuzzy c-means clustering method.

A segmentation editing framework based on shape change statistics

NASA Astrophysics Data System (ADS)

Mostapha, Mahmoud; Vicory, Jared; Styner, Martin; Pizer, Stephen

2017-02-01

Segmentation is a key task in medical image analysis because its accuracy significantly affects successive steps. Automatic segmentation methods often produce inadequate segmentations, which require the user to manually edit the produced segmentation slice by slice. Because editing is time-consuming, an editing tool that enables the user to produce accurate segmentations by only drawing a sparse set of contours would be needed. This paper describes such a framework as applied to a single object. Constrained by the additional information enabled by the manually segmented contours, the proposed framework utilizes object shape statistics to transform the failed automatic segmentation to a more accurate version. Instead of modeling the object shape, the proposed framework utilizes shape change statistics that were generated to capture the object deformation from the failed automatic segmentation to its corresponding correct segmentation. An optimization procedure was used to minimize an energy function that consists of two terms, an external contour match term and an internal shape change regularity term. The high accuracy of the proposed segmentation editing approach was confirmed by testing it on a simulated data set based on 10 in-vivo infant magnetic resonance brain data sets using four similarity metrics. Segmentation results indicated that our method can provide efficient and adequately accurate segmentations (Dice segmentation accuracy increase of 10%), with very sparse contours (only 10%), which is promising in greatly decreasing the work expected from the user.

Preference segmentation of health care services: the old-fashioneds, value conscious, affluents and professional want-it-alls.

PubMed

Woodside, A G; Nielsen, R L; Walters, F; Muller, G D

1988-06-01

The results of a national segmentation study are reported. The findings extend the empirical work of Finn and Lamb and the benefit-seeking conjectures by Kotler and Clarke that consumers with preferences toward specific hospitals can be segmented into a few distinct groups. The groups described in the findings are identified as the value conscious, the affluents, the old-fashioneds, and the professional want-it-alls. Each segment has a unique demographic profile. The substantial importance of doctors' recommendations in influencing hospital choice is supported for all four consumer segments. Suggestions for additional research and hospital marketing strategies are provided.

Pathways to increase consumer trust in meat as a safe and wholesome food.

PubMed

Gellynck, Xavier; Verbeke, Wim; Vermeire, Bert

2006-09-01

This paper focuses on the effect of information about meat safety and wholesomeness on consumer trust based on several studies with data collected in Belgium. The research is grounded in the observation that despite the abundant rise of information through labelling, traceability systems and quality assurance schemes, the effect on consumer trust in meat as a safe and wholesome product is only limited. The overload and complexity of information on food products results in misunderstanding and misinterpretation. Functional traceability attributes such as organisational efficiency and chain monitoring are considered to be highly important but not as a basis for market segmentation. However, process traceability attributes such as origin and production method are of interest for particular market segments as a response to meat quality concerns. Quality assurance schemes and associated labels have a poor impact on consumers' perception. It is argued that the high interest of retailers in such schemes is driven by procurement management efficiency rather than safety or overall quality. Future research could concentrate on the distribution of costs and benefits associated with meat quality initiatives among the chain participants.

Salted and preserved duck eggs: a consumer market segmentation analysis.

PubMed

Arthur, Jennifer; Wiseman, Kelleen; Cheng, K M

2015-08-01

The combination of increasing ethnic diversity in North America and growing consumer support for local food products may present opportunities for local producers and processors in the ethnic foods product category. Our study examined the ethnic Chinese (pop. 402,000) market for salted and preserved duck eggs in Vancouver, British Columbia (BC), Canada. The objective of the study was to develop a segmentation model using survey data to categorize consumer groups based on their attitudes and the importance they placed on product attributes. We further used post-segmentation acculturation score, demographics and buyer behaviors to define these groups. Data were gathered via a survey of randomly selected Vancouver households with Chinese surnames (n = 410), targeting the adult responsible for grocery shopping. Results from principal component analysis and a 2-step cluster analysis suggest the existence of 4 market segments, described as Enthusiasts, Potentialists, Pragmatists, Health Skeptics (salted duck eggs), and Neutralists (preserved duck eggs). Kruskal Wallis tests and post hoc Mann-Whitney tests found significant differences between segments in terms of attitudes and the importance placed on product characteristics. Health Skeptics, preserved egg Potentialists, and Pragmatists of both egg products were significantly biased against Chinese imports compared to others. Except for Enthusiasts, segments disagreed that eggs are 'Healthy Products'. Preserved egg Enthusiasts had a significantly lower acculturation score (AS) compared to all others, while salted egg Enthusiasts had a lower AS compared to Health Skeptics. All segments rated "produced in BC, not mainland China" products in the "neutral to very likely" range for increasing their satisfaction with the eggs. Results also indicate that buyers of each egg type are willing to pay an average premium of at least 10% more for BC produced products versus imports, with all other characteristics equal. Overall results indicate that opportunities exist for local producers and processors: Chinese Canadians with lower AS form a core part of the potential market. © 2015 Poultry Science Association Inc.

Multi-atlas-based segmentation of the parotid glands of MR images in patients following head-and-neck cancer radiotherapy

NASA Astrophysics Data System (ADS)

Cheng, Guanghui; Yang, Xiaofeng; Wu, Ning; Xu, Zhijian; Zhao, Hongfu; Wang, Yuefeng; Liu, Tian

2013-02-01

Xerostomia (dry mouth), resulting from radiation damage to the parotid glands, is one of the most common and distressing side effects of head-and-neck cancer radiotherapy. Recent MRI studies have demonstrated that the volume reduction of parotid glands is an important indicator for radiation damage and xerostomia. In the clinic, parotid-volume evaluation is exclusively based on physicians' manual contours. However, manual contouring is time-consuming and prone to inter-observer and intra-observer variability. Here, we report a fully automated multi-atlas-based registration method for parotid-gland delineation in 3D head-and-neck MR images. The multi-atlas segmentation utilizes a hybrid deformable image registration to map the target subject to multiple patients' images, applies the transformation to the corresponding segmented parotid glands, and subsequently uses the multiple patient-specific pairs (head-and-neck MR image and transformed parotid-gland mask) to train support vector machine (SVM) to reach consensus to segment the parotid gland of the target subject. This segmentation algorithm was tested with head-and-neck MRIs of 5 patients following radiotherapy for the nasopharyngeal cancer. The average parotid-gland volume overlapped 85% between the automatic segmentations and the physicians' manual contours. In conclusion, we have demonstrated the feasibility of an automatic multi-atlas based segmentation algorithm to segment parotid glands in head-and-neck MR images.

Firm strategy and consumer behaviour under a complex tobacco tax system: implications for the effectiveness of taxation on tobacco control.

PubMed

Atuk, Oğuz; Özmen, M Utku

2017-05-01

The current tobacco taxation scheme in Turkey, a mix of high ad valorem tax and low specific tax, contains incentives for firms and consumers to change pricing and consumption patterns, respectively. The association between tax structure and price and tax revenue stability has not been studied in detail with micro data containing price segment information. In this study, we analyse whether incentives for firms and consumers undermine the effectiveness of tax policy in reducing consumption. We calculate alternative taxation scheme outcomes using differing ad valorem and specific tax rates through simulation analysis. We also estimate price elasticity of demand using detailed price and volume statistics between segments via regression analysis. A very high ad valorem rate provides strong incentives to firms to reduce prices. Therefore, this sort of tax strategy may induce even more consumption despite its initial aim of discouraging consumption. While higher prices dramatically reduce consumption of economy and medium price segment cigarettes, demand for premium segment cigarettes is found to be highly price-inelastic. The current tax scheme, based on both ad valorem and specific components, introduces various incentives to firms as well as to consumers which reduce the effectiveness of the tax policy. Therefore, on the basis of our theoretical predictions, an appropriate tax scheme should involve a balanced combination of ad valorem and specific rates, away from extreme ( ad valorem or specific dominant) cases to enhance the effectiveness of tax policy for curbing consumption. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images.

PubMed

Nandy, Kaustav; Gudla, Prabhakar R; Amundsen, Ryan; Meaburn, Karen J; Misteli, Tom; Lockett, Stephen J

2012-09-01

Analysis of preferential localization of certain genes within the cell nuclei is emerging as a new technique for the diagnosis of breast cancer. Quantitation requires accurate segmentation of 100-200 cell nuclei in each tissue section to draw a statistically significant result. Thus, for large-scale analysis, manual processing is too time consuming and subjective. Fortuitously, acquired images generally contain many more nuclei than are needed for analysis. Therefore, we developed an integrated workflow that selects, following automatic segmentation, a subpopulation of accurately delineated nuclei for positioning of fluorescence in situ hybridization-labeled genes of interest. Segmentation was performed by a multistage watershed-based algorithm and screening by an artificial neural network-based pattern recognition engine. The performance of the workflow was quantified in terms of the fraction of automatically selected nuclei that were visually confirmed as well segmented and by the boundary accuracy of the well-segmented nuclei relative to a 2D dynamic programming-based reference segmentation method. Application of the method was demonstrated for discriminating normal and cancerous breast tissue sections based on the differential positioning of the HES5 gene. Automatic results agreed with manual analysis in 11 out of 14 cancers, all four normal cases, and all five noncancerous breast disease cases, thus showing the accuracy and robustness of the proposed approach. Published 2012 Wiley Periodicals, Inc.

Consumers' willingness to pay for nutritional claims fighting the obesity epidemic: the case of reduced-fat and low salt cheese in Spain.

PubMed

de-Magistris, T; Lopéz-Galán, B

2016-06-01

The aim of this study was to investigate consumers' willingness to pay (WTP) for cheeses bearing reduced-fat and low salt claims in Spain. An experiment with 219 cheese consumers was conducted in the period March-May 2015. We used different versions of cheese bearing reduced-fat and low salt claims. A choice experiment was used to estimate WTP for reduced-fat and/or low salt cheeses. Participants faced eight choice sets, each consisting of two packages of cheese with different combinations of two claims. Individuals chose one of the two packages of cheese in each choice set, or decided not to choose either. Moreover, to consider possible heterogeneity in WTP across consumers, a random parameters logit model (RPL), a Chi-squared test, and analysis of variance tests were used. Spanish cheese consumers were willing to pay a positive premium for packages of cheese with reduced-fat claims (€0.538/100 g), and for cheese with reduced-fat and low salt claims (€1.15/100 g). Conversely, consumers valued low-salt content claims negatively. They preferred to pay €0.38/100 g for a conventional cheese rather than one low in salt content. As there was heterogeneity in consumers' WTP, two different consumer segments were identified. Segment 1 consisted of normal weight and younger consumers with higher incomes and levels of education, who valued low salt cheese more negatively than those individuals in Segment 2, predominantly comprising overweight and older consumers with low income and educational level. This means that individuals in Segment 1 would pay more for conventional cheese (€1/100 g) than those in Segment 2 (€0.50/100 g). However, no difference between the two segments was found in WTP for reduced-fat cheese. The findings suggest that consumers are willing to pay a price premium for a package of cheese with a reduced-fat claim or cheese with reduced-fat and low salt claims appearing together; however, they are not willing to pay for a package of cheese with only a low salt claim. In comparison with overweight people, normal weight consumers would prefer to pay more for conventional cheese than low salt cheese. Finally, the results of this study contribute to insights in the promotion of healthier food choices among consumers. In this regard, outreach activities promoted by food companies could drive consumers to increase their knowledge of the benefits of eating reduced-fat and low salt food products in relation to their health status. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

User-guided segmentation for volumetric retinal optical coherence tomography images

PubMed Central

Yin, Xin; Chao, Jennifer R.; Wang, Ruikang K.

2014-01-01

Abstract. Despite the existence of automatic segmentation techniques, trained graders still rely on manual segmentation to provide retinal layers and features from clinical optical coherence tomography (OCT) images for accurate measurements. To bridge the gap between this time-consuming need of manual segmentation and currently available automatic segmentation techniques, this paper proposes a user-guided segmentation method to perform the segmentation of retinal layers and features in OCT images. With this method, by interactively navigating three-dimensional (3-D) OCT images, the user first manually defines user-defined (or sketched) lines at regions where the retinal layers appear very irregular for which the automatic segmentation method often fails to provide satisfactory results. The algorithm is then guided by these sketched lines to trace the entire 3-D retinal layer and anatomical features by the use of novel layer and edge detectors that are based on robust likelihood estimation. The layer and edge boundaries are finally obtained to achieve segmentation. Segmentation of retinal layers in mouse and human OCT images demonstrates the reliability and efficiency of the proposed user-guided segmentation method. PMID:25147962

User-guided segmentation for volumetric retinal optical coherence tomography images.

PubMed

Yin, Xin; Chao, Jennifer R; Wang, Ruikang K

2014-08-01

Despite the existence of automatic segmentation techniques, trained graders still rely on manual segmentation to provide retinal layers and features from clinical optical coherence tomography (OCT) images for accurate measurements. To bridge the gap between this time-consuming need of manual segmentation and currently available automatic segmentation techniques, this paper proposes a user-guided segmentation method to perform the segmentation of retinal layers and features in OCT images. With this method, by interactively navigating three-dimensional (3-D) OCT images, the user first manually defines user-defined (or sketched) lines at regions where the retinal layers appear very irregular for which the automatic segmentation method often fails to provide satisfactory results. The algorithm is then guided by these sketched lines to trace the entire 3-D retinal layer and anatomical features by the use of novel layer and edge detectors that are based on robust likelihood estimation. The layer and edge boundaries are finally obtained to achieve segmentation. Segmentation of retinal layers in mouse and human OCT images demonstrates the reliability and efficiency of the proposed user-guided segmentation method.

Automatic segmentation of coronary arteries from computed tomography angiography data cloud using optimal thresholding

NASA Astrophysics Data System (ADS)

Ansari, Muhammad Ahsan; Zai, Sammer; Moon, Young Shik

2017-01-01

Manual analysis of the bulk data generated by computed tomography angiography (CTA) is time consuming, and interpretation of such data requires previous knowledge and expertise of the radiologist. Therefore, an automatic method that can isolate the coronary arteries from a given CTA dataset is required. We present an automatic yet effective segmentation method to delineate the coronary arteries from a three-dimensional CTA data cloud. Instead of a region growing process, which is usually time consuming and prone to leakages, the method is based on the optimal thresholding, which is applied globally on the Hessian-based vesselness measure in a localized way (slice by slice) to track the coronaries carefully to their distal ends. Moreover, to make the process automatic, we detect the aorta using the Hough transform technique. The proposed segmentation method is independent of the starting point to initiate its process and is fast in the sense that coronary arteries are obtained without any preprocessing or postprocessing steps. We used 12 real clinical datasets to show the efficiency and accuracy of the presented method. Experimental results reveal that the proposed method achieves 95% average accuracy.

Media and message strategies: consumer input for hospital advertising.

PubMed

Flexner, W A; Berkowitz, E N

1981-01-01

In summary, the results of the study suggest that a potentially large segment of consumers views advertising as an appropriate way to communicate about hospital services and rates. These consumers are unique not by traditional measures of audience/patient sociodemographic characteristics, but rather by their values and outlook toward hospitals and health care providers. Effective hospital advertising should recognize this segment's perspective in the message that are part of overall advertising strategy.

Segmenting human from photo images based on a coarse-to-fine scheme.

PubMed

Lu, Huchuan; Fang, Guoliang; Shao, Xinqing; Li, Xuelong

2012-06-01

Human segmentation in photo images is a challenging and important problem that finds numerous applications ranging from album making and photo classification to image retrieval. Previous works on human segmentation usually demand a time-consuming training phase for complex shape-matching processes. In this paper, we propose a straightforward framework to automatically recover human bodies from color photos. Employing a coarse-to-fine strategy, we first detect a coarse torso (CT) using the multicue CT detection algorithm and then extract the accurate region of the upper body. Then, an iterative multiple oblique histogram algorithm is presented to accurately recover the lower body based on human kinematics. The performance of our algorithm is evaluated on our own data set (contains 197 images with human body region ground truth data), VOC 2006, and the 2010 data set. Experimental results demonstrate the merits of the proposed method in segmenting a person with various poses.

Exploring the Constraint Profile of Winter Sports Resort Tourist Segments.

PubMed

Priporas, Constantinos-Vasilios; Vassiliadis, Chris A; Bellou, Victoria; Andronikidis, Andreas

2015-09-01

Many studies have confirmed the importance of market segmentation both theoretically and empirically. Surprisingly though, no study has so far addressed the issue from the perspective of leisure constraints. Since different consumers face different barriers, we look at participation in leisure activities as an outcome of the negotiation process that winter sports resort tourists go through, to balance between related motives and constraints. This empirical study reports the findings on the applicability of constraining factors in segmenting the tourists who visit winter sports resorts. Utilizing data from 1,391 tourists of winter sports resorts in Greece, five segments were formed based on their constraint, demographic, and behavioral profile. Our findings indicate that such segmentation sheds light on factors that could potentially limit the full utilization of the market. To maximize utilization, we suggest customizing marketing to the profile of each distinct winter sports resort tourist segment that emerged.

Statistical evaluation of manual segmentation of a diffuse low-grade glioma MRI dataset.

PubMed

Ben Abdallah, Meriem; Blonski, Marie; Wantz-Mezieres, Sophie; Gaudeau, Yann; Taillandier, Luc; Moureaux, Jean-Marie

2016-08-01

Software-based manual segmentation is critical to the supervision of diffuse low-grade glioma patients and to the optimal treatment's choice. However, manual segmentation being time-consuming, it is difficult to include it in the clinical routine. An alternative to circumvent the time cost of manual segmentation could be to share the task among different practitioners, providing it can be reproduced. The goal of our work is to assess diffuse low-grade gliomas' manual segmentation's reproducibility on MRI scans, with regard to practitioners, their experience and field of expertise. A panel of 13 experts manually segmented 12 diffuse low-grade glioma clinical MRI datasets using the OSIRIX software. A statistical analysis gave promising results, as the practitioner factor, the medical specialty and the years of experience seem to have no significant impact on the average values of the tumor volume variable.

Exploring the Constraint Profile of Winter Sports Resort Tourist Segments

PubMed Central

Priporas, Constantinos-Vasilios; Vassiliadis, Chris A.; Bellou, Victoria; Andronikidis, Andreas

2014-01-01

Many studies have confirmed the importance of market segmentation both theoretically and empirically. Surprisingly though, no study has so far addressed the issue from the perspective of leisure constraints. Since different consumers face different barriers, we look at participation in leisure activities as an outcome of the negotiation process that winter sports resort tourists go through, to balance between related motives and constraints. This empirical study reports the findings on the applicability of constraining factors in segmenting the tourists who visit winter sports resorts. Utilizing data from 1,391 tourists of winter sports resorts in Greece, five segments were formed based on their constraint, demographic, and behavioral profile. Our findings indicate that such segmentation sheds light on factors that could potentially limit the full utilization of the market. To maximize utilization, we suggest customizing marketing to the profile of each distinct winter sports resort tourist segment that emerged. PMID:29708114

Plexiform neurofibroma tissue classification

NASA Astrophysics Data System (ADS)

Weizman, L.; Hoch, L.; Ben Sira, L.; Joskowicz, L.; Pratt, L.; Constantini, S.; Ben Bashat, D.

2011-03-01

Plexiform Neurofibroma (PN) is a major complication of NeuroFibromatosis-1 (NF1), a common genetic disease that involving the nervous system. PNs are peripheral nerve sheath tumors extending along the length of the nerve in various parts of the body. Treatment decision is based on tumor volume assessment using MRI, which is currently time consuming and error prone, with limited semi-automatic segmentation support. We present in this paper a new method for the segmentation and tumor mass quantification of PN from STIR MRI scans. The method starts with a user-based delineation of the tumor area in a single slice and automatically detects the PN lesions in the entire image based on the tumor connectivity. Experimental results on seven datasets yield a mean volume overlap difference of 25% as compared to manual segmentation by expert radiologist with a mean computation and interaction time of 12 minutes vs. over an hour for manual annotation. Since the user interaction in the segmentation process is minimal, our method has the potential to successfully become part of the clinical workflow.

Segmentation in local hospital markets.

PubMed

Dranove, D; White, W D; Wu, L

1993-01-01

This study examines evidence of market segmentation on the basis of patients' insurance status, demographic characteristics, and medical condition in selected local markets in California in the years 1983 and 1989. Substantial differences exist in the probability patients may be admitted to particular hospitals based on insurance coverage, particularly Medicaid, and race. Segmentation based on insurance and race is related to hospital characteristics, but not the characteristics of the hospital's community. Medicaid patients are more likely to go to hospitals with lower costs and fewer service offerings. Privately insured patients go to hospitals offering more services, although cost concerns are increasing. Hispanic patients also go to low-cost hospitals, ceteris paribus. Results indicate little evidence of segmentation based on medical condition in either 1983 or 1989, suggesting that "centers of excellence" have yet to play an important role in patient choice of hospital. The authors found that distance matters, and that patients prefer nearby hospitals, moreso for some medical conditions than others, in ways consistent with economic theories of consumer choice.

Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

PubMed Central

Maji, Pradipta; Roy, Shaswati

2015-01-01

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices. PMID:25848961

Perception of oyster-based products by French consumers. The effect of processing and role of social representations.

PubMed

Debucquet, Gervaise; Cornet, Josiane; Adam, Isabelle; Cardinal, Mireille

2012-12-01

The search for new markets in the seafood sector, associated with the question of the continuity of raw oyster consumption over generations can be an opportunity for processors to extend their ranges with oyster-based products. The twofold aim of this study was to evaluate the impact of processing and social representation on perception of oyster-based products by French consumers and to identify the best means of development in order to avoid possible failure in the market. Five products with different degrees of processing (cooked oysters in a half-shell, hot preparation for toast, potted oyster, oyster butter and oyster-based soup) were presented within focus groups and consumer tests, at home and in canteens with the staff of several companies in order to reach consumers with different ages and professional activities. The results showed that social representation had a strong impact and that behaviours were contrasted according to the initial profile of the consumer (traditional raw oyster consumers or non-consumers) and their age distribution (younger and older people). The degree of processing has to be adapted to each segment. It is suggested to develop early exposure to influence the food choices and preferences of the youngest consumers on a long-term basis. Copyright © 2012 Elsevier Ltd. All rights reserved.

Explaining fruit and vegetable intake using a consumer marketing tool.

PubMed

Della, Lindsay J; Dejoy, David M; Lance, Charles E

2009-10-01

In response to calls to reinvent the 5 A Day fruit and vegetable campaign, this study assesses the utility of VALS, a consumer-based audience segmentation tool that divides the U.S. population into groups leading similar lifestyles. The study examines whether the impact of theory of planned behavior (TPB) constructs varies across VALS groups in a cross-sectional sample of 1,588 U.S. adults. In a multigroup structural equation model, the VALS audience group variable moderated latent TPB relationships. Attitudes, subjective norms, and perceived behavioral control explained 57% to 70% of the variation in intention to eat fruit and vegetables across 5 different VALS groups. Perceived behavioral control and intention also predicted self-reported consumption behavior (R2 = 20% to 71% across VALS groups). Bivariate z tests were calculated to determine statistical differences in parameter estimates across groups. Nine of the bivariate z tests were statistically significant (p < or = .04), with standardized coefficients ranging from .05 to .70. These findings confirm the efficacy of using the TPB to explain variation in fruit and vegetable consumption as well as the validity of using a consumer-based algorithm to segment audiences for fruit and vegetable consumption messaging.

Spanish, French and British consumers' acceptability of Uruguayan beef, and consumers' beef choice associated with country of origin, finishing diet and meat price.

PubMed

Realini, C E; Font i Furnols, M; Sañudo, C; Montossi, F; Oliver, M A; Guerrero, L

2013-09-01

The effect of country of origin (local, Switzerland, Argentina, Uruguay), finishing diet (grass, grass plus concentrate, concentrate), and price (low, medium, high) on consumer's beef choice and segmentation was evaluated in Spain, France and United Kingdom. Sensory acceptability of Uruguayan beef from different production systems was also evaluated and contrasted with consumers' beef choices. Origin was the most important characteristic for the choice of beef with preference for meat produced locally. The second most important factor was animal feed followed by price with preference for beef from grass-fed animals and lowest price. The least preferred product was beef from Uruguay, concentrate-fed animals and highest price. Sensory data showed higher acceptability scores for Uruguayan beef from grass-fed animals with or without concentrate supplementation than animals fed concentrate only. Consumer segments with distinct preferences were identified. Foreign country promotion seems to be fundamental for marketing beef in Europe, as well as the development of different marketing strategies to satisfy each consumer segment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Direct volume estimation without segmentation

NASA Astrophysics Data System (ADS)

Zhen, X.; Wang, Z.; Islam, A.; Bhaduri, M.; Chan, I.; Li, S.

2015-03-01

Volume estimation plays an important role in clinical diagnosis. For example, cardiac ventricular volumes including left ventricle (LV) and right ventricle (RV) are important clinical indicators of cardiac functions. Accurate and automatic estimation of the ventricular volumes is essential to the assessment of cardiac functions and diagnosis of heart diseases. Conventional methods are dependent on an intermediate segmentation step which is obtained either manually or automatically. However, manual segmentation is extremely time-consuming, subjective and highly non-reproducible; automatic segmentation is still challenging, computationally expensive, and completely unsolved for the RV. Towards accurate and efficient direct volume estimation, our group has been researching on learning based methods without segmentation by leveraging state-of-the-art machine learning techniques. Our direct estimation methods remove the accessional step of segmentation and can naturally deal with various volume estimation tasks. Moreover, they are extremely flexible to be used for volume estimation of either joint bi-ventricles (LV and RV) or individual LV/RV. We comparatively study the performance of direct methods on cardiac ventricular volume estimation by comparing with segmentation based methods. Experimental results show that direct estimation methods provide more accurate estimation of cardiac ventricular volumes than segmentation based methods. This indicates that direct estimation methods not only provide a convenient and mature clinical tool for cardiac volume estimation but also enables diagnosis of cardiac diseases to be conducted in a more efficient and reliable way.

Use of market segmentation to identify untapped consumer needs in vision correction surgery for future growth.

PubMed

Loarie, Thomas M; Applegate, David; Kuenne, Christopher B; Choi, Lawrence J; Horowitz, Diane P

2003-01-01

Market segmentation analysis identifies discrete segments of the population whose beliefs are consistent with exhibited behaviors such as purchase choice. This study applies market segmentation analysis to low myopes (-1 to -3 D with less than 1 D cylinder) in their consideration and choice of a refractive surgery procedure to discover opportunities within the market. A quantitative survey based on focus group research was sent to a demographically balanced sample of myopes using contact lenses and/or glasses. A variable reduction process followed by a clustering analysis was used to discover discrete belief-based segments. The resulting segments were validated both analytically and through in-market testing. Discontented individuals who wear contact lenses are the primary target for vision correction surgery. However, 81% of the target group is apprehensive about laser in situ keratomileusis (LASIK). They are nervous about the procedure and strongly desire reversibility and exchangeability. There exists a large untapped opportunity for vision correction surgery within the low myope population. Market segmentation analysis helped determine how to best meet this opportunity through repositioning existing procedures or developing new vision correction technology, and could also be applied to identify opportunities in other vision correction populations.

Consumerism as a branding opportunity.

PubMed

Treash, M; Adams, R

1998-01-01

Managing a customer portfolio at the individual level is the most difficult and most promising endeavor. An individual level consumer portfolio does not mean creating marketing materials and advertising campaigns customized for every member of your health plan. What it does mean is developing segmentation models based on consumer preferences extracted directly from your members, not socioeconomic or other demographic models. The most important information to extract is perceptions on how much and what kind of value members want from the organization.

Using the animal to the last bit: Consumer preferences for different beef cuts.

PubMed

Scozzafava, Gabriele; Corsi, Armando Maria; Casini, Leonardo; Contini, Caterina; Loose, Simone Mueller

2016-01-01

Meat is expensive to produce, making it is essential to understand the importance consumers pay to different meat cuts. Previous research on consumers' meat choices has mainly focused on meat species, while consumer preferences for meat cuts has so far only received limited interest. The aim of this study is to shed some light into this relatively unexplored area by answering four research questions. First, this study intends to show the relative importance meat cuts play in relation to other extrinsic product attributes. Secondly, this paper looks into differences in choice criteria between regular and special occasions. Third, consumer segments that differ in their preferences and beef purchase are identified, and, finally, the meat purchase portfolios of these segments are revealed. A stated preference methodology of a discrete choice experiment with cut-specific prices covering several meat cuts simultaneously is proposed to answer the research questions. The sample consists of 1500 respondents representative of the Italian population in terms of age, gender and geographic location The results shows that meat cut is the most important factor when choosing bovine meat followed by quality certification (origin), production technique, the type of breed and price. In terms of consumption occasions, we observe significantly lower price sensitivity for marbled steaks and cutlets for special occasions compared to normal occasions. Segmentation analysis shows that while the choices of two segments (comprising about 40% of the sample) are mostly driven by extrinsic product attributes, the remaining segments are mostly driven by meat cuts. These varying preferences are also reflected in the purchase portfolios of the different segments, while less variability is detected from a socio-demographic perspective. Copyright © 2015 Elsevier Ltd. All rights reserved.

Segmentation of overweight Americans and opportunities for social marketing

PubMed Central

Kolodinsky, Jane; Reynolds, Travis

2009-01-01

Background The food industry uses market segmentation to target products toward specific groups of consumers with similar attitudinal, demographic, or lifestyle characteristics. Our aims were to identify distinguishable segments within the US overweight population to be targeted with messages and media aimed at moving Americans toward more healthy weights. Methods Cluster analysis was used to identify segments of consumers based on both food and lifestyle behaviors related to unhealthy weights. Drawing from Social Learning Theory, the Health Belief Model, and existing market segmentation literature, the study identified five distinct, recognizable market segments based on knowledge and behavioral and environmental factors. Implications for social marketing campaigns designed to move Americans toward more healthy weights were explored. Results The five clusters identified were: Highest Risk (19%); At Risk (22%); Right Behavior/Wrong Results (33%); Getting Best Results (13%); and Doing OK (12%). Ninety-nine percent of those in the Highest Risk cluster were overweight; members watched the most television and exercised the least. Fifty-five percent of those in the At Risk cluster were overweight; members logged the most computer time and almost half rarely or never read food labels. Sixty-six percent of those in the Right Behavior/Wrong Results cluster were overweight; however, 95% of them were familiar with the food pyramid. Members reported eating a low percentage of fast food meals (8%) compared to other groups but a higher percentage of other restaurant meals (15%). Less than six percent of those in the Getting Best Results cluster were overweight; every member read food labels and 75% of members' meals were "made from scratch." Eighteen percent of those in the Doing OK cluster were overweight; members watched the least television and reported eating 78% of their meals "made from scratch." Conclusion This study demonstrated that five distinct market segments can be identified for social marketing efforts aimed at addressing the obesity epidemic. Through the identification of these five segments, social marketing campaigns can utilize selected channels and messages that communicate the most relevant and important information. The results of this study offer insight into how segmentation strategies and social marketing messages may improve public health. PMID:19267936

Segmentation of overweight Americans and opportunities for social marketing.

PubMed

Kolodinsky, Jane; Reynolds, Travis

2009-03-08

The food industry uses market segmentation to target products toward specific groups of consumers with similar attitudinal, demographic, or lifestyle characteristics. Our aims were to identify distinguishable segments within the US overweight population to be targeted with messages and media aimed at moving Americans toward more healthy weights. Cluster analysis was used to identify segments of consumers based on both food and lifestyle behaviors related to unhealthy weights. Drawing from Social Learning Theory, the Health Belief Model, and existing market segmentation literature, the study identified five distinct, recognizable market segments based on knowledge and behavioral and environmental factors. Implications for social marketing campaigns designed to move Americans toward more healthy weights were explored. The five clusters identified were: Highest Risk (19%); At Risk (22%); Right Behavior/Wrong Results (33%); Getting Best Results (13%); and Doing OK (12%). Ninety-nine percent of those in the Highest Risk cluster were overweight; members watched the most television and exercised the least. Fifty-five percent of those in the At Risk cluster were overweight; members logged the most computer time and almost half rarely or never read food labels. Sixty-six percent of those in the Right Behavior/Wrong Results cluster were overweight; however, 95% of them were familiar with the food pyramid. Members reported eating a low percentage of fast food meals (8%) compared to other groups but a higher percentage of other restaurant meals (15%). Less than six percent of those in the Getting Best Results cluster were overweight; every member read food labels and 75% of members' meals were "made from scratch." Eighteen percent of those in the Doing OK cluster were overweight; members watched the least television and reported eating 78% of their meals "made from scratch." This study demonstrated that five distinct market segments can be identified for social marketing efforts aimed at addressing the obesity epidemic. Through the identification of these five segments, social marketing campaigns can utilize selected channels and messages that communicate the most relevant and important information. The results of this study offer insight into how segmentation strategies and social marketing messages may improve public health.

Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.

PubMed

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V; Robles, Montserrat; Aparici, F; Martí-Bonmatí, L; García-Gómez, Juan M

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

Product involvement and consumer food-elicited emotional associations: Insights from emoji questionnaires.

PubMed

Jaeger, Sara R; Lee, Pui-Yee; Ares, Gastón

2018-04-01

Individual differences in food-related consumer behaviour are well documented, but lack thorough exploration in relation to product-elicited emotional associations. In this research, focus is directed to product involvement as a factor that modulates emotional associations to tasted products (dried fruit, n = 4) and written descriptions of consumption situations (drinking red wine, cooking dinner using seafood). Emoji questionnaires were used (as check-all-that-apply questions: CATA), and across two studies with consumers in New Zealand (n = 352) and China (n = 450), higher levels of involvement were associated with more positive emotional associations. For example, consumers with higher involvement for dried fruit used emoji with positive meanings (e.g., face savouring delicious food (), smiling face with heart-shaped eyes () and smiling face with smiling eyes () more frequently than those with lower levels of involvement. Conversely, emoji with negative or neutral meanings (e.g., confused face (), confounded face (), neutral face ()), were more frequently used by consumers with lower levels of product involvement. The number of significant differences between the samples of dried fruit were lower in the less involved consumer segment, and these consumers, on average, used less emoji to characterise the samples. A similar pattern of results were established for the written stimuli, which were used with Chinese consumers. For example, in the segment with greater involvement with seafood, associations to emoji with positive meanings were higher when responding to the situation "cooking dinner using frozen seafood as one of the ingredients." In the case of "drinking French red wine," the strategy used to define segments (median vs. triadic split of summed involvement scores) additionally influenced the results, and bigger differences were established when comparing more discrete segments (two extreme groups following triadic split). Copyright © 2018 Elsevier Ltd. All rights reserved.

Comparison of parameter-adapted segmentation methods for fluorescence micrographs.

PubMed

Held, Christian; Palmisano, Ralf; Häberle, Lothar; Hensel, Michael; Wittenberg, Thomas

2011-11-01

Interpreting images from fluorescence microscopy is often a time-consuming task with poor reproducibility. Various image processing routines that can help investigators evaluate the images are therefore useful. The critical aspect for a reliable automatic image analysis system is a robust segmentation algorithm that can perform accurate segmentation for different cell types. In this study, several image segmentation methods were therefore compared and evaluated in order to identify the most appropriate segmentation schemes that are usable with little new parameterization and robustly with different types of fluorescence-stained cells for various biological and biomedical tasks. The study investigated, compared, and enhanced four different methods for segmentation of cultured epithelial cells. The maximum-intensity linking (MIL) method, an improved MIL, a watershed method, and an improved watershed method based on morphological reconstruction were used. Three manually annotated datasets consisting of 261, 817, and 1,333 HeLa or L929 cells were used to compare the different algorithms. The comparisons and evaluations showed that the segmentation performance of methods based on the watershed transform was significantly superior to the performance of the MIL method. The results also indicate that using morphological opening by reconstruction can improve the segmentation of cells stained with a marker that exhibits the dotted surface of cells. Copyright © 2011 International Society for Advancement of Cytometry.

Importance-performance analysis: an application to Michigan's natural resources

Treesearch

Gloria Sanders; Erin White; Lori Pennington-Gray

2001-01-01

In the state of Michigan, the nature-based tourist is becoming an increasingly important target market for providers of natural resources. To meet the demands of this growing market segment, evaluation strategies for nature-based sites are needed to maintain and improve customer satisfaction and loyalty. Evaluation strategies that incorporate consumer input can help to...

Clinical evaluation of semi-automatic open-source algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action.

PubMed

Wallner, Jürgen; Hochegger, Kerstin; Chen, Xiaojun; Mischak, Irene; Reinbacher, Knut; Pau, Mauro; Zrnc, Tomislav; Schwenzer-Zimmerer, Katja; Zemann, Wolfgang; Schmalstieg, Dieter; Egger, Jan

2018-01-01

Computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However-due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice. In this retrospective, randomized, controlled trail the accuracy and accordance of the open-source based segmentation algorithm GrowCut was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score and the Hausdorff distance. Overall semi-automatic GrowCut segmentation times were about one minute. Mean Dice Score values of over 85% and Hausdorff Distances below 33.5 voxel could be achieved between the algorithmic GrowCut-based segmentations and the manual generated ground truth schemes. Statistical differences between the assessment parameters were not significant (p<0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the two groups. Complete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Due to an open-source basis the used method could be further developed by other groups or specialists. Systematic comparisons to other segmentation approaches or with a greater data amount are areas of future works.

Organic Food Market Segmentation in Lebanon

NASA Astrophysics Data System (ADS)

Tleis, Malak; Roma, Rocco; Callieris, Roberta

2015-04-01

Organic farming in Lebanon is not a new concept. It started with the efforts of the private sector more than a decade ago and is still present even with the limited agricultural production. The local market is quite developed in comparison to neighboring countries, depending mainly on imports. Few studies were addressed to organic consumption in Lebanon, were none of them dealt with organic consumers analysis. Therefore, our objectives were to identify the profiles of Lebanese organic consumer and non organic consumer and to propose appropriate marketing strategies for each segment of consumer with the final aim of developing the Lebanese organic market. A survey, based on the use of closed-ended questionnaire, was addressed to 400 consumers in the capital, Beirut, from the end of February till the end of March 2014. Data underwent descriptive analyses, principal component analyses (PCA) and cluster analyses (k-means method) through the statistical software SPSS. Four cluster were obtained based on psychographic characteristics and willingness to pay (WTP) for the principal organic products purchased. "Localists" and "Health conscious" clusters constituted the largest proportion of the selected sample, thus were the most critical to be addressed by specific marketing strategies emphasizing the combination of local and organic food and the healthy properties of organic products. "Rational" and "Irregular" cluster were relatively small groups, addressed by pricing and promotional strategies. This study showed a positive attitude among Lebanese consumer towards organic food, where egoistic motives are prevailing over altruistic motives. High prices of organic commodities and low trust in organic farming, remain a constraint to levitating organic consumption. The combined efforts of the public and the private sector are required to spread the knowledge about positive environmental payback of organic agriculture and for the promotion of locally produced organic goods.

Experiences of Users from Online Grocery Stores

NASA Astrophysics Data System (ADS)

Freeman, Mark

Grocery shopping, traditionally considered as the pinnacle of the self-service industry, is used as the case study in this chapter. As the Internet has become widely used by many segments of the population, the opportunity to shop online for groceries has been presented to consumers. This chapter considers issues that need to be addressed to make online grocery shopping systems more usable for these consumers, based on feedback from individuals who participated in a study of user interactions with Australian online grocery stores.

Computed tomography landmark-based semi-automated mesh morphing and mapping techniques: generation of patient specific models of the human pelvis without segmentation.

PubMed

Salo, Zoryana; Beek, Maarten; Wright, David; Whyne, Cari Marisa

2015-04-13

Current methods for the development of pelvic finite element (FE) models generally are based upon specimen specific computed tomography (CT) data. This approach has traditionally required segmentation of CT data sets, which is time consuming and necessitates high levels of user intervention due to the complex pelvic anatomy. The purpose of this research was to develop and assess CT landmark-based semi-automated mesh morphing and mapping techniques to aid the generation and mechanical analysis of specimen-specific FE models of the pelvis without the need for segmentation. A specimen-specific pelvic FE model (source) was created using traditional segmentation methods and morphed onto a CT scan of a different (target) pelvis using a landmark-based method. The morphed model was then refined through mesh mapping by moving the nodes to the bone boundary. A second target model was created using traditional segmentation techniques. CT intensity based material properties were assigned to the morphed/mapped model and to the traditionally segmented target models. Models were analyzed to evaluate their geometric concurrency and strain patterns. Strains generated in a double-leg stance configuration were compared to experimental strain gauge data generated from the same target cadaver pelvis. CT landmark-based morphing and mapping techniques were efficiently applied to create a geometrically multifaceted specimen-specific pelvic FE model, which was similar to the traditionally segmented target model and better replicated the experimental strain results (R(2)=0.873). This study has shown that mesh morphing and mapping represents an efficient validated approach for pelvic FE model generation without the need for segmentation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Texture segmentation by genetic programming.

PubMed

Song, Andy; Ciesielski, Vic

2008-01-01

This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.

Does the Valuation of Nutritional Claims Differ among Consumers? Insights from Spain.

PubMed

Jurado, Francesc; Gracia, Azucena

2017-02-13

The presence in the market of food products with nutritional claims is increasing. The objective of this paper is to assess consumers' valuation of some nutritional claims ('high in fiber' and 'reduced saturated fat') in a European country and to test for differences among consumers. An artefactual non-hypothetical experiment was carried out in a realistic setting (mock/real brick-and-mortar supermarket) with a sample of 121 Spanish consumers stratified by gender, age, and body mass index. A latent class model was specified and estimated with the data from the experiment. Results indicate that consumers positively valued both nutritional claims, but the valuation was heterogeneous, and three consumer segments were detected. Two of them positively valued both nutritional claims (named 'nutritional claim seekers'), while the third segment's valuation was negative (named 'nutritional claim avoiders'). This last segment is characterized by being younger males with university studies who give the least importance to health, natural ingredients, and the calorie/sugar/fat content when shopping. They pay less attention to nutritional information, and they stated that they use this information to a lesser extent. These consumers showed the least interest in healthy eating, and they reported that they do not have health problems related to their diet.

European consumer response to packaging technologies for improved beef safety.

PubMed

Van Wezemael, Lynn; Ueland, Øydis; Verbeke, Wim

2011-09-01

Beef packaging can influence consumer perceptions of beef. Although consumer perceptions and acceptance are considered to be among the most limiting factors in the application of new technologies, there is a lack of knowledge about the acceptability to consumers of beef packaging systems aimed at improved safety. This paper explores European consumers' acceptance levels of different beef packaging technologies. An online consumer survey was conducted in five European countries (n=2520). Acceptance levels among the sample ranged between 23% for packaging releasing preservative additives up to 73% for vacuum packaging. Factor analysis revealed that familiar packaging technologies were clearly preferred over non-familiar technologies. Four consumer segments were identified: the negative (31% of the sample), cautious (30%), conservative (17%) and enthusiast (22%) consumers, which were profiled based on their attitudes and beef consumption behaviour. Differences between consumer acceptance levels should be taken into account while optimising beef packaging and communicating its benefits. Copyright © 2011 Elsevier Ltd. All rights reserved.

GPU based contouring method on grid DEM data

NASA Astrophysics Data System (ADS)

Tan, Liheng; Wan, Gang; Li, Feng; Chen, Xiaohui; Du, Wenlong

2017-08-01

This paper presents a novel method to generate contour lines from grid DEM data based on the programmable GPU pipeline. The previous contouring approaches often use CPU to construct a finite element mesh from the raw DEM data, and then extract contour segments from the elements. They also need a tracing or sorting strategy to generate the final continuous contours. These approaches can be heavily CPU-costing and time-consuming. Meanwhile the generated contours would be unsmooth if the raw data is sparsely distributed. Unlike the CPU approaches, we employ the GPU's vertex shader to generate a triangular mesh with arbitrary user-defined density, in which the height of each vertex is calculated through a third-order Cardinal spline function. Then in the same frame, segments are extracted from the triangles by the geometry shader, and translated to the CPU-side with an internal order in the GPU's transform feedback stage. Finally we propose a "Grid Sorting" algorithm to achieve the continuous contour lines by travelling the segments only once. Our method makes use of multiple stages of GPU pipeline for computation, which can generate smooth contour lines, and is significantly faster than the previous CPU approaches. The algorithm can be easily implemented with OpenGL 3.3 API or higher on consumer-level PCs.

Watershed-based segmentation of the corpus callosum in diffusion MRI

NASA Astrophysics Data System (ADS)

Freitas, Pedro; Rittner, Leticia; Appenzeller, Simone; Lapa, Aline; Lotufo, Roberto

2012-02-01

The corpus callosum (CC) is one of the most important white matter structures of the brain, interconnecting the two cerebral hemispheres, and is related to several neurodegenerative diseases. Since segmentation is usually the first step for studies in this structure, and manual volumetric segmentation is a very time-consuming task, it is important to have a robust automatic method for CC segmentation. We propose here an approach for fully automatic 3D segmentation of the CC in the magnetic resonance diffusion tensor images. The method uses the watershed transform and is performed on the fractional anisotropy (FA) map weighted by the projection of the principal eigenvector in the left-right direction. The section of the CC in the midsagittal slice is used as seed for the volumetric segmentation. Experiments with real diffusion MRI data showed that the proposed method is able to quickly segment the CC without any user intervention, with great results when compared to manual segmentation. Since it is simple, fast and does not require parameter settings, the proposed method is well suited for clinical applications.

Science in liquid dietary supplement promotion: the misleading case of mangosteen juice.

PubMed

Lobb, Ano L

2012-02-01

Liquid dietary supplements represent a fast growing market segment, including botanically-based beverages containing mangosteen, acai, and noni. These products often resemble fruit juice in packaging and appearance, but may contain pharmacologically active ingredients. While little is known about the human health effects or safety of consuming such products, manufacturers make extensive use of low-quality published research to promote their products. This report analyzes the science-based marketing claims of two of the most widely consumed mangosteen liquid dietary supplements, and compares them to the findings of the research being cited. The reviewer found that analyzed marketing claims overstate the significance of findings, and fail to disclose severe methodological weaknesses of the research they cite. If this trend extends to other related products that are similarly widely consumed, it may pose a public health threat by misleading consumers into assuming that product safety and effectiveness are backed by rigorous scientific data.

Science in Liquid Dietary Supplement Promotion: The Misleading Case of Mangosteen Juice

PubMed Central

2012-01-01

Liquid dietary supplements represent a fast growing market segment, including botanically-based beverages containing mangosteen, acai, and noni. These products often resemble fruit juice in packaging and appearance, but may contain pharmacologically active ingredients. While little is known about the human health effects or safety of consuming such products, manufacturers make extensive use of low-quality published research to promote their products. This report analyzes the science-based marketing claims of two of the most widely consumed mangosteen liquid dietary supplements, and compares them to the findings of the research being cited. The reviewer found that analyzed marketing claims overstate the significance of findings, and fail to disclose severe methodological weaknesses of the research they cite. If this trend extends to other related products that are similarly widely consumed, it may pose a public health threat by misleading consumers into assuming that product safety and effectiveness are backed by rigorous scientific data. PMID:22454810

Segmenting Broadcast News Audiences in the New Media Environment.

ERIC Educational Resources Information Center

Wicks, Robert H.

1989-01-01

Examines the "benefit segmentation model," a marketing strategy for local news media which is capable of sorting consumers into discrete segments interested in similar salient product attributes or benefits. Concludes that benefit segmentation may provide a means by which news programmers may respond to their audience. (RS)

Beef customer satisfaction: factors affecting consumer evaluations of clod steaks.

PubMed

Goodson, K J; Morgan, W W; Reagan, J O; Gwartney, B L; Courington, S M; Wise, J W; Savell, J W

2002-02-01

An in-home beef study evaluated consumer ratings of clod steaks (n = 1,264) as influenced by USDA quality grade (Top Choice, Low Choice, High Select, and Low Select), city (Chicago and Philadelphia), consumer segment (Beef Loyals, who are heavy consumers of beef; Budget Rotators, who are cost-driven and split meat consumption between beef and chicken; and Variety Rotators, who have higher incomes and education and split their meat consumption among beef, poultry, and other foods), degree of doneness, and cooking method. Consumers evaluated each steak for Overall Like, Tenderness, Juiciness, Flavor Like, and Flavor Amount using 10-point scales. Grilling was the predominant cooking method used, and steaks were cooked to medium-well and greater degrees of doneness. Interactions existed involving the consumer-controlled factors of degree of doneness and(or) cooking method for all consumer-evaluated traits for the clod steak (P < 0.05). USDA grade did not affect any consumer evaluation traits or Warner-Bratzler shear force values (P > 0.05). One significant main effect, segment (P = 0.006), and one significant interaction, cooking method x city (P = 0.0407), existed for Overall Like ratings. Consumers in the Beef Loyals segment rated clod steaks higher in Overall Like than the other segments. Consumers in Chicago tended to give more uniform Overall Like ratings to clod steaks cooked by various methods; however, consumers in Philadelphia gave among the highest ratings to clod steaks that were fried and among the lowest to those that were grilled. Additionally, although clod steaks that were fried were given generally high ratings by consumers in Philadelphia, consumers in Chicago rated clod steaks cooked in this manner significantly lower than those in Philadelphia. Conversely, consumers in Chicago rated clod steaks that were grilled significantly higher than consumers in Philadelphia. Correlation and stepwise regression analyses indicated that Flavor Like was driving customer satisfaction of the clod steak. Flavor Like was the sensory trait most highly correlated to Overall Like, followed by Tenderness, Flavor Amount, and Juiciness. Flavor Like was the first variable to enter into the stepwise regression equation for predicting Overall Like, followed by Tenderness and Flavor Amount. For the clod steak, it is likely that preparation techniques that improve flavor without reducing tenderness positively affect customer satisfaction.

Consumer evaluations of processed meat products reformulated to be healthier - A conjoint analysis study.

PubMed

Shan, Liran C; De Brún, Aoife; Henchion, Maeve; Li, Chenguang; Murrin, Celine; Wall, Patrick G; Monahan, Frank J

2017-09-01

Recent innovations in processed meats focus on healthier reformulations through reducing negative constituents and/or adding health beneficial ingredients. This study explored the influence of base meat product (ham, sausages, beef burger), salt and/or fat content (reduced or not), healthy ingredients (omega 3, vitamin E, none), and price (average or higher than average) on consumers' purchase intention and quality judgement of processed meats. A survey (n=481) using conjoint methodology and cluster analysis was conducted. Price and base meat product were most important for consumers' purchase intention, followed by healthy ingredient and salt and/or fat content. In reformulation, consumers had a preference for ham and sausages over beef burgers, and for reduced salt and/or fat over non reduction. In relation to healthy ingredients, omega 3 was preferred over none, and vitamin E was least preferred. Healthier reformulations improved the perceived healthiness of processed meats. Cluster analyses identified three consumer segments with different product preferences. Copyright © 2017 Elsevier Ltd. All rights reserved.

TH-CD-206-02: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in MR Images Using Patch-Based Anatomical Signature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, X; Jani, A; Rossi, P

Purpose: MRI has shown promise in identifying prostate tumors with high sensitivity and specificity for the detection of prostate cancer. Accurate segmentation of the prostate plays a key role various tasks: to accurately localize prostate boundaries for biopsy needle placement and radiotherapy, to initialize multi-modal registration algorithms or to obtain the region of interest for computer-aided detection of prostate cancer. However, manual segmentation during biopsy or radiation therapy can be time consuming and subject to inter- and intra-observer variation. This study’s purpose it to develop an automated method to address this technical challenge. Methods: We present an automated multi-atlas segmentationmore » for MR prostate segmentation using patch-based label fusion. After an initial preprocessing for all images, all the atlases are non-rigidly registered to a target image. And then, the resulting transformation is used to propagate the anatomical structure labels of the atlas into the space of the target image. The top L similar atlases are further chosen by measuring intensity and structure difference in the region of interest around prostate. Finally, using voxel weighting based on patch-based anatomical signature, the label that the majority of all warped labels predict for each voxel is used for the final segmentation of the target image. Results: This segmentation technique was validated with a clinical study of 13 patients. The accuracy of our approach was assessed using the manual segmentation (gold standard). The mean volume Dice Overlap Coefficient was 89.5±2.9% between our and manual segmentation, which indicate that the automatic segmentation method works well and could be used for 3D MRI-guided prostate intervention. Conclusion: We have developed a new prostate segmentation approach based on the optimal feature learning label fusion framework, demonstrated its clinical feasibility, and validated its accuracy. This segmentation technique could be a useful tool in image-guided interventions for prostate-cancer diagnosis and treatment.« less

Analysis and segmentation of images in case of solving problems of detecting and tracing objects on real-time video

NASA Astrophysics Data System (ADS)

Ezhova, Kseniia; Fedorenko, Dmitriy; Chuhlamov, Anton

2016-04-01

The article deals with the methods of image segmentation based on color space conversion, and allow the most efficient way to carry out the detection of a single color in a complex background and lighting, as well as detection of objects on a homogeneous background. The results of the analysis of segmentation algorithms of this type, the possibility of their implementation for creating software. The implemented algorithm is very time-consuming counting, making it a limited application for the analysis of the video, however, it allows us to solve the problem of analysis of objects in the image if there is no dictionary of images and knowledge bases, as well as the problem of choosing the optimal parameters of the frame quantization for video analysis.

[Research on K-means clustering segmentation method for MRI brain image based on selecting multi-peaks in gray histogram].

PubMed

Chen, Zhaoxue; Yu, Haizhong; Chen, Hao

2013-12-01

To solve the problem of traditional K-means clustering in which initial clustering centers are selected randomly, we proposed a new K-means segmentation algorithm based on robustly selecting 'peaks' standing for White Matter, Gray Matter and Cerebrospinal Fluid in multi-peaks gray histogram of MRI brain image. The new algorithm takes gray value of selected histogram 'peaks' as the initial K-means clustering center and can segment the MRI brain image into three parts of tissue more effectively, accurately, steadily and successfully. Massive experiments have proved that the proposed algorithm can overcome many shortcomings caused by traditional K-means clustering method such as low efficiency, veracity, robustness and time consuming. The histogram 'peak' selecting idea of the proposed segmentootion method is of more universal availability.

Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation.

PubMed

Ilunga-Mbuyamba, Elisee; Avina-Cervantes, Juan Gabriel; Cepeda-Negrete, Jonathan; Ibarra-Manzano, Mario Alberto; Chalopin, Claire

2017-12-01

Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately. Copyright © 2017 Elsevier Ltd. All rights reserved.

Consumer preferences for fresh tomato at the European scale: a common segmentation on taste and firmness.

PubMed

Causse, Mathilde; Friguet, Chloé; Coiret, Clément; Lépicier, Mélanie; Navez, Brigitte; Lee, Monica; Holthuysen, Nancy; Sinesio, Fiorella; Moneta, Elisabetta; Grandillo, Silvana

2010-01-01

Although tomato flavor has not been a major goal for breeders, nowadays it becomes important as it is a subject of consumer complaint. A better knowledge of tomato consumer preferences, at the European level, should provide the basis for improvement of fruit quality and for market segmentation. In the framework of a large European project, 806 consumers from 3 countries, The Netherlands, France, and Italy, were presented with a set of 16 varieties representing the diversity of fresh tomato offer in order to evaluate their preferences. In parallel, sensory profiles were constructed by expert panels in each country. Preference maps were then constructed in each country revealing the structure of consumer preferences and allowing identification of the most important characteristics. Then a global analysis revealed that preferences were quite homogeneous across countries. This study identified the overall flavor and firmness as the most important traits for improving tomato fruit quality. It showed that consumer preferences from different European countries, with different cultures and food practices, are segmented following similar patterns when projected onto a common referential plan. Moreover, the results clearly showed that diversification of taste and texture is required to satisfy all consumers' expectations as some consumers preferred firm tomatoes, while others preferred melting ones and were more or less demanding in terms of sweetness and flavor intensity. Detailed comparisons also showed the importance of the fruit appearance in consumer preference. © 2010 Institute of Food Technologists®

Quantitative characterization of metastatic disease in the spine. Part I. Semiautomated segmentation using atlas-based deformable registration and the level set method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hardisty, M.; Gordon, L.; Agarwal, P.

2007-08-15

Quantitative assessment of metastatic disease in bone is often considered immeasurable and, as such, patients with skeletal metastases are often excluded from clinical trials. In order to effectively quantify the impact of metastatic tumor involvement in the spine, accurate segmentation of the vertebra is required. Manual segmentation can be accurate but involves extensive and time-consuming user interaction. Potential solutions to automating segmentation of metastatically involved vertebrae are demons deformable image registration and level set methods. The purpose of this study was to develop a semiautomated method to accurately segment tumor-bearing vertebrae using the aforementioned techniques. By maintaining morphology of anmore » atlas, the demons-level set composite algorithm was able to accurately differentiate between trans-cortical tumors and surrounding soft tissue of identical intensity. The algorithm successfully segmented both the vertebral body and trabecular centrum of tumor-involved and healthy vertebrae. This work validates our approach as equivalent in accuracy to an experienced user.« less

Active learning based segmentation of Crohns disease from abdominal MRI.

PubMed

Mahapatra, Dwarikanath; Vos, Franciscus M; Buhmann, Joachim M

2016-05-01

This paper proposes a novel active learning (AL) framework, and combines it with semi supervised learning (SSL) for segmenting Crohns disease (CD) tissues from abdominal magnetic resonance (MR) images. Robust fully supervised learning (FSL) based classifiers require lots of labeled data of different disease severities. Obtaining such data is time consuming and requires considerable expertise. SSL methods use a few labeled samples, and leverage the information from many unlabeled samples to train an accurate classifier. AL queries labels of most informative samples and maximizes gain from the labeling effort. Our primary contribution is in designing a query strategy that combines novel context information with classification uncertainty and feature similarity. Combining SSL and AL gives a robust segmentation method that: (1) optimally uses few labeled samples and many unlabeled samples; and (2) requires lower training time. Experimental results show our method achieves higher segmentation accuracy than FSL methods with fewer samples and reduced training effort. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A medical software system for volumetric analysis of cerebral pathologies in magnetic resonance imaging (MRI) data.

PubMed

Egger, Jan; Kappus, Christoph; Freisleben, Bernd; Nimsky, Christopher

2012-08-01

In this contribution, a medical software system for volumetric analysis of different cerebral pathologies in magnetic resonance imaging (MRI) data is presented. The software system is based on a semi-automatic segmentation algorithm and helps to overcome the time-consuming process of volume determination during monitoring of a patient. After imaging, the parameter settings-including a seed point-are set up in the system and an automatic segmentation is performed by a novel graph-based approach. Manually reviewing the result leads to reseeding, adding seed points or an automatic surface mesh generation. The mesh is saved for monitoring the patient and for comparisons with follow-up scans. Based on the mesh, the system performs a voxelization and volume calculation, which leads to diagnosis and therefore further treatment decisions. The overall system has been tested with different cerebral pathologies-glioblastoma multiforme, pituitary adenomas and cerebral aneurysms- and evaluated against manual expert segmentations using the Dice Similarity Coefficient (DSC). Additionally, intra-physician segmentations have been performed to provide a quality measure for the presented system.

Why consumers behave as they do with respect to food safety and risk information.

PubMed

Verbeke, Wim; Frewer, Lynn J; Scholderer, Joachim; De Brabander, Hubert F

2007-03-14

In recent years, it seems that consumers are generally uncertain about the safety and quality of their food and their risk perception differs substantially from that of experts. Hormone and veterinary drug residues in meat persist to occupy a high position in European consumers' food concern rankings. The aim of this contribution is to provide a better understanding to food risk analysts of why consumers behave as they do with respect to food safety and risk information. This paper presents some cases of seemingly irrational and inconsistent consumer behaviour with respect to food safety and risk information and provides explanations for these behaviours based on the nature of the risk and individual psychological processes. Potential solutions for rebuilding consumer confidence in food safety and bridging between lay and expert opinions towards food risks are reviewed. These include traceability and labelling, segmented communication approaches and public involvement in risk management decision-making.

Twelve automated thresholding methods for segmentation of PET images: a phantom study.

PubMed

Prieto, Elena; Lecumberri, Pablo; Pagola, Miguel; Gómez, Marisol; Bilbao, Izaskun; Ecay, Margarita; Peñuelas, Iván; Martí-Climent, Josep M

2012-06-21

Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical (18)F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools.

Twelve automated thresholding methods for segmentation of PET images: a phantom study

NASA Astrophysics Data System (ADS)

Prieto, Elena; Lecumberri, Pablo; Pagola, Miguel; Gómez, Marisol; Bilbao, Izaskun; Ecay, Margarita; Peñuelas, Iván; Martí-Climent, Josep M.

2012-06-01

Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical 18F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools.

Multiatlas segmentation of thoracic and abdominal anatomy with level set-based local search.

PubMed

Schreibmann, Eduard; Marcus, David M; Fox, Tim

2014-07-08

Segmentation of organs at risk (OARs) remains one of the most time-consuming tasks in radiotherapy treatment planning. Atlas-based segmentation methods using single templates have emerged as a practical approach to automate the process for brain or head and neck anatomy, but pose significant challenges in regions where large interpatient variations are present. We show that significant changes are needed to autosegment thoracic and abdominal datasets by combining multi-atlas deformable registration with a level set-based local search. Segmentation is hierarchical, with a first stage detecting bulk organ location, and a second step adapting the segmentation to fine details present in the patient scan. The first stage is based on warping multiple presegmented templates to the new patient anatomy using a multimodality deformable registration algorithm able to cope with changes in scanning conditions and artifacts. These segmentations are compacted in a probabilistic map of organ shape using the STAPLE algorithm. Final segmentation is obtained by adjusting the probability map for each organ type, using customized combinations of delineation filters exploiting prior knowledge of organ characteristics. Validation is performed by comparing automated and manual segmentation using the Dice coefficient, measured at an average of 0.971 for the aorta, 0.869 for the trachea, 0.958 for the lungs, 0.788 for the heart, 0.912 for the liver, 0.884 for the kidneys, 0.888 for the vertebrae, 0.863 for the spleen, and 0.740 for the spinal cord. Accurate atlas segmentation for abdominal and thoracic regions can be achieved with the usage of a multi-atlas and perstructure refinement strategy. To improve clinical workflow and efficiency, the algorithm was embedded in a software service, applying the algorithm automatically on acquired scans without any user interaction.

Insurees' preferences in hospital choice-A population-based study.

PubMed

Schuldt, Johannes; Doktor, Anna; Lichters, Marcel; Vogt, Bodo; Robra, Bernt-Peter

2017-10-01

In Germany, the patient himself makes the choice for or against a health service provider. Hospital comparison websites offer him possibilities to inform himself before choosing. However, it remains unclear, how health care consumers use those websites, and there is little information about how preferences in hospital choice differ interpersonally. We conducted a Discrete-Choice-Experiment (DCE) on hospital choice with 1500 randomly selected participants (age 40-70) in three different German cities selecting four attributes for hospital vignettes. The analysis of the study draws on multilevel mixed effects logit regression analyses with the dependent variables: "chance to select a hospital" and "choice confidence". Subsequently, we performed a Latent-Class-Analysis to uncover consumer segments with distinct preferences. 590 of the questionnaires were evaluable. All four attributes of the hospital vignettes have a significant impact on hospital choice. The attribute "complication rate" exerts the highest impact on consumers' decisions and reported choice confidence. Latent-Class-Analysis results in one dominant consumer segment that considered the complication rate the most important decision criterion. Using DCE, we were able to show that the complication rate is an important trusted criterion in hospital choice to a large group of consumers. Our study supports current governmental efforts in Germany to concentrate the provision of specialized health care services. We suggest further national and cross-national research on the topic. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

PubMed Central

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.

PubMed

Ma, Jinlian; Wu, Fa; Jiang, Tian'an; Zhao, Qiyu; Kong, Dexing

2017-11-01

Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images. Our CNN-based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi-view strategy is used to improve the performance of the CNN-based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset. The experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN-based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN-based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] on overall folds, respectively. Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.

Message strategies in direct-to-consumer pharmaceutical advertising: a content analysis using Taylor's six-segment message strategy wheel.

PubMed

Tsai, Wan-Hsiu Sunny; Lancaster, Alyse R

2012-01-01

This exploratory study applies Taylor's (1999) six-segment message strategy wheel to direct-to-consumer (DTC) pharmaceutical television commercials to understand message strategies adopted by pharmaceutical advertisers to persuade consumers. A convenience sample of 96 DTC commercial campaigns was analyzed. The results suggest that most DTC drug ads used a combination approach, providing consumers with medical and drug information while simultaneously appealing to the viewer's ego-related needs and desires. In contrast to ration and ego strategies, other approaches including routine, acute need, and social are relatively uncommon while sensory was the least common message strategy. Findings thus recognized the educational value of DTC commercials.

Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation.

PubMed

Daisne, Jean-François; Blumhofer, Andreas

2013-06-26

Intensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions. The updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for "manual to automatic" and "manual to corrected" volumes comparisons. In both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors. The updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert.

Breast tumor segmentation in DCE-MRI using fully convolutional networks with an application in radiogenomics

NASA Astrophysics Data System (ADS)

Zhang, Jun; Saha, Ashirbani; Zhu, Zhe; Mazurowski, Maciej A.

2018-02-01

Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) remains an active as well as a challenging problem. Previous studies often rely on manual annotation for tumor regions, which is not only time-consuming but also error-prone. Recent studies have shown high promise of deep learning-based methods in various segmentation problems. However, these methods are usually faced with the challenge of limited number (e.g., tens or hundreds) of medical images for training, leading to sub-optimal segmentation performance. Also, previous methods cannot efficiently deal with prevalent class-imbalance problems in tumor segmentation, where the number of voxels in tumor regions is much lower than that in the background area. To address these issues, in this study, we propose a mask-guided hierarchical learning (MHL) framework for breast tumor segmentation via fully convolutional networks (FCN). Our strategy is first decomposing the original difficult problem into several sub-problems and then solving these relatively simpler sub-problems in a hierarchical manner. To precisely identify locations of tumors that underwent a biopsy, we further propose an FCN model to detect two landmarks defined on nipples. Finally, based on both segmentation probability maps and our identified landmarks, we proposed to select biopsied tumors from all detected tumors via a tumor selection strategy using the pathology location. We validate our MHL method using data for 272 patients, and achieve a mean Dice similarity coefficient (DSC) of 0.72 in breast tumor segmentation. Finally, in a radiogenomic analysis, we show that a previously developed image features show a comparable performance for identifying luminal A subtype when applied to the automatic segmentation and a semi-manual segmentation demonstrating a high promise for fully automated radiogenomic analysis in breast cancer.

Clustering consumers based on trust, confidence and giving behaviour: data-driven model building for charitable involvement in the Australian not-for-profit sector.

PubMed

de Vries, Natalie Jane; Reis, Rodrigo; Moscato, Pablo

2015-01-01

Organisations in the Not-for-Profit and charity sector face increasing competition to win time, money and efforts from a common donor base. Consequently, these organisations need to be more proactive than ever. The increased level of communications between individuals and organisations today, heightens the need for investigating the drivers of charitable giving and understanding the various consumer groups, or donor segments, within a population. It is contended that `trust' is the cornerstone of the not-for-profit sector's survival, making it an inevitable topic for research in this context. It has become imperative for charities and not-for-profit organisations to adopt for-profit's research, marketing and targeting strategies. This study provides the not-for-profit sector with an easily-interpretable segmentation method based on a novel unsupervised clustering technique (MST-kNN) followed by a feature saliency method (the CM1 score). A sample of 1,562 respondents from a survey conducted by the Australian Charities and Not-for-profits Commission is analysed to reveal donor segments. Each cluster's most salient features are identified using the CM1 score. Furthermore, symbolic regression modelling is employed to find cluster-specific models to predict `low' or `high' involvement in clusters. The MST-kNN method found seven clusters. Based on their salient features they were labelled as: the `non-institutionalist charities supporters', the `resource allocation critics', the `information-seeking financial sceptics', the `non-questioning charity supporters', the `non-trusting sceptics', the `charity management believers' and the `institutionalist charity believers'. Each cluster exhibits their own characteristics as well as different drivers of `involvement'. The method in this study provides the not-for-profit sector with a guideline for clustering, segmenting, understanding and potentially targeting their donor base better. If charities and not-for-profit organisations adopt these strategies, they will be more successful in today's competitive environment.

Clustering Consumers Based on Trust, Confidence and Giving Behaviour: Data-Driven Model Building for Charitable Involvement in the Australian Not-For-Profit Sector

PubMed Central

de Vries, Natalie Jane; Reis, Rodrigo; Moscato, Pablo

2015-01-01

Organisations in the Not-for-Profit and charity sector face increasing competition to win time, money and efforts from a common donor base. Consequently, these organisations need to be more proactive than ever. The increased level of communications between individuals and organisations today, heightens the need for investigating the drivers of charitable giving and understanding the various consumer groups, or donor segments, within a population. It is contended that `trust' is the cornerstone of the not-for-profit sector's survival, making it an inevitable topic for research in this context. It has become imperative for charities and not-for-profit organisations to adopt for-profit's research, marketing and targeting strategies. This study provides the not-for-profit sector with an easily-interpretable segmentation method based on a novel unsupervised clustering technique (MST-kNN) followed by a feature saliency method (the CM1 score). A sample of 1,562 respondents from a survey conducted by the Australian Charities and Not-for-profits Commission is analysed to reveal donor segments. Each cluster's most salient features are identified using the CM1 score. Furthermore, symbolic regression modelling is employed to find cluster-specific models to predict `low' or `high' involvement in clusters. The MST-kNN method found seven clusters. Based on their salient features they were labelled as: the `non-institutionalist charities supporters', the `resource allocation critics', the `information-seeking financial sceptics', the `non-questioning charity supporters', the `non-trusting sceptics', the `charity management believers' and the `institutionalist charity believers'. Each cluster exhibits their own characteristics as well as different drivers of `involvement'. The method in this study provides the not-for-profit sector with a guideline for clustering, segmenting, understanding and potentially targeting their donor base better. If charities and not-for-profit organisations adopt these strategies, they will be more successful in today's competitive environment. PMID:25849547

Clinical evaluation of semi-automatic open-source algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action

PubMed Central

Wallner, Jürgen; Hochegger, Kerstin; Chen, Xiaojun; Mischak, Irene; Reinbacher, Knut; Pau, Mauro; Zrnc, Tomislav; Schwenzer-Zimmerer, Katja; Zemann, Wolfgang; Schmalstieg, Dieter

2018-01-01

Introduction Computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However—due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice. Material and methods In this retrospective, randomized, controlled trail the accuracy and accordance of the open-source based segmentation algorithm GrowCut was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score and the Hausdorff distance. Results Overall semi-automatic GrowCut segmentation times were about one minute. Mean Dice Score values of over 85% and Hausdorff Distances below 33.5 voxel could be achieved between the algorithmic GrowCut-based segmentations and the manual generated ground truth schemes. Statistical differences between the assessment parameters were not significant (p<0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the two groups. Discussion Complete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Due to an open-source basis the used method could be further developed by other groups or specialists. Systematic comparisons to other segmentation approaches or with a greater data amount are areas of future works. PMID:29746490

Automated segmentation of blood-flow regions in large thoracic arteries using 3D-cine PC-MRI measurements.

PubMed

van Pelt, Roy; Nguyen, Huy; ter Haar Romeny, Bart; Vilanova, Anna

2012-03-01

Quantitative analysis of vascular blood flow, acquired by phase-contrast MRI, requires accurate segmentation of the vessel lumen. In clinical practice, 2D-cine velocity-encoded slices are inspected, and the lumen is segmented manually. However, segmentation of time-resolved volumetric blood-flow measurements is a tedious and time-consuming task requiring automation. Automated segmentation of large thoracic arteries, based solely on the 3D-cine phase-contrast MRI (PC-MRI) blood-flow data, was done. An active surface model, which is fast and topologically stable, was used. The active surface model requires an initial surface, approximating the desired segmentation. A method to generate this surface was developed based on a voxel-wise temporal maximum of blood-flow velocities. The active surface model balances forces, based on the surface structure and image features derived from the blood-flow data. The segmentation results were validated using volunteer studies, including time-resolved 3D and 2D blood-flow data. The segmented surface was intersected with a velocity-encoded PC-MRI slice, resulting in a cross-sectional contour of the lumen. These cross-sections were compared to reference contours that were manually delineated on high-resolution 2D-cine slices. The automated approach closely approximates the manual blood-flow segmentations, with error distances on the order of the voxel size. The initial surface provides a close approximation of the desired luminal geometry. This improves the convergence time of the active surface and facilitates parametrization. An active surface approach for vessel lumen segmentation was developed, suitable for quantitative analysis of 3D-cine PC-MRI blood-flow data. As opposed to prior thresholding and level-set approaches, the active surface model is topologically stable. A method to generate an initial approximate surface was developed, and various features that influence the segmentation model were evaluated. The active surface segmentation results were shown to closely approximate manual segmentations.

Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms.

PubMed

Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza

2012-05-01

Effective abnormality detection and diagnosis in Magnetic Resonance Images (MRIs) requires a robust segmentation strategy. Since manual segmentation is a time-consuming task which engages valuable human resources, automatic MRI segmentations received an enormous amount of attention. For this goal, various techniques have been applied. However, Markov Random Field (MRF) based algorithms have produced reasonable results in noisy images compared to other methods. MRF seeks a label field which minimizes an energy function. The traditional minimization method, simulated annealing (SA), uses Monte Carlo simulation to access the minimum solution with heavy computation burden. For this reason, MRFs are rarely used in real time processing environments. This paper proposed a novel method based on MRF and a hybrid of social algorithms that contain an ant colony optimization (ACO) and a Gossiping algorithm which can be used for segmenting single and multispectral MRIs in real time environments. Combining ACO with the Gossiping algorithm helps find the better path using neighborhood information. Therefore, this interaction causes the algorithm to converge to an optimum solution faster. Several experiments on phantom and real images were performed. Results indicate that the proposed algorithm outperforms the traditional MRF and hybrid of MRF-ACO in speed and accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

Do Consumers Want More Nutritional and Health Information on Wine Labels? Insights from the EU and USA.

PubMed

Annunziata, Azzurra; Pomarici, Eugenio; Vecchio, Riccardo; Mariani, Angela

2016-07-07

The global strategy to reduce the harmful use of alcohol launched in 2010 by the World Health Organization includes, amongst several areas of recommended actions, providing consumer information about, and labelling, alcoholic beverages to indicate alcohol-related harm. Labelling requirements worldwide for alcoholic drinks are currently quite diverse and somewhat limited compared to labelling on food products and on tobacco. In this context, the current paper contributes to the academic and political debate on the inclusion of nutritional and health information on wine labelling, providing some insights into consumer interest in, and preferences for, such information in four core wine-producing and -consuming countries: Italy, France, Spain, and the United States of America. A rating-based conjoint analysis was performed in order to ascertain consumer preferences for different formats of additional information on wine labels, and a segmentation of the sample was performed to determine the existence of homogeneous groups of consumers in relation to the degrees of usefulness attached to the nutritional and health information on wine labels. Our results highlight the interest expressed by European and United States consumers for introducing nutrition and health information on wine labels. However, the results of conjoint analysis show some significant differences among stated preferences of the information delivery modes in different countries. In addition, segmentation analysis reveal the existence of significant differences between consumer groups with respect to their interest in receiving additional information on wine labels. These differences are not only linked to the geographic origin of the consumers, or to socio-demographic variables, but are also related to wine consumption habits, attitudes towards nutritional information, and the degree of involvement with wine. This heterogeneity of consumer preferences indicates a need for a careful consideration of wine labelling regulations and merits further investigation in order to identify labelling guidelines in terms of the message content and presentation method to be used.

Do Consumers Want More Nutritional and Health Information on Wine Labels? Insights from the EU and USA

PubMed Central

Annunziata, Azzurra; Pomarici, Eugenio; Vecchio, Riccardo; Mariani, Angela

2016-01-01

The global strategy to reduce the harmful use of alcohol launched in 2010 by the World Health Organization includes, amongst several areas of recommended actions, providing consumer information about, and labelling, alcoholic beverages to indicate alcohol-related harm. Labelling requirements worldwide for alcoholic drinks are currently quite diverse and somewhat limited compared to labelling on food products and on tobacco. In this context, the current paper contributes to the academic and political debate on the inclusion of nutritional and health information on wine labelling, providing some insights into consumer interest in, and preferences for, such information in four core wine-producing and -consuming countries: Italy, France, Spain, and the United States of America. A rating-based conjoint analysis was performed in order to ascertain consumer preferences for different formats of additional information on wine labels, and a segmentation of the sample was performed to determine the existence of homogeneous groups of consumers in relation to the degrees of usefulness attached to the nutritional and health information on wine labels. Our results highlight the interest expressed by European and United States consumers for introducing nutrition and health information on wine labels. However, the results of conjoint analysis show some significant differences among stated preferences of the information delivery modes in different countries. In addition, segmentation analysis reveal the existence of significant differences between consumer groups with respect to their interest in receiving additional information on wine labels. These differences are not only linked to the geographic origin of the consumers, or to socio-demographic variables, but are also related to wine consumption habits, attitudes towards nutritional information, and the degree of involvement with wine. This heterogeneity of consumer preferences indicates a need for a careful consideration of wine labelling regulations and merits further investigation in order to identify labelling guidelines in terms of the message content and presentation method to be used. PMID:27399767

Segmentation in low-penetration and low-involvement categories: an application to lottery games.

PubMed

Guesalaga, Rodrigo; Marshall, Pablo

2013-09-01

Market segmentation is accepted as a fundamental concept in marketing and several authors have recently proposed a segmentation model where personal and environmental variables intersect with each other to form motivating conditions that drive behavior and preferences. This model of segmentation has been applied to packaged goods. This paper extends this literature by proposing a segmentation model for low-penetration and low involvement (LP-LI) products. An application to the lottery games in Chile supports the proposed model. The results of the study show that in this type of products (LP-LI), the attitude towards the product category is the most important factor that distinguishes consumers from non consumers, and heavy users from light users, and consequently, a critical segmentation variable. In addition, a cluster analysis shows the existence of three segments: (1) the impulsive dreamers, who believe in chance, and in that lottery games can change their life, (2) the skeptical, that do not believe in chance, nor in that lottery games can change their life and (3) the willing, who value the benefits of playing.

Muslim consumer trust in halal meat status and control in Belgium.

PubMed

Bonne, Karijn; Verbeke, Wim

2008-05-01

This paper focuses on public trust of Belgian Muslims in information sources of halal meat and their confidence in key actors and institutions for monitoring and controlling the halal meat chain. Cross-sectional consumer data were collected through a survey with 367 Muslims during the summer of 2006 in Belgium. Findings reveal that Islamic institutions and especially the Islamic butcher receive in general most confidence for monitoring and controlling the halal status of meat, and for communicating about halal meat. However, based on Muslims' confidence, four distinct market segments were identified: indifferent (29.1%), concerned (9.7%), confident (33.1%) and Islamic idealist (26.7%). These segments differ significantly with respect to trust in information sources and institutions, health and safety perception of halal meat, perceived halal meat consumption barriers, behavioural variables (halal meat consumption frequency and place of purchase), and socio-cultural (acculturation and self-identity) and individual characteristics. Indifferent consumers are rather undecided about who should monitor the halal status of meat, and they are most open to purchasing halal meat in the supermarket. Concerned Muslim consumers display higher confidence in Belgian than in Islamic institutions, which associates with perceiving a lack of information, poor hygiene and safety concern as barriers to purchasing halal meat. Confident consumers display a clear preference for Islamic institutions to monitor and communicate about halal. Islamic idealists, who are typified by younger age, second generation and high Muslim self-identity, differ from the confident consumers through their very low confidence in local Belgian sources and institutions.

Automated intraretinal layer segmentation of optical coherence tomography images using graph-theoretical methods

NASA Astrophysics Data System (ADS)

Roy, Priyanka; Gholami, Peyman; Kuppuswamy Parthasarathy, Mohana; Zelek, John; Lakshminarayanan, Vasudevan

2018-02-01

Segmentation of spectral-domain Optical Coherence Tomography (SD-OCT) images facilitates visualization and quantification of sub-retinal layers for diagnosis of retinal pathologies. However, manual segmentation is subjective, expertise dependent, and time-consuming, which limits applicability of SD-OCT. Efforts are therefore being made to implement active-contours, artificial intelligence, and graph-search to automatically segment retinal layers with accuracy comparable to that of manual segmentation, to ease clinical decision-making. Although, low optical contrast, heavy speckle noise, and pathologies pose challenges to automated segmentation. Graph-based image segmentation approach stands out from the rest because of its ability to minimize the cost function while maximising the flow. This study has developed and implemented a shortest-path based graph-search algorithm for automated intraretinal layer segmentation of SD-OCT images. The algorithm estimates the minimal-weight path between two graph-nodes based on their gradients. Boundary position indices (BPI) are computed from the transition between pixel intensities. The mean difference between BPIs of two consecutive layers quantify individual layer thicknesses, which shows statistically insignificant differences when compared to a previous study [for overall retina: p = 0.17, for individual layers: p > 0.05 (except one layer: p = 0.04)]. These results substantiate the accurate delineation of seven intraretinal boundaries in SD-OCT images by this algorithm, with a mean computation time of 0.93 seconds (64-bit Windows10, core i5, 8GB RAM). Besides being self-reliant for denoising, the algorithm is further computationally optimized to restrict segmentation within the user defined region-of-interest. The efficiency and reliability of this algorithm, even in noisy image conditions, makes it clinically applicable.

Lifestyle segmentation of US food shoppers to examine organic and local food consumption.

PubMed

Nie, Cong; Zepeda, Lydia

2011-08-01

The food related lifestyle (FRL) model, widely used on European data, is applied to US data using a modified survey instrument to examine organic and local food consumption. Since empirical studies indicate these shoppers are motivated by environmental and health concerns and limited by access, the conceptual framework employs an environmental behavior model, Attitude Behavior Context (ABC), which is consistent with means-end chain theory, the Health Belief (HB) model, and the FRL model. ABC theory incorporates contextual factors that may limit consumers' ability to act on their intentions. US food shopper data was collected in 2003 (n=956) utilizing an instrument with variables adapted from the FRL, ABC, and HB models. Cluster analysis segmented food shoppers into four FRL groups: rational, adventurous, careless, and a fourth segment that had some characteristics of both conservative and uninvolved consumers. The segments exhibited significant differences in organic and local food consumption. These were correlated with consumers' environmental concerns, knowledge and practices, health concerns and practices, as well as some demographic characteristics (race, gender, age, education), income, and variables that measured access to these foods. Implications for marketing and public policy strategies to promote organic and local foods include: emphasizing taste, nutrition, value, children, and enjoyment of cooking for rational consumers; and emphasizing health, fitness, and freshness, and providing ethnic foods for adventurous consumers. While both careless and conservative/uninvolved consumers valued convenience, the former tended to be in the highest income group, while the latter were in the lowest, were more likely to be either in the youngest or oldest age groups, and were very concerned about food safety and health. Copyright © 2011 Elsevier Ltd. All rights reserved.

Measurement of thermally ablated lesions in sonoelastographic images using level set methods

NASA Astrophysics Data System (ADS)

Castaneda, Benjamin; Tamez-Pena, Jose Gerardo; Zhang, Man; Hoyt, Kenneth; Bylund, Kevin; Christensen, Jared; Saad, Wael; Strang, John; Rubens, Deborah J.; Parker, Kevin J.

2008-03-01

The capability of sonoelastography to detect lesions based on elasticity contrast can be applied to monitor the creation of thermally ablated lesion. Currently, segmentation of lesions depicted in sonoelastographic images is performed manually which can be a time consuming process and prone to significant intra- and inter-observer variability. This work presents a semi-automated segmentation algorithm for sonoelastographic data. The user starts by planting a seed in the perceived center of the lesion. Fast marching methods use this information to create an initial estimate of the lesion. Subsequently, level set methods refine its final shape by attaching the segmented contour to edges in the image while maintaining smoothness. The algorithm is applied to in vivo sonoelastographic images from twenty five thermal ablated lesions created in porcine livers. The estimated area is compared to results from manual segmentation and gross pathology images. Results show that the algorithm outperforms manual segmentation in accuracy, inter- and intra-observer variability. The processing time per image is significantly reduced.

CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

PubMed Central

2013-01-01

The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening. PMID:23938087

CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation.

PubMed

Hodneland, Erlend; Kögel, Tanja; Frei, Dominik Michael; Gerdes, Hans-Hermann; Lundervold, Arvid

2013-08-09

: The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening.

Sensory factors affecting female consumers' acceptability of nail polish.

PubMed

Sun, C; Koppel, K; Adhikari, K

2015-12-01

The objectives of this study were to determine what sensory factors impact consumers' acceptability of nail polishes, to explore how these sensory factors impact consumers' acceptability of nail polishes, to investigate whether there are any consumer segments according to their overall acceptability on different nail polishes and to scrutinize how the consumer segments are related to the sensory factors. Ninety-eight females participated in a nail polish consumer study at Kansas State University. Eight commercial products belonging to four categories - regular (REG), gel (GEL), flake (FLK) and water-based (WAT) - were evaluated. Each nail polish sample was evaluated twice by each participant in two different tasks - a task devoted to applying and evaluating the product and a task devoted to observing the appearance and evaluating the product. Pearson's correlation analysis, analysis of variance (ANOVA), external preference mapping, cluster analysis and internal preference mapping were applied for data analysis. Participants' scores of overall liking of the nail polishes were similar in the application task and in the observation task. In general, participants liked the REG and GEL product samples more than the FLK and WAT samples. Among all the sensory attributes, appearance attributes were the major factors that affected participants' overall liking. Aroma seemed to be a minor factor to participants' overall liking. Some sensory attributes, such as runny, shininess, opacity, spreadability, smoothness, coverage and wet appearance, were found to drive participants' overall acceptability positively, whereas others such as pinhole, fatty-edges, blister, brushlines, pearl-like, flake-protrusion, glittery and initial-drag impacted participants' overall acceptability negatively. Four clusters of participants were identified according to their overall liking scores from both the application task and the observation task. Participants' acceptability, based on different sensory attributes, could help a nail polish manufacturer modify or improve their nail polish formulas. Nail polish manufacturers could use the consumer cluster information to improve their marketing strategies for specific categories of their products and to target their advertising on particular consumer groups. © 2015 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Cardiac Multi-detector CT Segmentation Based on Multiscale Directional Edge Detector and 3D Level Set.

PubMed

Antunes, Sofia; Esposito, Antonio; Palmisano, Anna; Colantoni, Caterina; Cerutti, Sergio; Rizzo, Giovanna

2016-05-01

Extraction of the cardiac surfaces of interest from multi-detector computed tomographic (MDCT) data is a pre-requisite step for cardiac analysis, as well as for image guidance procedures. Most of the existing methods need manual corrections, which is time-consuming. We present a fully automatic segmentation technique for the extraction of the right ventricle, left ventricular endocardium and epicardium from MDCT images. The method consists in a 3D level set surface evolution approach coupled to a new stopping function based on a multiscale directional second derivative Gaussian filter, which is able to stop propagation precisely on the real boundary of the structures of interest. We validated the segmentation method on 18 MDCT volumes from healthy and pathologic subjects using manual segmentation performed by a team of expert radiologists as gold standard. Segmentation errors were assessed for each structure resulting in a surface-to-surface mean error below 0.5 mm and a percentage of surface distance with errors less than 1 mm above 80%. Moreover, in comparison to other segmentation approaches, already proposed in previous work, our method presented an improved accuracy (with surface distance errors less than 1 mm increased of 8-20% for all structures). The obtained results suggest that our approach is accurate and effective for the segmentation of ventricular cavities and myocardium from MDCT images.

The Importance of Marketing Segmentation

ERIC Educational Resources Information Center

Martin, Gillian

2011-01-01

The rationale behind marketing segmentation is to allow businesses to focus on their consumers' behaviors and purchasing patterns. If done effectively, marketing segmentation allows an organization to achieve its highest return on investment (ROI) in turn for its marketing and sales expenses. If an organization markets its products or services to…

Intensity-based hierarchical clustering in CT-scans: application to interactive segmentation in cardiology

NASA Astrophysics Data System (ADS)

Hadida, Jonathan; Desrosiers, Christian; Duong, Luc

2011-03-01

The segmentation of anatomical structures in Computed Tomography Angiography (CTA) is a pre-operative task useful in image guided surgery. Even though very robust and precise methods have been developed to help achieving a reliable segmentation (level sets, active contours, etc), it remains very time consuming both in terms of manual interactions and in terms of computation time. The goal of this study is to present a fast method to find coarse anatomical structures in CTA with few parameters, based on hierarchical clustering. The algorithm is organized as follows: first, a fast non-parametric histogram clustering method is proposed to compute a piecewise constant mask. A second step then indexes all the space-connected regions in the piecewise constant mask. Finally, a hierarchical clustering is achieved to build a graph representing the connections between the various regions in the piecewise constant mask. This step builds up a structural knowledge about the image. Several interactive features for segmentation are presented, for instance association or disassociation of anatomical structures. A comparison with the Mean-Shift algorithm is presented.

Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context.

PubMed

Dolz, Jose; Laprie, Anne; Ken, Soléakhéna; Leroy, Henri-Arthur; Reyns, Nicolas; Massoptier, Laurent; Vermandel, Maximilien

2016-01-01

To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI). SVM is proposed to segment the brainstem on MRI in multicenter brain cancer context. A dataset composed by 14 adult brain MRI scans is used to evaluate its performance. In addition to spatial and probabilistic information, five different image intensity values (IIVs) configurations are evaluated as features to train the SVM classifier. Segmentation accuracy is evaluated by computing the Dice similarity coefficient (DSC), absolute volumes difference (AVD) and percentage volume difference between automatic and manual contours. Mean DSC for all proposed IIVs configurations ranged from 0.89 to 0.90. Mean AVD values were below 1.5 cm(3), where the value for best performing IIVs configuration was 0.85 cm(3), representing an absolute mean difference of 3.99% with respect to the manual segmented volumes. Results suggest consistent volume estimation and high spatial similarity with respect to expert delineations. The proposed approach outperformed presented methods to segment the brainstem, not only in volume similarity metrics, but also in segmentation time. Preliminary results showed that the approach might be promising for adoption in clinical use.

Influence of quality labels on the formation of preferences of lamb meat consumers. A Spanish case study.

PubMed

Bernabéu, Rodolfo; Rabadán, Adrián; El Orche, Nour E; Díaz, Mónica

2018-01-01

Analysis of the attributes determining the formation of consumers' preferences when buying lamb meat is a key aspect in increasing the demand for this product. To this end, by means of conjoint analysis, we determined lamb meat consumers' preferences according to their frequency of consumption, and we used logistic simulation to analyse market shares of the most valued attributes. After segmenting the market into habitual and occasional consumers of lamb meat, our results seem to suggest that while regular consumers base their preferences mostly on origin, occasional consumers take other attributes into account, such as Protected Geographical Origin (PGI) and organic production. An analysis of market shares shows that PGI significantly influences consumer preferences, while ecological production has a less marked impact. This finding confirms the usefulness of PGI in the lamb meat market and highlights the urgent need to improve the communication strategy of the organic production sector as a synergistic effect to increase its acceptance among consumers. Copyright © 2017 Elsevier Ltd. All rights reserved.

Consumers young and old: segmenting the target markets for direct-to-consumer prescription drug advertising.

PubMed

Ball, Jennifer Gerard; Manika, Danae; Stout, Patricia

2011-10-01

Direct-to-consumer pharmaceutical advertising (DTCA) studies have typically focused on older adults or a general population of adults. However, college students are viable targets for DTCA and are receiving more research attention in this area. In this article, we compare college students with two adult age segments. Our findings indicate all age groups had relatively high awareness of DTCA and similar attitudes and behavioral responses to the ads. However, there were significant differences in media use and health characteristics as well as the factors predicting DTCA ad trust, attitudes, and behavioral intentions. Implications and future research suggestions are discussed.

WE-EF-210-08: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in Ultrasound Images Using Patch-Based Anatomical Feature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, X; Rossi, P; Jani, A

Purpose: Transrectal ultrasound (TRUS) is the standard imaging modality for the image-guided prostate-cancer interventions (e.g., biopsy and brachytherapy) due to its versatility and real-time capability. Accurate segmentation of the prostate plays a key role in biopsy needle placement, treatment planning, and motion monitoring. As ultrasound images have a relatively low signal-to-noise ratio (SNR), automatic segmentation of the prostate is difficult. However, manual segmentation during biopsy or radiation therapy can be time consuming. We are developing an automated method to address this technical challenge. Methods: The proposed segmentation method consists of two major stages: the training stage and the segmentation stage.more » During the training stage, patch-based anatomical features are extracted from the registered training images with patient-specific information, because these training images have been mapped to the new patient’ images, and the more informative anatomical features are selected to train the kernel support vector machine (KSVM). During the segmentation stage, the selected anatomical features are extracted from newly acquired image as the input of the well-trained KSVM and the output of this trained KSVM is the segmented prostate of this patient. Results: This segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentation. The mean volume Dice Overlap Coefficient was 89.7±2.3%, and the average surface distance was 1.52 ± 0.57 mm between our and manual segmentation, which indicate that the automatic segmentation method works well and could be used for 3D ultrasound-guided prostate intervention. Conclusion: We have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentation (gold standard). This segmentation technique could be a useful tool for image-guided interventions in prostate-cancer diagnosis and treatment. This research is supported in part by DOD PCRP Award W81XWH-13-1-0269, and National Cancer Institute (NCI) Grant CA114313.« less

Model-based segmentation in orbital volume measurement with cone beam computed tomography and evaluation against current concepts.

PubMed

Wagner, Maximilian E H; Gellrich, Nils-Claudius; Friese, Karl-Ingo; Becker, Matthias; Wolter, Franz-Erich; Lichtenstein, Juergen T; Stoetzer, Marcus; Rana, Majeed; Essig, Harald

2016-01-01

Objective determination of the orbital volume is important in the diagnostic process and in evaluating the efficacy of medical and/or surgical treatment of orbital diseases. Tools designed to measure orbital volume with computed tomography (CT) often cannot be used with cone beam CT (CBCT) because of inferior tissue representation, although CBCT has the benefit of greater availability and lower patient radiation exposure. Therefore, a model-based segmentation technique is presented as a new method for measuring orbital volume and compared to alternative techniques. Both eyes from thirty subjects with no known orbital pathology who had undergone CBCT as a part of routine care were evaluated (n = 60 eyes). Orbital volume was measured with manual, atlas-based, and model-based segmentation methods. Volume measurements, volume determination time, and usability were compared between the three methods. Differences in means were tested for statistical significance using two-tailed Student's t tests. Neither atlas-based (26.63 ± 3.15 mm(3)) nor model-based (26.87 ± 2.99 mm(3)) measurements were significantly different from manual volume measurements (26.65 ± 4.0 mm(3)). However, the time required to determine orbital volume was significantly longer for manual measurements (10.24 ± 1.21 min) than for atlas-based (6.96 ± 2.62 min, p < 0.001) or model-based (5.73 ± 1.12 min, p < 0.001) measurements. All three orbital volume measurement methods examined can accurately measure orbital volume, although atlas-based and model-based methods seem to be more user-friendly and less time-consuming. The new model-based technique achieves fully automated segmentation results, whereas all atlas-based segmentations at least required manipulations to the anterior closing. Additionally, model-based segmentation can provide reliable orbital volume measurements when CT image quality is poor.

Identification of the varietal origin of loose leaf tea based on analysis of a single leaf by SNP nanofluidic array

USDA-ARS?s Scientific Manuscript database

Tea [Camellia sinensis (L.) O Kuntze] is an economically important crop cultivated in more than 50 countries. Production and marketing of premium specialty tea products provides opportunities for tea growers, the tea industry and consumers. Rapid market segmentation in the tea industry has resulted ...

Classification and characterization of Japanese consumers' beef preferences by external preference mapping.

PubMed

Sasaki, Keisuke; Ooi, Motoki; Nagura, Naoto; Motoyama, Michiyo; Narita, Takumi; Oe, Mika; Nakajima, Ikuyo; Hagi, Tatsuro; Ojima, Koichi; Kobayashi, Miho; Nomura, Masaru; Muroya, Susumu; Hayashi, Takeshi; Akama, Kyoko; Fujikawa, Akira; Hokiyama, Hironao; Kobayashi, Kuniyuki; Nishimura, Takanori

2017-08-01

Over the past few decades, beef producers in Japan have improved marbling in their beef products. It was recently reported that marbling is not well correlated with palatability as rated by Japanese consumers. This study sought to identify the consumer segments in Japan that prefer sensory characteristics of beef other than high marbling. Three Wagyu beef, one Holstein beef and two lean imported beef longissimus samples were subjected to a descriptive sensory test, physicochemical analysis and a consumer (n = 307) preference test. According to consumer classification and external preference mapping, four consumer segments were identified as 'gradual high-fat likers', 'moderate-fat and distinctive taste likers', 'Wagyu likers' and 'distinctive texture likers'. Although the major trend of Japanese consumers' beef preference was 'marbling liking', 16.9% of the consumers preferred beef samples that had moderate marbling and distinctive taste. The consumers' attitudes expressed in a questionnaire survey were in good agreement with the preference for marbling among the 'moderate-fat and distinctive taste likers'. These results indicate that moderately marbled beef is a potent category in the Japanese beef market. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: A clinical study.

PubMed

Dolz, Jose; Betrouni, Nacim; Quidet, Mathilde; Kharroubi, Dris; Leroy, Henri A; Reyns, Nicolas; Massoptier, Laurent; Vermandel, Maximilien

2016-09-01

Delineation of organs at risk (OARs) is a crucial step in surgical and treatment planning in brain cancer, where precise OARs volume delineation is required. However, this task is still often manually performed, which is time-consuming and prone to observer variability. To tackle these issues a deep learning approach based on stacking denoising auto-encoders has been proposed to segment the brainstem on magnetic resonance images in brain cancer context. Additionally to classical features used in machine learning to segment brain structures, two new features are suggested. Four experts participated in this study by segmenting the brainstem on 9 patients who underwent radiosurgery. Analysis of variance on shape and volume similarity metrics indicated that there were significant differences (p<0.05) between the groups of manual annotations and automatic segmentations. Experimental evaluation also showed an overlapping higher than 90% with respect to the ground truth. These results are comparable, and often higher, to those of the state of the art segmentation methods but with a considerably reduction of the segmentation time. Copyright © 2016 Elsevier Ltd. All rights reserved.

Performing label-fusion-based segmentation using multiple automatically generated templates.

PubMed

Chakravarty, M Mallar; Steadman, Patrick; van Eede, Matthijs C; Calcott, Rebecca D; Gu, Victoria; Shaw, Philip; Raznahan, Armin; Collins, D Louis; Lerch, Jason P

2013-10-01

Classically, model-based segmentation procedures match magnetic resonance imaging (MRI) volumes to an expertly labeled atlas using nonlinear registration. The accuracy of these techniques are limited due to atlas biases, misregistration, and resampling error. Multi-atlas-based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates. This approach yields numerous independent segmentations that are fused using a voxel-by-voxel label-voting procedure. In this article, we demonstrate how the multi-atlas approach can be extended to work with input atlases that are unique and extremely time consuming to construct by generating a library of multiple automatically generated templates of different brains (MAGeT Brain). We demonstrate the efficacy of our method for the mouse and human using two different nonlinear registration algorithms (ANIMAL and ANTs). The input atlases consist a high-resolution mouse brain atlas and an atlas of the human basal ganglia and thalamus derived from serial histological data. MAGeT Brain segmentation improves the identification of the mouse anterior commissure (mean Dice Kappa values (κ = 0.801), but may be encountering a ceiling effect for hippocampal segmentations. Applying MAGeT Brain to human subcortical structures improves segmentation accuracy for all structures compared to regular model-based techniques (κ = 0.845, 0.752, and 0.861 for the striatum, globus pallidus, and thalamus, respectively). Experiments performed with three manually derived input templates suggest that MAGeT Brain can approach or exceed the accuracy of multi-atlas label-fusion segmentation (κ = 0.894, 0.815, and 0.895 for the striatum, globus pallidus, and thalamus, respectively). Copyright © 2012 Wiley Periodicals, Inc.

Communication Strategies in Direct-to-Consumer Prescription Drug Advertising (DTCA): Application of the Six Segment Message Strategy Wheel.

PubMed

Ju, Ilwoo; Park, Jin Seong

2015-01-01

This study addresses a void in the literature on direct-to-consumer prescription drug advertising (DTCA) with a theory-based content analysis. The findings indicate that Taylor's communication strategy wheel provides insight into what and how pharmaceutical marketers communicate with consumers by means of DTCA. Major findings are summarized as follows: (a) In most DTC ads, informational and transformational message themes and creative approaches were simultaneously used, indicating a combination strategy; (b) DTCA message themes were associated with creative strategies in alignment with Taylor's framework; and (c) message themes and creative strategies varied across therapeutic categories and DTCA categories with different levels of ad spending. Theoretical and practical implications of the findings are discussed.

Aspects of quality related to the consumption and production of lamb meat. Consumers versus producers.

PubMed

Sepúlveda, Wilmer S; Maza, María T; Pardos, Luis

2011-04-01

The purpose of this study was to identify and compare the different evaluations made by the agents at either end of the lamb meat chain, i.e. producers and consumers, in relation to the parameters that consumers use when purchasing lamb meat and the factors that affect the production of quality lamb meat. In addition, consumer segments that can be targeted for action by the different agents in the chain were examined. The study was carried out in Aragón, a region in north east Spain that is a producer and consumer of lamb meat. 371 surveys were carried out on purchasers of lamb meat and 49 surveys on sheep farmers. Bivariant analyses and a cluster analysis were performed. The results suggest that there are certain congruencies and divergences between producers and consumers. Also, a segment of consumers for whom the hygiene and sanitary conditions on the farm, animal welfare and the environment are of great importance were found. © 2010 The American Meat Science Association. Published by Elsevier Ltd. All rights reserved.

To eat or not to eat pork, how frequently and how varied? Insights from the quantitative Q-PorkChains consumer survey in four European countries.

PubMed

Verbeke, Wim; Pérez-Cueto, Federico J A; Grunert, Klaus G

2011-08-01

This study uses pork consumption frequency and variety to identify and profile European pork consumer segments. Data (n=1931) were collected in January 2008 in Belgium, Denmark, Germany and Poland. "Non-pork eaters" are profiled as predominantly younger (<35 years) females, with a high likelihood of living single and being underweight (BMI<18.5 kg/m²). Three segments of pork eaters were identified. The "Low variety, Low frequency" segment (17.4%) has a similar profile as the non-pork eaters, though it is a largely non-Polish and non-German segment. The "High variety, High frequency" segment (18.6%) consists mainly of rural, lower educated and overweight or obese (BMI>30 kg/m²) males. The segment "High variety, Medium frequency" (50.1%) includes families and other non-single households, with a profile that matches the overall sample. Their pork consumption is balanced over a wide range of pork cuts and pork meat products. Each segment entails specific challenges for the industry and the public health sector. Copyright © 2011 Elsevier Ltd. All rights reserved.

Spatially varying accuracy and reproducibility of prostate segmentation in magnetic resonance images using manual and semiautomated methods.

PubMed

Shahedi, Maysam; Cool, Derek W; Romagnoli, Cesare; Bauman, Glenn S; Bastian-Jordan, Matthew; Gibson, Eli; Rodrigues, George; Ahmad, Belal; Lock, Michael; Fenster, Aaron; Ward, Aaron D

2014-11-01

Three-dimensional (3D) prostate image segmentation is useful for cancer diagnosis and therapy guidance, but can be time-consuming to perform manually and involves varying levels of difficulty and interoperator variability within the prostatic base, midgland (MG), and apex. In this study, the authors measured accuracy and interobserver variability in the segmentation of the prostate on T2-weighted endorectal magnetic resonance (MR) imaging within the whole gland (WG), and separately within the apex, midgland, and base regions. The authors collected MR images from 42 prostate cancer patients. Prostate border delineation was performed manually by one observer on all images and by two other observers on a subset of ten images. The authors used complementary boundary-, region-, and volume-based metrics [mean absolute distance (MAD), Dice similarity coefficient (DSC), recall rate, precision rate, and volume difference (ΔV)] to elucidate the different types of segmentation errors that they observed. Evaluation for expert manual and semiautomatic segmentation approaches was carried out. Compared to manual segmentation, the authors' semiautomatic approach reduces the necessary user interaction by only requiring an indication of the anteroposterior orientation of the prostate and the selection of prostate center points on the apex, base, and midgland slices. Based on these inputs, the algorithm identifies candidate prostate boundary points using learned boundary appearance characteristics and performs regularization based on learned prostate shape information. The semiautomated algorithm required an average of 30 s of user interaction time (measured for nine operators) for each 3D prostate segmentation. The authors compared the segmentations from this method to manual segmentations in a single-operator (mean whole gland MAD = 2.0 mm, DSC = 82%, recall = 77%, precision = 88%, and ΔV = - 4.6 cm(3)) and multioperator study (mean whole gland MAD = 2.2 mm, DSC = 77%, recall = 72%, precision = 86%, and ΔV = - 4.0 cm(3)). These results compared favorably with observed differences between manual segmentations and a simultaneous truth and performance level estimation reference for this data set (whole gland differences as high as MAD = 3.1 mm, DSC = 78%, recall = 66%, precision = 77%, and ΔV = 15.5 cm(3)). The authors found that overall, midgland segmentation was more accurate and repeatable than the segmentation of the apex and base, with the base posing the greatest challenge. The main conclusions of this study were that (1) the semiautomated approach reduced interobserver segmentation variability; (2) the segmentation accuracy of the semiautomated approach, as well as the accuracies of recently published methods from other groups, were within the range of observed expert variability in manual prostate segmentation; and (3) further efforts in the development of computer-assisted segmentation would be most productive if focused on improvement of segmentation accuracy and reduction of variability within the prostatic apex and base.

Unsupervised motion-based object segmentation refined by color

NASA Astrophysics Data System (ADS)

Piek, Matthijs C.; Braspenning, Ralph; Varekamp, Chris

2003-06-01

For various applications, such as data compression, structure from motion, medical imaging and video enhancement, there is a need for an algorithm that divides video sequences into independently moving objects. Because our focus is on video enhancement and structure from motion for consumer electronics, we strive for a low complexity solution. For still images, several approaches exist based on colour, but these lack in both speed and segmentation quality. For instance, colour-based watershed algorithms produce a so-called oversegmentation with many segments covering each single physical object. Other colour segmentation approaches exist which somehow limit the number of segments to reduce this oversegmentation problem. However, this often results in inaccurate edges or even missed objects. Most likely, colour is an inherently insufficient cue for real world object segmentation, because real world objects can display complex combinations of colours. For video sequences, however, an additional cue is available, namely the motion of objects. When different objects in a scene have different motion, the motion cue alone is often enough to reliably distinguish objects from one another and the background. However, because of the lack of sufficient resolution of efficient motion estimators, like the 3DRS block matcher, the resulting segmentation is not at pixel resolution, but at block resolution. Existing pixel resolution motion estimators are more sensitive to noise, suffer more from aperture problems or have less correspondence to the true motion of objects when compared to block-based approaches or are too computationally expensive. From its tendency to oversegmentation it is apparent that colour segmentation is particularly effective near edges of homogeneously coloured areas. On the other hand, block-based true motion estimation is particularly effective in heterogeneous areas, because heterogeneous areas improve the chance a block is unique and thus decrease the chance of the wrong position producing a good match. Consequently, a number of methods exist which combine motion and colour segmentation. These methods use colour segmentation as a base for the motion segmentation and estimation or perform an independent colour segmentation in parallel which is in some way combined with the motion segmentation. The presented method uses both techniques to complement each other by first segmenting on motion cues and then refining the segmentation with colour. To our knowledge few methods exist which adopt this approach. One example is te{meshrefine}. This method uses an irregular mesh, which hinders its efficient implementation in consumer electronics devices. Furthermore, the method produces a foreground/background segmentation, while our applications call for the segmentation of multiple objects. NEW METHOD As mentioned above we start with motion segmentation and refine the edges of this segmentation with a pixel resolution colour segmentation method afterwards. There are several reasons for this approach: + Motion segmentation does not produce the oversegmentation which colour segmentation methods normally produce, because objects are more likely to have colour discontinuities than motion discontinuities. In this way, the colour segmentation only has to be done at the edges of segments, confining the colour segmentation to a smaller part of the image. In such a part, it is more likely that the colour of an object is homogeneous. + This approach restricts the computationally expensive pixel resolution colour segmentation to a subset of the image. Together with the very efficient 3DRS motion estimation algorithm, this helps to reduce the computational complexity. + The motion cue alone is often enough to reliably distinguish objects from one another and the background. To obtain the motion vector fields, a variant of the 3DRS block-based motion estimator which analyses three frames of input was used. The 3DRS motion estimator is known for its ability to estimate motion vectors which closely resemble the true motion. BLOCK-BASED MOTION SEGMENTATION As mentioned above we start with a block-resolution segmentation based on motion vectors. The presented method is inspired by the well-known K-means segmentation method te{K-means}. Several other methods (e.g. te{kmeansc}) adapt K-means for connectedness by adding a weighted shape-error. This adds the additional difficulty of finding the correct weights for the shape-parameters. Also, these methods often bias one particular pre-defined shape. The presented method, which we call K-regions, encourages connectedness because only blocks at the edges of segments may be assigned to another segment. This constrains the segmentation method to such a degree that it allows the method to use least squares for the robust fitting of affine motion models for each segment. Contrary to te{parmkm}, the segmentation step still operates on vectors instead of model parameters. To make sure the segmentation is temporally consistent, the segmentation of the previous frame will be used as initialisation for every new frame. We also present a scheme which makes the algorithm independent of the initially chosen amount of segments. COLOUR-BASED INTRA-BLOCK SEGMENTATION The block resolution motion-based segmentation forms the starting point for the pixel resolution segmentation. The pixel resolution segmentation is obtained from the block resolution segmentation by reclassifying pixels only at the edges of clusters. We assume that an edge between two objects can be found in either one of two neighbouring blocks that belong to different clusters. This assumption allows us to do the pixel resolution segmentation on each pair of such neighbouring blocks separately. Because of the local nature of the segmentation, it largely avoids problems with heterogeneously coloured areas. Because no new segments are introduced in this step, it also does not suffer from oversegmentation problems. The presented method has no problems with bifurcations. For the pixel resolution segmentation itself we reclassify pixels such that we optimize an error norm which favour similarly coloured regions and straight edges. SEGMENTATION MEASURE To assist in the evaluation of the proposed algorithm we developed a quality metric. Because the problem does not have an exact specification, we decided to define a ground truth output which we find desirable for a given input. We define the measure for the segmentation quality as being how different the segmentation is from the ground truth. Our measure enables us to evaluate oversegmentation and undersegmentation seperately. Also, it allows us to evaluate which parts of a frame suffer from oversegmentation or undersegmentation. The proposed algorithm has been tested on several typical sequences. CONCLUSIONS In this abstract we presented a new video segmentation method which performs well in the segmentation of multiple independently moving foreground objects from each other and the background. It combines the strong points of both colour and motion segmentation in the way we expected. One of the weak points is that the segmentation method suffers from undersegmentation when adjacent objects display similar motion. In sequences with detailed backgrounds the segmentation will sometimes display noisy edges. Apart from these results, we think that some of the techniques, and in particular the K-regions technique, may be useful for other two-dimensional data segmentation problems.

Consumer preference models: fuzzy theory approach

NASA Astrophysics Data System (ADS)

Turksen, I. B.; Wilson, I. A.

1993-12-01

Consumer preference models are widely used in new product design, marketing management, pricing and market segmentation. The purpose of this article is to develop and test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation) and how much to make (market share prediction).

Validation of automatic segmentation of ribs for NTCP modeling.

PubMed

Stam, Barbara; Peulen, Heike; Rossi, Maddalena M G; Belderbos, José S A; Sonke, Jan-Jakob

2016-03-01

Determination of a dose-effect relation for rib fractures in a large patient group has been limited by the time consuming manual delineation of ribs. Automatic segmentation could facilitate such an analysis. We determine the accuracy of automatic rib segmentation in the context of normal tissue complication probability modeling (NTCP). Forty-one patients with stage I/II non-small cell lung cancer treated with SBRT to 54 Gy in 3 fractions were selected. Using the 4DCT derived mid-ventilation planning CT, all ribs were manually contoured and automatically segmented. Accuracy of segmentation was assessed using volumetric, shape and dosimetric measures. Manual and automatic dosimetric parameters Dx and EUD were tested for equivalence using the Two One-Sided T-test (TOST), and assessed for agreement using Bland-Altman analysis. NTCP models based on manual and automatic segmentation were compared. Automatic segmentation was comparable with the manual delineation in radial direction, but larger near the costal cartilage and vertebrae. Manual and automatic Dx and EUD were significantly equivalent. The Bland-Altman analysis showed good agreement. The two NTCP models were very similar. Automatic rib segmentation was significantly equivalent to manual delineation and can be used for NTCP modeling in a large patient group. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching.

PubMed

Liao, Miao; Zhao, Yu-Qian; Liu, Xi-Yao; Zeng, Ye-Zhan; Zou, Bei-Ji; Wang, Xiao-Fang; Shih, Frank Y

2017-05-01

Identifying liver regions from abdominal computed tomography (CT) volumes is an important task for computer-aided liver disease diagnosis and surgical planning. This paper presents a fully automatic method for liver segmentation from CT volumes based on graph cuts and border marching. An initial slice is segmented by density peak clustering. Based on pixel- and patch-wise features, an intensity model and a PCA-based regional appearance model are developed to enhance the contrast between liver and background. Then, these models as well as the location constraint estimated iteratively are integrated into graph cuts in order to segment the liver in each slice automatically. Finally, a vessel compensation method based on the border marching is used to increase the segmentation accuracy. Experiments are conducted on a clinical data set we created and also on the MICCAI2007 Grand Challenge liver data. The results show that the proposed intensity, appearance models, and the location constraint are significantly effective for liver recognition, and the undersegmented vessels can be compensated by the border marching based method. The segmentation performances in terms of VOE, RVD, ASD, RMSD, and MSD as well as the average running time achieved by our method on the SLIVER07 public database are 5.8 ± 3.2%, -0.1 ± 4.1%, 1.0 ± 0.5mm, 2.0 ± 1.2mm, 21.2 ± 9.3mm, and 4.7 minutes, respectively, which are superior to those of existing methods. The proposed method does not require time-consuming training process and statistical model construction, and is capable of dealing with complicated shapes and intensity variations successfully. Copyright © 2017 Elsevier B.V. All rights reserved.

Segmentation of large periapical lesions toward dental computer-aided diagnosis in cone-beam CT scans

NASA Astrophysics Data System (ADS)

Rysavy, Steven; Flores, Arturo; Enciso, Reyes; Okada, Kazunori

2008-03-01

This paper presents an experimental study for assessing the applicability of general-purpose 3D segmentation algorithms for analyzing dental periapical lesions in cone-beam computed tomography (CBCT) scans. In the field of Endodontics, clinical studies have been unable to determine if a periapical granuloma can heal with non-surgical methods. Addressing this issue, Simon et al. recently proposed a diagnostic technique which non-invasively classifies target lesions using CBCT. Manual segmentation exploited in their study, however, is too time consuming and unreliable for real world adoption. On the other hand, many technically advanced algorithms have been proposed to address segmentation problems in various biomedical and non-biomedical contexts, but they have not yet been applied to the field of dentistry. Presented in this paper is a novel application of such segmentation algorithms to the clinically-significant dental problem. This study evaluates three state-of-the-art graph-based algorithms: a normalized cut algorithm based on a generalized eigen-value problem, a graph cut algorithm implementing energy minimization techniques, and a random walks algorithm derived from discrete electrical potential theory. In this paper, we extend the original 2D formulation of the above algorithms to segment 3D images directly and apply the resulting algorithms to the dental CBCT images. We experimentally evaluate quality of the segmentation results for 3D CBCT images, as well as their 2D cross sections. The benefits and pitfalls of each algorithm are highlighted.

Segmentation of breast ultrasound images based on active contours using neutrosophic theory.

PubMed

Lotfollahi, Mahsa; Gity, Masoumeh; Ye, Jing Yong; Mahlooji Far, A

2018-04-01

Ultrasound imaging is an effective approach for diagnosing breast cancer, but it is highly operator-dependent. Recent advances in computer-aided diagnosis have suggested that it can assist physicians in diagnosis. Definition of the region of interest before computer analysis is still needed. Since manual outlining of the tumor contour is tedious and time-consuming for a physician, developing an automatic segmentation method is important for clinical application. The present paper represents a novel method to segment breast ultrasound images. It utilizes a combination of region-based active contour and neutrosophic theory to overcome the natural properties of ultrasound images including speckle noise and tissue-related textures. First, due to inherent speckle noise and low contrast of these images, we have utilized a non-local means filter and fuzzy logic method for denoising and image enhancement, respectively. This paper presents an improved weighted region-scalable active contour to segment breast ultrasound images using a new feature derived from neutrosophic theory. This method has been applied to 36 breast ultrasound images. It generates true-positive and false-positive results, and similarity of 95%, 6%, and 90%, respectively. The purposed method indicates clear advantages over other conventional methods of active contour segmentation, i.e., region-scalable fitting energy and weighted region-scalable fitting energy.

DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

PubMed

Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

2017-11-27

In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

Automatic identification of the number of food items in a meal using clustering techniques based on the monitoring of swallowing and chewing.

PubMed

Lopez-Meyer, Paulo; Schuckers, Stephanie; Makeyev, Oleksandr; Fontana, Juan M; Sazonov, Edward

2012-09-01

The number of distinct foods consumed in a meal is of significant clinical concern in the study of obesity and other eating disorders. This paper proposes the use of information contained in chewing and swallowing sequences for meal segmentation by food types. Data collected from experiments of 17 volunteers were analyzed using two different clustering techniques. First, an unsupervised clustering technique, Affinity Propagation (AP), was used to automatically identify the number of segments within a meal. Second, performance of the unsupervised AP method was compared to a supervised learning approach based on Agglomerative Hierarchical Clustering (AHC). While the AP method was able to obtain 90% accuracy in predicting the number of food items, the AHC achieved an accuracy >95%. Experimental results suggest that the proposed models of automatic meal segmentation may be utilized as part of an integral application for objective Monitoring of Ingestive Behavior in free living conditions.

Automatic Testing and Assessment of Neuroanatomy Using a Digital Brain Atlas: Method and Development of Computer- and Mobile-Based Applications

ERIC Educational Resources Information Center

Nowinski, Wieslaw L.; Thirunavuukarasuu, Arumugam; Ananthasubramaniam, Anand; Chua, Beng Choon; Qian, Guoyu; Nowinska, Natalia G.; Marchenko, Yevgen; Volkau, Ihar

2009-01-01

Preparation of tests and student's assessment by the instructor are time consuming. We address these two tasks in neuroanatomy education by employing a digital media application with a three-dimensional (3D), interactive, fully segmented, and labeled brain atlas. The anatomical and vascular models in the atlas are linked to "Terminologia…

TAS::89 0927::TAS RECOVERY - The Lean Green Energy Controller Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Teeter, John; Wang, Gene; Moss, David

Achieving efficiency improvements and providing demand-response programs have been identified as key elements of our national energy initiative. The residential market is the largest, yet most difficult, segment to engage in efforts to meet these objectives. This project developed Energy Management System that engages the consumer and enables Smart Grid services, applications, and business processes to address this need. Our innovative solution provides smart controller providing dynamic optimization of energy consumption for the residential energy consumer. Our solution extends the technical platform to include a cloud based Internet of Things (IoT) aggregation of data sensors and actuators the go beyondmore » energy management and extend to life style services provided through compelling mobile and console based user experiences.« less

Analysing Smart Metering Systems from a Consumer Perspective

NASA Astrophysics Data System (ADS)

Yesudas, Rani

Many countries are deploying smart meters and Advanced Metering Infrastructure systems as part of demand management and grid modernisation efforts. Several of these projects are facing consumer resistance. The advertised benefits to the consumer appear mainly monetary but detailed analysis shows that financial benefits are hard to realise since the fixed services charges are high. Additionally, the data collected from smart meters have security and privacy implications for the consumer. These projects failed to consider end-users as an important stakeholder group during planning stages resulting in the design and roll-out of expensive systems, which do not demonstrate clear consumer benefits. The overall goal of the research reported in this thesis was to improve the smart metering system to deliver consumer benefits that increase confidence and acceptance of these projects. The smart metering system was examined from an end-user perspective for realistic insights into consumer concerns. Processes from Design Science Research methodology were utilised to conduct this research due to the utilitarian nature of the objective. Consumer segmentation was central to the proposed measures. Initially, a consumer-friendly risk analysis framework was devised, and appropriate requirement elicitation techniques were identified. Control options for smart meter data transfer and storage were explored. Various scenarios were analysed to determine consumer-friendly features in the smart metering system, including control options for smart meter data transfer and storage. Proposed functionalities (billing choices, feedback information and specific configurations to match the needs of different user segments) were studied using the Australian smart metering system. Smart meters vary in capabilities depending on the manufacturer, mode and place of deployment. The research showed that features proposed in this thesis are implementable in smart meters, by examining their applicability to those used in Victoria (Australia). This study demonstrated that intelligent systems for demand and distribution-side management can be built without the use of detailed consumption data from the consumer. Many issues related to smart meter data could be avoided by distributing intelligent metering devices across the network. A check-list was generated to guide project proponents to achieve a consumer-friendly outcome. This research establishes that by applying well-established theories during the planning process, in particular, requirement elicitation and risk analysis, consumer support can be gained leading to the deployment of user-friendly and sustainable systems. The check-list generated will help the industry to appropriately plan and develop systems that can avoid opposition and even stimulate adoption. Options proposed provide choices for different consumer segments without affecting major operations such as billing. On evaluation, it has been identified that the proposed measures do not affect the quality attributes of the system. Since the proposals presented in this thesis were based on smart meters used in Victoria (Australia), smart meters used in other areas may require upgrades or revisions to support these functions. The scope of this research is limited to identifying improvements in the system that will benefit the residential consumer and does not extend to the analysis of the effects of these improvements on the profitability of the investors.

Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint.

PubMed

Liukkonen, Mimmi K; Mononen, Mika E; Tanska, Petri; Saarakkala, Simo; Nieminen, Miika T; Korhonen, Rami K

2017-10-01

Manual segmentation of articular cartilage from knee joint 3D magnetic resonance images (MRI) is a time consuming and laborious task. Thus, automatic methods are needed for faster and reproducible segmentations. In the present study, we developed a semi-automatic segmentation method based on radial intensity profiles to generate 3D geometries of knee joint cartilage which were then used in computational biomechanical models of the knee joint. Six healthy volunteers were imaged with a 3T MRI device and their knee cartilages were segmented both manually and semi-automatically. The values of cartilage thicknesses and volumes produced by these two methods were compared. Furthermore, the influences of possible geometrical differences on cartilage stresses and strains in the knee were evaluated with finite element modeling. The semi-automatic segmentation and 3D geometry construction of one knee joint (menisci, femoral and tibial cartilages) was approximately two times faster than with manual segmentation. Differences in cartilage thicknesses, volumes, contact pressures, stresses, and strains between segmentation methods in femoral and tibial cartilage were mostly insignificant (p > 0.05) and random, i.e. there were no systematic differences between the methods. In conclusion, the devised semi-automatic segmentation method is a quick and accurate way to determine cartilage geometries; it may become a valuable tool for biomechanical modeling applications with large patient groups.

Automated 3D Ultrasound Image Segmentation to Aid Breast Cancer Image Interpretation

PubMed Central

Gu, Peng; Lee, Won-Mean; Roubidoux, Marilyn A.; Yuan, Jie; Wang, Xueding; Carson, Paul L.

2015-01-01

Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer. PMID:26547117

Automated 3D ultrasound image segmentation for assistant diagnosis of breast cancer

NASA Astrophysics Data System (ADS)

Wang, Yuxin; Gu, Peng; Lee, Won-Mean; Roubidoux, Marilyn A.; Du, Sidan; Yuan, Jie; Wang, Xueding; Carson, Paul L.

2016-04-01

Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.

A hybrid segmentation approach for geographic atrophy in fundus auto-fluorescence images for diagnosis of age-related macular degeneration.

PubMed

Lee, Noah; Laine, Andrew F; Smith, R Theodore

2007-01-01

Fundus auto-fluorescence (FAF) images with hypo-fluorescence indicate geographic atrophy (GA) of the retinal pigment epithelium (RPE) in age-related macular degeneration (AMD). Manual quantification of GA is time consuming and prone to inter- and intra-observer variability. Automatic quantification is important for determining disease progression and facilitating clinical diagnosis of AMD. In this paper we describe a hybrid segmentation method for GA quantification by identifying hypo-fluorescent GA regions from other interfering retinal vessel structures. First, we employ background illumination correction exploiting a non-linear adaptive smoothing operator. Then, we use the level set framework to perform segmentation of hypo-fluorescent areas. Finally, we present an energy function combining morphological scale-space analysis with a geometric model-based approach to perform segmentation refinement of false positive hypo- fluorescent areas due to interfering retinal structures. The clinically apparent areas of hypo-fluorescence were drawn by an expert grader and compared on a pixel by pixel basis to our segmentation results. The mean sensitivity and specificity of the ROC analysis were 0.89 and 0.98%.

Exploring the functional mealtime associations of older adults through consumer segmentation and a means-end chain approach.

PubMed

den Uijl, Louise C; Jager, Gerry; de Graaf, Cees; Kremer, Stefanie

2016-12-01

Senior consumers are a rapidly growing and highly heterogeneous part of the world's population. This group does not always meet its recommended protein intake, which can negatively impact on their physical functioning and quality of life. To date, little is known about their motivations to consume protein-rich meals. In the current study, we therefore aim to identify consumer segments within the group of vital community-dwelling older adults on the basis of mealtime functionality (for example 'I eat because I'm hungry', or 'I eat because it is cosy'). To this end, we first conducted an online survey to identify these functional mealtime expectations of older consumers (study I, n = 398, 158 males, mean age 65.8 (y) ± 5.9 (SD)). To obtain further insights regarding mealtime functionality and proteins/protein enrichment, laddering interviews were conducted with a subgroup of the segmentation study participants (study II, n = 40, 20 males, mean age 66.9 (y) ± 4.8 (SD)). The results of the online survey showed three consumer clusters: cosy socialisers, physical nutritioners, and thoughtless rewarders. Thoughtless rewarders tend to eat without having explicit thoughts about it, they eat for the reward, and score highest on environmental awareness. Both the segmentation and the in-depth interviews showed that, for the cosy socialisers, the cosiness and social function of a meal are important motivators, whereas for the physical nutritioners the focus is more on the health and nutrient aspects of a meal. For cosy socialisers, protein enrichment can best be achieved through addition of protein-rich ingredients, whereas, for physical nutritioners, addition of protein powder is preferred. These results provide practical guidelines for the development of protein-rich meals and communication strategies tailored to the needs of specific vital community-dwelling older subgroups. Copyright © 2016 Elsevier Ltd. All rights reserved.

Investigating Greek consumers' attitudes towards low-fat food products: a segmentation study.

PubMed

Krystallis, Athanasios; Arvanitoyannis, Ioannis S; Kapirti, A

2003-05-01

The present study aims at gaining a first insight into Greek consumers' attitudes towards low-fat food products. Although Greece, and in particular Crete, have enjoyed a great popularity in terms of the Mediterranean diet, there has been an almost complete lack of low-fat-related surveys concerning the Greek food consumer. Using this as a research trigger, the current investigation evolves around the conflict between 'sensory appeal' and 'healthiness' of low-fat products, widely described in the international literature. Other crucial factors examined are consumers' awareness, occasional use and conscious purchase of, and willingness to pay for, food products with the 'low-fat' claim. Overall, the study has the objective to segment the Greek market in terms of users' perceptions of light products and to identify a number of well-described clusters with clear-cut socio-demographic and behavioural profile. Three clusters are identified, comprised of consumers with favourable attitudes towards low-fat foods and willing to pay premiums to purchase them.

Highlight summarization in golf videos using audio signals

NASA Astrophysics Data System (ADS)

Kim, Hyoung-Gook; Kim, Jin Young

2008-01-01

In this paper, we present an automatic summarization of highlights in golf videos based on audio information alone without video information. The proposed highlight summarization system is carried out based on semantic audio segmentation and detection on action units from audio signals. Studio speech, field speech, music, and applause are segmented by means of sound classification. Swing is detected by the methods of impulse onset detection. Sounds like swing and applause form a complete action unit, while studio speech and music parts are used to anchor the program structure. With the advantage of highly precise detection of applause, highlights are extracted effectively. Our experimental results obtain high classification precision on 18 golf games. It proves that the proposed system is very effective and computationally efficient to apply the technology to embedded consumer electronic devices.

An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images.

PubMed

Wang, Liansheng; Li, Shusheng; Chen, Rongzhen; Liu, Sze-Yu; Chen, Jyh-Cheng

2016-01-01

Accurate segmentation and classification of different anatomical structures of teeth from medical images plays an essential role in many clinical applications. Usually, the anatomical structures of teeth are manually labelled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing three dimensional (3D) information, and classify the tooth by employing unsupervised learning Pulse Coupled Neural Networks (PCNN) model. In order to evaluate the proposed method, the experiments are conducted on the different datasets of mandibular molars and the experimental results show that our method can achieve better accuracy and robustness compared to other four state of the art clustering methods.

Interactive 3D segmentation of the prostate in magnetic resonance images using shape and local appearance similarity analysis

NASA Astrophysics Data System (ADS)

Shahedi, Maysam; Fenster, Aaron; Cool, Derek W.; Romagnoli, Cesare; Ward, Aaron D.

2013-03-01

3D segmentation of the prostate in medical images is useful to prostate cancer diagnosis and therapy guidance, but is time-consuming to perform manually. Clinical translation of computer-assisted segmentation algorithms for this purpose requires a comprehensive and complementary set of evaluation metrics that are informative to the clinical end user. We have developed an interactive 3D prostate segmentation method for 1.5T and 3.0T T2-weighted magnetic resonance imaging (T2W MRI) acquired using an endorectal coil. We evaluated our method against manual segmentations of 36 3D images using complementary boundary-based (mean absolute distance; MAD), regional overlap (Dice similarity coefficient; DSC) and volume difference (ΔV) metrics. Our technique is based on inter-subject prostate shape and local boundary appearance similarity. In the training phase, we calculated a point distribution model (PDM) and a set of local mean intensity patches centered on the prostate border to capture shape and appearance variability. To segment an unseen image, we defined a set of rays - one corresponding to each of the mean intensity patches computed in training - emanating from the prostate centre. We used a radial-based search strategy and translated each mean intensity patch along its corresponding ray, selecting as a candidate the boundary point with the highest normalized cross correlation along each ray. These boundary points were then regularized using the PDM. For the whole gland, we measured a mean+/-std MAD of 2.5+/-0.7 mm, DSC of 80+/-4%, and ΔV of 1.1+/-8.8 cc. We also provided an anatomic breakdown of these metrics within the prostatic base, mid-gland, and apex.

SU-E-J-132: Automated Segmentation with Post-Registration Atlas Selection Based On Mutual Information

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, X; Gao, H; Sharp, G

2015-06-15

Purpose: The delineation of targets and organs-at-risk is a critical step during image-guided radiation therapy, for which manual contouring is the gold standard. However, it is often time-consuming and may suffer from intra- and inter-rater variability. The purpose of this work is to investigate the automated segmentation. Methods: The automatic segmentation here is based on mutual information (MI), with the atlas from Public Domain Database for Computational Anatomy (PDDCA) with manually drawn contours.Using dice coefficient (DC) as the quantitative measure of segmentation accuracy, we perform leave-one-out cross-validations for all PDDCA images sequentially, during which other images are registered to eachmore » chosen image and DC is computed between registered contour and ground truth. Meanwhile, six strategies, including MI, are selected to measure the image similarity, with MI to be the best. Then given a target image to be segmented and an atlas, automatic segmentation consists of: (a) the affine registration step for image positioning; (b) the active demons registration method to register the atlas to the target image; (c) the computation of MI values between the deformed atlas and the target image; (d) the weighted image fusion of three deformed atlas images with highest MI values to form the segmented contour. Results: MI was found to be the best among six studied strategies in the sense that it had the highest positive correlation between similarity measure (e.g., MI values) and DC. For automated segmentation, the weighted image fusion of three deformed atlas images with highest MI values provided the highest DC among four proposed strategies. Conclusion: MI has the highest correlation with DC, and therefore is an appropriate choice for post-registration atlas selection in atlas-based segmentation. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)« less

Identifying Degenerative Brain Disease Using Rough Set Classifier Based on Wavelet Packet Method.

PubMed

Cheng, Ching-Hsue; Liu, Wei-Xiang

2018-05-28

Population aging has become a worldwide phenomenon, which causes many serious problems. The medical issues related to degenerative brain disease have gradually become a concern. Magnetic Resonance Imaging is one of the most advanced methods for medical imaging and is especially suitable for brain scans. From the literature, although the automatic segmentation method is less laborious and time-consuming, it is restricted in several specific types of images. In addition, hybrid techniques segmentation improves the shortcomings of the single segmentation method. Therefore, this study proposed a hybrid segmentation combined with rough set classifier and wavelet packet method to identify degenerative brain disease. The proposed method is a three-stage image process method to enhance accuracy of brain disease classification. In the first stage, this study used the proposed hybrid segmentation algorithms to segment the brain ROI (region of interest). In the second stage, wavelet packet was used to conduct the image decomposition and calculate the feature values. In the final stage, the rough set classifier was utilized to identify the degenerative brain disease. In verification and comparison, two experiments were employed to verify the effectiveness of the proposed method and compare with the TV-seg (total variation segmentation) algorithm, Discrete Cosine Transform, and the listing classifiers. Overall, the results indicated that the proposed method outperforms the listing methods.

Accelerating Convolutional Sparse Coding for Curvilinear Structures Segmentation by Refining SCIRD-TS Filter Banks.

PubMed

Annunziata, Roberto; Trucco, Emanuele

2016-11-01

Deep learning has shown great potential for curvilinear structure (e.g., retinal blood vessels and neurites) segmentation as demonstrated by a recent auto-context regression architecture based on filter banks learned by convolutional sparse coding. However, learning such filter banks is very time-consuming, thus limiting the amount of filters employed and the adaptation to other data sets (i.e., slow re-training). We address this limitation by proposing a novel acceleration strategy to speed-up convolutional sparse coding filter learning for curvilinear structure segmentation. Our approach is based on a novel initialisation strategy (warm start), and therefore it is different from recent methods improving the optimisation itself. Our warm-start strategy is based on carefully designed hand-crafted filters (SCIRD-TS), modelling appearance properties of curvilinear structures which are then refined by convolutional sparse coding. Experiments on four diverse data sets, including retinal blood vessels and neurites, suggest that the proposed method reduces significantly the time taken to learn convolutional filter banks (i.e., up to -82%) compared to conventional initialisation strategies. Remarkably, this speed-up does not worsen performance; in fact, filters learned with the proposed strategy often achieve a much lower reconstruction error and match or exceed the segmentation performance of random and DCT-based initialisation, when used as input to a random forest classifier.

Thigh muscle segmentation of chemical shift encoding-based water-fat magnetic resonance images: The reference database MyoSegmenTUM.

PubMed

Schlaeger, Sarah; Freitag, Friedemann; Klupp, Elisabeth; Dieckmeyer, Michael; Weidlich, Dominik; Inhuber, Stephanie; Deschauer, Marcus; Schoser, Benedikt; Bublitz, Sarah; Montagnese, Federica; Zimmer, Claus; Rummeny, Ernst J; Karampinos, Dimitrios C; Kirschke, Jan S; Baum, Thomas

2018-01-01

Magnetic resonance imaging (MRI) can non-invasively assess muscle anatomy, exercise effects and pathologies with different underlying causes such as neuromuscular diseases (NMD). Quantitative MRI including fat fraction mapping using chemical shift encoding-based water-fat MRI has emerged for reliable determination of muscle volume and fat composition. The data analysis of water-fat images requires segmentation of the different muscles which has been mainly performed manually in the past and is a very time consuming process, currently limiting the clinical applicability. An automatization of the segmentation process would lead to a more time-efficient analysis. In the present work, the manually segmented thigh magnetic resonance imaging database MyoSegmenTUM is presented. It hosts water-fat MR images of both thighs of 15 healthy subjects and 4 patients with NMD with a voxel size of 3.2x2x4 mm3 with the corresponding segmentation masks for four functional muscle groups: quadriceps femoris, sartorius, gracilis, hamstrings. The database is freely accessible online at https://osf.io/svwa7/?view_only=c2c980c17b3a40fca35d088a3cdd83e2. The database is mainly meant as ground truth which can be used as training and test dataset for automatic muscle segmentation algorithms. The segmentation allows extraction of muscle cross sectional area (CSA) and volume. Proton density fat fraction (PDFF) of the defined muscle groups from the corresponding images and quadriceps muscle strength measurements/neurological muscle strength rating can be used for benchmarking purposes.

Inferior vena cava segmentation with parameter propagation and graph cut.

PubMed

Yan, Zixu; Chen, Feng; Wu, Fa; Kong, Dexing

2017-09-01

The inferior vena cava (IVC) is one of the vital veins inside the human body. Accurate segmentation of the IVC from contrast-enhanced CT images is of great importance. This extraction not only helps the physician understand its quantitative features such as blood flow and volume, but also it is helpful during the hepatic preoperative planning. However, manual delineation of the IVC is time-consuming and poorly reproducible. In this paper, we propose a novel method to segment the IVC with minimal user interaction. The proposed method performs the segmentation block by block between user-specified beginning and end masks. At each stage, the proposed method builds the segmentation model based on information from image regional appearances, image boundaries, and a prior shape. The intensity range and the prior shape for this segmentation model are estimated based on the segmentation result from the last block, or from user- specified beginning mask if at first stage. Then, the proposed method minimizes the energy function and generates the segmentation result for current block using graph cut. Finally, a backward tracking step from the end of the IVC is performed if necessary. We have tested our method on 20 clinical datasets and compared our method to three other vessel extraction approaches. The evaluation was performed using three quantitative metrics: the Dice coefficient (Dice), the mean symmetric distance (MSD), and the Hausdorff distance (MaxD). The proposed method has achieved a Dice of [Formula: see text], an MSD of [Formula: see text] mm, and a MaxD of [Formula: see text] mm, respectively, in our experiments. The proposed approach can achieve a sound performance with a relatively low computational cost and a minimal user interaction. The proposed algorithm has high potential to be applied for the clinical applications in the future.

Sensory properties and consumer acceptance of imported and domestic sliced black ripe olives.

PubMed

Lee, Soh Min; Kitsawad, Kamolnate; Sigal, Abdulkadir; Flynn, Dan; Guinard, Jean-Xavier

2012-12-01

Table olives are healthy and nutritious products with high contents of monounsaturated fatty acids, phenolics, vitamins, minerals, and fiber. Understanding sensory cues affecting consumer preferences would enable the increase of olive consumption. The objectives of this study were to characterize the sensory properties of commercial sliced black ripe olives from different regions, including California, Egypt, Morocco, Portugal, and Spain, and to examine the preferences of California consumers for sliced black ripe olives. Sensory profiles and preferences for 20 sliced olive samples were determined using descriptive analysis with a trained panel and a consumer test with 104 users and likers of table olives. Aroma and flavor characteristics separated the olives according to country of origin, and were the main determinants of consumer preferences for sliced olives, even though the biggest differences among the samples were in appearance and texture. Total of 2 consumer segments were identified with 51 and 53 consumers, respectively, that both liked Californian products, but differed in the olives they disliked. Negative drivers of liking for both segments included alcohol, oak barrel, and artificial fruity/floral characteristics; however, consumers from Cluster 1 were further negatively influenced by rancid, gassy, and bitter characteristics. This study stresses the need for sound and appealing flavor quality for table olives to gain wider acceptance among U.S. consumers. © 2012 Institute of Food Technologists®

The Influence of Materialism on Purebred Dogs' Welfare Among Two Different Generations in Colombia (South America).

PubMed

Luna-Cortés, Gonzalo

2018-03-27

Some consumers in Colombia show a clear preference for purebred dogs. At the same time, there are many abandoned dogs on the streets and in shelters in this country. Previous research has revealed that appearances of the breeds influence the caregivers' (owners') choice. A choice based on appearances has been connected with materialism in the psychology and consumer behavior literature. Buying purebred dogs based on materialistic standards could affect the welfare of these nonhuman animals. With the use of quantitative research and the methodology of structural equation modeling, this research demonstrated that more materialistic consumers in Colombia have purebred dogs who, in the owners' opinions, show more behavioral problems. Furthermore, the results showed that materialism influenced the owners' intentions to abandon their companion animals when they perceived these problems. Finally, this research examined the moderating effect of generational segmentation regarding these relationships. It was observed that the intention to abandon the dogs was greater among members of Generation X than among members of Generation Y.

A fuzzy set preference model for market share analysis

NASA Technical Reports Server (NTRS)

Turksen, I. B.; Willson, Ian A.

1992-01-01

Consumer preference models are widely used in new product design, marketing management, pricing, and market segmentation. The success of new products depends on accurate market share prediction and design decisions based on consumer preferences. The vague linguistic nature of consumer preferences and product attributes, combined with the substantial differences between individuals, creates a formidable challenge to marketing models. The most widely used methodology is conjoint analysis. Conjoint models, as currently implemented, represent linguistic preferences as ratio or interval-scaled numbers, use only numeric product attributes, and require aggregation of individuals for estimation purposes. It is not surprising that these models are costly to implement, are inflexible, and have a predictive validity that is not substantially better than chance. This affects the accuracy of market share estimates. A fuzzy set preference model can easily represent linguistic variables either in consumer preferences or product attributes with minimal measurement requirements (ordinal scales), while still estimating overall preferences suitable for market share prediction. This approach results in flexible individual-level conjoint models which can provide more accurate market share estimates from a smaller number of more meaningful consumer ratings. Fuzzy sets can be incorporated within existing preference model structures, such as a linear combination, using the techniques developed for conjoint analysis and market share estimation. The purpose of this article is to develop and fully test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation), and how much to make (market share prediction).

Optimal Dynamic Advertising Strategy Under Age-Specific Market Segmentation

NASA Astrophysics Data System (ADS)

Krastev, Vladimir

2011-12-01

We consider the model proposed by Faggian and Grosset for determining the advertising efforts and goodwill in the long run of a company under age segmentation of consumers. Reducing this model to optimal control sub problems we find the optimal advertising strategy and goodwill.

Iterative-cuts: longitudinal and scale-invariant segmentation via user-defined templates for rectosigmoid colon in gynecological brachytherapy.

PubMed

Lüddemann, Tobias; Egger, Jan

2016-04-01

Among all types of cancer, gynecological malignancies belong to the fourth most frequent type of cancer among women. In addition to chemotherapy and external beam radiation, brachytherapy is the standard procedure for the treatment of these malignancies. In the progress of treatment planning, localization of the tumor as the target volume and adjacent organs of risks by segmentation is crucial to accomplish an optimal radiation distribution to the tumor while simultaneously preserving healthy tissue. Segmentation is performed manually and represents a time-consuming task in clinical daily routine. This study focuses on the segmentation of the rectum/sigmoid colon as an organ-at-risk in gynecological brachytherapy. The proposed segmentation method uses an interactive, graph-based segmentation scheme with a user-defined template. The scheme creates a directed two-dimensional graph, followed by the minimal cost closed set computation on the graph, resulting in an outlining of the rectum. The graph's outline is dynamically adapted to the last calculated cut. Evaluation was performed by comparing manual segmentations of the rectum/sigmoid colon to results achieved with the proposed method. The comparison of the algorithmic to manual result yielded a dice similarity coefficient value of [Formula: see text], in comparison to [Formula: see text] for the comparison of two manual segmentations by the same physician. Utilizing the proposed methodology resulted in a median time of [Formula: see text], compared to 300 s needed for pure manual segmentation.

Interactive and scale invariant segmentation of the rectum/sigmoid via user-defined templates

NASA Astrophysics Data System (ADS)

Lüddemann, Tobias; Egger, Jan

2016-03-01

Among all types of cancer, gynecological malignancies belong to the 4th most frequent type of cancer among women. Besides chemotherapy and external beam radiation, brachytherapy is the standard procedure for the treatment of these malignancies. In the progress of treatment planning, localization of the tumor as the target volume and adjacent organs of risks by segmentation is crucial to accomplish an optimal radiation distribution to the tumor while simultaneously preserving healthy tissue. Segmentation is performed manually and represents a time-consuming task in clinical daily routine. This study focuses on the segmentation of the rectum/sigmoid colon as an Organ-At-Risk in gynecological brachytherapy. The proposed segmentation method uses an interactive, graph-based segmentation scheme with a user-defined template. The scheme creates a directed two dimensional graph, followed by the minimal cost closed set computation on the graph, resulting in an outlining of the rectum. The graphs outline is dynamically adapted to the last calculated cut. Evaluation was performed by comparing manual segmentations of the rectum/sigmoid colon to results achieved with the proposed method. The comparison of the algorithmic to manual results yielded to a Dice Similarity Coefficient value of 83.85+/-4.08%, in comparison to 83.97+/-8.08% for the comparison of two manual segmentations of the same physician. Utilizing the proposed methodology resulted in a median time of 128 seconds per dataset, compared to 300 seconds needed for pure manual segmentation.

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm.

PubMed

Bahadure, Nilesh Bhaskarrao; Ray, Arun Kumar; Thethi, Har Pal

2018-01-17

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images

PubMed Central

Jain, Saurabh; Sima, Diana M.; Ribbens, Annemie; Cambron, Melissa; Maertens, Anke; Van Hecke, Wim; De Mey, Johan; Barkhof, Frederik; Steenwijk, Martijn D.; Daams, Marita; Maes, Frederik; Van Huffel, Sabine; Vrenken, Hugo; Smeets, Dirk

2015-01-01

The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter settings. PMID:26106562

Effects of aircraft and flight parameters on energy-efficient profile descents in time-based metered traffic

NASA Technical Reports Server (NTRS)

Dejarnette, F. R.

1984-01-01

Attention is given to a computer algorithm yielding the data required for a flight crew to navigate from an entry fix, about 100 nm from an airport, to a metering fix, and arrive there at a predetermined time, altitude, and airspeed. The flight path is divided into several descent and deceleration segments. Results for the case of a B-737 airliner indicate that wind and nonstandard atmospheric properties have a significant effect on the flight path and must be taken into account. While a range of combinations of Mach number and calibrated airspeed is possible for the descent segments leading to the metering fix, only small changes in the fuel consumed were observed for this range of combinations. A combination that is based on scheduling flexibility therefore seems preferable.

DOS Design/Application Tools System/Segment Specification. Volume 3

DTIC Science & Technology

1990-09-01

consume the same information to obtain that information without "manual" translation by people. Solving the information management problem effectively...and consumes ’ even more information than centralized development. Distributed systems cannot be developed successfully by experiment without...human intervention because all tools consume input from and produce output to the same repository. New tools are easily absorbed into the environment

Consumers' acceptance of innovations in traditional cheese. A comparative study in France and Norway.

PubMed

Almli, Valérie Lengard; Naes, Tormod; Enderli, Géraldine; Sulmont-Rossé, Claire; Issanchou, Sylvie; Hersleth, Margrethe

2011-08-01

This study explores consumers' acceptance of innovations in traditional cheese in France (n=120) and Norway (n=119). The respondents were presented with 16 photographs of a traditional cheese from their respective countries, varying according to six factors: pasteurisation, organic production, omega-3, packaging, price and appropriateness. For each of the scenarios the consumers indicated their willingness to buy the cheese on a nine-point scale. Results show that consumers' willingness to buy traditional cheese is highly driven by price, appropriateness and pasteurisation in both countries. However, on average consumers in the French sample prefer buying raw milk cheese, while consumers in the Norwegian sample prefer buying pasteurised cheese. These general trends are led by a pro-raw milk segment in France and a pro-pasteurised milk segment in Norway. Several interaction effects involving appropriateness are detected, indicating the importance of the consumption context on the acceptance of innovations in traditional cheese. On a general level, the results indicate that well-accepted innovations in traditional cheese are those that reinforce the traditional and authentic character of the product. Copyright © 2011 Elsevier Ltd. All rights reserved.

Automated brain tumor segmentation in magnetic resonance imaging based on sliding-window technique and symmetry analysis.

PubMed

Lian, Yanyun; Song, Zhijian

2014-01-01

Brain tumor segmentation from magnetic resonance imaging (MRI) is an important step toward surgical planning, treatment planning, monitoring of therapy. However, manual tumor segmentation commonly used in clinic is time-consuming and challenging, and none of the existed automated methods are highly robust, reliable and efficient in clinic application. An accurate and automated tumor segmentation method has been developed for brain tumor segmentation that will provide reproducible and objective results close to manual segmentation results. Based on the symmetry of human brain, we employed sliding-window technique and correlation coefficient to locate the tumor position. At first, the image to be segmented was normalized, rotated, denoised, and bisected. Subsequently, through vertical and horizontal sliding-windows technique in turn, that is, two windows in the left and the right part of brain image moving simultaneously pixel by pixel in two parts of brain image, along with calculating of correlation coefficient of two windows, two windows with minimal correlation coefficient were obtained, and the window with bigger average gray value is the location of tumor and the pixel with biggest gray value is the locating point of tumor. At last, the segmentation threshold was decided by the average gray value of the pixels in the square with center at the locating point and 10 pixels of side length, and threshold segmentation and morphological operations were used to acquire the final tumor region. The method was evaluated on 3D FSPGR brain MR images of 10 patients. As a result, the average ratio of correct location was 93.4% for 575 slices containing tumor, the average Dice similarity coefficient was 0.77 for one scan, and the average time spent on one scan was 40 seconds. An fully automated, simple and efficient segmentation method for brain tumor is proposed and promising for future clinic use. Correlation coefficient is a new and effective feature for tumor location.

Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.

PubMed

Sauwen, Nicolas; Acou, Marjan; Sima, Diana M; Veraart, Jelle; Maes, Frederik; Himmelreich, Uwe; Achten, Eric; Huffel, Sabine Van

2017-05-04

Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments. We present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method. L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to an MP-MRI dataset of 21 high-grade glioma patients, including conventional, perfusion-weighted and diffusion-weighted MRI. To assess the effect of using MP-MRI data and the L1-regularization term, analyses are also run using only conventional MRI and without L1-regularization. Robustness against user input variability is verified by considering the statistical distribution of the segmentation results when repeatedly analyzing each patient's dataset with a different set of random seeding points. Using L1-regularized semi-automated NMF segmentation, mean Dice-scores of 65%, 74 and 80% are found for active tumor, the tumor core and the whole tumor region. Mean Hausdorff distances of 6.1 mm, 7.4 mm and 8.2 mm are found for active tumor, the tumor core and the whole tumor region. Lower Dice-scores and higher Hausdorff distances are found without L1-regularization and when only considering conventional MRI data. Based on the mean Dice-scores and Hausdorff distances, segmentation results are competitive with state-of-the-art in literature. Robust results were found for most patients, although careful voxel selection is mandatory to avoid sub-optimal segmentation.

Product Matching in Television News Using Benefit Segmentation.

ERIC Educational Resources Information Center

Wicks, Robert H.

Because local television news appears to be resilient to audience erosion, programmers may find it beneficial to develop strategies that are accommodating to the interests of audience segments. This also suggests that advertisers may communicate more effectively with consumers sorted according to benefit orientation. After telephone interviews…

Segmentation of solid subregion of high grade gliomas in MRI images based on active contour model (ACM)

NASA Astrophysics Data System (ADS)

Seow, P.; Win, M. T.; Wong, J. H. D.; Abdullah, N. A.; Ramli, N.

2016-03-01

Gliomas are tumours arising from the interstitial tissue of the brain which are heterogeneous, infiltrative and possess ill-defined borders. Tumour subregions (e.g. solid enhancing part, edema and necrosis) are often used for tumour characterisation. Tumour demarcation into substructures facilitates glioma staging and provides essential information. Manual segmentation had several drawbacks that include laborious, time consuming, subjected to intra and inter-rater variability and hindered by diversity in the appearance of tumour tissues. In this work, active contour model (ACM) was used to segment the solid enhancing subregion of the tumour. 2D brain image acquisition data using 3T MRI fast spoiled gradient echo sequence in post gadolinium of four histologically proven high-grade glioma patients were obtained. Preprocessing of the images which includes subtraction and skull stripping were performed and then followed by ACM segmentation. The results of the automatic segmentation method were compared against the manual delineation of the tumour by a trainee radiologist. Both results were further validated by an experienced neuroradiologist and a brief quantitative evaluations (pixel area and difference ratio) were performed. Preliminary results of the clinical data showed the potential of ACM model in the application of fast and large scale tumour segmentation in medical imaging.

A New Method for Automated Identification and Morphometry of Myelinated Fibers Through Light Microscopy Image Analysis.

PubMed

Novas, Romulo Bourget; Fazan, Valeria Paula Sassoli; Felipe, Joaquim Cezar

2016-02-01

Nerve morphometry is known to produce relevant information for the evaluation of several phenomena, such as nerve repair, regeneration, implant, transplant, aging, and different human neuropathies. Manual morphometry is laborious, tedious, time consuming, and subject to many sources of error. Therefore, in this paper, we propose a new method for the automated morphometry of myelinated fibers in cross-section light microscopy images. Images from the recurrent laryngeal nerve of adult rats and the vestibulocochlear nerve of adult guinea pigs were used herein. The proposed pipeline for fiber segmentation is based on the techniques of competitive clustering and concavity analysis. The evaluation of the proposed method for segmentation of images was done by comparing the automatic segmentation with the manual segmentation. To further evaluate the proposed method considering morphometric features extracted from the segmented images, the distributions of these features were tested for statistical significant difference. The method achieved a high overall sensitivity and very low false-positive rates per image. We detect no statistical difference between the distribution of the features extracted from the manual and the pipeline segmentations. The method presented a good overall performance, showing widespread potential in experimental and clinical settings allowing large-scale image analysis and, thus, leading to more reliable results.

Automated methods for hippocampus segmentation: the evolution and a review of the state of the art.

PubMed

Dill, Vanderson; Franco, Alexandre Rosa; Pinho, Márcio Sarroglia

2015-04-01

The segmentation of the hippocampus in Magnetic Resonance Imaging (MRI) has been an important procedure to diagnose and monitor several clinical situations. The precise delineation of the borders of this brain structure makes it possible to obtain a measure of the volume and estimate its shape, which can be used to diagnose some diseases, such as Alzheimer's disease, schizophrenia and epilepsy. As the manual segmentation procedure in three-dimensional images is highly time consuming and the reproducibility is low, automated methods introduce substantial gains. On the other hand, the implementation of those methods is a challenge because of the low contrast of this structure in relation to the neighboring areas of the brain. Within this context, this research presents a review of the evolution of automatized methods for the segmentation of the hippocampus in MRI. Many proposed methods for segmentation of the hippocampus have been published in leading journals in the medical image processing area. This paper describes these methods presenting the techniques used and quantitatively comparing the methods based on Dice Similarity Coefficient. Finally, we present an evaluation of those methods considering the degree of user intervention, computational cost, segmentation accuracy and feasibility of application in a clinical routine.

Automatic media-adventitia IVUS image segmentation based on sparse representation framework and dynamic directional active contour model.

PubMed

Zakeri, Fahimeh Sadat; Setarehdan, Seyed Kamaledin; Norouzi, Somayye

2017-10-01

Segmentation of the arterial wall boundaries from intravascular ultrasound images is an important image processing task in order to quantify arterial wall characteristics such as shape, area, thickness and eccentricity. Since manual segmentation of these boundaries is a laborious and time consuming procedure, many researchers attempted to develop (semi-) automatic segmentation techniques as a powerful tool for educational and clinical purposes in the past but as yet there is no any clinically approved method in the market. This paper presents a deterministic-statistical strategy for automatic media-adventitia border detection by a fourfold algorithm. First, a smoothed initial contour is extracted based on the classification in the sparse representation framework which is combined with the dynamic directional convolution vector field. Next, an active contour model is utilized for the propagation of the initial contour toward the interested borders. Finally, the extracted contour is refined in the leakage, side branch openings and calcification regions based on the image texture patterns. The performance of the proposed algorithm is evaluated by comparing the results to those manually traced borders by an expert on 312 different IVUS images obtained from four different patients. The statistical analysis of the results demonstrates the efficiency of the proposed method in the media-adventitia border detection with enough consistency in the leakage and calcification regions. Copyright © 2017 Elsevier Ltd. All rights reserved.

The study of infrared target recognition at sea background based on visual attention computational model

NASA Astrophysics Data System (ADS)

Wang, Deng-wei; Zhang, Tian-xu; Shi, Wen-jun; Wei, Long-sheng; Wang, Xiao-ping; Ao, Guo-qing

2009-07-01

Infrared images at sea background are notorious for the low signal-to-noise ratio, therefore, the target recognition of infrared image through traditional methods is very difficult. In this paper, we present a novel target recognition method based on the integration of visual attention computational model and conventional approach (selective filtering and segmentation). The two distinct techniques for image processing are combined in a manner to utilize the strengths of both. The visual attention algorithm searches the salient regions automatically, and represented them by a set of winner points, at the same time, demonstrated the salient regions in terms of circles centered at these winner points. This provides a priori knowledge for the filtering and segmentation process. Based on the winner point, we construct a rectangular region to facilitate the filtering and segmentation, then the labeling operation will be added selectively by requirement. Making use of the labeled information, from the final segmentation result we obtain the positional information of the interested region, label the centroid on the corresponding original image, and finish the localization for the target. The cost time does not depend on the size of the image but the salient regions, therefore the consumed time is greatly reduced. The method is used in the recognition of several kinds of real infrared images, and the experimental results reveal the effectiveness of the algorithm presented in this paper.

Three-dimensional murine airway segmentation in micro-CT images

NASA Astrophysics Data System (ADS)

Shi, Lijun; Thiesse, Jacqueline; McLennan, Geoffrey; Hoffman, Eric A.; Reinhardt, Joseph M.

2007-03-01

Thoracic imaging for small animals has emerged as an important tool for monitoring pulmonary disease progression and therapy response in genetically engineered animals. Micro-CT is becoming the standard thoracic imaging modality in small animal imaging because it can produce high-resolution images of the lung parenchyma, vasculature, and airways. Segmentation, measurement, and visualization of the airway tree is an important step in pulmonary image analysis. However, manual analysis of the airway tree in micro-CT images can be extremely time-consuming since a typical dataset is usually on the order of several gigabytes in size. Automated and semi-automated tools for micro-CT airway analysis are desirable. In this paper, we propose an automatic airway segmentation method for in vivo micro-CT images of the murine lung and validate our method by comparing the automatic results to manual tracing. Our method is based primarily on grayscale morphology. The results show good visual matches between manually segmented and automatically segmented trees. The average true positive volume fraction compared to manual analysis is 91.61%. The overall runtime for the automatic method is on the order of 30 minutes per volume compared to several hours to a few days for manual analysis.

Segmentation of bone structures in 3D CT images based on continuous max-flow optimization

NASA Astrophysics Data System (ADS)

Pérez-Carrasco, J. A.; Acha-Piñero, B.; Serrano, C.

2015-03-01

In this paper an algorithm to carry out the automatic segmentation of bone structures in 3D CT images has been implemented. Automatic segmentation of bone structures is of special interest for radiologists and surgeons to analyze bone diseases or to plan some surgical interventions. This task is very complicated as bones usually present intensities overlapping with those of surrounding tissues. This overlapping is mainly due to the composition of bones and to the presence of some diseases such as Osteoarthritis, Osteoporosis, etc. Moreover, segmentation of bone structures is a very time-consuming task due to the 3D essence of the bones. Usually, this segmentation is implemented manually or with algorithms using simple techniques such as thresholding and thus providing bad results. In this paper gray information and 3D statistical information have been combined to be used as input to a continuous max-flow algorithm. Twenty CT images have been tested and different coefficients have been computed to assess the performance of our implementation. Dice and Sensitivity values above 0.91 and 0.97 respectively were obtained. A comparison with Level Sets and thresholding techniques has been carried out and our results outperformed them in terms of accuracy.

Hybrid Clustering And Boundary Value Refinement for Tumor Segmentation using Brain MRI

NASA Astrophysics Data System (ADS)

Gupta, Anjali; Pahuja, Gunjan

2017-08-01

The method of brain tumor segmentation is the separation of tumor area from Brain Magnetic Resonance (MR) images. There are number of methods already exist for segmentation of brain tumor efficiently. However it’s tedious task to identify the brain tumor from MR images. The segmentation process is extraction of different tumor tissues such as active, tumor, necrosis, and edema from the normal brain tissues such as gray matter (GM), white matter (WM), as well as cerebrospinal fluid (CSF). As per the survey study, most of time the brain tumors are detected easily from brain MR image using region based approach but required level of accuracy, abnormalities classification is not predictable. The segmentation of brain tumor consists of many stages. Manually segmenting the tumor from brain MR images is very time consuming hence there exist many challenges in manual segmentation. In this research paper, our main goal is to present the hybrid clustering which consists of Fuzzy C-Means Clustering (for accurate tumor detection) and level set method(for handling complex shapes) for the detection of exact shape of tumor in minimal computational time. using this approach we observe that for a certain set of images 0.9412 sec of time is taken to detect tumor which is very less in comparison to recent existing algorithm i.e. Hybrid clustering (Fuzzy C-Means and K Means clustering).

Volumetric glioma quantification: comparison of manual and semi-automatic tumor segmentation for the quantification of tumor growth.

PubMed

Odland, Audun; Server, Andres; Saxhaug, Cathrine; Breivik, Birger; Groote, Rasmus; Vardal, Jonas; Larsson, Christopher; Bjørnerud, Atle

2015-11-01

Volumetric magnetic resonance imaging (MRI) is now widely available and routinely used in the evaluation of high-grade gliomas (HGGs). Ideally, volumetric measurements should be included in this evaluation. However, manual tumor segmentation is time-consuming and suffers from inter-observer variability. Thus, tools for semi-automatic tumor segmentation are needed. To present a semi-automatic method (SAM) for segmentation of HGGs and to compare this method with manual segmentation performed by experts. The inter-observer variability among experts manually segmenting HGGs using volumetric MRIs was also examined. Twenty patients with HGGs were included. All patients underwent surgical resection prior to inclusion. Each patient underwent several MRI examinations during and after adjuvant chemoradiation therapy. Three experts performed manual segmentation. The results of tumor segmentation by the experts and by the SAM were compared using Dice coefficients and kappa statistics. A relatively close agreement was seen among two of the experts and the SAM, while the third expert disagreed considerably with the other experts and the SAM. An important reason for this disagreement was a different interpretation of contrast enhancement as either surgically-induced or glioma-induced. The time required for manual tumor segmentation was an average of 16 min per scan. Editing of the tumor masks produced by the SAM required an average of less than 2 min per sample. Manual segmentation of HGG is very time-consuming and using the SAM could increase the efficiency of this process. However, the accuracy of the SAM ultimately depends on the expert doing the editing. Our study confirmed a considerable inter-observer variability among experts defining tumor volume from volumetric MRIs. © The Foundation Acta Radiologica 2014.

Monitoring service for the Gran Telescopio Canarias control system

NASA Astrophysics Data System (ADS)

Huertas, Manuel; Molgo, Jordi; Macías, Rosa; Ramos, Francisco

2016-07-01

The Monitoring Service collects, persists and propagates the Telescope and Instrument telemetry, for the Gran Telescopio CANARIAS (GTC), an optical-infrared 10-meter segmented mirror telescope at the ORM observatory in Canary Islands (Spain). A new version of the Monitoring Service has been developed in order to improve performance, provide high availability, guarantee fault tolerance and scalability to cope with high volume of data. The architecture is based on a distributed in-memory data store with a Product/Consumer pattern design. The producer generates the data samples. The consumers either persists the samples to a database for further analysis or propagates them to the consoles in the control room to monitorize the state of the whole system.

Optimisation of the formulation of a bubble bath by a chemometric approach market segmentation and optimisation.

PubMed

Marengo, Emilio; Robotti, Elisa; Gennaro, Maria Carla; Bertetto, Mariella

2003-03-01

The optimisation of the formulation of a commercial bubble bath was performed by chemometric analysis of Panel Tests results. A first Panel Test was performed to choose the best essence, among four proposed to the consumers; the best essence chosen was used in the revised commercial bubble bath. Afterwards, the effect of changing the amount of four components (the amount of primary surfactant, the essence, the hydratant and the colouring agent) of the bubble bath was studied by a fractional factorial design. The segmentation of the bubble bath market was performed by a second Panel Test, in which the consumers were requested to evaluate the samples coming from the experimental design. The results were then treated by Principal Component Analysis. The market had two segments: people preferring a product with a rich formulation and people preferring a poor product. The final target, i.e. the optimisation of the formulation for each segment, was obtained by the calculation of regression models relating the subjective evaluations given by the Panel and the compositions of the samples. The regression models allowed to identify the best formulations for the two segments ofthe market.

Offering-level strategy formulation in health service organizations.

PubMed

Pointer, D D

1990-01-01

One of six different strategies must be selected for a health service offering to provide consumers with distinctive value and achieve sustainable competitive advantage in a market or market segment. Decisions must be made regarding objectives sought, market segmentation, market scope, and the customer-value proposition that will be pursued.

New Trends in the Chinese Diet: Cultural Influences on Consumer Behaviour

PubMed Central

Cicia, Gianni; Grunert, Klaus G.; Krystallis, Athanasios K.; Zhou, Yanfeng; Cembalo, Luigi; Verneau, Fabio; Caracciolo, Francesco

2016-01-01

China is one of the most dynamic regions in the world in terms of economic growth and development. Such development has inevitably influenced the structure and habits of Chinese society. Whilst the economic condition of the middle class and high-income segment has steadily improved, cultural changes are also under way: ancient Chinese traditions now include major elements from other cultures, most notably the West. The above scenario is the background to this paper. A structured research-administered survey was developed to investigate the changes in the Chinese consumer food culture: 500 urban participants were randomly selected from six reference cities, covering geographically almost the whole country. This study aims not only to analyze the propensity of consumers to include food products from other countries in their ancient Chinese culinary culture, but also represents an initial attempt to perform a market segmentation of Chinese consumers according to their degree of cultural openness towards non-Chinese food, taking into account socio-demographic, cognitive and psychographic variables. PMID:27800438

New Trends in the Chinese Diet: Cultural Influences on Consumer Behaviour.

PubMed

Del Giudice, Teresa; Cicia, Gianni; Grunert, Klaus G; Krystallis, Athanasios K; Zhou, Yanfeng; Cembalo, Luigi; Verneau, Fabio; Caracciolo, Francesco

2016-04-19

China is one of the most dynamic regions in the world in terms of economic growth and development. Such development has inevitably influenced the structure and habits of Chinese society. Whilst the economic condition of the middle class and high-income segment has steadily improved, cultural changes are also under way: ancient Chinese traditions now include major elements from other cultures, most notably the West. The above scenario is the background to this paper. A structured research-administered survey was developed to investigate the changes in the Chinese consumer food culture: 500 urban participants were randomly selected from six reference cities, covering geographically almost the whole country. This study aims not only to analyze the propensity of consumers to include food products from other countries in their ancient Chinese culinary culture, but also represents an initial attempt to perform a market segmentation of Chinese consumers according to their degree of cultural openness towards non-Chinese food, taking into account socio-demographic, cognitive and psychographic variables.

Dogmatism as a mediating influence on the perception of risk in consumer choice decisions.

PubMed

Durand, R M; Davis, D L; Bearden, W O

1977-01-01

The risk perceived by individual consumers when faced with an unfamiliar purchase situation was examined across three groups of females for three product categories. Group membership was determined on the basis of high, medium, and low scores on the Trodahl-Powell dogmatism instrument. Ss were 155 housewives of a medium size midwestern city in the United States surveyed as part of a two-tiered sampling process. The results of a multivariate analysis of variance procedure supported the hypothesis that consumers of a less dogmatic nature perceive lower levels of risk inherent within unfamiliar purchase situations than more dogmatic individuals. The implication for management is that the likelihood of obtaining successful new product introductions may be substantially enhanced through the process of risk reduction across dogmatic consumer segments by use of direct testimonial promotional themes stressing product acceptance in support of more traditional and informative advertising messages. The feasibility of this approach is based upon the premise that the behavior of dogmatic individuals is more frequently affected by pressures from peers and significant others than the behavior of individuals low in dogmatism which is generally based on more factual and relevant information.

Automatic identification of IASLC-defined mediastinal lymph node stations on CT scans using multi-atlas organ segmentation

NASA Astrophysics Data System (ADS)

Hoffman, Joanne; Liu, Jiamin; Turkbey, Evrim; Kim, Lauren; Summers, Ronald M.

2015-03-01

Station-labeling of mediastinal lymph nodes is typically performed to identify the location of enlarged nodes for cancer staging. Stations are usually assigned in clinical radiology practice manually by qualitative visual assessment on CT scans, which is time consuming and highly variable. In this paper, we developed a method that automatically recognizes the lymph node stations in thoracic CT scans based on the anatomical organs in the mediastinum. First, the trachea, lungs, and spines are automatically segmented to locate the mediastinum region. Then, eight more anatomical organs are simultaneously identified by multi-atlas segmentation. Finally, with the segmentation of those anatomical organs, we convert the text definitions of the International Association for the Study of Lung Cancer (IASLC) lymph node map into patient-specific color-coded CT image maps. Thus, a lymph node station is automatically assigned to each lymph node. We applied this system to CT scans of 86 patients with 336 mediastinal lymph nodes measuring equal or greater than 10 mm. 84.8% of mediastinal lymph nodes were correctly mapped to their stations.

Risk Segmentation Related to the Offering of a Consumer-Directed Health Plan: A Case Study of Humana Inc.

PubMed Central

Tollen, Laura A; Ross, Murray N; Poor, Stephen

2004-01-01

Objective To determine whether the offering of a consumer-directed health plan (CDHP) is likely to cause risk segmentation in an employer group. Study Setting and Data Source The study population comprises the approximately 10,000 people (employees and dependents) enrolled as members of the employee health benefit program of Humana Inc. at its headquarters in Louisville, Kentucky, during the benefit years starting July 1, 2000, and July 1, 2001. This analysis is based on primary collection of claims, enrollment, and employment data for those employees and dependents. Study Design This is a case study of the experience of a single employer in offering two consumer-directed health plan options (“Coverage First 1” and “Coverage First 2”) to its employees. We assessed the risk profile of those choosing the Coverage First plans and those remaining in more traditional health maintenance organization (HMO) and preferred provider organization (PPO) coverage. Risk was measured using prior claims (in dollars per member per month), prior utilization (admissions/1,000; average length of stay; prescriptions/1,000; physician office visit services/1,000), a pharmacy-based risk assessment tool (developed by Ingenix), and demographics. Data Collection/Extraction Methods Complete claims and administrative data were provided by Humana Inc. for the two-year study period. Unique identifiers enabled us to track subscribers' individual enrollment and utilization over this period. Principal Findings Based on demographic data alone, there did not appear to be a difference in the risk profiles of those choosing versus not choosing Coverage First. However, based on prior claims and prior use data, it appeared that those who chose Coverage First were healthier than those electing to remain in more traditional coverage. For each of five services, prior-year usage by people who subsequently enrolled in Coverage First 1 (CF1) was below 60 percent of the average for the whole group. Hospital and maternity admissions per thousand were less than 30 percent of the overall average; length of stay per hospital admission, physician office services per thousand, and prescriptions per thousand were all between 50 and 60 percent of the overall average. Coverage First 2 (CF2) subscribers' prior use of services was somewhat higher than CF1 subscribers', but it was still below average in every category. As with prior use, prior claims data indicated that Coverage First subscribers were healthier than average, with prior total claims less than 50 percent of average. Conclusions In this case, the offering of high-deductible or consumer-directed health plan options alongside more traditional options caused risk segmentation within an employer group. The extent to which these findings are applicable to other cases will depend on many factors, including the employer premium contribution policies and employees' perception of the value of the various plan options. Further research is needed to determine whether risk segmentation will worsen in future years for this employer and if so, whether it will cause premiums for more traditional health plans to increase. PMID:15230919

Impact of flavor attributes on consumer liking of Swiss cheese.

PubMed

Liggett, R E; Drake, M A; Delwiche, J F

2008-02-01

Although Swiss cheese is growing in popularity, no research has examined what flavor characteristics consumers desire in Swiss cheese, which was the main objective of this study. To this end, a large group of commercially available Swiss-type cheeses (10 domestic Swiss cheeses, 4 domestic Baby Swiss cheeses, and one imported Swiss Emmenthal) were assessed both by 12 trained panelists for flavor and feeling factors and by 101 consumers for overall liking. In addition, a separate panel of 24 consumers rated the same cheeses for dissimilarity. On the basis of liking ratings, the 101 consumers were segmented by cluster analysis into 2 groups: nondistinguishers (n = 40) and varying responders (n = 61). Partial least squares regression, a statistical modeling technique that relates 2 data sets (in this case, a set of descriptive analysis data and a set of consumer liking data), was used to determine which flavor attributes assessed by the trained panel were important variables in overall liking of the cheeses for the varying responders. The model explained 93% of the liking variance on 3 normally distributed components and had 49% predictability. Diacetyl, whey, milk fat, and umami were found to be drivers of liking, whereas cabbage, cooked, and vinegar were drivers of disliking. Nutty flavor was not particularly important to liking and it was present in only 2 of the cheeses. The dissimilarity ratings were combined with the liking ratings of both segments and analyzed by probabilistic multidimensional scaling. The ideals of each segment completely overlapped, with the variance of the varying responders being smaller than the variance of the non-distinguishers. This model indicated that the Baby Swiss cheeses were closer to the consumers' ideals than were the other cheeses. Taken together, the 2 models suggest that the partial least squares regression failed to capture one or more attributes that contribute to consumer acceptance, although the descriptive analysis of flavor and feeling factors was able to account for 93% of the variance in the liking ratings. These findings indicate the flavor characteristics Swiss cheese producers should optimize, and minimize, to create cheeses that best match consumer desires.

Sensory properties and consumer acceptance of sweet tamarind varieties grown in Thailand.

PubMed

Oupadissakoon, Chintana; Chambers, Edgar; Kongpensook, Varapha; Suwonsichon, Suntaree; Yenket, Renoo; Retiveau, Annlyse

2010-04-30

Sweet tamarind is a major edible fruit and flavoring ingredient particularly in south-east and southern Asia. Little research has focused on the fruit and almost nothing is known of its particular sensory properties. The aims of this research were to develop a lexicon for describing sweet tamarind, to compare varieties grown in Thailand, determine if orchard impacts sensory properties, and determine consumer acceptance of the varieties. A descriptive sensory lexicon of 25 terms was developed and six varieties were grouped into three clusters based on their sensory properties. The clusters appear to represent varieties that differ in their dark fruity notes and firm, fibrous texture. Generally, the orchard in which the plants were grown had little effect on sensory properties. In general, Sithong was liked by consumers along with Kunthee and Pragaithong. Intapalum was liked less but one small segment of consumers disliked Sithong and liked the Intapalum variety more. This research provides a foundation for further sensory and consumer research on sweet tamarind varieties by providing the initial data on the sensory properties of sweet tamarind, a lexicon that can be used for future research, and information on the consumer acceptance of tamarind varieties.

Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations

NASA Astrophysics Data System (ADS)

Nanayakkara, Nuwan D.; Samarabandu, Jagath; Fenster, Aaron

2006-04-01

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 ± 0.51 pixels (0.54 ± 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts.

Automated segmentation of the atrial region and fossa ovalis towards computer-aided planning of inter-atrial wall interventions.

PubMed

Morais, Pedro; Vilaça, João L; Queirós, Sandro; Marchi, Alberto; Bourier, Felix; Deisenhofer, Isabel; D'hooge, Jan; Tavares, João Manuel R S

2018-07-01

Image-fusion strategies have been applied to improve inter-atrial septal (IAS) wall minimally-invasive interventions. Hereto, several landmarks are initially identified on richly-detailed datasets throughout the planning stage and then combined with intra-operative images, enhancing the relevant structures and easing the procedure. Nevertheless, such planning is still performed manually, which is time-consuming and not necessarily reproducible, hampering its regular application. In this article, we present a novel automatic strategy to segment the atrial region (left/right atrium and aortic tract) and the fossa ovalis (FO). The method starts by initializing multiple 3D contours based on an atlas-based approach with global transforms only and refining them to the desired anatomy using a competitive segmentation strategy. The obtained contours are then applied to estimate the FO by evaluating both IAS wall thickness and the expected FO spatial location. The proposed method was evaluated in 41 computed tomography datasets, by comparing the atrial region segmentation and FO estimation results against manually delineated contours. The automatic segmentation method presented a performance similar to the state-of-the-art techniques and a high feasibility, failing only in the segmentation of one aortic tract and of one right atrium. The FO estimation method presented an acceptable result in all the patients with a performance comparable to the inter-observer variability. Moreover, it was faster and fully user-interaction free. Hence, the proposed method proved to be feasible to automatically segment the anatomical models for the planning of IAS wall interventions, making it exceptionally attractive for use in the clinical practice. Copyright © 2018 Elsevier B.V. All rights reserved.

The Flexitarian Flip™ : Testing the Modalities of Flavor as Sensory Strategies to Accomplish the Shift from Meat-Centered to Vegetable-Forward Mixed Dishes.

PubMed

Spencer, Molly; Guinard, Jean-Xavier

2018-01-01

The American diet is lacking in plant-based foods and vegetables, higher in protein than necessary, and too centered on meat and poultry. Two major dietary shifts recommended by the 2015-2020 U.S. Dietary Guidelines are to increase vegetable intake and to increase the variety of protein food sources. One suggested strategy for doing this is to partially replace meat and poultry with vegetables and plant-based ingredients in mixed dishes. This research tested the potential of flavor modalities (taste, aroma, trigeminal, and their combination) as strategies to increase the sensory appeal of plant-forward dishes. Consumer testing (n = 141) was conducted in a cross-sectional design in a laboratory setting on 24 recipe variations. Three factors were tested: cuisine (Latin American, Mediterranean, and Asian), meat proportion (high-meat/low-vegetable versus low-meat/high-vegetable), and flavor strategy (taste, aroma, trigeminal, and a reduced-intensity trimodal combination). Statistical analysis was performed in R and XLSTAT-Sensory ® 2017. Four consumer preference segments were uncovered. The low-meat dishes achieved parity or higher in consumer acceptance across all recipes and flavor strategies. The taste and trigeminal strategies both had higher overall acceptability scores than the aroma strategy, and the differences were significant (P < 0.05) in some consumer preference segments. The consumers successfully characterized the samples using a Check-All-That-Apply task, verifying the flavor strategy design. This research provides insight into consumer preferences regarding flavor strategies to partially replace meat with vegetables in mixed dishes. The trigeminal and trimodal combination strategies were found to be the most promising flavor modalities to use to implement this shift. There is little knowledge of American consumer preferences regarding vegetables in mixed dishes. Mixed dishes are a strategy recommended by the U.S. Dietary Guidelines to increase vegetable consumption and variety of protein sources. This research explores various flavor and culinary strategies with which to carry out the mixed dish meat-vegetable swap and to test the potential of the Flexitarian Flip ™ (the shift from meat-centric to plant-centric diets). This research shows that individuals have different preferences regarding the type of flavor they prefer in mixed dishes (for example, some consumers prefer salty and some prefer spicy), so if the dietitian can recommend recipes that cater to that client's food and flavor preferences, the client will be more likely to adhere to their diet. © 2017 Institute of Food Technologists®.

Has the Web really empowered health care consumers? The truth is customers may not have changed as much as we think.

PubMed

Wilkins, S T; Navarro, F H

2001-01-01

The experts tell us that fueled by unprecedented access to health information online, today's new health care consumer will revolutionize the way health care services are organized and delivered. An examination of consumers from a health value-graphic perspective, however, casts some doubt on these predictions. Patterns emerging online are simply making us more aware of existing consumer segments that have always been actively involved in their own health.

Iterative-cuts: longitudinal and scale-invariant segmentation via user-defined templates for rectosigmoid colon in gynecological brachytherapy

PubMed Central

Lüddemann, Tobias; Egger, Jan

2016-01-01

Abstract. Among all types of cancer, gynecological malignancies belong to the fourth most frequent type of cancer among women. In addition to chemotherapy and external beam radiation, brachytherapy is the standard procedure for the treatment of these malignancies. In the progress of treatment planning, localization of the tumor as the target volume and adjacent organs of risks by segmentation is crucial to accomplish an optimal radiation distribution to the tumor while simultaneously preserving healthy tissue. Segmentation is performed manually and represents a time-consuming task in clinical daily routine. This study focuses on the segmentation of the rectum/sigmoid colon as an organ-at-risk in gynecological brachytherapy. The proposed segmentation method uses an interactive, graph-based segmentation scheme with a user-defined template. The scheme creates a directed two-dimensional graph, followed by the minimal cost closed set computation on the graph, resulting in an outlining of the rectum. The graph’s outline is dynamically adapted to the last calculated cut. Evaluation was performed by comparing manual segmentations of the rectum/sigmoid colon to results achieved with the proposed method. The comparison of the algorithmic to manual result yielded a dice similarity coefficient value of 83.85±4.08, in comparison to 83.97±8.08% for the comparison of two manual segmentations by the same physician. Utilizing the proposed methodology resulted in a median time of 128  s/dataset, compared to 300 s needed for pure manual segmentation. PMID:27403448

Cerebrovascular plaque segmentation using object class uncertainty snake in MR images

NASA Astrophysics Data System (ADS)

Das, Bipul; Saha, Punam K.; Wolf, Ronald; Song, Hee Kwon; Wright, Alexander C.; Wehrli, Felix W.

2005-04-01

Atherosclerotic cerebrovascular disease leads to formation of lipid-laden plaques that can form emboli when ruptured causing blockage to cerebral vessels. The clinical manifestation of this event sequence is stroke; a leading cause of disability and death. In vivo MR imaging provides detailed image of vascular architecture for the carotid artery making it suitable for analysis of morphological features. Assessing the status of carotid arteries that supplies blood to the brain is of primary interest to such investigations. Reproducible quantification of carotid artery dimensions in MR images is essential for plaque analysis. Manual segmentation being the only method presently makes it time consuming and sensitive to inter and intra observer variability. This paper presents a deformable model for lumen and vessel wall segmentation of carotid artery from MR images. The major challenges of carotid artery segmentation are (a) low signal-to-noise ratio, (b) background intensity inhomogeneity and (c) indistinct inner and/or outer vessel wall. We propose a new, effective object-class uncertainty based deformable model with additional features tailored toward this specific application. Object-class uncertainty optimally utilizes MR intensity characteristics of various anatomic entities that enable the snake to avert leakage through fuzzy boundaries. To strengthen the deformable model for this application, some other properties are attributed to it in the form of (1) fully arc-based deformation using a Gaussian model to maximally exploit vessel wall smoothness, (2) construction of a forbidden region for outer-wall segmentation to reduce interferences by prominent lumen features and (3) arc-based landmark for efficient user interaction. The algorithm has been tested upon T1- and PD- weighted images. Measures of lumen area and vessel wall area are computed from segmented data of 10 patient MR images and their accuracy and reproducibility are examined. These results correspond exceptionally well with manual segmentation completed by radiology experts. Reproducibility of the proposed method is estimated for both intra- and inter-operator studies.

A robust firearm identification algorithm of forensic ballistics specimens

NASA Astrophysics Data System (ADS)

Chuan, Z. L.; Jemain, A. A.; Liong, C.-Y.; Ghani, N. A. M.; Tan, L. K.

2017-09-01

There are several inherent difficulties in the existing firearm identification algorithms, include requiring the physical interpretation and time consuming. Therefore, the aim of this study is to propose a robust algorithm for a firearm identification based on extracting a set of informative features from the segmented region of interest (ROI) using the simulated noisy center-firing pin impression images. The proposed algorithm comprises Laplacian sharpening filter, clustering-based threshold selection, unweighted least square estimator, and segment a square ROI from the noisy images. A total of 250 simulated noisy images collected from five different pistols of the same make, model and caliber are used to evaluate the robustness of the proposed algorithm. This study found that the proposed algorithm is able to perform the identical task on the noisy images with noise levels as high as 70%, while maintaining a firearm identification accuracy rate of over 90%.

Microscopic image analysis for reticulocyte based on watershed algorithm

NASA Astrophysics Data System (ADS)

Wang, J. Q.; Liu, G. F.; Liu, J. G.; Wang, G.

2007-12-01

We present a watershed-based algorithm in the analysis of light microscopic image for reticulocyte (RET), which will be used in an automated recognition system for RET in peripheral blood. The original images, obtained by micrography, are segmented by modified watershed algorithm and are recognized in term of gray entropy and area of connective area. In the process of watershed algorithm, judgment conditions are controlled according to character of the image, besides, the segmentation is performed by morphological subtraction. The algorithm was simulated with MATLAB software. It is similar for automated and manual scoring and there is good correlation(r=0.956) between the methods, which is resulted from 50 pieces of RET images. The result indicates that the algorithm for peripheral blood RETs is comparable to conventional manual scoring, and it is superior in objectivity. This algorithm avoids time-consuming calculation such as ultra-erosion and region-growth, which will speed up the computation consequentially.

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Ming; Xie, Fei; Zhao, Jing; Sun, Rui; Zhang, Lei; Zhang, Yue

2018-04-01

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

Nutritional therapy - Facing the gap between coeliac disease and gluten-free food.

PubMed

Foschia, Martina; Horstmann, Stefan; Arendt, Elke K; Zannini, Emanuele

2016-12-19

The market of gluten-free bakery products is considerably growing since better diagnostic methods allow identifying an increasing number of people suffering coeliac disease and other gluten-related disorders such as dermatitis herpetiformis, gluten ataxia, wheat allergy and non-coeliac gluten sensitivity. The only and safe treatment available nowadays for these types of disorders is to follow a strict and permanent lifelong gluten-free diet. Beside the people needing to follow a gluten-free diet for health reasons, a new segment of consumers has arisen who consume gluten-free products as a lifestyle choice. Among the bakery products, bread is a major staple food consumed daily all over the world. The dough and bread quality characteristics (such as gas retaining ability, mixing tolerance, resistance to stretch and extensibility and crumb structure) are mostly attributed to the presence of gluten. Despite the improved quality of gluten-free breads in the last number of years, most products on the market are still described as low quality product. In addition to the low overall quality of gluten-free products, the nutritional value of a large number of them is quite poor. In this context, this review gives an overview on the consumers, which need to follow a gluten-free diet for health reasons. The trends in this gluten-free bakery segment will also be reviewed based on the current analysis of marketing studies. An overview of the major ingredients used in gluten-free bread products will be given. The choice of the ingredients discussed in this paper is based on a comprehensive study of the leading gluten-free breads available on the market, as well as a detailed study of the scientific literature. The impact of the various ingredients on bread-making process and bread quality is also part of this review. Major emphasis will be placed on the application of sourdough as a means to improve gluten-free bread quality. Copyright © 2016 Elsevier B.V. All rights reserved.

Consumer liking of fruit juices with different açaí (Euterpe oleracea Mart.) concentrations.

PubMed

Sabbe, Sara; Verbeke, Wim; Deliza, Rosires; Matta, Virginia M; Van Damme, Patrick

2009-06-01

Overall liking, flavor, and perceived healthiness of one newly developed fruit juice with high açaí content (40% açaí) and 5 commercially available fruit juices with lower (4% to 20%) açaí concentrations were evaluated by consumers in Belgium. General trends for the overall sample were examined by means of analysis of variance, whereas individual consumer preferences were evaluated using internal preference mapping and hierarchical cluster analysis. The relative contribution of flavor and perceived health benefits as predictors of consumers' overall liking of the 6 açaí-based fruit juices was estimated through linear regression analysis. The results showed a negative relationship between the juices' overall liking and their açaí concentrations. Although the vast majority of consumers preferred the juices having a low açaí content (4% to 5% açaí), a small consumer segment liked the juice with 40% açaí. Flavor or taste experience superseded consumers' perceived health benefits as the primary determinant of the fruit juices' overall liking. The impact of perceived health benefits on the overall liking of the açaí juices decreased with higher taste dissatisfaction.

The Influence of Segmental Impedance Analysis in Predicting Validity of Consumer Grade Bioelectrical Impedance Analysis Devices

NASA Astrophysics Data System (ADS)

Sharp, Andy; Heath, Jennifer; Peterson, Janet

2008-05-01

Consumer grade bioelectric impedance analysis (BIA) instruments measure the body's impedance at 50 kHz, and yield a quick estimate of percent body fat. The frequency dependence of the impedance gives more information about the current pathway and the response of different tissues. This study explores the impedance response of human tissue at a range of frequencies from 0.2 - 102 kHz using a four probe method and probe locations standard for segmental BIA research of the arm. The data at 50 kHz, for a 21 year old healthy Caucasian male (resistance of 180φ±10 and reactance of 33φ±2) is in agreement with previously reported values [1]. The frequency dependence is not consistent with simple circuit models commonly used in evaluating BIA data, and repeatability of measurements is problematic. This research will contribute to a better understanding of the inherent difficulties in estimating body fat using consumer grade BIA devices. [1] Chumlea, William C., Richard N. Baumgartner, and Alex F. Roche. ``Specific resistivity used to estimate fat-free mass from segmental body measures of bioelectrical impedance.'' Am J Clin Nutr 48 (1998): 7-15.

NeuroSeg: automated cell detection and segmentation for in vivo two-photon Ca2+ imaging data.

PubMed

Guan, Jiangheng; Li, Jingcheng; Liang, Shanshan; Li, Ruijie; Li, Xingyi; Shi, Xiaozhe; Huang, Ciyu; Zhang, Jianxiong; Pan, Junxia; Jia, Hongbo; Zhang, Le; Chen, Xiaowei; Liao, Xiang

2018-01-01

Two-photon Ca 2+ imaging has become a popular approach for monitoring neuronal population activity with cellular or subcellular resolution in vivo. This approach allows for the recording of hundreds to thousands of neurons per animal and thus leads to a large amount of data to be processed. In particular, manually drawing regions of interest is the most time-consuming aspect of data analysis. However, the development of automated image analysis pipelines, which will be essential for dealing with the likely future deluge of imaging data, remains a major challenge. To address this issue, we developed NeuroSeg, an open-source MATLAB program that can facilitate the accurate and efficient segmentation of neurons in two-photon Ca 2+ imaging data. We proposed an approach using a generalized Laplacian of Gaussian filter to detect cells and weighting-based segmentation to separate individual cells from the background. We tested this approach on an in vivo two-photon Ca 2+ imaging dataset obtained from mouse cortical neurons with differently sized view fields. We show that this approach exhibits superior performance for cell detection and segmentation compared with the existing published tools. In addition, we integrated the previously reported, activity-based segmentation into our approach and found that this combined method was even more promising. The NeuroSeg software, including source code and graphical user interface, is freely available and will be a useful tool for in vivo brain activity mapping.

Does the Valuation of Nutritional Claims Differ among Consumers? Insights from Spain

PubMed Central

Jurado, Francesc; Gracia, Azucena

2017-01-01

The presence in the market of food products with nutritional claims is increasing. The objective of this paper is to assess consumers’ valuation of some nutritional claims (‘high in fiber’ and ‘reduced saturated fat’) in a European country and to test for differences among consumers. An artefactual non-hypothetical experiment was carried out in a realistic setting (mock/real brick-and-mortar supermarket) with a sample of 121 Spanish consumers stratified by gender, age, and body mass index. A latent class model was specified and estimated with the data from the experiment. Results indicate that consumers positively valued both nutritional claims, but the valuation was heterogeneous, and three consumer segments were detected. Two of them positively valued both nutritional claims (named ‘nutritional claim seekers’), while the third segment’s valuation was negative (named ‘nutritional claim avoiders’). This last segment is characterized by being younger males with university studies who give the least importance to health, natural ingredients, and the calorie/sugar/fat content when shopping. They pay less attention to nutritional information, and they stated that they use this information to a lesser extent. These consumers showed the least interest in healthy eating, and they reported that they do not have health problems related to their diet. PMID:28208811

Consumer attitudes and preferences for fresh market tomatoes.

PubMed

Oltman, A E; Jervis, S M; Drake, M A

2014-10-01

This study established attractive attributes and consumer desires for fresh tomatoes. Three focus groups (n = 28 participants) were conducted to explore how consumers perceived tomatoes, including how they purchased and consumed them. Subsequently, an Adaptive Choice Based Conjoint (ACBC) survey was conducted to understand consumer preferences toward traditional tomatoes. The ACBC survey with Kano questions (n = 1037 consumers in Raleigh, NC) explored the importance of color, firmness, size, skin, texture, interior, seed presence, flavor, and health benefits. The most important tomato attribute was color, then juice when sliced, followed by size, followed by seed presence, which was at parity with firmness. An attractive tomato was red, firm, medium/small sized, crisp, meaty, juicy, flavorful, and with few seeds. Deviations from these features resulted in a tomato that was rejected by consumers. Segmentations of consumers were determined by patterns in utility scores. External attributes were the main drivers of tomato liking, but different groups of tomato consumers exist with distinct preferences for juiciness, firmness, flavor, and health benefits. Conjoint analysis is a research technique that collects a large amount of data from consumers in a format designed to be reflective of a real life market setting and can be combined with qualitative insight from focus groups to gain information on consumer consumption and purchase behaviors. This study established that the most important fresh tomato attributes were color, amount of juice when sliced, and size. Distinct consumer clusters were differentiated by preference for color/appearance, juiciness and firm texture. Tomato growers can utilize the results to target attributes that drive consumer choice for fresh tomatoes. © 2014 Institute of Food Technologists®

Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring

PubMed Central

Moghbel, Mehrdad; Mashohor, Syamsiah; Mahmud, Rozi; Saripan, M. Iqbal Bin

2016-01-01

Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset. PMID:27540353

Understanding tobacco industry pricing strategy and whether it undermines tobacco tax policy: the example of the UK cigarette market.

PubMed

Gilmore, Anna B; Tavakoly, Behrooz; Taylor, Gordon; Reed, Howard

2013-07-01

Tobacco tax increases are the most effective means of reducing tobacco use and inequalities in smoking, but effectiveness depends on transnational tobacco company (TTC) pricing strategies, specifically whether TTCs overshift tax increases (increase prices on top of the tax increase) or undershift the taxes (absorb the tax increases so they are not passed onto consumers), about which little is known. Review of literature on brand segmentation. Analysis of 1999-2009 data to explore the extent to which tax increases are shifted to consumers, if this differs by brand segment and whether cigarette price indices accurately reflect cigarette prices. UK. UK smokers. Real cigarette prices, volumes and net-of-tax- revenue by price segment. TTCs categorise brands into four price segments: premium, economy, mid and 'ultra-low price' (ULP). TTCs have sold ULP brands since 2006; since then, their real price has remained virtually static and market share doubled. The price gap between premium and ULP brands is increasing because the industry differentially shifts tax increases between brand segments; while, on average, taxes are overshifted, taxes on ULP brands are not always fully passed onto consumers (being absorbed at the point each year when tobacco taxes increase). Price indices reflect the price of premium brands only and fail to detect these problems. Industry-initiated cigarette price changes in the UK appear timed to accentuate the price gap between premium and ULP brands. Increasing the prices of more expensive cigarettes on top of tobacco tax increases should benefit public health, but the growing price gap enables smokers to downtrade to cheaper tobacco products and may explain smoking-related inequalities. Governments must monitor cigarette prices by price segment and consider industry pricing strategies in setting tobacco tax policies. © 2013 Society for the Study of Addiction.

Understanding tobacco industry pricing strategy and whether it undermines tobacco tax policy: the example of the UK cigarette market

PubMed Central

Gilmore, Anna B; Tavakoly, Behrooz; Taylor, Gordon; Reed, Howard

2013-01-01

Aims Tobacco tax increases are the most effective means of reducing tobacco use and inequalities in smoking, but effectiveness depends on transnational tobacco company (TTC) pricing strategies, specifically whether TTCs overshift tax increases (increase prices on top of the tax increase) or undershift the taxes (absorb the tax increases so they are not passed onto consumers), about which little is known. Design Review of literature on brand segmentation. Analysis of 1999–2009 data to explore the extent to which tax increases are shifted to consumers, if this differs by brand segment and whether cigarette price indices accurately reflect cigarette prices. Setting UK. Participants UK smokers. Measurements Real cigarette prices, volumes and net-of-tax- revenue by price segment. Findings TTCs categorise brands into four price segments: premium, economy, mid and ‘ultra-low price’ (ULP). TTCs have sold ULP brands since 2006; since then, their real price has remained virtually static and market share doubled. The price gap between premium and ULP brands is increasing because the industry differentially shifts tax increases between brand segments; while, on average, taxes are overshifted, taxes on ULP brands are not always fully passed onto consumers (being absorbed at the point each year when tobacco taxes increase). Price indices reflect the price of premium brands only and fail to detect these problems. Conclusions Industry-initiated cigarette price changes in the UK appear timed to accentuate the price gap between premium and ULP brands. Increasing the prices of more expensive cigarettes on top of tobacco tax increases should benefit public health, but the growing price gap enables smokers to downtrade to cheaper tobacco products and may explain smoking-related inequalities. Governments must monitor cigarette prices by price segment and consider industry pricing strategies in setting tobacco tax policies. PMID:23445255

Counterconformity: an attribution model of adolescents' uniqueness-seeking behaviors in dressing.

PubMed

Ling, I-Ling

2008-01-01

This article explores how an attribution model will illustrate uniqueness-seeking behavior in dressing in the Taiwanese adolescent subculture. The study employed 443 senior high school students. Results show that the tendency of uniqueness-seeking behavior in dressing is moderate. However, using cluster analysis to segment the counterconformity behavior of the subjects, the study demonstrates that there are two conspicuous types of segmentation "markets": rubber stamp and self-determined. The attribution models investigate the susceptibilities to informational and normative influence which have different direction impacts and weights on the adolescents' counterconformity behavior. More interestingly, path analyses indicate that consumer self-confidence mediates the relationship between informational influence and counterconformity behavior only on the rubber stamp type. This study then discusses how the adolescent consumers' need for uniqueness could be used in better understanding consumer behavior and the role consumption plays in their expression of identity.

Using conjoint and cluster analysis in developing new product for micro, small and medium enterprises (SMEs) based on customer preferences (Case study: Lampung province's banana chips)

NASA Astrophysics Data System (ADS)

Kosasih, Wilson; Salomon, Lithrone Laricha; Hutomo, Reynaldo

2017-08-01

This paper discusses the development of new products of Micro, Small and Medium Entreprises (SMEs) to identify what attributes are considered by consumers, as well as combinations of attributes that need to be analyzed into the main preferences of consumers. The purpose of this research is to increase the added value and competitiveness of SMEs through product innovation. The object of this study is banana chips produced by SMEs from the province of Lampung which it considered to be unique souvenirs of the province. The research data were collected by distributing questionnaires in Jakarta which has heterogeneous population, in order to develop banana chip's marketing and increase its market share in Indonesia. Data processing was performed using conjoint analysis and cluster analysis. Segmentation was performed using conjoint analysis based on the importance level of attributes and part-worth of level attributes of each cluster. Finally, characteristics and consumer preferences of each cluster will be a consideration in determining the product development and marketing strategies.

Is food-related lifestyle (FRL) able to reveal food consumption patterns in non-Western cultural environments? Its adaptation and application in urban China.

PubMed

Grunert, Klaus G; Perrea, Toula; Zhou, Yanfeng; Huang, Guang; Sørensen, Bjarne T; Krystallis, Athanasios

2011-04-01

Research related to food-related behaviour in China is still scarce, one reason being the fact that food consumption patterns in East Asia do not appear to be easily analyzed by models originating in Western cultures. The objective of the present work is to examine the ability of the food related lifestyle (FRL) instrument to reveal food consumption patterns in a Chinese context. Data were collected from 479 respondents in 6 major Chinese cities using a Chinese version of the FRL instrument. Analysis of reliability and dimensionality of the scales resulted in a revised version of the instrument, in which a number of dimensions of the original instrument had to be omitted. This revised instrument was tested for statistical robustness and used as a basis for the derivation of consumer segments. Construct validity of the instrument was then investigated by profiling the segments in terms of consumer values, attitudes and purchase behaviour, using frequency of consumption of pork products as an example. Three consumer segments were identified: concerned, uninvolved and traditional. This pattern replicates partly those identified in Western cultures. Moreover, all three segments showed consistent value-attitude-behaviour profiles. The results also suggest which dimensions may be missing in the instrument in a more comprehensive instrument adapted to Chinese conditions, most notably a broader treatment of eating out activities. Copyright © 2010 Elsevier Ltd. All rights reserved.

Automatic ultrasound image enhancement for 2D semi-automatic breast-lesion segmentation

NASA Astrophysics Data System (ADS)

Lu, Kongkuo; Hall, Christopher S.

2014-03-01

Breast cancer is the fastest growing cancer, accounting for 29%, of new cases in 2012, and second leading cause of cancer death among women in the United States and worldwide. Ultrasound (US) has been used as an indispensable tool for breast cancer detection/diagnosis and treatment. In computer-aided assistance, lesion segmentation is a preliminary but vital step, but the task is quite challenging in US images, due to imaging artifacts that complicate detection and measurement of the suspect lesions. The lesions usually present with poor boundary features and vary significantly in size, shape, and intensity distribution between cases. Automatic methods are highly application dependent while manual tracing methods are extremely time consuming and have a great deal of intra- and inter- observer variability. Semi-automatic approaches are designed to counterbalance the advantage and drawbacks of the automatic and manual methods. However, considerable user interaction might be necessary to ensure reasonable segmentation for a wide range of lesions. This work proposes an automatic enhancement approach to improve the boundary searching ability of the live wire method to reduce necessary user interaction while keeping the segmentation performance. Based on the results of segmentation of 50 2D breast lesions in US images, less user interaction is required to achieve desired accuracy, i.e. < 80%, when auto-enhancement is applied for live-wire segmentation.

[Differentiated perception of transgenic tomato sauce in the southern Chile].

PubMed

Schnettler Morales, B; Sepúlveda Bravo, O; Ruiz Fuentes, D; Denegri Coria, M

2008-03-01

The present study considers the debate generated in developed countries by genetically modified foods, the importance of this variable to consumers in Temuco (Araucanía Region, Chile) when purchasing tomato sauce and different market segments were studied through a personal survey administered to 400 people. Using conjoint analysis, it was determined that the presence of genetic modification in food was generally more important than the brand and purchase price. Using cluster analysis, three segments were distinguished, with the most numerous (49.3%) placing the greatest importance on the presence of genetic modification (GM) in food and rejecting the transgenic product. The second group (39.4%) gave the greatest importance to the brand and preferred tomato sauce with genetically modified ingredients. The smallest segment (11.3%) placed the greatest value on price and preferred transgenic tomato sauce. The three segments prefer the national brand, reject the store brand and react positively to lower prices. The segment sensitive to the presence of GM in food comprised mainly those younger than 35 years of age, single and with no children. The absence of GM in food of vegetable origin is desirable for young consumers in the Araucanía Region, but a significant proportion accepts genetic modification in food (50.7%).

A segmentation and classification scheme for single tooth in MicroCT images based on 3D level set and k-means+.

PubMed

Wang, Liansheng; Li, Shusheng; Chen, Rongzhen; Liu, Sze-Yu; Chen, Jyh-Cheng

2017-04-01

Accurate classification of different anatomical structures of teeth from medical images provides crucial information for the stress analysis in dentistry. Usually, the anatomical structures of teeth are manually labeled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing 3 dimensional (3D) information, and classify the tooth by employing unsupervised learning i.e., k-means++ method. In order to evaluate the proposed method, the experiments are conducted on the sufficient and extensive datasets of mandibular molars. The experimental results show that our method can achieve higher accuracy and robustness compared to other three clustering methods. Copyright © 2016 Elsevier Ltd. All rights reserved.

Consumer preferences for general practitioner services.

PubMed

Morrison, Mark; Murphy, Tom; Nalder, Craig

2003-01-01

This study focuses on segmenting the market for General Practitioner services in a regional setting. Using factor analysis, five main service attributes are identified. These are clear communication, ongoing doctor-patient relationship, same gender as the patient, provides advice to the patient, and empowers the patient to make his/her own decisions. These service attributes are used as a basis for market segmentation, using both socio-demographic variables and cluster analysis. Four distinct market segments are identified, with varying degrees of viability in terms of target marketing.

U.S. consumer demand for restaurant calorie information: targeting demographic and behavioral segments in labeling initiatives.

PubMed

Kolodinsky, Jane; Reynolds, Travis William; Cannella, Mark; Timmons, David; Bromberg, Daniel

2009-01-01

To identify different segments of U.S. consumers based on food choices, exercise patterns, and desire for restaurant calorie labeling. Using a stratified (by region) random sample of the U.S. population, trained interviewers collected data for this cross-sectional study through telephone surveys. Center for Rural Studies U.S. national health survey. The final sample included 580 responses (22% response rate); data were weighted to be representative of age and gender characteristics of the U.S. population. Self-reported behaviors related to food choices, exercise patterns, desire for calorie information in restaurants, and sample demographics. Clusters were identified using Schwartz Bayesian criteria. Impacts of demographic characteristics on cluster membership were analyzed using bivariate tests of association and multinomial logit regression. Cluster analysis revealed three clusters based on respondents' food choices, activity levels, and desire for restaurant labeling. Two clusters, comprising three quarters of the sample, desired calorie labeling in restaurants. The remaining cluster opposed restaurant labeling. Demographic variables significantly predicting cluster membership included region of residence (p < .10), income (p < .05), gender (p < .01), and age (p < .10). Though limited by a low response and potential self-reporting bias in the phone survey, this study suggests that several groups are likely to benefit from restaurant calorie labeling. Specific demographic clusters could be targeted through labeling initiatives.

Automatic lumbar spine measurement in CT images

NASA Astrophysics Data System (ADS)

Mao, Yunxiang; Zheng, Dong; Liao, Shu; Peng, Zhigang; Yan, Ruyi; Liu, Junhua; Dong, Zhongxing; Gong, Liyan; Zhou, Xiang Sean; Zhan, Yiqiang; Fei, Jun

2017-03-01

Accurate lumbar spine measurement in CT images provides an essential way for quantitative spinal diseases analysis such as spondylolisthesis and scoliosis. In today's clinical workflow, the measurements are manually performed by radiologists and surgeons, which is time consuming and irreproducible. Therefore, automatic and accurate lumbar spine measurement algorithm becomes highly desirable. In this study, we propose a method to automatically calculate five different lumbar spine measurements in CT images. There are three main stages of the proposed method: First, a learning based spine labeling method, which integrates both the image appearance and spine geometry information, is used to detect lumbar and sacrum vertebrae in CT images. Then, a multiatlases based image segmentation method is used to segment each lumbar vertebra and the sacrum based on the detection result. Finally, measurements are derived from the segmentation result of each vertebra. Our method has been evaluated on 138 spinal CT scans to automatically calculate five widely used clinical spine measurements. Experimental results show that our method can achieve more than 90% success rates across all the measurements. Our method also significantly improves the measurement efficiency compared to manual measurements. Besides benefiting the routine clinical diagnosis of spinal diseases, our method also enables the large scale data analytics for scientific and clinical researches.

Sensory quality of fresh French and Dutch market tomatoes: a preference mapping study with Italian consumers.

PubMed

Sinesio, Fiorella; Cammareri, Maria; Moneta, Elisabetta; Navez, Brigitte; Peparaio, Marina; Causse, Mathilde; Grandillo, Silvana

2010-01-01

Sensory properties are important elements to evaluate the qualities of vegetable products and are also determinant factors in purchasing decision. Here we report the Italian results of a preference mapping study conducted within a larger European project with the aim of describing the preferences of European consumers in regard to the diversity of traditional and modern tomato varieties, available on the market. This study has allowed the assessment of fruit quality at 3 levels: objective description of sensory properties, consumer preference tests, and physicochemical measurements. A set of 16 tomato cultivars, with different fruit sizes and shapes, was described and classified according to 18 sensory attributes including flavor, appearance, and texture characteristics. The same cultivars were evaluated by 179 consumers in a "preference mapping" experiment with the goal of identifying the preferred varieties and the reasons for the choice. The consumer data are referred to hedonic ratings (aspect liking and overall liking), familiarity for the analyzed cultivars, and individual features collected by a questionnaire. A hierarchical analysis of the clusters allowed to distinguish, within the sampled Italian consumers, 4 segments with different preferences which represented 19%, 25%, 41%, and 15% of the population, respectively. A partial least square regression model allowed the identification of the sensory attributes that best described consumer cluster preferences for tomato cultivars. Both texture and flavor descriptors were important drivers of consumer preferences, but the relevance (predictive value) of individual descriptors to model tomato liking was different for each consumer segment. Information on demographic and behavioral characteristics, usage habits, and factors relevant for purchasing were also provided on the 4 groups of consumers.

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines.

PubMed

Khan, Arif Ul Maula; Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts.

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines

PubMed Central

Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts. PMID:27764213

Lymph node segmentation on CT images by a shape model guided deformable surface methodh

NASA Astrophysics Data System (ADS)

Maleike, Daniel; Fabel, Michael; Tetzlaff, Ralf; von Tengg-Kobligk, Hendrik; Heimann, Tobias; Meinzer, Hans-Peter; Wolf, Ivo

2008-03-01

With many tumor entities, quantitative assessment of lymph node growth over time is important to make therapy choices or to evaluate new therapies. The clinical standard is to document diameters on transversal slices, which is not the best measure for a volume. We present a new algorithm to segment (metastatic) lymph nodes and evaluate the algorithm with 29 lymph nodes in clinical CT images. The algorithm is based on a deformable surface search, which uses statistical shape models to restrict free deformation. To model lymph nodes, we construct an ellipsoid shape model, which strives for a surface with strong gradients and user-defined gray values. The algorithm is integrated into an application, which also allows interactive correction of the segmentation results. The evaluation shows that the algorithm gives good results in the majority of cases and is comparable to time-consuming manual segmentation. The median volume error was 10.1% of the reference volume before and 6.1% after manual correction. Integrated into an application, it is possible to perform lymph node volumetry for a whole patient within the 10 to 15 minutes time limit imposed by clinical routine.

Milk fat threshold determination and the effect of milk fat content on consumer preference for fluid milk.

PubMed

McCarthy, K S; Lopetcharat, K; Drake, M A

2017-03-01

Milk consumption in the United States has been in decline since the 1960s. Milk fat plays a critical role in sensory properties of fluid milk. The first objective of this study was to determine the change in percent milk fat needed to produce a detectable or just noticeable difference (JND) to consumers in skim, 1%, 2%, and whole milks. The second objective was to evaluate how milk fat affected consumer preferences for fluid milk. Threshold tests were conducted to determine the JND for each reference milk (skim, 1%, 2%, and whole milk), with a minimum of 60 consumers for each JND. The JND was determined for milks by visual appearance without tasting and tasting without visual cues. Serving temperature effect (4, 8, or 15°C) on tasting JND values were also investigated. The established JND values were then used to conduct ascending forced-choice preference tests with milks. Consumers were assigned to 3 groups based on self-reported milk consumption: skim milk drinkers (n = 59), low-fat milk drinkers (consumed 1% or 2% milk, n = 64), and whole milk drinkers (n = 49). Follow-up interviews were conducted where consumers were asked to taste and explain their preference between milks that showed the most polarization within each consumer segment. Descriptive sensory analysis was performed on the milks used in the follow-up interviews to quantify sensory differences. Visual-only JND were lower than tasting-only JND values. Preference testing revealed 3 distinct preference curves among the consumer segments. Skim milk drinkers preferred skim milk and up to 2% milk fat, but disliked milk higher in fat due to it being "too thick," "too heavy," "flavor and texture like cream," "too fatty," and "looks like half and half." Low-fat milk drinkers preferred 2% milk up to 3.25% (whole milk), but then disliked higher milk fat content. Whole milk drinkers preferred whichever milk was higher in milk fat regardless of how high the fat content was, distinct from skim and low-fat milk drinkers. The findings of this study provide insights on sensory characteristics of milk fat in fluid milk and consumer sensory perception of these properties. These results also provide insights on how the industry might adjust milk fat references for adjusting milk sensory properties to increase milk preference and remain within the standards of identity of milk. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification.

PubMed

Vrooman, Henri A; Cocosco, Chris A; van der Lijn, Fedde; Stokking, Rik; Ikram, M Arfan; Vernooij, Meike W; Breteler, Monique M B; Niessen, Wiro J

2007-08-01

Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue in MR data, requires training on manually labeled subjects. This manual labeling is a laborious and time-consuming procedure. In this work, a new fully automated brain tissue classification procedure is presented, in which kNN training is automated. This is achieved by non-rigidly registering the MR data with a tissue probability atlas to automatically select training samples, followed by a post-processing step to keep the most reliable samples. The accuracy of the new method was compared to rigid registration-based training and to conventional kNN-based segmentation using training on manually labeled subjects for segmenting gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in 12 data sets. Furthermore, for all classification methods, the performance was assessed when varying the free parameters. Finally, the robustness of the fully automated procedure was evaluated on 59 subjects. The automated training method using non-rigid registration with a tissue probability atlas was significantly more accurate than rigid registration. For both automated training using non-rigid registration and for the manually trained kNN classifier, the difference with the manual labeling by observers was not significantly larger than inter-observer variability for all tissue types. From the robustness study, it was clear that, given an appropriate brain atlas and optimal parameters, our new fully automated, non-rigid registration-based method gives accurate and robust segmentation results. A similarity index was used for comparison with manually trained kNN. The similarity indices were 0.93, 0.92 and 0.92, for CSF, GM and WM, respectively. It can be concluded that our fully automated method using non-rigid registration may replace manual segmentation, and thus that automated brain tissue segmentation without laborious manual training is feasible.

SU-F-J-113: Multi-Atlas Based Automatic Organ Segmentation for Lung Radiotherapy Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, J; Han, J; Ailawadi, S

Purpose: Normal organ segmentation is one time-consuming and labor-intensive step for lung radiotherapy treatment planning. The aim of this study is to evaluate the performance of a multi-atlas based segmentation approach for automatic organs at risk (OAR) delineation. Methods: Fifteen Lung stereotactic body radiation therapy patients were randomly selected. Planning CT images and OAR contours of the heart - HT, aorta - AO, vena cava - VC, pulmonary trunk - PT, and esophagus – ES were exported and used as reference and atlas sets. For automatic organ delineation for a given target CT, 1) all atlas sets were deformably warpedmore » to the target CT, 2) the deformed sets were accumulated and normalized to produce organ probability density (OPD) maps, and 3) the OPD maps were converted to contours via image thresholding. Optimal threshold for each organ was empirically determined by comparing the auto-segmented contours against their respective reference contours. The delineated results were evaluated by measuring contour similarity metrics: DICE, mean distance (MD), and true detection rate (TD), where DICE=(intersection volume/sum of two volumes) and TD = {1.0 - (false positive + false negative)/2.0}. Diffeomorphic Demons algorithm was employed for CT-CT deformable image registrations. Results: Optimal thresholds were determined to be 0.53 for HT, 0.38 for AO, 0.28 for PT, 0.43 for VC, and 0.31 for ES. The mean similarity metrics (DICE[%], MD[mm], TD[%]) were (88, 3.2, 89) for HT, (79, 3.2, 82) for AO, (75, 2.7, 77) for PT, (68, 3.4, 73) for VC, and (51,2.7, 60) for ES. Conclusion: The investigated multi-atlas based approach produced reliable segmentations for the organs with large and relatively clear boundaries (HT and AO). However, the detection of small and narrow organs with diffused boundaries (ES) were challenging. Sophisticated atlas selection and multi-atlas fusion algorithms may further improve the quality of segmentations.« less

Understanding consumer acceptance of intervention strategies for healthy food choices: a qualitative study.

PubMed

Bos, Colin; Van der Lans, Ivo A; Van Rijnsoever, Frank J; Van Trijp, Hans C M

2013-11-13

The increasing prevalence of overweight and obesity poses a major threat to public health. Intervention strategies for healthy food choices potentially reduce obesity rates. Reviews of the effectiveness of interventions, however, show mixed results. To maximise effectiveness, interventions need to be accepted by consumers. The aim of the present study is to explore consumer acceptance of intervention strategies for low-calorie food choices. Beliefs that are associated with consumer acceptance are identified. Data was collected in the Netherlands in 8 semi-structured interviews and 4 focus group discussions (N = 39). Nine archetypical strategies representing educational, marketing and legal interventions served as reference points. Verbatim transcriptions were coded both inductively and deductively with the framework approach. We found that three beliefs are related to consumer acceptance: 1) general beliefs regarding obesity, such as who is responsible for food choice; 2) the perceived effectiveness of interventions; and 3) the perceived fairness of interventions. Furthermore, the different aspects underlying these general and intervention-specific beliefs were identified. General and intervention-specific beliefs are associated with consumer acceptance of interventions for low-calorie food choices. Policymakers in the food domain can use the findings to negotiate the development of interventions and to assess the feasibility of interventions. With respect to future research, we recommend that segments of consumers based on perceptions of intervention strategies are identified.

Understanding consumer acceptance of intervention strategies for healthy food choices: a qualitative study

PubMed Central

2013-01-01

Background The increasing prevalence of overweight and obesity poses a major threat to public health. Intervention strategies for healthy food choices potentially reduce obesity rates. Reviews of the effectiveness of interventions, however, show mixed results. To maximise effectiveness, interventions need to be accepted by consumers. The aim of the present study is to explore consumer acceptance of intervention strategies for low-calorie food choices. Beliefs that are associated with consumer acceptance are identified. Methods Data was collected in the Netherlands in 8 semi-structured interviews and 4 focus group discussions (N = 39). Nine archetypical strategies representing educational, marketing and legal interventions served as reference points. Verbatim transcriptions were coded both inductively and deductively with the framework approach. Results We found that three beliefs are related to consumer acceptance: 1) general beliefs regarding obesity, such as who is responsible for food choice; 2) the perceived effectiveness of interventions; and 3) the perceived fairness of interventions. Furthermore, the different aspects underlying these general and intervention-specific beliefs were identified. Conclusions General and intervention-specific beliefs are associated with consumer acceptance of interventions for low-calorie food choices. Policymakers in the food domain can use the findings to negotiate the development of interventions and to assess the feasibility of interventions. With respect to future research, we recommend that segments of consumers based on perceptions of intervention strategies are identified. PMID:24225034

Survey of contemporary trends in color image segmentation

NASA Astrophysics Data System (ADS)

Vantaram, Sreenath Rao; Saber, Eli

2012-10-01

In recent years, the acquisition of image and video information for processing, analysis, understanding, and exploitation of the underlying content in various applications, ranging from remote sensing to biomedical imaging, has grown at an unprecedented rate. Analysis by human observers is quite laborious, tiresome, and time consuming, if not infeasible, given the large and continuously rising volume of data. Hence the need for systems capable of automatically and effectively analyzing the aforementioned imagery for a variety of uses that span the spectrum from homeland security to elderly care. In order to achieve the above, tools such as image segmentation provide the appropriate foundation for expediting and improving the effectiveness of subsequent high-level tasks by providing a condensed and pertinent representation of image information. We provide a comprehensive survey of color image segmentation strategies adopted over the last decade, though notable contributions in the gray scale domain will also be discussed. Our taxonomy of segmentation techniques is sampled from a wide spectrum of spatially blind (or feature-based) approaches such as clustering and histogram thresholding as well as spatially guided (or spatial domain-based) methods such as region growing/splitting/merging, energy-driven parametric/geometric active contours, supervised/unsupervised graph cuts, and watersheds, to name a few. In addition, qualitative and quantitative results of prominent algorithms on several images from the Berkeley segmentation dataset are shown in order to furnish a fair indication of the current quality of the state of the art. Finally, we provide a brief discussion on our current perspective of the field as well as its associated future trends.

A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer.

PubMed

Echegaray, Sebastian; Nair, Viswam; Kadoch, Michael; Leung, Ann; Rubin, Daniel; Gevaert, Olivier; Napel, Sandy

2016-12-01

Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required.

Large deep neural networks for MS lesion segmentation

NASA Astrophysics Data System (ADS)

Prieto, Juan C.; Cavallari, Michele; Palotai, Miklos; Morales Pinzon, Alfredo; Egorova, Svetlana; Styner, Martin; Guttmann, Charles R. G.

2017-02-01

Multiple sclerosis (MS) is a multi-factorial autoimmune disorder, characterized by spatial and temporal dissemination of brain lesions that are visible in T2-weighted and Proton Density (PD) MRI. Assessment of lesion burden and is useful for monitoring the course of the disease, and assessing correlates of clinical outcomes. Although there are established semi-automated methods to measure lesion volume, most of them require human interaction and editing, which are time consuming and limits the ability to analyze large sets of data with high accuracy. The primary objective of this work is to improve existing segmentation algorithms and accelerate the time consuming operation of identifying and validating MS lesions. In this paper, a Deep Neural Network for MS Lesion Segmentation is implemented. The MS lesion samples are extracted from the Partners Comprehensive Longitudinal Investigation of Multiple Sclerosis (CLIMB) study. A set of 900 subjects with T2, PD and a manually corrected label map images were used to train a Deep Neural Network and identify MS lesions. Initial tests using this network achieved a 90% accuracy rate. A secondary goal was to enable this data repository for big data analysis by using this algorithm to segment the remaining cases available in the CLIMB repository.

Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans

NASA Astrophysics Data System (ADS)

Lassen, B. C.; Jacobs, C.; Kuhnigk, J.-M.; van Ginneken, B.; van Rikxoort, E. M.

2015-02-01

The malignancy of lung nodules is most often detected by analyzing changes of the nodule diameter in follow-up scans. A recent study showed that comparing the volume or the mass of a nodule over time is much more significant than comparing the diameter. Since the survival rate is higher when the disease is still in an early stage it is important to detect the growth rate as soon as possible. However manual segmentation of a volume is time-consuming. Whereas there are several well evaluated methods for the segmentation of solid nodules, less work is done on subsolid nodules which actually show a higher malignancy rate than solid nodules. In this work we present a fast, semi-automatic method for segmentation of subsolid nodules. As minimal user interaction the method expects a user-drawn stroke on the largest diameter of the nodule. First, a threshold-based region growing is performed based on intensity analysis of the nodule region and surrounding parenchyma. In the next step the chest wall is removed by a combination of a connected component analyses and convex hull calculation. Finally, attached vessels are detached by morphological operations. The method was evaluated on all nodules of the publicly available LIDC/IDRI database that were manually segmented and rated as non-solid or part-solid by four radiologists (Dataset 1) and three radiologists (Dataset 2). For these 59 nodules the Jaccard index for the agreement of the proposed method with the manual reference segmentations was 0.52/0.50 (Dataset 1/Dataset 2) compared to an inter-observer agreement of the manual segmentations of 0.54/0.58 (Dataset 1/Dataset 2). Furthermore, the inter-observer agreement using the proposed method (i.e. different input strokes) was analyzed and gave a Jaccard index of 0.74/0.74 (Dataset 1/Dataset 2). The presented method provides satisfactory segmentation results with minimal observer effort in minimal time and can reduce the inter-observer variability for segmentation of subsolid nodules in clinical routine.

[Consumer demands concerning meat between price and ethics].

PubMed

Branscheid, W

1998-08-01

A review is given about consumer demands on meat. Mainly the problems of decreasing meat consumption, health aspects and special questions of ecological products are considered. The market gives evidence of a further drop in meat consumption and a more consistent differentiation of product lines. Eco-products, emphasising the animal welfare aspect, will have their place in that context but persist in the state of a minor market segment. Nevertheless it has to be expected, that also the conventional products will increasingly comply to modern consumer demands.

Implementation and evaluation of a new workflow for registration and segmentation of pulmonary MRI data for regional lung perfusion assessment.

PubMed

Böttger, T; Grunewald, K; Schöbinger, M; Fink, C; Risse, F; Kauczor, H U; Meinzer, H P; Wolf, Ivo

2007-03-07

Recently it has been shown that regional lung perfusion can be assessed using time-resolved contrast-enhanced magnetic resonance (MR) imaging. Quantification of the perfusion images has been attempted, based on definition of small regions of interest (ROIs). Use of complete lung segmentations instead of ROIs could possibly increase quantification accuracy. Due to the low signal-to-noise ratio, automatic segmentation algorithms cannot be applied. On the other hand, manual segmentation of the lung tissue is very time consuming and can become inaccurate, as the borders of the lung to adjacent tissues are not always clearly visible. We propose a new workflow for semi-automatic segmentation of the lung from additionally acquired morphological HASTE MR images. First the lung is delineated semi-automatically in the HASTE image. Next the HASTE image is automatically registered with the perfusion images. Finally, the transformation resulting from the registration is used to align the lung segmentation from the morphological dataset with the perfusion images. We evaluated rigid, affine and locally elastic transformations, suitable optimizers and different implementations of mutual information (MI) metrics to determine the best possible registration algorithm. We located the shortcomings of the registration procedure and under which conditions automatic registration will succeed or fail. Segmentation results were evaluated using overlap and distance measures. Integration of the new workflow reduces the time needed for post-processing of the data, simplifies the perfusion quantification and reduces interobserver variability in the segmentation process. In addition, the matched morphological data set can be used to identify morphologic changes as the source for the perfusion abnormalities.

Validation of automatic landmark identification for atlas-based segmentation for radiation treatment planning of the head-and-neck region

NASA Astrophysics Data System (ADS)

Leavens, Claudia; Vik, Torbjørn; Schulz, Heinrich; Allaire, Stéphane; Kim, John; Dawson, Laura; O'Sullivan, Brian; Breen, Stephen; Jaffray, David; Pekar, Vladimir

2008-03-01

Manual contouring of target volumes and organs at risk in radiation therapy is extremely time-consuming, in particular for treating the head-and-neck area, where a single patient treatment plan can take several hours to contour. As radiation treatment delivery moves towards adaptive treatment, the need for more efficient segmentation techniques will increase. We are developing a method for automatic model-based segmentation of the head and neck. This process can be broken down into three main steps: i) automatic landmark identification in the image dataset of interest, ii) automatic landmark-based initialization of deformable surface models to the patient image dataset, and iii) adaptation of the deformable models to the patient-specific anatomical boundaries of interest. In this paper, we focus on the validation of the first step of this method, quantifying the results of our automatic landmark identification method. We use an image atlas formed by applying thin-plate spline (TPS) interpolation to ten atlas datasets, using 27 manually identified landmarks in each atlas/training dataset. The principal variation modes returned by principal component analysis (PCA) of the landmark positions were used by an automatic registration algorithm, which sought the corresponding landmarks in the clinical dataset of interest using a controlled random search algorithm. Applying a run time of 60 seconds to the random search, a root mean square (rms) distance to the ground-truth landmark position of 9.5 +/- 0.6 mm was calculated for the identified landmarks. Automatic segmentation of the brain, mandible and brain stem, using the detected landmarks, is demonstrated.

Towards Automatic Image Segmentation Using Optimised Region Growing Technique

NASA Astrophysics Data System (ADS)

Alazab, Mamoun; Islam, Mofakharul; Venkatraman, Sitalakshmi

Image analysis is being adopted extensively in many applications such as digital forensics, medical treatment, industrial inspection, etc. primarily for diagnostic purposes. Hence, there is a growing interest among researches in developing new segmentation techniques to aid the diagnosis process. Manual segmentation of images is labour intensive, extremely time consuming and prone to human errors and hence an automated real-time technique is warranted in such applications. There is no universally applicable automated segmentation technique that will work for all images as the image segmentation is quite complex and unique depending upon the domain application. Hence, to fill the gap, this paper presents an efficient segmentation algorithm that can segment a digital image of interest into a more meaningful arrangement of regions and objects. Our algorithm combines region growing approach with optimised elimination of false boundaries to arrive at more meaningful segments automatically. We demonstrate this using X-ray teeth images that were taken for real-life dental diagnosis.

Consumer acceptance of irradiated chicken and produce in the U.S.A.

NASA Astrophysics Data System (ADS)

Cottee, Jim; Kunstadt, Peter; Fraser, Frank

1995-02-01

There is a demonstrated dichotomy between perceived consumer acceptance of irradiated foods, and the consumers' choice of food in grocery stores. Indeed the perception has been that most consumers were against irradiated foods and that massive educational campaigns would be needed to change their minds. Meanwhile, some initial sales of irradiated foods have been unexpectedly brisk when supported by limited, point-of-sale information. There is strong agreement between recent studies, with respect to consumers willing to buy irradiated foods once the benefits are explained. A large segment of approximately 50% of all respondents indicate that they would buy irradiated foods. Consumers have also shown that they put a great deal of trust in their grocers and in regulatory bodies.

Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis.

PubMed

Weijers, Gert; Starke, Alexander; Haudum, Alois; Thijssen, Johan M; Rehage, Jürgen; De Korte, Chris L

2010-07-01

The aim of this study was to test the hypothesis that automatic segmentation of vessels in ultrasound (US) images can produce similar or better results in grading fatty livers than interactive segmentation. A study was performed in postpartum dairy cows (N=151), as an animal model of human fatty liver disease, to test this hypothesis. Five transcutaneous and five intraoperative US liver images were acquired in each animal and a liverbiopsy was taken. In liver tissue samples, triacylglycerol (TAG) was measured by biochemical analysis and hepatic diseases other than hepatic lipidosis were excluded by histopathologic examination. Ultrasonic tissue characterization (UTC) parameters--Mean echo level, standard deviation (SD) of echo level, signal-to-noise ratio (SNR), residual attenuation coefficient (ResAtt) and axial and lateral speckle size--were derived using a computer-aided US (CAUS) protocol and software package. First, the liver tissue was interactively segmented by two observers. With increasing fat content, fewer hepatic vessels were visible in the ultrasound images and, therefore, a smaller proportion of the liver needed to be excluded from these images. Automatic-segmentation algorithms were implemented and it was investigated whether better results could be achieved than with the subjective and time-consuming interactive-segmentation procedure. The automatic-segmentation algorithms were based on both fixed and adaptive thresholding techniques in combination with a 'speckle'-shaped moving-window exclusion technique. All data were analyzed with and without postprocessing as contained in CAUS and with different automated-segmentation techniques. This enabled us to study the effect of the applied postprocessing steps on single and multiple linear regressions ofthe various UTC parameters with TAG. Improved correlations for all US parameters were found by using automatic-segmentation techniques. Stepwise multiple linear-regression formulas where derived and used to predict TAG level in the liver. Receiver-operating-characteristics (ROC) analysis was applied to assess the performance and area under the curve (AUC) of predicting TAG and to compare the sensitivity and specificity of the methods. Best speckle-size estimates and overall performance (R2 = 0.71, AUC = 0.94) were achieved by using an SNR-based adaptive automatic-segmentation method (used TAG threshold: 50 mg/g liver wet weight). Automatic segmentation is thus feasible and profitable.

Automated grain extraction and classification by combining improved region growing segmentation and shape descriptors in electromagnetic mill classification system

NASA Astrophysics Data System (ADS)

Budzan, Sebastian

2018-04-01

In this paper, the automatic method of grain detection and classification has been presented. As input, it uses a single digital image obtained from milling process of the copper ore with an high-quality digital camera. The grinding process is an extremely energy and cost consuming process, thus granularity evaluation process should be performed with high efficiency and time consumption. The method proposed in this paper is based on the three-stage image processing. First, using Seeded Region Growing (SRG) segmentation with proposed adaptive thresholding based on the calculation of Relative Standard Deviation (RSD) all grains are detected. In the next step results of the detection are improved using information about the shape of the detected grains using distance map. Finally, each grain in the sample is classified into one of the predefined granularity class. The quality of the proposed method has been obtained by using nominal granularity samples, also with a comparison to the other methods.

Ross filter pairs for metal artefact reduction in x-ray tomography: a case study based on imaging and segmentation of metallic implants

NASA Astrophysics Data System (ADS)

Arhatari, Benedicta D.; Abbey, Brian

2018-01-01

Ross filter pairs have recently been demonstrated as a highly effective means of producing quasi-monoenergetic beams from polychromatic X-ray sources. They have found applications in both X-ray spectroscopy and for elemental separation in X-ray computed tomography (XCT). Here we explore whether they could be applied to the problem of metal artefact reduction (MAR) for applications in medical imaging. Metal artefacts are a common problem in X-ray imaging of metal implants embedded in bone and soft tissue. A number of data post-processing approaches to MAR have been proposed in the literature, however these can be time-consuming and sometimes have limited efficacy. Here we describe and demonstrate an alternative approach based on beam conditioning using Ross filter pairs. This approach obviates the need for any complex post-processing of the data and enables MAR and segmentation from the surrounding tissue by exploiting the absorption edge contrast of the implant.

Maximized Inter-Class Weighted Mean for Fast and Accurate Mitosis Cells Detection in Breast Cancer Histopathology Images.

PubMed

Nateghi, Ramin; Danyali, Habibollah; Helfroush, Mohammad Sadegh

2017-08-14

Based on the Nottingham criteria, the number of mitosis cells in histopathological slides is an important factor in diagnosis and grading of breast cancer. For manual grading of mitosis cells, histopathology slides of the tissue are examined by pathologists at 40× magnification for each patient. This task is very difficult and time-consuming even for experts. In this paper, a fully automated method is presented for accurate detection of mitosis cells in histopathology slide images. First a method based on maximum-likelihood is employed for segmentation and extraction of mitosis cell. Then a novel Maximized Inter-class Weighted Mean (MIWM) method is proposed that aims at reducing the number of extracted non-mitosis candidates that results in reducing the false positive mitosis detection rate. Finally, segmented candidates are classified into mitosis and non-mitosis classes by using a support vector machine (SVM) classifier. Experimental results demonstrate a significant improvement in accuracy of mitosis cells detection in different grades of breast cancer histopathological images.

Cultural affiliation and the importance of health care attributes. Marketers can develop segmentation strategies for targeted patient groups.

PubMed

Dolinsky, A L; Stinerock, R

1998-01-01

Culturally based values are known to influence consumer purchase decisions, but little is known about how those values affect health care choices. To rectify that situation and provide health care marketers with a framework for developing culturally based segmentation strategies, the authors undertook an exploratory research project in which Hispanic-, African-, and Anglo-Americans were asked to rate the importance of 16 different health care attributes. Those attributes can be grouped under five categories: quality of physician, quality of nurses and other medical staff, economic issues, access to health care, and nonmedically related experiential aspects. Survey responses identified distinct differences in the importance attached to the various attributes by the three cultural groups. The study also looks at the impact of six demographic and social characteristics on the evaluations made by each cultural group. Those characteristics are educational level, gender, age, health status, marital status, and number of people living in the household.

Retinal blood vessel segmentation in high resolution fundus photographs using automated feature parameter estimation

NASA Astrophysics Data System (ADS)

Orlando, José Ignacio; Fracchia, Marcos; del Río, Valeria; del Fresno, Mariana

2017-11-01

Several ophthalmological and systemic diseases are manifested through pathological changes in the properties and the distribution of the retinal blood vessels. The characterization of such alterations requires the segmentation of the vasculature, which is a tedious and time-consuming task that is infeasible to be performed manually. Numerous attempts have been made to propose automated methods for segmenting the retinal vasculature from fundus photographs, although their application in real clinical scenarios is usually limited by their ability to deal with images taken at different resolutions. This is likely due to the large number of parameters that have to be properly calibrated according to each image scale. In this paper we propose to apply a novel strategy for automated feature parameter estimation, combined with a vessel segmentation method based on fully connected conditional random fields. The estimation model is learned by linear regression from structural properties of the images and known optimal configurations, that were previously obtained for low resolution data sets. Our experiments in high resolution images show that this approach is able to estimate appropriate configurations that are suitable for performing the segmentation task without requiring to re-engineer parameters. Furthermore, our combined approach reported state of the art performance on the benchmark data set HRF, as measured in terms of the F1-score and the Matthews correlation coefficient.

Consumers' perceptions of preconception health.

PubMed

Squiers, Linda; Mitchell, Elizabeth W; Levis, Denise M; Lynch, Molly; Dolina, Suzanne; Margolis, Marjorie; Scales, Monica; Kish-Doto, Julia

2013-01-01

To inform the development of a preconception health (PCH) social marketing plan, we conducted qualitative research with prospective consumers. We present formative findings based on the four Ps of social marketing: product, price, promotion, and place. We conducted focus groups with 10 groups of women in Atlanta, Georgia, in fall 2010. We classified women aged 18 to 44 into five groups based on their pregnancy plans, and then further segmented the groups based on socioeconomic status for a total of 10 groups. The focus group guide was designed to elicit participants' responses about the product, price, promotion, and placement of PCH. We used NVivo 9 software to analyze focus group data. Women planning a pregnancy in the future had different perspectives on PCH as a product than women not planning a pregnancy. Barriers to PCH included lack of social support, addiction, and lack of awareness about PCH. Participants preferred to think of PCH behaviors as "promoting" a healthy baby rather than preventing an unhealthy birth outcome. Many women in the focus groups preferred to hear PCH messages from a health care provider, among other channels. The results from this research will inform the development of a social marketing plan for PCH and the development of concepts that will be tested with consumers to determine their viability for use in a national campaign.

Imagers for digital still photography

NASA Astrophysics Data System (ADS)

Bosiers, Jan; Dillen, Bart; Draijer, Cees; Manoury, Erik-Jan; Meessen, Louis; Peters, Inge

2006-04-01

This paper gives an overview of the requirements for, and current state-of-the-art of, CCD and CMOS imagers for use in digital still photography. Four market segments will be reviewed: mobile imaging, consumer "point-and-shoot cameras", consumer digital SLR cameras and high-end professional camera systems. The paper will also present some challenges and innovations with respect to packaging, testing, and system integration.

A distributed algorithm for demand-side management: Selling back to the grid.

PubMed

Latifi, Milad; Khalili, Azam; Rastegarnia, Amir; Zandi, Sajad; Bazzi, Wael M

2017-11-01

Demand side energy consumption scheduling is a well-known issue in the smart grid research area. However, there is lack of a comprehensive method to manage the demand side and consumer behavior in order to obtain an optimum solution. The method needs to address several aspects, including the scale-free requirement and distributed nature of the problem, consideration of renewable resources, allowing consumers to sell electricity back to the main grid, and adaptivity to a local change in the solution point. In addition, the model should allow compensation to consumers and ensurance of certain satisfaction levels. To tackle these issues, this paper proposes a novel autonomous demand side management technique which minimizes consumer utility costs and maximizes consumer comfort levels in a fully distributed manner. The technique uses a new logarithmic cost function and allows consumers to sell excess electricity (e.g. from renewable resources) back to the grid in order to reduce their electric utility bill. To develop the proposed scheme, we first formulate the problem as a constrained convex minimization problem. Then, it is converted to an unconstrained version using the segmentation-based penalty method. At each consumer location, we deploy an adaptive diffusion approach to obtain the solution in a distributed fashion. The use of adaptive diffusion makes it possible for consumers to find the optimum energy consumption schedule with a small number of information exchanges. Moreover, the proposed method is able to track drifts resulting from changes in the price parameters and consumer preferences. Simulations and numerical results show that our framework can reduce the total load demand peaks, lower the consumer utility bill, and improve the consumer comfort level.

A content relevance model for social media health information.

PubMed

Prybutok, Gayle Linda; Koh, Chang; Prybutok, Victor R

2014-04-01

Consumer health informatics includes the development and implementation of Internet-based systems to deliver health risk management information and health intervention applications to the public. The application of consumer health informatics to educational and interventional efforts such as smoking reduction and cessation has garnered attention from both consumers and health researchers in recent years. Scientists believe that smoking avoidance or cessation before the age of 30 years can prevent more than 90% of smoking-related cancers and that individuals who stop smoking fare as well in preventing cancer as those who never start. The goal of this study was to determine factors that were most highly correlated with content relevance for health information provided on the Internet for a study group of 18- to 30-year-old college students. Data analysis showed that the opportunity for convenient entertainment, social interaction, health information-seeking behavior, time spent surfing on the Internet, the importance of available activities on the Internet (particularly e-mail), and perceived site relevance for Internet-based sources of health information were significantly correlated with content relevance for 18- to 30-year-old college students, an educated subset of this population segment.

Combining registration and active shape models for the automatic segmentation of the lymph node regions in head and neck CT images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen Antong; Deeley, Matthew A.; Niermann, Kenneth J.

2010-12-15

Purpose: Intensity-modulated radiation therapy (IMRT) is the state of the art technique for head and neck cancer treatment. It requires precise delineation of the target to be treated and structures to be spared, which is currently done manually. The process is a time-consuming task of which the delineation of lymph node regions is often the longest step. Atlas-based delineation has been proposed as an alternative, but, in the authors' experience, this approach is not accurate enough for routine clinical use. Here, the authors improve atlas-based segmentation results obtained for level II-IV lymph node regions using an active shape model (ASM)more » approach. Methods: An average image volume was first created from a set of head and neck patient images with minimally enlarged nodes. The average image volume was then registered using affine, global, and local nonrigid transformations to the other volumes to establish a correspondence between surface points in the atlas and surface points in each of the other volumes. Once the correspondence was established, the ASMs were created for each node level. The models were then used to first constrain the results obtained with an atlas-based approach and then to iteratively refine the solution. Results: The method was evaluated through a leave-one-out experiment. The ASM- and atlas-based segmentations were compared to manual delineations via the Dice similarity coefficient (DSC) for volume overlap and the Euclidean distance between manual and automatic 3D surfaces. The mean DSC value obtained with the ASM-based approach is 10.7% higher than with the atlas-based approach; the mean and median surface errors were decreased by 13.6% and 12.0%, respectively. Conclusions: The ASM approach is effective in reducing segmentation errors in areas of low CT contrast where purely atlas-based methods are challenged. Statistical analysis shows that the improvements brought by this approach are significant.« less

Human body segmentation via data-driven graph cut.

PubMed

Li, Shifeng; Lu, Huchuan; Shao, Xingqing

2014-11-01

Human body segmentation is a challenging and important problem in computer vision. Existing methods usually entail a time-consuming training phase for prior knowledge learning with complex shape matching for body segmentation. In this paper, we propose a data-driven method that integrates top-down body pose information and bottom-up low-level visual cues for segmenting humans in static images within the graph cut framework. The key idea of our approach is first to exploit human kinematics to search for body part candidates via dynamic programming for high-level evidence. Then, by using the body parts classifiers, obtaining bottom-up cues of human body distribution for low-level evidence. All the evidence collected from top-down and bottom-up procedures are integrated in a graph cut framework for human body segmentation. Qualitative and quantitative experiment results demonstrate the merits of the proposed method in segmenting human bodies with arbitrary poses from cluttered backgrounds.

Phonotactic Probability of Brand Names: I'd buy that!

PubMed Central

Vitevitch, Michael S.; Donoso, Alexander J.

2011-01-01

Psycholinguistic research shows that word-characteristics influence the speed and accuracy of various language-related processes. Analogous characteristics of brand names influence the retrieval of product information and the perception of risks associated with that product. In the present experiment we examined how phonotactic probability—the frequency with which phonological segments and sequences of segments appear in a word—might influence consumer behavior. Participants rated brand names that varied in phonotactic probability on the likelihood that they would buy the product. Participants indicated that they were more likely to purchase a product if the brand name was comprised of common segments and sequences of segments rather than less common segments and sequences of segments. This result suggests that word-characteristics may influence higher-level cognitive processes, in addition to language-related processes. Furthermore, the benefits of using objective measures of word characteristics in the design of brand names are discussed. PMID:21870135

Embedded Implementation of VHR Satellite Image Segmentation

PubMed Central

Li, Chao; Balla-Arabé, Souleymane; Ginhac, Dominique; Yang, Fan

2016-01-01

Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage. PMID:27240370

Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images.

PubMed

Cunefare, David; Cooper, Robert F; Higgins, Brian; Katz, David F; Dubra, Alfredo; Carroll, Joseph; Farsiu, Sina

2016-05-01

Quantitative analysis of the cone photoreceptor mosaic in the living retina is potentially useful for early diagnosis and prognosis of many ocular diseases. Non-confocal split detector based adaptive optics scanning light ophthalmoscope (AOSLO) imaging reveals the cone photoreceptor inner segment mosaics often not visualized on confocal AOSLO imaging. Despite recent advances in automated cone segmentation algorithms for confocal AOSLO imagery, quantitative analysis of split detector AOSLO images is currently a time-consuming manual process. In this paper, we present the fully automatic adaptive filtering and local detection (AFLD) method for detecting cones in split detector AOSLO images. We validated our algorithm on 80 images from 10 subjects, showing an overall mean Dice's coefficient of 0.95 (standard deviation 0.03), when comparing our AFLD algorithm to an expert grader. This is comparable to the inter-observer Dice's coefficient of 0.94 (standard deviation 0.04). To the best of our knowledge, this is the first validated, fully-automated segmentation method which has been applied to split detector AOSLO images.

Real-time door detection for indoor autonomous vehicle

NASA Astrophysics Data System (ADS)

He, Zhihao; Zhu, Ming

2017-07-01

Indoor Autonomous Vehicle(IAV) is used in many indoor scenes. Such as hotels and hospitals. Door detection is a key issue to guide the IAV into rooms. In this paper, we consider door detection in the use of indoor navigation of IAV. Since real-time properties are important for real-world IAV, the detection algorithm must be fast enough. Most monocular-camera based door detection model need a perfect detection of the four line segments of the door or the four corners. But in many situations, line segments could be extended or cut off. And there could be many false detected corners. And few of them can distinguish doors from door-like objects with door-like shape effectively. We proposed a 2-D vision model of the door that is made up of line segments. The number of parts detected is used to determine the possibility of a door. Our algorithm is tested on a database of doors.1 The robustness and real-time are verified. The precision is 89.4%. Average time consumed for processing a 640x320 figure is 44.73ms.

Automated image segmentation-assisted flattening of atomic force microscopy images.

PubMed

Wang, Yuliang; Lu, Tongda; Li, Xiaolai; Wang, Huimin

2018-01-01

Atomic force microscopy (AFM) images normally exhibit various artifacts. As a result, image flattening is required prior to image analysis. To obtain optimized flattening results, foreground features are generally manually excluded using rectangular masks in image flattening, which is time consuming and inaccurate. In this study, a two-step scheme was proposed to achieve optimized image flattening in an automated manner. In the first step, the convex and concave features in the foreground were automatically segmented with accurate boundary detection. The extracted foreground features were taken as exclusion masks. In the second step, data points in the background were fitted as polynomial curves/surfaces, which were then subtracted from raw images to get the flattened images. Moreover, sliding-window-based polynomial fitting was proposed to process images with complex background trends. The working principle of the two-step image flattening scheme were presented, followed by the investigation of the influence of a sliding-window size and polynomial fitting direction on the flattened images. Additionally, the role of image flattening on the morphological characterization and segmentation of AFM images were verified with the proposed method.

A consumption value-gap analysis for sustainable consumption.

PubMed

Biswas, Aindrila

2017-03-01

Recent studies on consumption behavior have depicted environmental apprehension resulting from across wide consumer segments. However, this has not been widely reflected upon the growth in the market shares for green or environment-friendly products mostly because gaps exist between consumers' expectations and perceptions for those products. Previous studies have highlighted the impact of perceived value on potential demand, consumer satisfaction and behavioral intentions. The necessity to understand the effects of gaps in expected and perceived values on consumers' behavioral intention and potential demand for green products cannot be undermined as it shapes the consumers' inclination to repeated purchase and consumption and thus foster potential market demand. Pertaining to this reason, the study aims to adopt a consumption value-gap model based on the theory of consumption values to assess their impact on sustainable consumption behavior and market demand of green products. Consumption value refers to the level of fulfillment of consumer needs by assessment of net utility derived after effective comparison between the benefits (financial or emotional) and the gives (money, time, or energy). The larger the gaps the higher will be the adversarial impact on behavioral intentions. A structural equation modeling was applied to assess data collected through questionnaire survey. The results indicate that functional value-gap and environmental value-gap has the most adversarial impact on sustainable consumption behavior and market demand for green products.

Consumer attitude and purchase intention towards processed meat products with natural compounds and a reduced level of nitrite.

PubMed

Hung, Yung; de Kok, Theo M; Verbeke, Wim

2016-11-01

This study investigates consumer attitude and purchase intention towards processed meat products with added natural compounds and a reduced level of nitrite. The rationale for such innovation relates to nitrite's negative health image as a chemical additive among consumers, versus the perception of compounds from fruits and vegetables as being natural and healthy. Cross-sectional data were collected through online questionnaires on knowledge about, interest in, attitude and intentions towards such new type of processed meat products in Belgium, The Netherlands, Italy and Germany (n=2057). Consumers generally had limited knowledge about nitrite being added to meat products. Yet, they expressed favourable attitudes and purchase intentions towards the new processed meat products. Purchase intention associated positively with: attitude; preference for natural over chemical additives; perceived harmfulness of chemical additives; risk importance; domain specific innovativeness; awareness of nitrite added; education; general health interest; and processed meat consumption frequency. Consumers from Italy and Germany had a lower level of purchase intention compared to Belgium. Four consumer segments were identified based on attitude and purchase intention: 'enthusiasts' (39.3% of the sample), 'accepters' (11.9%), 'half-hearted' (42.3%) and 'uninterested' (6.6%). This study provides valuable insight for further product development and effective tailoring of marketing communication strategies of innovative processed meat products. Copyright © 2016 Elsevier Ltd. All rights reserved.

Buying higher welfare poultry products? Profiling Flemish consumers who do and do not.

PubMed

Vanhonacker, F; Verbeke, W

2009-12-01

A substantial number of studies has already investigated differences within the consumer market with regard to attitudes and perceptions in relation to farm animal welfare. Likewise, several studies focused on the gap that exists between positive attitudes and reported consumption or purchase intentions for sustainable food products in general and higher welfare products more specific, and on the factors influencing this attitude-behavior gap. Little or no studies, however, have started from reported pro-welfare behavior to distinguish between consumer groups and to explore the motivations of the respective behavior. With this study, we aim to group consumers according to their reported buying frequency of higher welfare eggs and higher welfare chicken meat. Similarities and dissimilarities between these groups are mapped in terms of individual characteristics, product attribute importance, perceived consumer effectiveness, perception of higher welfare products, and attitude toward a welfare label. The research methodology applied was a quantitative study with cross-sectional consumer survey data collected in Flanders in spring 2007 (n = 469). Pro-welfare behavior was unevenly distributed across different consumer segments, despite a general interest and concern for bird welfare. A consistent choice for standard (no welfare premium) poultry products was related to strong perceived price and availability barriers, to a low importance attached to ethical issues as product attributes, and to a low perceived consumer effectiveness. A consistent choice for products with higher welfare standards to the contrast associated with a high importance attached to ethical issues; a low effect of price and availability perception; a strong association of higher welfare products with product attributes like health, taste, and quality; and a high perceived consumer effectiveness. The identification of market segments with common characteristics is essential for positioning higher welfare products and developing effective communication strategies. Finally, a welfare label emerged as an appropriate communication vehicle for consumers who engage in pro-welfare behavior and who experienced the label as a solution to lower the search costs for higher welfare products.

Design of a Template for Handwriting Based Hindi Text Entry in Handheld Devices

NASA Astrophysics Data System (ADS)

Gangopadhyay, Diya; Vasal, Ityam; Yammiyavar, Pradeep

Mobile phones, in the recent times, have become affordable and accessible to a wider range of users including the hitherto technologically and economically under-represented segments. Indian users are a gigantic consumer base for mobile phones. With Hindi being one of the most widely spoken languages in the country and the primary tool of communication for about a third of its population, an effective solution for Hindi text entry in mobile devices is expected to be immensely useful to the non English speaking users. This paper proposes a mobile phone handwriting based text entry solution for Hindi language, which allows for an easy text entry method, while facilitating better recognition accuracy.

Consumer-perceived quality in 'traditional' food chains: the case of the Greek meat supply chain.

PubMed

Krystallis, Athanassios; Chryssochoidis, George; Scholderer, Joachim

2007-01-01

Recent food scares have increased consumer concern about meat safety. However, the Greek 'traditional' meat supply chain from producers to local butchers does not seem to realise the pressing consumer demand for certified meat quality. Or is it that, in such food chains, this demand is not so pressing yet? The present paper seeks to answer this question based on a survey conducted in the Athens area, involving a sample of 268 participants responsible for food purchasing decisions. The survey mainly aims to develop an integrated model of factors that affect consumer-perceived meat quality and to develop the profile of different consumer segments in relation to these perceptions. The substantial findings of the survey include the fact that, despite their enormous per capita consumption, the majority of consumers are not particularly involved in the meat-purchasing process. Rather they attach importance to visual intrinsic quality cues evaluated in a pre-purchasing context. In this respect, intrinsic quality cues are assigned a role similar to that of quality certification; coupled with the choice of traditional channels and the resulting personal relation with the butcher, they can be understood as efforts to decrease risk of the purchasing decision. Moreover, consumers with such behaviour seem to relate domestic country of origin of meat mostly with perceptions of general safety. Finally, a small, but promising trend with substantial marketing implications of frequent purchases of chicken and pork at supermarkets should not be ignored.

SU-C-BRA-01: Interactive Auto-Segmentation for Bowel in Online Adaptive MRI-Guided Radiation Therapy by Using a Multi-Region Labeling Algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Y; Chen, I; Kashani, R

Purpose: In MRI-guided online adaptive radiation therapy, re-contouring of bowel is time-consuming and can impact the overall time of patients on table. The study aims to auto-segment bowel on volumetric MR images by using an interactive multi-region labeling algorithm. Methods: 5 Patients with locally advanced pancreatic cancer underwent fractionated radiotherapy (18–25 fractions each, total 118 fractions) on an MRI-guided radiation therapy system with a 0.35 Tesla magnet and three Co-60 sources. At each fraction, a volumetric MR image of the patient was acquired when the patient was in the treatment position. An interactive two-dimensional multi-region labeling technique based on graphmore » cut solver was applied on several typical MRI images to segment the large bowel and small bowel, followed by a shape based contour interpolation for generating entire bowel contours along all image slices. The resulted contours were compared with the physician’s manual contouring by using metrics of Dice coefficient and Hausdorff distance. Results: Image data sets from the first 5 fractions of each patient were selected (total of 25 image data sets) for the segmentation test. The algorithm segmented the large and small bowel effectively and efficiently. All bowel segments were successfully identified, auto-contoured and matched with manual contours. The time cost by the algorithm for each image slice was within 30 seconds. For large bowel, the calculated Dice coefficients and Hausdorff distances (mean±std) were 0.77±0.07 and 13.13±5.01mm, respectively; for small bowel, the corresponding metrics were 0.73±0.08and 14.15±4.72mm, respectively. Conclusion: The preliminary results demonstrated the potential of the proposed algorithm in auto-segmenting large and small bowel on low field MRI images in MRI-guided adaptive radiation therapy. Further work will be focused on improving its segmentation accuracy and lessening human interaction.« less

Applied Warfighter Ergonomics: A Research Method for Evaluating Military Individual Equipment

DTIC Science & Technology

2005-09-01

innovations, as well. 6 Subsequent studies have established that the top official, head of household, or other nominal leader of the organization...alternative products have no meaningful differentiation between them (such as shampoo and instant coffee), consumers preferences can be significantly...example, with his weapon slung over his shoulder . Admin The conventional segment of the scenario was identical for each RPDA. The RPDA segment was

Electronic Gaming Machine (EGM) Environments: Market Segments and Risk.

PubMed

Rockloff, Matthew; Moskovsky, Neda; Thorne, Hannah; Browne, Matthew; Bryden, Gabrielle

2017-12-01

This study used a marketing-research paradigm to explore gamblers' attraction to EGMs based on different elements of the environment. A select set of environmental features was sourced from a prior study (Thorne et al. in J Gambl Issues 2016b), and a discrete choice experiment was conducted through an online survey. Using the same dataset first described by Rockloff et al. (EGM Environments that contribute to excess consumption and harm, 2015), a sample of 245 EGM gamblers were sourced from clubs in Victoria, Australia, and 7516 gamblers from an Australian national online survey-panel. Participants' choices amongst sets of hypothetical gambling environments allowed for an estimation of the implied individual-level utilities for each feature (e.g., general sounds, location, etc.). K-means clustering on these utilities identified four unique market segments for EGM gambling, representing four different types of consumers. The segments were named according to their dominant features: Social, Value, High Roller and Internet. We found that the environments orientated towards the Social and Value segments were most conducive to attracting players with relatively few gambling problems, while the High Roller and Internet-focused environments had greater appeal for players with problems and vulnerabilities. This study has generated new insights into the kinds of gambling environments that are most consistent with safe play.

A Method for the Evaluation of Thousands of Automated 3D Stem Cell Segmentations

PubMed Central

Bajcsy, Peter; Simon, Mylene; Florczyk, Stephen; Simon, Carl G.; Juba, Derek; Brady, Mary

2016-01-01

There is no segmentation method that performs perfectly with any data set in comparison to human segmentation. Evaluation procedures for segmentation algorithms become critical for their selection. The problems associated with segmentation performance evaluations and visual verification of segmentation results are exaggerated when dealing with thousands of 3D image volumes because of the amount of computation and manual inputs needed. We address the problem of evaluating 3D segmentation performance when segmentation is applied to thousands of confocal microscopy images (z-stacks). Our approach is to incorporate experimental imaging and geometrical criteria, and map them into computationally efficient segmentation algorithms that can be applied to a very large number of z-stacks. This is an alternative approach to considering existing segmentation methods and evaluating most state-of-the-art algorithms. We designed a methodology for 3D segmentation performance characterization that consists of design, evaluation and verification steps. The characterization integrates manual inputs from projected surrogate “ground truth” of statistically representative samples and from visual inspection into the evaluation. The novelty of the methodology lies in (1) designing candidate segmentation algorithms by mapping imaging and geometrical criteria into algorithmic steps, and constructing plausible segmentation algorithms with respect to the order of algorithmic steps and their parameters, (2) evaluating segmentation accuracy using samples drawn from probability distribution estimates of candidate segmentations, and (3) minimizing human labor needed to create surrogate “truth” by approximating z-stack segmentations with 2D contours from three orthogonal z-stack projections and by developing visual verification tools. We demonstrate the methodology by applying it to a dataset of 1253 mesenchymal stem cells. The cells reside on 10 different types of biomaterial scaffolds, and are stained for actin and nucleus yielding 128 460 image frames (on average 125 cells/scaffold × 10 scaffold types × 2 stains × 51 frames/cell). After constructing and evaluating six candidates of 3D segmentation algorithms, the most accurate 3D segmentation algorithm achieved an average precision of 0.82 and an accuracy of 0.84 as measured by the Dice similarity index where values greater than 0.7 indicate a good spatial overlap. A probability of segmentation success was 0.85 based on visual verification, and a computation time was 42.3 h to process all z-stacks. While the most accurate segmentation technique was 4.2 times slower than the second most accurate algorithm, it consumed on average 9.65 times less memory per z-stack segmentation. PMID:26268699

Multiplanar visualization in 3D transthoracic echocardiography for precise delineation of mitral valve pathology.

PubMed

Kuppahally, Suman S; Paloma, Allan; Craig Miller, D; Schnittger, Ingela; Liang, David

2008-01-01

A novel multiplanar reformatting (MPR) technique in three-dimensional transthoracic echocardiography (3D TTE) was used to precisely localize the prolapsed lateral segment of posterior mitral valve leaflet in a patient symptomatic with mitral valve prolapse (MVP) and moderate mitral regurgitation (MR) before undergoing mitral valve repair surgery. Transesophageal echocardiography was avoided based on the findings of this new technique by 3D TTE. It was noninvasive, quick, reproducible and reliable. Also, it did not need the time-consuming reconstruction of multiple cardiac images. Mitral valve repair surgery was subsequently performed based on the MPR findings and corroborated the findings from the MPR examination.

Drugs of abuse in maternal hair and paired neonatal meconium: an objective assessment of foetal exposure to gestational consumption.

PubMed

Joya, Xavier; Marchei, Emilia; Salat-Batlle, Judith; García-Algar, Oscar; Calvaresi, Valeria; Pacifici, Roberta; Pichini, Simona

2016-08-01

In a prospective sample of 80 mother-infant dyads, we investigated whether drugs of abuse in maternal hair measured during the pregnancy trimesters were also present in neonatal meconium. Principal drugs of abuse were analyzed in the three consecutive maternal hair segments and meconium samples by ultra-performance liquid chromatography tandem mass spectrometry assay. Of the 80 mothers, 32 (40%) presented one or more hair shafts with at least one of the analyzed drugs of abuse and/or its metabolites. The drug of abuse with a higher prevalence in our study population was methamphetamine: 19 mothers had methamphetamine in one or more hair segments (59.4%). The second most detected drug of abuse was cocaine; nine mothers presented cocaine in one or more hair segments (28.1%). Nineteen pregnant women consumed at least one drug of abuse during the first trimester, ten continued consuming drugs of abuse during the second trimester; and nine consumed until the end of pregnancy. Five of the nine newborns from mothers who consumed drugs during the whole pregnancy showed drugs of abuse in meconium samples. Newborns from the 23 remaining mothers with one or two hair shafts positive to drugs of abuse did not present drugs in their meconium. Indeed from these results, it seems that discontinuous and/or sporadic consumption during pregnancy could produce a negligible transplacental passage and hence negative results in meconium. Furthermore, the role of placenta in the metabolism and excretion of drugs of abuse is still to be precisely investigated. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data

PubMed Central

2017-01-01

Remote-Field Eddy-Current (RFEC) technology is often used as a Non-Destructive Evaluation (NDE) method to prevent water pipe failures. By analyzing the RFEC data, it is possible to quantify the corrosion present in pipes. Quantifying the corrosion involves detecting defects and extracting their depth and shape. For large sections of pipelines, this can be extremely time-consuming if performed manually. Automated approaches are therefore well motivated. In this article, we propose an automated framework to locate and segment defects in individual pipe segments, starting from raw RFEC measurements taken over large pipelines. The framework relies on a novel feature to robustly detect these defects and a segmentation algorithm applied to the deconvolved RFEC signal. The framework is evaluated using both simulated and real datasets, demonstrating its ability to efficiently segment the shape of corrosion defects. PMID:28984823

Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data.

PubMed

Falque, Raphael; Vidal-Calleja, Teresa; Miro, Jaime Valls

2017-10-06

Remote-Field Eddy-Current (RFEC) technology is often used as a Non-Destructive Evaluation (NDE) method to prevent water pipe failures. By analyzing the RFEC data, it is possible to quantify the corrosion present in pipes. Quantifying the corrosion involves detecting defects and extracting their depth and shape. For large sections of pipelines, this can be extremely time-consuming if performed manually. Automated approaches are therefore well motivated. In this article, we propose an automated framework to locate and segment defects in individual pipe segments, starting from raw RFEC measurements taken over large pipelines. The framework relies on a novel feature to robustly detect these defects and a segmentation algorithm applied to the deconvolved RFEC signal. The framework is evaluated using both simulated and real datasets, demonstrating its ability to efficiently segment the shape of corrosion defects.

Automated skin segmentation in ultrasonic evaluation of skin toxicity in breast cancer radiotherapy.

PubMed

Gao, Yi; Tannenbaum, Allen; Chen, Hao; Torres, Mylin; Yoshida, Emi; Yang, Xiaofeng; Wang, Yuefeng; Curran, Walter; Liu, Tian

2013-11-01

Skin toxicity is the most common side effect of breast cancer radiotherapy and impairs the quality of life of many breast cancer survivors. We, along with other researchers, have recently found quantitative ultrasound to be effective as a skin toxicity assessment tool. Although more reliable than standard clinical evaluations (visual observation and palpation), the current procedure for ultrasound-based skin toxicity measurements requires manual delineation of the skin layers (i.e., epidermis-dermis and dermis-hypodermis interfaces) on each ultrasound B-mode image. Manual skin segmentation is time consuming and subjective. Moreover, radiation-induced skin injury may decrease image contrast between the dermis and hypodermis, which increases the difficulty of delineation. Therefore, we have developed an automatic skin segmentation tool (ASST) based on the active contour model with two significant modifications: (i) The proposed algorithm introduces a novel dual-curve scheme for the double skin layer extraction, as opposed to the original single active contour method. (ii) The proposed algorithm is based on a geometric contour framework as opposed to the previous parametric algorithm. This ASST algorithm was tested on a breast cancer image database of 730 ultrasound breast images (73 ultrasound studies of 23 patients). We compared skin segmentation results obtained with the ASST with manual contours performed by two physicians. The average percentage differences in skin thickness between the ASST measurement and that of each physician were less than 5% (4.8 ± 17.8% and -3.8 ± 21.1%, respectively). In summary, we have developed an automatic skin segmentation method that ensures objective assessment of radiation-induced changes in skin thickness. Our ultrasound technology offers a unique opportunity to quantify tissue injury in a more meaningful and reproducible manner than the subjective assessments currently employed in the clinic. Copyright © 2013 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms.

PubMed

Wiesmann, Veit; Bergler, Matthias; Palmisano, Ralf; Prinzen, Martin; Franz, Daniela; Wittenberg, Thomas

2017-03-18

Manual assessment and evaluation of fluorescent micrograph cell experiments is time-consuming and tedious. Automated segmentation pipelines can ensure efficient and reproducible evaluation and analysis with constant high quality for all images of an experiment. Such cell segmentation approaches are usually validated and rated in comparison to manually annotated micrographs. Nevertheless, manual annotations are prone to errors and display inter- and intra-observer variability which influence the validation results of automated cell segmentation pipelines. We present a new approach to simulate fluorescent cell micrographs that provides an objective ground truth for the validation of cell segmentation methods. The cell simulation was evaluated twofold: (1) An expert observer study shows that the proposed approach generates realistic fluorescent cell micrograph simulations. (2) An automated segmentation pipeline on the simulated fluorescent cell micrographs reproduces segmentation performances of that pipeline on real fluorescent cell micrographs. The proposed simulation approach produces realistic fluorescent cell micrographs with corresponding ground truth. The simulated data is suited to evaluate image segmentation pipelines more efficiently and reproducibly than it is possible on manually annotated real micrographs.

Automatic repositioning of jaw segments for three-dimensional virtual treatment planning of orthognathic surgery.

PubMed

Santos, Rodrigo Mologni Gonçalves Dos; De Martino, José Mario; Passeri, Luis Augusto; Attux, Romis Ribeiro de Faissol; Haiter Neto, Francisco

2017-09-01

To develop a computer-based method for automating the repositioning of jaw segments in the skull during three-dimensional virtual treatment planning of orthognathic surgery. The method speeds up the planning phase of the orthognathic procedure, releasing surgeons from laborious and time-consuming tasks. The method finds the optimal positions for the maxilla, mandibular body, and bony chin in the skull. Minimization of cephalometric differences between measured and standard values is considered. Cone-beam computed tomographic images acquired from four preoperative patients with skeletal malocclusion were used for evaluating the method. Dentofacial problems of the four patients were rectified, including skeletal malocclusion, facial asymmetry, and jaw discrepancies. The results show that the method is potentially able to be used in routine clinical practice as support for treatment-planning decisions in orthognathic surgery. Copyright © 2017 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Geomagnetism-Aided Indoor Wi-Fi Radio-Map Construction via Smartphone Crowdsourcing.

PubMed

Li, Wen; Wei, Dongyan; Lai, Qifeng; Li, Xianghong; Yuan, Hong

2018-05-08

Wi-Fi radio-map construction is an important phase in indoor fingerprint localization systems. Traditional methods for Wi-Fi radio-map construction have the problems of being time-consuming and labor-intensive. In this paper, an indoor Wi-Fi radio-map construction method is proposed which utilizes crowdsourcing data contributed by smartphone users. We draw indoor pathway map and construct Wi-Fi radio-map without requiring manual site survey, exact floor layout and extra infrastructure support. The key novelty is that it recognizes road segments from crowdsourcing traces by a cluster based on magnetism sequence similarity and constructs an indoor pathway map with Wi-Fi signal strengths annotated on. Through experiments in real world indoor areas, the method is proved to have good performance on magnetism similarity calculation, road segment clustering and pathway map construction. The Wi-Fi radio maps constructed by crowdsourcing data are validated to provide competitive indoor localization accuracy.

Geomagnetism-Aided Indoor Wi-Fi Radio-Map Construction via Smartphone Crowdsourcing

PubMed Central

Li, Wen; Wei, Dongyan; Lai, Qifeng; Li, Xianghong; Yuan, Hong

2018-01-01

Wi-Fi radio-map construction is an important phase in indoor fingerprint localization systems. Traditional methods for Wi-Fi radio-map construction have the problems of being time-consuming and labor-intensive. In this paper, an indoor Wi-Fi radio-map construction method is proposed which utilizes crowdsourcing data contributed by smartphone users. We draw indoor pathway map and construct Wi-Fi radio-map without requiring manual site survey, exact floor layout and extra infrastructure support. The key novelty is that it recognizes road segments from crowdsourcing traces by a cluster based on magnetism sequence similarity and constructs an indoor pathway map with Wi-Fi signal strengths annotated on. Through experiments in real world indoor areas, the method is proved to have good performance on magnetism similarity calculation, road segment clustering and pathway map construction. The Wi-Fi radio maps constructed by crowdsourcing data are validated to provide competitive indoor localization accuracy. PMID:29738454

Consumer beliefs regarding farmed versus wild fish.

PubMed

Claret, Anna; Guerrero, Luis; Ginés, Rafael; Grau, Amàlia; Hernández, M Dolores; Aguirre, Enaitz; Peleteiro, José Benito; Fernández-Pato, Carlos; Rodríguez-Rodríguez, Carmen

2014-08-01

Aquaculture is a food-producing activity, alternative to traditional extractive fishing, which still acts as a reference for most consumers. The main objective of the present paper was to study which consumer beliefs, regarding farmed versus wild fish, hinder the potential development of the aquaculture sector. To achieve this purpose the study was organized into two complementary steps: a qualitative approach (focus groups) aimed at assessing consumer perception about wild and farmed fish and to identify the salient beliefs that differentiate them; and a quantitative approach (survey by means of a questionnaire) to validate the results obtained in the focus group discussions over a representative sample of participants (n = 919). Results showed that participants perceive clear differences between farmed and wild fish. Although no significant differences between both kinds of fish were detected on safety, in general farmed fish was perceived to be less affected by marine pollution, heavy metals and parasites. In the contrary, wild fish was considered to have healthier feeding, to contain fewer antibiotics and to be fresher, healthier, less handled and more natural. Beliefs related to quality were in favour of wild fish, while those related to availability and price were in favour of farmed fish. Significant differences were observed in the perception of both kinds of fish depending on the consumers' objective knowledge about fish, on the level of education, age and gender and on the three segments of consumers identified: "Traditional/Conservative", "Connoisseur", "Open to aquaculture". The results provided could play an important role when planning and designing efficient marketing strategies for promoting farmed fish by adapting the information provided to the perception of each segment of consumers identified by the present study. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automatic and quantitative measurement of collagen gel contraction using model-guided segmentation

NASA Astrophysics Data System (ADS)

Chen, Hsin-Chen; Yang, Tai-Hua; Thoreson, Andrew R.; Zhao, Chunfeng; Amadio, Peter C.; Sun, Yung-Nien; Su, Fong-Chin; An, Kai-Nan

2013-08-01

Quantitative measurement of collagen gel contraction plays a critical role in the field of tissue engineering because it provides spatial-temporal assessment (e.g., changes of gel area and diameter during the contraction process) reflecting the cell behavior and tissue material properties. So far the assessment of collagen gels relies on manual segmentation, which is time-consuming and suffers from serious intra- and inter-observer variability. In this study, we propose an automatic method combining various image processing techniques to resolve these problems. The proposed method first detects the maximal feasible contraction range of circular references (e.g., culture dish) and avoids the interference of irrelevant objects in the given image. Then, a three-step color conversion strategy is applied to normalize and enhance the contrast between the gel and background. We subsequently introduce a deformable circular model which utilizes regional intensity contrast and circular shape constraint to locate the gel boundary. An adaptive weighting scheme was employed to coordinate the model behavior, so that the proposed system can overcome variations of gel boundary appearances at different contraction stages. Two measurements of collagen gels (i.e., area and diameter) can readily be obtained based on the segmentation results. Experimental results, including 120 gel images for accuracy validation, showed high agreement between the proposed method and manual segmentation with an average dice similarity coefficient larger than 0.95. The results also demonstrated obvious improvement in gel contours obtained by the proposed method over two popular, generic segmentation methods.

Meat, beyond the plate. Data-driven hypotheses for understanding consumer willingness to adopt a more plant-based diet.

PubMed

Graça, João; Oliveira, Abílio; Calheiros, Maria Manuela

2015-07-01

A shift towards reduced meat consumption and a more plant-based diet is endorsed to promote sustainability, improve public health, and minimize animal suffering. However, large segments of consumers do not seem willing to make such transition. While it may take a profound societal change to achieve significant progresses on this regard, there have been limited attempts to understand the psychosocial processes that may hinder or facilitate this shift. This study provides an in-depth exploration of how consumer representations of meat, the impact of meat, and rationales for changing or not habits relate with willingness to adopt a more plant-based diet. Multiple Correspondence Analysis was employed to examine participant responses (N = 410) to a set of open-ended questions, free word association tasks and closed questions. Three clusters with two hallmarks each were identified: (1) a pattern of disgust towards meat coupled with moral internalization; (2) a pattern of low affective connection towards meat and willingness to change habits; and (3) a pattern of attachment to meat and unwillingness to change habits. The findings raise two main propositions. The first is that an affective connection towards meat relates to the perception of the impacts of meat and to willingness to change consumption habits. The second proposition is that a set of rationales resembling moral disengagement mechanisms (e.g., pro-meat justifications; self-exonerations) arise when some consumers contemplate the consequences of meat production and consumption, and the possibility of changing habits. Copyright © 2015 Elsevier Ltd. All rights reserved.

Changing trends in health care tourism.

PubMed

Karuppan, Corinne M; Karuppan, Muthu

2010-01-01

Despite much coverage in the popular press, only anecdotal evidence is available on medical tourists. At first sight, they seemed confined to small and narrowly defined consumer segments: individuals seeking bargains in cosmetic surgery or uninsured and financially distressed individuals in desperate need of medical care. The study reported in this article is the first empirical investigation of the medical tourism consumer market. It provides the demographic profile, motivations, and value perceptions of health care consumers who traveled abroad specifically to receive medical care. The findings suggest a much broader market of educated and savvy health care consumers than previously thought. In the backdrop of the health care reform, the article concludes with implications for health care providers.

Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging.

PubMed

Agner, Shannon C; Xu, Jun; Madabhushi, Anant

2013-03-01

Segmentation of breast lesions on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is the first step in lesion diagnosis in a computer-aided diagnosis framework. Because manual segmentation of such lesions is both time consuming and highly susceptible to human error and issues of reproducibility, an automated lesion segmentation method is highly desirable. Traditional automated image segmentation methods such as boundary-based active contour (AC) models require a strong gradient at the lesion boundary. Even when region-based terms are introduced to an AC model, grayscale image intensities often do not allow for clear definition of foreground and background region statistics. Thus, there is a need to find alternative image representations that might provide (1) strong gradients at the margin of the object of interest (OOI); and (2) larger separation between intensity distributions and region statistics for the foreground and background, which are necessary to halt evolution of the AC model upon reaching the border of the OOI. In this paper, the authors introduce a spectral embedding (SE) based AC (SEAC) for lesion segmentation on breast DCE-MRI. SE, a nonlinear dimensionality reduction scheme, is applied to the DCE time series in a voxelwise fashion to reduce several time point images to a single parametric image where every voxel is characterized by the three dominant eigenvectors. This parametric eigenvector image (PrEIm) representation allows for better capture of image region statistics and stronger gradients for use with a hybrid AC model, which is driven by both boundary and region information. They compare SEAC to ACs that employ fuzzy c-means (FCM) and principal component analysis (PCA) as alternative image representations. Segmentation performance was evaluated by boundary and region metrics as well as comparing lesion classification using morphological features from SEAC, PCA+AC, and FCM+AC. On a cohort of 50 breast DCE-MRI studies, PrEIm yielded overall better region and boundary-based statistics compared to the original DCE-MR image, FCM, and PCA based image representations. Additionally, SEAC outperformed a hybrid AC applied to both PCA and FCM image representations. Mean dice similarity coefficient (DSC) for SEAC was significantly better (DSC = 0.74 ± 0.21) than FCM+AC (DSC = 0.50 ± 0.32) and similar to PCA+AC (DSC = 0.73 ± 0.22). Boundary-based metrics of mean absolute difference and Hausdorff distance followed the same trends. Of the automated segmentation methods, breast lesion classification based on morphologic features derived from SEAC segmentation using a support vector machine classifier also performed better (AUC = 0.67 ± 0.05; p < 0.05) than FCM+AC (AUC = 0.50 ± 0.07), and PCA+AC (AUC = 0.49 ± 0.07). In this work, we presented SEAC, an accurate, general purpose AC segmentation tool that could be applied to any imaging domain that employs time series data. SE allows for projection of time series data into a PrEIm representation so that every voxel is characterized by the dominant eigenvectors, capturing the global and local time-intensity curve similarities in the data. This PrEIm allows for the calculation of strong tensor gradients and better region statistics than the original image intensities or alternative image representations such as PCA and FCM. The PrEIm also allows for building a more accurate hybrid AC scheme.

Intensity-based segmentation and visualization of cells in 3D microscopic images using the GPU

NASA Astrophysics Data System (ADS)

Kang, Mi-Sun; Lee, Jeong-Eom; Jeon, Woong-ki; Choi, Heung-Kook; Kim, Myoung-Hee

2013-02-01

3D microscopy images contain abundant astronomical data, rendering 3D microscopy image processing time-consuming and laborious on a central processing unit (CPU). To solve these problems, many people crop a region of interest (ROI) of the input image to a small size. Although this reduces cost and time, there are drawbacks at the image processing level, e.g., the selected ROI strongly depends on the user and there is a loss in original image information. To mitigate these problems, we developed a 3D microscopy image processing tool on a graphics processing unit (GPU). Our tool provides efficient and various automatic thresholding methods to achieve intensity-based segmentation of 3D microscopy images. Users can select the algorithm to be applied. Further, the image processing tool provides visualization of segmented volume data and can set the scale, transportation, etc. using a keyboard and mouse. However, the 3D objects visualized fast still need to be analyzed to obtain information for biologists. To analyze 3D microscopic images, we need quantitative data of the images. Therefore, we label the segmented 3D objects within all 3D microscopic images and obtain quantitative information on each labeled object. This information can use the classification feature. A user can select the object to be analyzed. Our tool allows the selected object to be displayed on a new window, and hence, more details of the object can be observed. Finally, we validate the effectiveness of our tool by comparing the CPU and GPU processing times by matching the specification and configuration.

A low-cost three-dimensional laser surface scanning approach for defining body segment parameters.

PubMed

Pandis, Petros; Bull, Anthony Mj

2017-11-01

Body segment parameters are used in many different applications in ergonomics as well as in dynamic modelling of the musculoskeletal system. Body segment parameters can be defined using different methods, including techniques that involve time-consuming manual measurements of the human body, used in conjunction with models or equations. In this study, a scanning technique for measuring subject-specific body segment parameters in an easy, fast, accurate and low-cost way was developed and validated. The scanner can obtain the body segment parameters in a single scanning operation, which takes between 8 and 10 s. The results obtained with the system show a standard deviation of 2.5% in volumetric measurements of the upper limb of a mannequin and 3.1% difference between scanning volume and actual volume. Finally, the maximum mean error for the moment of inertia by scanning a standard-sized homogeneous object was 2.2%. This study shows that a low-cost system can provide quick and accurate subject-specific body segment parameter estimates.

Fast Appearance Modeling for Automatic Primary Video Object Segmentation.

PubMed

Yang, Jiong; Price, Brian; Shen, Xiaohui; Lin, Zhe; Yuan, Junsong

2016-02-01

Automatic segmentation of the primary object in a video clip is a challenging problem as there is no prior knowledge of the primary object. Most existing techniques thus adapt an iterative approach for foreground and background appearance modeling, i.e., fix the appearance model while optimizing the segmentation and fix the segmentation while optimizing the appearance model. However, these approaches may rely on good initialization and can be easily trapped in local optimal. In addition, they are usually time consuming for analyzing videos. To address these limitations, we propose a novel and efficient appearance modeling technique for automatic primary video object segmentation in the Markov random field (MRF) framework. It embeds the appearance constraint as auxiliary nodes and edges in the MRF structure, and can optimize both the segmentation and appearance model parameters simultaneously in one graph cut. The extensive experimental evaluations validate the superiority of the proposed approach over the state-of-the-art methods, in both efficiency and effectiveness.

Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier.

PubMed

Barbosa, Jocelyn; Lee, Kyubum; Lee, Sunwon; Lodhi, Bilal; Cho, Jae-Gu; Seo, Woo-Keun; Kang, Jaewoo

2016-03-12

Facial palsy or paralysis (FP) is a symptom that loses voluntary muscles movement in one side of the human face, which could be very devastating in the part of the patients. Traditional methods are solely dependent to clinician's judgment and therefore time consuming and subjective in nature. Hence, a quantitative assessment system becomes apparently invaluable for physicians to begin the rehabilitation process; and to produce a reliable and robust method is challenging and still underway. We introduce a novel approach for a quantitative assessment of facial paralysis that tackles classification problem for FP type and degree of severity. Specifically, a novel method of quantitative assessment is presented: an algorithm that extracts the human iris and detects facial landmarks; and a hybrid approach combining the rule-based and machine learning algorithm to analyze and prognosticate facial paralysis using the captured images. A method combining the optimized Daugman's algorithm and Localized Active Contour (LAC) model is proposed to efficiently extract the iris and facial landmark or key points. To improve the performance of LAC, appropriate parameters of initial evolving curve for facial features' segmentation are automatically selected. The symmetry score is measured by the ratio between features extracted from the two sides of the face. Hybrid classifiers (i.e. rule-based with regularized logistic regression) were employed for discriminating healthy and unhealthy subjects, FP type classification, and for facial paralysis grading based on House-Brackmann (H-B) scale. Quantitative analysis was performed to evaluate the performance of the proposed approach. Experiments show that the proposed method demonstrates its efficiency. Facial movement feature extraction on facial images based on iris segmentation and LAC-based key point detection along with a hybrid classifier provides a more efficient way of addressing classification problem on facial palsy type and degree of severity. Combining iris segmentation and key point-based method has several merits that are essential for our real application. Aside from the facial key points, iris segmentation provides significant contribution as it describes the changes of the iris exposure while performing some facial expressions. It reveals the significant difference between the healthy side and the severe palsy side when raising eyebrows with both eyes directed upward, and can model the typical changes in the iris region.

Quantification of osteolytic bone lesions in a preclinical rat trial

NASA Astrophysics Data System (ADS)

Fränzle, Andrea; Bretschi, Maren; Bäuerle, Tobias; Giske, Kristina; Hillengass, Jens; Bendl, Rolf

2013-10-01

In breast cancer, most of the patients who died, have developed bone metastasis as disease progression. Bone metastases in case of breast cancer are mainly bone destructive (osteolytic). To understand pathogenesis and to analyse response to different treatments, animal models, in our case rats, are examined. For assessment of treatment response to bone remodelling therapies exact segmentations of osteolytic lesions are needed. Manual segmentations are not only time-consuming but lack in reproducibility. Computerized segmentation tools are essential. In this paper we present an approach for the computerized quantification of osteolytic lesion volumes using a comparison to a healthy reference model. The presented qualitative and quantitative evaluation of the reconstructed bone volumes show, that the automatically segmented lesion volumes complete missing bone in a reasonable way.

Automatic right ventricle (RV) segmentation by propagating a basal spatio-temporal characterization

NASA Astrophysics Data System (ADS)

Atehortúa, Angélica; Zuluaga, María. A.; Martínez, Fabio; Romero, Eduardo

2015-12-01

An accurate right ventricular (RV) function quantification is important to support the evaluation, diagnosis and prognosis of several cardiac pathologies and to complement the left ventricular function assessment. However, expert RV delineation is a time consuming task with high inter-and-intra observer variability. In this paper we present an automatic segmentation method of the RV in MR-cardiac sequences. Unlike atlas or multi-atlas methods, this approach estimates the RV using exclusively information from the sequence itself. For so doing, a spatio-temporal analysis segments the heart at the basal slice, segmentation that is then propagated to the apex by using a non-rigid-registration strategy. The proposed approach achieves an average Dice Score of 0:79 evaluated with a set of 48 patients.

How the media influences women's perceptions of health care.

PubMed

Kahn, C

2001-01-01

To better understand the effectiveness of media sources that marketers use to channel direct-to-consumer (DTC) campaigns to women, researchers devised a study that segmented the female participants according to their degree of involvement in health care decisions, marital status, age, employment, income, and education. The findings show that women in certain population segments reacted far differently to health care information depending on whether it was presented through the Internet, magazines, newspapers, radio, or TV.

Who chooses a consumer-directed health plan?

PubMed

Barry, Colleen L; Cullen, Mark R; Galusha, Deron; Slade, Martin D; Busch, Susan H

2008-01-01

Consumer-directed health plans (CDHPs) hold the promise of reining in health spending by giving consumers a greater stake in health care purchasing, yet little is known about employers' experience with these products. In examining the characteristics of those selecting a CDHP offered by one large employer, we found stronger evidence of selection than has been identified in prior research. Our findings suggest that in the context of plan choice, CDHPs may offer little opportunity to greatly lower employers' cost burden, and they highlight concerns about the potential for risk segmentation and the value of conferring preferential tax treatment to CDHPs.

Who Chooses A Consumer-Directed Health Plan?

PubMed Central

Barry, Colleen L.; Cullen, Mark R.; Galusha, Deron; Slade, Martin D.; Busch, Susan H.

2012-01-01

Consumer-directed health plans (CDHPs) hold the promise of reining in health spending by giving consumers a greater stake in health care purchasing, yet little is known about employers’ experience with these products. In examining the characteristics of those selecting a CDHP offered by one large employer, we found stronger evidence of selection than has been identified in prior research. Our findings suggest that in the context of plan choice, CDHPs may offer little opportunity to greatly lower employers’ cost burden, and they highlight concerns about the potential for risk segmentation and the value of conferring preferential tax treatment to CDHPs. PMID:18997225

Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging.

PubMed

Ravi, Daniele; Fabelo, Himar; Callic, Gustavo Marrero; Yang, Guang-Zhong

2017-09-01

Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing, and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumor classification map for intra-operative margin definition during brain surgery. However, existing approaches to dimensionality reduction based on manifold embedding can be time consuming and may not guarantee a consistent result, thus hindering final tissue classification. The proposed framework aims to overcome these problems through a process divided into two steps: dimensionality reduction based on an extension of the T-distributed stochastic neighbor approach is first performed and then a semantic segmentation technique is applied to the embedded results by using a Semantic Texton Forest for tissue classification. Detailed in vivo validation of the proposed method has been performed to demonstrate the potential clinical value of the system.

Effects of aircraft and flight parameters on energy-efficient profile descents in time-based metered traffic

NASA Technical Reports Server (NTRS)

Dejarnette, F. R.

1984-01-01

Concepts to save fuel while preserving airport capacity by combining time based metering with profile descent procedures were developed. A computer algorithm is developed to provide the flight crew with the information needed to fly from an entry fix to a metering fix and arrive there at a predetermined time, altitude, and airspeed. The flight from the metering fix to an aim point near the airport was calculated. The flight path is divided into several descent and deceleration segments. Descents are performed at constant Mach numbers or calibrated airspeed, whereas decelerations occur at constant altitude. The time and distance associated with each segment are calculated from point mass equations of motion for a clean configuration with idle thrust. Wind and nonstandard atmospheric properties have a large effect on the flight path. It is found that uncertainty in the descent Mach number has a large effect on the predicted flight time. Of the possible combinations of Mach number and calibrated airspeed for a descent, only small changes were observed in the fuel consumed.

Food Choice Questionnaire (FCQ) revisited. Suggestions for the development of an enhanced general food motivation model.

PubMed

Fotopoulos, Christos; Krystallis, Athanasios; Vassallo, Marco; Pagiaslis, Anastasios

2009-02-01

Recognising the need for a more statistically robust instrument to investigate general food selection determinants, the research validates and confirms Food Choice Questionnaire (FCQ's) factorial design, develops ad hoc a more robust FCQ version and tests its ability to discriminate between consumer segments in terms of the importance they assign to the FCQ motivational factors. The original FCQ appears to represent a comprehensive and reliable research instrument. However, the empirical data do not support the robustness of its 9-factorial design. On the other hand, segmentation results at the subpopulation level based on the enhanced FCQ version bring about an optimistic message for the FCQ's ability to predict food selection behaviour. The paper concludes that some of the basic components of the original FCQ can be used as a basis for a new general food motivation typology. The development of such a new instrument, with fewer, of higher abstraction FCQ-based dimensions and fewer items per dimension, is a right step forward; yet such a step should be theory-driven, while a rigorous statistical testing across and within population would be necessary.

Morphing Wing Weight Predictors and Their Application in a Template-Based Morphing Aircraft Sizing Environment II. Part 2; Morphing Aircraft Sizing via Multi-level Optimization

NASA Technical Reports Server (NTRS)

Skillen, Michael D.; Crossley, William A.

2008-01-01

This report presents an approach for sizing of a morphing aircraft based upon a multi-level design optimization approach. For this effort, a morphing wing is one whose planform can make significant shape changes in flight - increasing wing area by 50% or more from the lowest possible area, changing sweep 30 or more, and/or increasing aspect ratio by as much as 200% from the lowest possible value. The top-level optimization problem seeks to minimize the gross weight of the aircraft by determining a set of "baseline" variables - these are common aircraft sizing variables, along with a set of "morphing limit" variables - these describe the maximum shape change for a particular morphing strategy. The sub-level optimization problems represent each segment in the morphing aircraft's design mission; here, each sub-level optimizer minimizes fuel consumed during each mission segment by changing the wing planform within the bounds set by the baseline and morphing limit variables from the top-level problem.

Segmentation of Brain Lesions in MRI and CT Scan Images: A Hybrid Approach Using k-Means Clustering and Image Morphology

NASA Astrophysics Data System (ADS)

Agrawal, Ritu; Sharma, Manisha; Singh, Bikesh Kumar

2018-04-01

Manual segmentation and analysis of lesions in medical images is time consuming and subjected to human errors. Automated segmentation has thus gained significant attention in recent years. This article presents a hybrid approach for brain lesion segmentation in different imaging modalities by combining median filter, k means clustering, Sobel edge detection and morphological operations. Median filter is an essential pre-processing step and is used to remove impulsive noise from the acquired brain images followed by k-means segmentation, Sobel edge detection and morphological processing. The performance of proposed automated system is tested on standard datasets using performance measures such as segmentation accuracy and execution time. The proposed method achieves a high accuracy of 94% when compared with manual delineation performed by an expert radiologist. Furthermore, the statistical significance test between lesion segmented using automated approach and that by expert delineation using ANOVA and correlation coefficient achieved high significance values of 0.986 and 1 respectively. The experimental results obtained are discussed in lieu of some recently reported studies.

A speeded-up saliency region-based contrast detection method for small targets

NASA Astrophysics Data System (ADS)

Li, Zhengjie; Zhang, Haiying; Bai, Jiaojiao; Zhou, Zhongjun; Zheng, Huihuang

2018-04-01

To cope with the rapid development of the real applications for infrared small targets, the researchers have tried their best to pursue more robust detection methods. At present, the contrast measure-based method has become a promising research branch. Following the framework, in this paper, a speeded-up contrast measure scheme is proposed based on the saliency detection and density clustering. First, the saliency region is segmented by saliency detection method, and then, the Multi-scale contrast calculation is carried out on it instead of traversing the whole image. Second, the target with a certain "integrity" property in spatial is exploited to distinguish the target from the isolated noises by density clustering. Finally, the targets are detected by a self-adaptation threshold. Compared with time-consuming MPCM (Multiscale Patch Contrast Map), the time cost of the speeded-up version is within a few seconds. Additional, due to the use of "clustering segmentation", the false alarm caused by heavy noises can be restrained to a lower level. The experiments show that our method has a satisfied FASR (False alarm suppression ratio) and real-time performance compared with the state-of-art algorithms no matter in cloudy sky or sea-sky background.

A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling.

PubMed

Shahedi, Maysam; Halicek, Martin; Guo, Rongrong; Zhang, Guoyi; Schuster, David M; Fei, Baowei

2018-06-01

Prostate segmentation in computed tomography (CT) images is useful for treatment planning and procedure guidance such as external beam radiotherapy and brachytherapy. However, because of the low, soft tissue contrast of CT images, manual segmentation of the prostate is a time-consuming task with high interobserver variation. In this study, we proposed a semiautomated, three-dimensional (3D) segmentation for prostate CT images using shape and texture analysis and we evaluated the method against manual reference segmentations. The prostate gland usually has a globular shape with a smoothly curved surface, and its shape could be accurately modeled or reconstructed having a limited number of well-distributed surface points. In a training dataset, using the prostate gland centroid point as the origin of a coordination system, we defined an intersubject correspondence between the prostate surface points based on the spherical coordinates. We applied this correspondence to generate a point distribution model for prostate shape using principal component analysis and to study the local texture difference between prostate and nonprostate tissue close to the different prostate surface subregions. We used the learned shape and texture characteristics of the prostate in CT images and then combined them with user inputs to segment a new image. We trained our segmentation algorithm using 23 CT images and tested the algorithm on two sets of 10 nonbrachytherapy and 37 postlow dose rate brachytherapy CT images. We used a set of error metrics to evaluate the segmentation results using two experts' manual reference segmentations. For both nonbrachytherapy and post-brachytherapy image sets, the average measured Dice similarity coefficient (DSC) was 88% and the average mean absolute distance (MAD) was 1.9 mm. The average measured differences between the two experts on both datasets were 92% (DSC) and 1.1 mm (MAD). The proposed, semiautomatic segmentation algorithm showed a fast, robust, and accurate performance for 3D prostate segmentation of CT images, specifically when no previous, intrapatient information, that is, previously segmented images, was available. The accuracy of the algorithm is comparable to the best performance results reported in the literature and approaches the interexpert variability observed in manual segmentation. © 2018 American Association of Physicists in Medicine.

Determining the functional form of density dependence: deductive approaches for consumer-resource systems having a single resource.

PubMed

Abrams, Peter A

2009-09-01

Consumer-resource models are used to deduce the functional form of density dependence in the consumer population. A general approach to determining the form of consumer density dependence is proposed; this involves determining the equilibrium (or average) population size for a series of different harvest rates. The relationship between a consumer's mortality and its equilibrium population size is explored for several one-consumer/one-resource models. The shape of density dependence in the resource and the shape of the numerical and functional responses all tend to be "inherited" by the consumer's density dependence. Consumer-resource models suggest that density dependence will very often have both concave and convex segments, something that is impossible under the commonly used theta-logistic model. A range of consumer-resource models predicts that consumer population size often declines at a decelerating rate with mortality at low mortality rates, is insensitive to or increases with mortality over a wide range of intermediate mortalities, and declines at a rapidly accelerating rate with increased mortality when mortality is high. This has important implications for management and conservation of natural populations.

Basic Concepts and Principles of Marketing.

ERIC Educational Resources Information Center

Beder, Hal

1986-01-01

Presents an overview of marketing concepts and principles. These include (1) organizational objectives, (2) exchange, (3) value, (4) market segmentation, (5) market position, (6) consumer analysis, (7) product, (8) promotion, (9) place, and (10) price. (CH)

A real-time visual inspection method of fastening bolts in freight car operation

NASA Astrophysics Data System (ADS)

Nan, Guo; Yao, JunEn

2015-10-01

A real-time inspection of the key components is necessary for ensuring safe operation of freight car. While traditional inspection depends on the trained human inspectors, which is time-consuming and lower efficient. With the development of machine vision, vision-based inspection methods get more railway on-spot applications. The cross rod end fastening bolts are important components on both sides of the train body that fixing locking plates together with the freight car main structure. In our experiment, we get the images containing fastening bolt components, and accurately locate the locking plate position using a linear Support Vector Machine (SVM) locating model trained with Histograms of Oriented Gradients (HOG) features. Then we extract the straight line segment using the Line Segment Detector (LSD) and encoding them in a range, which constitute a straight line segment dataset. Lastly we determine the locking plate's working state by the linear pattern. The experiment result shows that the localization accurate rate is over 99%, the fault detection rate is over 95%, and the module implementation time is 2f/s. The overall performance can completely meet the practical railway safety assurance application.

Study of the polysemic term of minerality in wine: Segmentation of consumers based on their textual responses to an open-ended survey.

PubMed

Deneulin, Pascale; Le Fur, Yves; Bavaud, François

2016-12-01

Over the past 20years, the word "minerality" has been increasingly used in the description of wines. However, a precise definition of the concept of minerality appears to be inexistent, and no consensual meaning, even among wine professionals, can be identified. Although this word usage seems to spread out from wine professionals to consumers, research on what consumers assume about minerality is scarce. This paper aims to study the various concepts about minerality held by consumers by using an open-ended questionnaire. A total of 1697 French-speaking consumers responded to an online survey and their free answers were analysed using statistical textual methods. The clustering around latent variables (CLV) method was used, taking into account both the lexicon used and the personal characteristics of consumers to classify them. Word associativities were then computed by means of renormalized Markov associativities, generating textual networks associated to each group, as well as to personal characteristics of the consumers. Typically, the most inexperienced consumers confess to have never heard about minerality in wine. Then, young women, also endowed with little wine competences, mainly associate minerality to mineral ions as those found in bottled water. Slightly older consumers embed the concept of minerality into the idea of terroir. Finally, the most experienced consumers refer to sensory perceptions such as gunflint or acidity. Those findings are consistent with a lexical innovation process, diffusing from wine professionals to consumers, referring to the mineral kingdom (as opposed to animal or vegetal), and aiming to stress that the style of their wines has changed towards more subtlety. Beyond the specific minerality issue investigated in this paper, the methodology (CLV approach used in conjunction with renormalized Markov associativities) demonstrates its ability to generate informative clusters of textual networks, highlighting the cores of prototypical sentences, and apt to investigate the meaning of new concepts. Copyright © 2016 Elsevier Ltd. All rights reserved.

Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Akhbardeh, Alireza; Jacobs, Michael A.; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205

2012-04-15

Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), andmore » diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B{sub 1} inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment both synthetic and clinical data. In the synthetic data, the authors demonstrated the performance of the NLDR method compared with conventional linear DR methods. The NLDR approach enabled successful segmentation of the structures, whereas, in most cases, PCA and MDS failed. The NLDR approach was able to segment different breast tissue types with a high accuracy and the embedded image of the breast MRI data demonstrated fuzzy boundaries between the different types of breast tissue, i.e., fatty, glandular, and tissue with lesions (>86%). Conclusions: The proposed hybrid NLDR methods were able to segment clinical breast data with a high accuracy and construct an embedded image that visualized the contribution of different radiological parameters.« less

Automated segmentation of thyroid gland on CT images with multi-atlas label fusion and random classification forest

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Chang, Kevin; Kim, Lauren; Turkbey, Evrim; Lu, Le; Yao, Jianhua; Summers, Ronald

2015-03-01

The thyroid gland plays an important role in clinical practice, especially for radiation therapy treatment planning. For patients with head and neck cancer, radiation therapy requires a precise delineation of the thyroid gland to be spared on the pre-treatment planning CT images to avoid thyroid dysfunction. In the current clinical workflow, the thyroid gland is normally manually delineated by radiologists or radiation oncologists, which is time consuming and error prone. Therefore, a system for automated segmentation of the thyroid is desirable. However, automated segmentation of the thyroid is challenging because the thyroid is inhomogeneous and surrounded by structures that have similar intensities. In this work, the thyroid gland segmentation is initially estimated by multi-atlas label fusion algorithm. The segmentation is refined by supervised statistical learning based voxel labeling with a random forest algorithm. Multiatlas label fusion (MALF) transfers expert-labeled thyroids from atlases to a target image using deformable registration. Errors produced by label transfer are reduced by label fusion that combines the results produced by all atlases into a consensus solution. Then, random forest (RF) employs an ensemble of decision trees that are trained on labeled thyroids to recognize features. The trained forest classifier is then applied to the thyroid estimated from the MALF by voxel scanning to assign the class-conditional probability. Voxels from the expert-labeled thyroids in CT volumes are treated as positive classes; background non-thyroid voxels as negatives. We applied this automated thyroid segmentation system to CT scans of 20 patients. The results showed that the MALF achieved an overall 0.75 Dice Similarity Coefficient (DSC) and the RF classification further improved the DSC to 0.81.

Automated segmentation of multifocal basal ganglia T2*-weighted MRI hypointensities

PubMed Central

Glatz, Andreas; Bastin, Mark E.; Kiker, Alexander J.; Deary, Ian J.; Wardlaw, Joanna M.; Valdés Hernández, Maria C.

2015-01-01

Multifocal basal ganglia T2*-weighted (T2*w) hypointensities, which are believed to arise mainly from vascular mineralization, were recently proposed as a novel MRI biomarker for small vessel disease and ageing. These T2*w hypointensities are typically segmented semi-automatically, which is time consuming, associated with a high intra-rater variability and low inter-rater agreement. To address these limitations, we developed a fully automated, unsupervised segmentation method for basal ganglia T2*w hypointensities. This method requires conventional, co-registered T2*w and T1-weighted (T1w) volumes, as well as region-of-interest (ROI) masks for the basal ganglia and adjacent internal capsule generated automatically from T1w MRI. The basal ganglia T2*w hypointensities were then segmented with thresholds derived with an adaptive outlier detection method from respective bivariate T2*w/T1w intensity distributions in each ROI. Artefacts were reduced by filtering connected components in the initial masks based on their standardised T2*w intensity variance. The segmentation method was validated using a custom-built phantom containing mineral deposit models, i.e. gel beads doped with 3 different contrast agents in 7 different concentrations, as well as with MRI data from 98 community-dwelling older subjects in their seventies with a wide range of basal ganglia T2*w hypointensities. The method produced basal ganglia T2*w hypointensity masks that were in substantial volumetric and spatial agreement with those generated by an experienced rater (Jaccard index = 0.62 ± 0.40). These promising results suggest that this method may have use in automatic segmentation of basal ganglia T2*w hypointensities in studies of small vessel disease and ageing. PMID:25451469

In vitro propagation of olive (Olea europaea L.) by nodal segmentation of elongated shoots.

PubMed

Lambardi, Maurizio; Ozudogru, Elif Aylin; Roncasaglia, Romano

2013-01-01

Olive (Olea europaea L.), long-living, ever-green fruit tree of the Old World, has been part of a traditional landscape in the Mediterranean area for centuries. Both the fruits consumed after processing and the oil extracted from the fruits are among the main components of the Mediterranean diet, widely used for salads and cooking, as well as for preserving other food. Documentations show that the ancient use of this beautiful tree also includes lamp fuel production, wool treatment, soap production, medicine, and cosmetics. However, unlike the majority of the fruit species, olive propagation is still a laborious practice. As regards traditional propagation, rooting of cuttings and grafting stem segments onto rootstocks are possible, former being achieved only when the cuttings are collected in specific periods (spring or beginning of autumn), and latter only when skilled grafters are available. In both the cases, performance of the cultivars varies considerably. The regeneration of whole plants from ovules, on the other hand, is used only occasionally. Micropropagation of olive is not easy mainly due to explant oxidation, difficulties in explant disinfection, and labor-oriented establishment of in vitro shoot cultures. However today, the progress in micropropagation technology has made available the complete protocols for several Mediterranean cultivars. This chapter describes a micropropagation protocol based on the segmentation of nodal segments obtained from elongated shoots.

Automated Inspection of Power Line Corridors to Measure Vegetation Undercut Using Uav-Based Images

NASA Astrophysics Data System (ADS)

Maurer, M.; Hofer, M.; Fraundorfer, F.; Bischof, H.

2017-08-01

Power line corridor inspection is a time consuming task that is performed mostly manually. As the development of UAVs made huge progress in recent years, and photogrammetric computer vision systems became well established, it is time to further automate inspection tasks. In this paper we present an automated processing pipeline to inspect vegetation undercuts of power line corridors. For this, the area of inspection is reconstructed, geo-referenced, semantically segmented and inter class distance measurements are calculated. The presented pipeline performs an automated selection of the proper 3D reconstruction method for on the one hand wiry (power line), and on the other hand solid objects (surrounding). The automated selection is realized by performing pixel-wise semantic segmentation of the input images using a Fully Convolutional Neural Network. Due to the geo-referenced semantic 3D reconstructions a documentation of areas where maintenance work has to be performed is inherently included in the distance measurements and can be extracted easily. We evaluate the influence of the semantic segmentation according to the 3D reconstruction and show that the automated semantic separation in wiry and dense objects of the 3D reconstruction routine improves the quality of the vegetation undercut inspection. We show the generalization of the semantic segmentation to datasets acquired using different acquisition routines and to varied seasons in time.

Toward accurate and fast iris segmentation for iris biometrics.

PubMed

He, Zhaofeng; Tan, Tieniu; Sun, Zhenan; Qiu, Xianchao

2009-09-01

Iris segmentation is an essential module in iris recognition because it defines the effective image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search of a large parameter space, which is time consuming and sensitive to noise. To address these problems, this paper presents a novel algorithm for accurate and fast iris segmentation. After efficient reflection removal, an Adaboost-cascade iris detector is first built to extract a rough position of the iris center. Edge points of iris boundaries are then detected, and an elastic model named pulling and pushing is established. Under this model, the center and radius of the circular iris boundaries are iteratively refined in a way driven by the restoring forces of Hooke's law. Furthermore, a smoothing spline-based edge fitting scheme is presented to deal with noncircular iris boundaries. After that, eyelids are localized via edge detection followed by curve fitting. The novelty here is the adoption of a rank filter for noise elimination and a histogram filter for tackling the shape irregularity of eyelids. Finally, eyelashes and shadows are detected via a learned prediction model. This model provides an adaptive threshold for eyelash and shadow detection by analyzing the intensity distributions of different iris regions. Experimental results on three challenging iris image databases demonstrate that the proposed algorithm outperforms state-of-the-art methods in both accuracy and speed.

Interactive approach to segment organs at risk in radiotherapy treatment planning

NASA Astrophysics Data System (ADS)

Dolz, Jose; Kirisli, Hortense A.; Viard, Romain; Massoptier, Laurent

2014-03-01

Accurate delineation of organs at risk (OAR) is required for radiation treatment planning (RTP). However, it is a very time consuming and tedious task. The use in clinic of image guided radiation therapy (IGRT) becomes more and more popular, thus increasing the need of (semi-)automatic methods for delineation of the OAR. In this work, an interactive segmentation approach to delineate OAR is proposed and validated. The method is based on the combination of watershed transformation, which groups small areas of similar intensities in homogeneous labels, and graph cuts approach, which uses these labels to create the graph. Segmentation information can be added in any view - axial, sagittal or coronal -, making the interaction with the algorithm easy and fast. Subsequently, this information is propagated within the whole volume, providing a spatially coherent result. Manual delineations made by experts of 6 OAR - lungs, kidneys, liver, spleen, heart and aorta - over a set of 9 computed tomography (CT) scans were used as reference standard to validate the proposed approach. With a maximum of 4 interactions, a Dice similarity coefficient (DSC) higher than 0.87 was obtained, which demonstrates that, with the proposed segmentation approach, only few interactions are required to achieve similar results as the ones obtained manually. The integration of this method in the RTP process may save a considerable amount of time, and reduce the annotation complexity.

Consumer understanding and use of health claims: the case of functional foods.

PubMed

Annunziata, Azzurra; Mariani, Angela; Vecchio, Riccardo

2014-01-01

As widely acknowledged functional foods (FFs) may contribute to improve human health due to the presence of specific components useful for their protective action against several diseases. However it is essential that consumers are able to comprehend and assess the properties of FFs health claims play a central role in helping consumers to select among food alternatives, beyond providing protection against unsupported or misleading statements about foods properties. At the same time health claims are the main marketing tool that the food industry could use to differentiate FFs from other products. Clearly, massive investments in research and development are necessary to enter the FF market segment, together with the possibility to protect innovation through patents. Current paper aims to examine factors influencing consumer understanding and use of food health claims on FFs, as well as providing several indications for developers, marketers and policy makers. After a brief review of the literature the results of a quantitative survey conducted online on 650 Italian consumers are presented. Results show that consumer use and understanding of health claims on FFs depend on different variables such as socio-demographic characteristics, knowledge and confidence with nutrition information but also wording and variables related specifically to the product. Furthermore, different segments with a diverse degree of use and understanding of health claims have been identified. Therefore, to boost market growth, more efforts are needed by policy makers and marketers to provide better information on nutrition and health aspects of FF using an approach capable to ensure truthful, significant and clear information. Finally some recent patents related to the FFs market with specific regard to components and/or functionality investigated in the current paper are reviewed.

Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen; Liu, Yuan; Liang, Fuxun; Wang, Yongjun

2017-04-01

In recent years, updating the inventory of road infrastructures based on field work is labor intensive, time consuming, and costly. Fortunately, vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. However, robust recognition of road facilities from huge volumes of 3D point clouds is still a challenging issue because of complicated and incomplete structures, occlusions and varied point densities. Most existing methods utilize point or object based features to recognize object candidates, and can only extract limited types of objects with a relatively low recognition rate, especially for incomplete and small objects. To overcome these drawbacks, this paper proposes a semantic labeling framework by combing multiple aggregation levels (point-segment-object) of features and contextual features to recognize road facilities, such as road surfaces, road boundaries, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, and cars, for highway infrastructure inventory. The proposed method first identifies ground and non-ground points, and extracts road surfaces facilities from ground points. Non-ground points are segmented into individual candidate objects based on the proposed multi-rule region growing method. Then, the multiple aggregation levels of features and the contextual features (relative positions, relative directions, and spatial patterns) associated with each candidate object are calculated and fed into a SVM classifier to label the corresponding candidate object. The recognition performance of combining multiple aggregation levels and contextual features was compared with single level (point, segment, or object) based features using large-scale highway scene point clouds. Comparative studies demonstrated that the proposed semantic labeling framework significantly improves road facilities recognition precision (90.6%) and recall (91.2%), particularly for incomplete and small objects.

Small rural hospitals: an example of market segmentation analysis.

PubMed

Mainous, A G; Shelby, R L

1991-01-01

In recent years, market segmentation analysis has shown increased popularity among health care marketers, although marketers tend to focus upon hospitals as sellers. The present analysis suggests that there is merit to viewing hospitals as a market of consumers. Employing a random sample of 741 small rural hospitals, the present investigation sought to determine, through the use of segmentation analysis, the variables associated with hospital success (occupancy). The results of a discriminant analysis yielded a model which classifies hospitals with a high degree of predictive accuracy. Successful hospitals have more beds and employees, and are generally larger and have more resources. However, there was no significant relationship between organizational success and number of services offered by the institution.

Software for hyperspectral, joint photographic experts group (.JPG), portable network graphics (.PNG) and tagged image file format (.TIFF) segmentation

NASA Astrophysics Data System (ADS)

Bruno, L. S.; Rodrigo, B. P.; Lucio, A. de C. Jorge

2016-10-01

This paper presents a system developed by an application of a neural network Multilayer Perceptron for drone acquired agricultural image segmentation. This application allows a supervised user training the classes that will posteriorly be interpreted by neural network. These classes will be generated manually with pre-selected attributes in the application. After the attribute selection a segmentation process is made to allow the relevant information extraction for different types of images, RGB or Hyperspectral. The application allows extracting the geographical coordinates from the image metadata, geo referencing all pixels on the image. In spite of excessive memory consume on hyperspectral images regions of interest, is possible to perform segmentation, using bands chosen by user that can be combined in different ways to obtain different results.

Generalized expectation-maximization segmentation of brain MR images

NASA Astrophysics Data System (ADS)

Devalkeneer, Arnaud A.; Robe, Pierre A.; Verly, Jacques G.; Phillips, Christophe L. M.

2006-03-01

Manual segmentation of medical images is unpractical because it is time consuming, not reproducible, and prone to human error. It is also very difficult to take into account the 3D nature of the images. Thus, semi- or fully-automatic methods are of great interest. Current segmentation algorithms based on an Expectation- Maximization (EM) procedure present some limitations. The algorithm by Ashburner et al., 2005, does not allow multichannel inputs, e.g. two MR images of different contrast, and does not use spatial constraints between adjacent voxels, e.g. Markov random field (MRF) constraints. The solution of Van Leemput et al., 1999, employs a simplified model (mixture coefficients are not estimated and only one Gaussian is used by tissue class, with three for the image background). We have thus implemented an algorithm that combines the features of these two approaches: multichannel inputs, intensity bias correction, multi-Gaussian histogram model, and Markov random field (MRF) constraints. Our proposed method classifies tissues in three iterative main stages by way of a Generalized-EM (GEM) algorithm: (1) estimation of the Gaussian parameters modeling the histogram of the images, (2) correction of image intensity non-uniformity, and (3) modification of prior classification knowledge by MRF techniques. The goal of the GEM algorithm is to maximize the log-likelihood across the classes and voxels. Our segmentation algorithm was validated on synthetic data (with the Dice metric criterion) and real data (by a neurosurgeon) and compared to the original algorithms by Ashburner et al. and Van Leemput et al. Our combined approach leads to more robust and accurate segmentation.

Fully-integrated framework for the segmentation and registration of the spinal cord white and gray matter.

PubMed

Dupont, Sara M; De Leener, Benjamin; Taso, Manuel; Le Troter, Arnaud; Nadeau, Sylvie; Stikov, Nikola; Callot, Virginie; Cohen-Adad, Julien

2017-04-15

The spinal cord white and gray matter can be affected by various pathologies such as multiple sclerosis, amyotrophic lateral sclerosis or trauma. Being able to precisely segment the white and gray matter could help with MR image analysis and hence be useful in further understanding these pathologies, and helping with diagnosis/prognosis and drug development. Up to date, white/gray matter segmentation has mostly been done manually, which is time consuming, induces a bias related to the rater and prevents large-scale multi-center studies. Recently, few methods have been proposed to automatically segment the spinal cord white and gray matter. However, no single method exists that combines the following criteria: (i) fully automatic, (ii) works on various MRI contrasts, (iii) robust towards pathology and (iv) freely available and open source. In this study we propose a multi-atlas based method for the segmentation of the spinal cord white and gray matter that addresses the previous limitations. Moreover, to study the spinal cord morphology, atlas-based approaches are increasingly used. These approaches rely on the registration of a spinal cord template to an MR image, however the registration usually doesn't take into account the spinal cord internal structure and thus lacks accuracy. In this study, we propose a new template registration framework that integrates the white and gray matter segmentation to account for the specific gray matter shape of each individual subject. Validation of segmentation was performed in 24 healthy subjects using T 2 * -weighted images, in 8 healthy subjects using diffusion weighted images (exhibiting inverted white-to-gray matter contrast compared to T 2 *-weighted), and in 5 patients with spinal cord injury. The template registration was validated in 24 subjects using T 2 *-weighted data. Results of automatic segmentation on T 2 *-weighted images was in close correspondence with the manual segmentation (Dice coefficient in the white/gray matter of 0.91/0.71 respectively). Similarly, good results were obtained in data with inverted contrast (diffusion-weighted image) and in patients. When compared to the classical template registration framework, the proposed framework that accounts for gray matter shape significantly improved the quality of the registration (comparing Dice coefficient in gray matter: p=9.5×10 -6 ). While further validation is needed to show the benefits of the new registration framework in large cohorts and in a variety of patients, this study provides a fully-integrated tool for quantitative assessment of white/gray matter morphometry and template-based analysis. All the proposed methods are implemented in the Spinal Cord Toolbox (SCT), an open-source software for processing spinal cord multi-parametric MRI data. Copyright © 2017 Elsevier Inc. All rights reserved.

Extremely frequent behavior in consumer research: theory and empirical evidence for chronic casino gambling.

PubMed

Perfetto, Ralph; Woodside, Arch G

2009-09-01

The present study informs understanding of customer segmentation strategies by extending Twedt's heavy-half propositions to include a segment of users that represent less than 2% of all households-consumers demonstrating extremely frequent behavior (EFB). Extremely frequent behavior (EFB) theory provides testable propositions relating to the observation that few (2%) consumers in many product and service categories constitute more than 25% of the frequency of product or service use. Using casino gambling as an example for testing EFB theory, an analysis of national survey data shows that extremely frequent casino gamblers do exist and that less than 2% of all casino gamblers are responsible for nearly 25% of all casino gambling usage. Approximately 14% of extremely frequent casino users have very low-household income, suggesting somewhat paradoxical consumption patterns (where do very low-income users find the money to gamble so frequently?). Understanding the differences light, heavy, and extreme users and non-users can help marketers and policymakers identify and exploit "blue ocean" opportunities (Kim and Mauborgne, Blue ocean strategy, Harvard Business School Press, Boston, 2005), for example, creating effective strategies to convert extreme users into non-users or non-users into new users.

Variations in mature market consumer behavior within a health care product: implications for marketing strategy.

PubMed

Hopper, J A; Busbin, J W

1995-01-01

America is undergoing a profound age shift in its demographic make-up with people 55 and over comprising an increasing proportion of the population. Marketers may need to increase their response rate to this shift, especially in refining the application of marketing theory and practice to older age consumers. To this end, a survey of older couple buying behavior for health insurance coverage is reported here. Results clarify evaluative criteria and the viability of multiple market segmentation for health care coverage among older consumers as couples. Commentary on the efficacy of present health coverage marketing programs is provided.

Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images.

PubMed

Wang, Jinke; Cheng, Yuanzhi; Guo, Changyong; Wang, Yadong; Tamura, Shinichi

2016-05-01

Propose a fully automatic 3D segmentation framework to segment liver on challenging cases that contain the low contrast of adjacent organs and the presence of pathologies from abdominal CT images. First, all of the atlases are weighted in the selected training datasets by calculating the similarities between the atlases and the test image to dynamically generate a subject-specific probabilistic atlas for the test image. The most likely liver region of the test image is further determined based on the generated atlas. A rough segmentation is obtained by a maximum a posteriori classification of probability map, and the final liver segmentation is produced by a shape-intensity prior level set in the most likely liver region. Our method is evaluated and demonstrated on 25 test CT datasets from our partner site, and its results are compared with two state-of-the-art liver segmentation methods. Moreover, our performance results on 10 MICCAI test datasets are submitted to the organizers for comparison with the other automatic algorithms. Using the 25 test CT datasets, average symmetric surface distance is [Formula: see text] mm (range 0.62-2.12 mm), root mean square symmetric surface distance error is [Formula: see text] mm (range 0.97-3.01 mm), and maximum symmetric surface distance error is [Formula: see text] mm (range 12.73-26.67 mm) by our method. Our method on 10 MICCAI test data sets ranks 10th in all the 47 automatic algorithms on the site as of July 2015. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our method is a promising tool to improve the efficiency of both techniques. The applicability of the proposed method to some challenging clinical problems and the segmentation of the liver are demonstrated with good results on both quantitative and qualitative experimentations. This study suggests that the proposed framework can be good enough to replace the time-consuming and tedious slice-by-slice manual segmentation approach.

Mining residential water and electricity demand data in Southern California to inform demand management strategies

NASA Astrophysics Data System (ADS)

Cominola, A.; Spang, E. S.; Giuliani, M.; Castelletti, A.; Loge, F. J.; Lund, J. R.

2016-12-01

Demand side management strategies are key to meet future water and energy demands in urban contexts, promote water and energy efficiency in the residential sector, provide customized services and communications to consumers, and reduce utilities' costs. Smart metering technologies allow gathering high temporal and spatial resolution water and energy consumption data and support the development of data-driven models of consumers' behavior. Modelling and predicting resource consumption behavior is essential to inform demand management. Yet, analyzing big, smart metered, databases requires proper data mining and modelling techniques, in order to extract useful information supporting decision makers to spot end uses towards which water and energy efficiency or conservation efforts should be prioritized. In this study, we consider the following research questions: (i) how is it possible to extract representative consumers' personalities out of big smart metered water and energy data? (ii) are residential water and energy consumption profiles interconnected? (iii) Can we design customized water and energy demand management strategies based on the knowledge of water- energy demand profiles and other user-specific psychographic information? To address the above research questions, we contribute a data-driven approach to identify and model routines in water and energy consumers' behavior. We propose a novel customer segmentation procedure based on data-mining techniques. Our procedure consists of three steps: (i) extraction of typical water-energy consumption profiles for each household, (ii) profiles clustering based on their similarity, and (iii) evaluation of the influence of candidate explanatory variables on the identified clusters. The approach is tested onto a dataset of smart metered water and energy consumption data from over 1000 households in South California. Our methodology allows identifying heterogeneous groups of consumers from the studied sample, as well as characterizing them with respect to consumption profiles features and socio- demographic information. Results show how such better understanding of the considered users' community allows spotting potentially interesting areas for water and energy demand management interventions.

Consumer acceptance of dishes in which beef has been partially substituted with mushrooms and sodium has been reduced.

PubMed

Guinard, Jean-Xavier; Myrdal Miller, Amy; Mills, Kelly; Wong, Thomas; Lee, Soh Min; Sirimuangmoon, Chirat; Schaefer, Sarah E; Drescher, Greg

2016-10-01

We tested the hypothesis that because of their flavor-enhancing properties, mushrooms could be used as a healthy substitute for meat and a mitigating agent for sodium (salt) reduction without reduction in sensory appeal among consumers. In a fully-randomized design for each product, 147 consumers evaluated blind two carne asada and six taco blend recipes in which beef had been partially substituted with mushrooms and/or salt had been reduced by 25%, for overall liking, liking of appearance, flavor, texture and mouth feel on the 9-point hedonic scale, and adequacy of level of saltiness, spiciness and moistness on 5-point just-about-right (JAR) scales. Overall consumer acceptance of the carne asada, and liking for its appearance, flavor and texture/mouth feel decreased significantly when half the steak was substituted with mushrooms. The taco blend recipes with full sodium were also liked more overall than those with 25% less sodium. But there was no significant difference in overall liking among the three full-salt recipes, nor among the three reduced-salt recipes, indicating that across the consumer population we tested, acceptance of the mushroom-containing recipes was on par with that of the 100% beef recipe. The preference mapping analysis of the overall liking ratings of the taco blends uncovered four preference segments, two of which, representing a majority of the consumers, gave higher acceptance scores to the mushroom-substituted recipes. Furthermore, the largest preference segment liked the full- and reduced-sodium recipes equally, and another liked the reduced-sodium recipes significantly more. This research demonstrates that through their flavor enhancing properties, mushrooms can be used successfully to substitute for beef and even possibly mitigate sodium reduction without significant change in acceptance for a majority of consumers. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images.

PubMed

Abdulhay, Enas; Mohammed, Mazin Abed; Ibrahim, Dheyaa Ahmed; Arunkumar, N; Venkatraman, V

2018-02-17

Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise. We suggest a strategy towards segmentation of blood leucocytes using static microscope images which is a resultant of three prevailing systems of computer vision fiction: enhancing the image, Support vector machine for segmenting the image, and filtering out non ROI (region of interest) on the basis of Local binary patterns and texture features. Every one of these strategies are modified for blood leucocytes division issue, in this manner the subsequent techniques are very vigorous when compared with its individual segments. Eventually, we assess framework based by compare the outcome and manual division. The findings outcome from this study have shown a new approach that automatically segments the blood leucocytes and identify it from a static microscope images. Initially, the method uses a trainable segmentation procedure and trained support vector machine classifier to accurately identify the position of the ROI. After that, filtering out non ROI have proposed based on histogram analysis to avoid the non ROI and chose the right object. Finally, identify the blood leucocytes type using the texture feature. The performance of the foreseen approach has been tried in appearing differently in relation to the system against manual examination by a gynaecologist utilizing diverse scales. A total of 100 microscope images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual segmentation method for accurately determining the ROI. We have evaluated the blood leucocytes identification using the ROI texture (LBP Feature). The identification accuracy in the technique used is about 95.3%., with 100 sensitivity and 91.66% specificity.

Semantic Segmentation and Unregistered Building Detection from Uav Images Using a Deconvolutional Network

NASA Astrophysics Data System (ADS)

Ham, S.; Oh, Y.; Choi, K.; Lee, I.

2018-05-01

Detecting unregistered buildings from aerial images is an important task for urban management such as inspection of illegal buildings in green belt or update of GIS database. Moreover, the data acquisition platform of photogrammetry is evolving from manned aircraft to UAVs (Unmanned Aerial Vehicles). However, it is very costly and time-consuming to detect unregistered buildings from UAV images since the interpretation of aerial images still relies on manual efforts. To overcome this problem, we propose a system which automatically detects unregistered buildings from UAV images based on deep learning methods. Specifically, we train a deconvolutional network with publicly opened geospatial data, semantically segment a given UAV image into a building probability map and compare the building map with existing GIS data. Through this procedure, we could detect unregistered buildings from UAV images automatically and efficiently. We expect that the proposed system can be applied for various urban management tasks such as monitoring illegal buildings or illegal land-use change.

Which dietary patterns are more likely to be associated with aspects of eco-sustainable food behaviours in Italy?

PubMed

Scalvedi, Maria Luisa; Turrini, Aida; Saba, Anna

2017-12-04

Sustainable food consumption (SFC) policies need further investigation into eating habits to improve interventions to encourage shifting to new consumption patterns respectful of human rights, environment and health. Reversing the usual approach focussed on sustainable consumer, the present study investigates how different eating patterns relate to eco-sustainable food. A cluster analysis was carried out on consumption frequencies of food groups recorded in an Italian national survey on 3004 respondents, providing four eating habit segments, further investigated as for sustainable food attitude and behaviour. Openness to eco-sustainable food is found mostly in the more balanced diet segment, accounting for about one third of the adult Italian population. Inaccessibility, non-affordability, unhealthy diet and a lack of information still negatively condition eating habits to the detriment of more sustainable consumption. These findings could support SFC stakeholders in targeting policies and strategies based on diversified approaches to enhance awareness of SFC issues.

Demand side management in recycling and electricity retail pricing

NASA Astrophysics Data System (ADS)

Kazan, Osman

This dissertation addresses several problems from the recycling industry and electricity retail market. The first paper addresses a real-life scheduling problem faced by a national industrial recycling company. Based on their practices, a scheduling problem is defined, modeled, analyzed, and a solution is approximated efficiently. The recommended application is tested on the real-life data and randomly generated data. The scheduling improvements and the financial benefits are presented. The second problem is from electricity retail market. There are well-known patterns in daily usage in hours. These patterns change in shape and magnitude by seasons and days of the week. Generation costs are multiple times higher during the peak hours of the day. Yet most consumers purchase electricity at flat rates. This work explores analytic pricing tools to reduce peak load electricity demand for retailers. For that purpose, a nonlinear model that determines optimal hourly prices is established based on two major components: unit generation costs and consumers' utility. Both are analyzed and estimated empirically in the third paper. A pricing model is introduced to maximize the electric retailer's profit. As a result, a closed-form expression for the optimal price vector is obtained. Possible scenarios are evaluated for consumers' utility distribution. For the general case, we provide a numerical solution methodology to obtain the optimal pricing scheme. The models recommended are tested under various scenarios that consider consumer segmentation and multiple pricing policies. The recommended model reduces the peak load significantly in most cases. Several utility companies offer hourly pricing to their customers. They determine prices using historical data of unit electricity cost over time. In this dissertation we develop a nonlinear model that determines optimal hourly prices with parameter estimation. The last paper includes a regression analysis of the unit generation cost function obtained from Independent Service Operators. A consumer experiment is established to replicate the peak load behavior. As a result, consumers' utility function is estimated and optimal retail electricity prices are computed.

Influences of packaging attributes on consumer purchase decisions for fresh produce.

PubMed

Koutsimanis, Georgios; Getter, Kristin; Behe, Bridget; Harte, Janice; Almenar, Eva

2012-10-01

Packaging attributes are considered to have an influence on consumer purchase decisions for food and, as a consequence, also on its consumption. To improve the current minimal understanding of these influences for fresh produce, a survey instrument in the form of an online questionnaire has been developed and launched in the US. The first part of the questionnaire covers consumers' preferences for packaging convenience features, characteristics, materials, disposal method, and others for fresh produces in general, and the second focuses on attributes like price, container size, produce shelf life for a specific fresh produce, sweet cherries, to allow us to supply specific values for these factors to the participants. Cluster and conjoint analyses of responses from 292 participants reveal that specific packaging and produce attributes affect consumer purchase decisions of fresh produce in general and of sweet cherries in particular (P ≤ 0.05) and that some are population segment dependent (P ≤ 0.05). For produce packaging in general, 'extend the "best by" date' was ranked as the top convenience feature, the type of packaging material was considered to affect the food product quality (92.7%) and containers made from bio-based materials were highly appealing (3.52 out of 5.00). The most important attributes that affect the purchasing decisions of consumers regarding a specific fresh produce like sweet cherries are price (25%), shelf life (19%) and container size (17.2%). Copyright © 2012 Elsevier Ltd. All rights reserved.

Automatic segmentation of the bone and extraction of the bone cartilage interface from magnetic resonance images of the knee

NASA Astrophysics Data System (ADS)

Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K.; Ourselin, Sébastien

2007-03-01

The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis.

Drug Dependency: A Legacy from the Past.

ERIC Educational Resources Information Center

Labianca, Dominick A.; Reeves, William J.

1984-01-01

In the nineteenth century people consumed opium in the form of laudanum to relieve their anxieties. Today drug abuse has become a problem of epic proportions. For a segment of our society, chemicals represent relief from physical and mental pain. (CS)

Rapid totally diverting loop sigmoid colostomy with noncontaminating rectal irrigation.

PubMed

Sachatello, C R; Maull, K I

1977-08-01

Loop sigmoid colostomy employing a stapling device and catheter irrigation of the distal segment is less time-consuming and has lest potential for contamination than the standard double-barrel colostomy. Unlike the standard loop colostomy, it is totally diverting.

Chilled milk-based desserts as emerging probiotic and prebiotic products.

PubMed

Buriti, Flávia C A; Saad, Susana M I

2014-01-01

Nowadays, food companies are endeavoring to differentiate their products through creative segmentation and positioning strategies based on superior functionality and quality. Some kinds of dairy desserts have shown a great market potential, as a function of consumers interested in healthier and functional products with fine taste and mouthfeel. In this context, chilled dairy desserts are emerging as attractive options for the incorporation of probiotic cultures and prebiotic ingredients, as seen in the previous launches from the food industry, as well as in the growing number of scientific studies dealing with this subject published in the last years. The main aspects involved in the development of probiotic and/or prebiotic dairy desserts for storage under refrigerated conditions are presented in this review.

Automatic testing and assessment of neuroanatomy using a digital brain atlas: method and development of computer- and mobile-based applications.

PubMed

Nowinski, Wieslaw L; Thirunavuukarasuu, Arumugam; Ananthasubramaniam, Anand; Chua, Beng Choon; Qian, Guoyu; Nowinska, Natalia G; Marchenko, Yevgen; Volkau, Ihar

2009-10-01

Preparation of tests and student's assessment by the instructor are time consuming. We address these two tasks in neuroanatomy education by employing a digital media application with a three-dimensional (3D), interactive, fully segmented, and labeled brain atlas. The anatomical and vascular models in the atlas are linked to Terminologia Anatomica. Because the cerebral models are fully segmented and labeled, our approach enables automatic and random atlas-derived generation of questions to test location and naming of cerebral structures. This is done in four steps: test individualization by the instructor, test taking by the students at their convenience, automatic student assessment by the application, and communication of the individual assessment to the instructor. A computer-based application with an interactive 3D atlas and a preliminary mobile-based application were developed to realize this approach. The application works in two test modes: instructor and student. In the instructor mode, the instructor customizes the test by setting the scope of testing and student performance criteria, which takes a few seconds. In the student mode, the student is tested and automatically assessed. Self-testing is also feasible at any time and pace. Our approach is automatic both with respect to test generation and student assessment. It is also objective, rapid, and customizable. We believe that this approach is novel from computer-based, mobile-based, and atlas-assisted standpoints.

Impact of communication on consumers' food choices.

PubMed

Verbeke, Wim

2008-08-01

Consumers' food choices and dietary behaviour can be markedly affected by communication and information. Whether the provided information is processed by the receiver, and thus becomes likely to be effective, depends on numerous factors. The role of selected determinants such as uncertainty, knowledge, involvement, health-related motives and trust, as well as message content variables, are discussed in the present paper based on previous empirical studies. The different studies indicate that: uncertainty about meat quality and safety does not automatically result in more active information search; subjective knowledge about fish is a better predictor of fish consumption than objective knowledge; high subjective knowledge about functional foods as a result of a low trusted information source such as mass media advertising leads to a lower probability of adopting these foods in the diet. Also, evidence of the stronger impact of negative news as compared with messages promoting positive outcomes of food choices is discussed. Finally, three audience-segmentation studies based on consumers' involvement with fresh meat, individuals' health-related-motive orientations and their use of and trust in fish information sources are presented. A clear message from these studies is that communication and information provision strategies targeted to a specific audience's needs, interests or motives stand a higher likelihood of being attended to and processed by the receiving audience, and therefore also stand a higher chance of yielding their envisaged impact in terms of food choice and dietary behaviour.

An exploration of the applicability of situational segmentation in the health care market: development of a situational taxonomy.

PubMed

Gehrt, K C; Pinto, M B

1990-01-01

Competition in the health care market has intensified in recent years. Health care providers are increasingly adopting innovative marketing techniques to secure their positions in the marketplace. This paper examines an innovative marketing technique, situational segmentation, and assesses its applicability to the health care market. Situational segmentation has proven useful in many consumer goods markets but has received little attention in the context of health care marketing. A two-stage research process is used to develop a taxonomy of situational factors pertinent to health care choice. In stage one, focus group interviews are used to gather information which is instrumental to questionnaire development. In stage two, the responses of 151 subjects to a 51 item questionnaire are factor analyzed. The results demonstrate that situational segmentation is a viable strategy in the health care market.

Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography

NASA Astrophysics Data System (ADS)

Yong, Yan Ling; Tan, Li Kuo; McLaughlin, Robert A.; Chee, Kok Han; Liew, Yih Miin

2017-12-01

Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback scans is challenging and time consuming. We propose a linear-regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space. Benchmarked against gold-standard manual segmentation, our proposed algorithm achieves average locational accuracy of the vessel wall of 22 microns, and 0.985 and 0.970 in Dice coefficient and Jaccard similarity index, respectively. The average absolute error of luminal area estimation is 1.38%. The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame.

A multiresolution prostate representation for automatic segmentation in magnetic resonance images.

PubMed

Alvarez, Charlens; Martínez, Fabio; Romero, Eduardo

2017-04-01

Accurate prostate delineation is necessary in radiotherapy processes for concentrating the dose onto the prostate and reducing side effects in neighboring organs. Currently, manual delineation is performed over magnetic resonance imaging (MRI) taking advantage of its high soft tissue contrast property. Nevertheless, as human intervention is a consuming task with high intra- and interobserver variability rates, (semi)-automatic organ delineation tools have emerged to cope with these challenges, reducing the time spent for these tasks. This work presents a multiresolution representation that defines a novel metric and allows to segment a new prostate by combining a set of most similar prostates in a dataset. The proposed method starts by selecting the set of most similar prostates with respect to a new one using the proposed multiresolution representation. This representation characterizes the prostate through a set of salient points, extracted from a region of interest (ROI) that encloses the organ and refined using structural information, allowing to capture main relevant features of the organ boundary. Afterward, the new prostate is automatically segmented by combining the nonrigidly registered expert delineations associated to the previous selected similar prostates using a weighted patch-based strategy. Finally, the prostate contour is smoothed based on morphological operations. The proposed approach was evaluated with respect to the expert manual segmentation under a leave-one-out scheme using two public datasets, obtaining averaged Dice coefficients of 82% ± 0.07 and 83% ± 0.06, and demonstrating a competitive performance with respect to atlas-based state-of-the-art methods. The proposed multiresolution representation provides a feature space that follows a local salient point criteria and a global rule of the spatial configuration among these points to find out the most similar prostates. This strategy suggests an easy adaptation in the clinical routine, as supporting tool for annotation. © 2017 American Association of Physicists in Medicine.

Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation.

PubMed

Saha, Monjoy; Chakraborty, Chandan

2018-05-01

We present an efficient deep learning framework for identifying, segmenting, and classifying cell membranes and nuclei from human epidermal growth factor receptor-2 (HER2)-stained breast cancer images with minimal user intervention. This is a long-standing issue for pathologists because the manual quantification of HER2 is error-prone, costly, and time-consuming. Hence, we propose a deep learning-based HER2 deep neural network (Her2Net) to solve this issue. The convolutional and deconvolutional parts of the proposed Her2Net framework consisted mainly of multiple convolution layers, max-pooling layers, spatial pyramid pooling layers, deconvolution layers, up-sampling layers, and trapezoidal long short-term memory (TLSTM). A fully connected layer and a softmax layer were also used for classification and error estimation. Finally, HER2 scores were calculated based on the classification results. The main contribution of our proposed Her2Net framework includes the implementation of TLSTM and a deep learning framework for cell membrane and nucleus detection, segmentation, and classification and HER2 scoring. Our proposed Her2Net achieved 96.64% precision, 96.79% recall, 96.71% F-score, 93.08% negative predictive value, 98.33% accuracy, and a 6.84% false-positive rate. Our results demonstrate the high accuracy and wide applicability of the proposed Her2Net in the context of HER2 scoring for breast cancer evaluation.

Consumer attitudes, knowledge, and consumption of organic yogurt.

PubMed

Van Loo, Ellen J; Diem, My Nguyen Hoang; Pieniak, Zuzanna; Verbeke, Wim

2013-04-01

The segment of organic products occupies an increasingly important place in dairy assortments. The European Union (EU) introduced a new EU organic logo in 2010 with the aim of harmonizing its organic sector and boosting consumer trust in organic food. This study focuses on organic yogurt and investigates consumer awareness and knowledge of the new EU logo. Consumers evaluate organic yogurt as superior compared with conventional yogurt on healthiness, environmental friendliness, quality, and safety. More frequent buyers of organic yogurt have a stronger belief that organic yogurt is superior. The willingness-to-pay for organic yogurt ranged from a premium of 15% for nonbuyers to 40% for habitual buyers, indicating the market potential for this product. A structural equations model reveals the positive association between knowledge, attitudes, and the frequency of purchasing and consuming organic yogurt. Nevertheless, consumer awareness of the EU organic logo remains rather low, which suggests a need for more effective information campaigns and marketing actions. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Heterogeneity and nonlinearity in consumers' preferences: An application to the olive oil shopping behavior in Chile.

PubMed

Romo-Muñoz, Rodrigo Alejandro; Cabas-Monje, Juan Hernán; Garrido-Henrríquez, Héctor Manuel; Gil, José María

2017-01-01

In relatively unknown products, consumers use prices as a quality reference. Under such circumstances, the utility function can be non-negative for a specific price range and generate an inverted U-shaped function. The extra virgin olive oil market in Chile is a good example. Although domestic production and consumption have increased significantly in the last few years, consumer knowledge of this product is still limited. The objective of this study was to analyze Chilean consumer preferences and willingness to pay for extra virgin olive oil attributes. Consumers were segmented taking into account purchasing frequency. A Random Parameter Logit model was estimated for preference heterogeneity. Results indicate that the utility function is nonlinear allowing us to differentiate between two regimes. In the first regime, olive oil behaves as a conspicuous good, that is, higher utility is assigned to higher prices and consumers prefer foreign products in smaller containers. Under the second regime, Chilean olive oil in larger containers is preferred.

Fuzzy classification for strawberry diseases-infection using machine vision and soft-computing techniques

NASA Astrophysics Data System (ADS)

Altıparmak, Hamit; Al Shahadat, Mohamad; Kiani, Ehsan; Dimililer, Kamil

2018-04-01

Robotic agriculture requires smart and doable techniques to substitute the human intelligence with machine intelligence. Strawberry is one of the important Mediterranean product and its productivity enhancement requires modern and machine-based methods. Whereas a human identifies the disease infected leaves by his eye, the machine should also be capable of vision-based disease identification. The objective of this paper is to practically verify the applicability of a new computer-vision method for discrimination between the healthy and disease infected strawberry leaves which does not require neural network or time consuming trainings. The proposed method was tested under outdoor lighting condition using a regular DLSR camera without any particular lens. Since the type and infection degree of disease is approximated a human brain a fuzzy decision maker classifies the leaves over the images captured on-site having the same properties of human vision. Optimizing the fuzzy parameters for a typical strawberry production area at a summer mid-day in Cyprus produced 96% accuracy for segmented iron deficiency and 93% accuracy for segmented using a typical human instant classification approximation as the benchmark holding higher accuracy than a human eye identifier. The fuzzy-base classifier provides approximate result for decision making on the leaf status as if it is healthy or not.

A quantification strategy for missing bone mass in case of osteolytic bone lesions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fränzle, Andrea, E-mail: a.fraenzle@dkfz.de; Giske, Kristina; Bretschi, Maren

Purpose: Most of the patients who died of breast cancer have developed bone metastases. To understand the pathogenesis of bone metastases and to analyze treatment response of different bone remodeling therapies, preclinical animal models are examined. In breast cancer, bone metastases are often bone destructive. To assess treatment response of bone remodeling therapies, the volumes of these lesions have to be determined during the therapy process. The manual delineation of missing structures, especially if large parts are missing, is very time-consuming and not reproducible. Reproducibility is highly important to have comparable results during the therapy process. Therefore, a computerized approachmore » is needed. Also for the preclinical research, a reproducible measurement of the lesions is essential. Here, the authors present an automated segmentation method for the measurement of missing bone mass in a preclinical rat model with bone metastases in the hind leg bones based on 3D CT scans. Methods: The affected bone structure is compared to a healthy model. Since in this preclinical rat trial the metastasis only occurs on the right hind legs, which is assured by using vessel clips, the authors use the left body side as a healthy model. The left femur is segmented with a statistical shape model which is initialised using the automatically segmented medullary cavity. The left tibia and fibula are segmented using volume growing starting at the tibia medullary cavity and stopping at the femur boundary. Masked images of both segmentations are mirrored along the median plane and transferred manually to the position of the affected bone by rigid registration. Affected bone and healthy model are compared based on their gray values. If the gray value of a voxel indicates bone mass in the healthy model and no bone in the affected bone, this voxel is considered to be osteolytic. Results: The lesion segmentations complete the missing bone structures in a reasonable way. The mean ratiov{sub r}/v{sub m} of the reconstructed bone volume v{sub r} and the healthy model bone volume v{sub m} is 1.07, which indicates a good reconstruction of the modified bone. Conclusions: The qualitative and quantitative comparison of manual and semi-automated segmentation results have shown that comparing a modified bone structure with a healthy model can be used to identify and measure missing bone mass in a reproducible way.« less

Drivers of high-involvement consumers' intention to buy PDO wines: Valpolicella PDO case study.

PubMed

Capitello, Roberta; Agnoli, Lara; Begalli, Diego

2016-08-01

This study investigates whether different sensory profiles of wines belonging to the same Protected Designation of Origin (PDO) are perceived as different products by consumers. It identifies the drivers of consumers' intention to buy preferred wines. Descriptive sensory analysis, consumer tests and consumer interviews were conducted to reach research aims. To perform the consumer tests and interviews, 443 consumers participated in the survey. The tasted wines comprised five samples representative of Valpolicella PDO wine. Analysis of variance tests, principal component analysis and linear and logit regressions were employed to verify the research hypotheses. The results demonstrated: (1) different sensory profiles exist within the Valpolicella PDO wine; (2) these sensory profiles result in consumers having the perception of diversified products; (3) the perception of differences was less marked for consumers than for trained assessors due to the different weight attributed to visual, aroma and the taste/mouthfeel hedonic dimensions; and (4) consumers' liking, as well as general perceptions, attitudes, preferences, wine knowledge and experience, contribute to consumers' intentions to buy more than the socio-demographic characteristics of consumers. The analysis of the drivers of consumers' intention to buy certain PDO wines provides new marketing insights into the roles of intrinsic quality, preferences and consumers' subjective characteristics in market segmentation. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Vision 20/20: perspectives on automated image segmentation for radiotherapy.

PubMed

Sharp, Gregory; Fritscher, Karl D; Pekar, Vladimir; Peroni, Marta; Shusharina, Nadya; Veeraraghavan, Harini; Yang, Jinzhong

2014-05-01

Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra- and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition. In this paper, the authors review the current autosegmentation methods particularly relevant for applications in RT. The authors outline the methods' strengths and limitations and propose strategies that could lead to wider acceptance of autosegmentation in routine clinical practice. The authors conclude that currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment. Modern hardware platforms including GPUs allow most of the autosegmentation tasks to be done in a range of a few minutes. In the nearest future, improvements in CT-based autosegmentation tools will be achieved through standardization of imaging and contouring protocols. In the longer term, the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology.

Vision 20/20: Perspectives on automated image segmentation for radiotherapy

PubMed Central

Sharp, Gregory; Fritscher, Karl D.; Pekar, Vladimir; Peroni, Marta; Shusharina, Nadya; Veeraraghavan, Harini; Yang, Jinzhong

2014-01-01

Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra- and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition. In this paper, the authors review the current autosegmentation methods particularly relevant for applications in RT. The authors outline the methods’ strengths and limitations and propose strategies that could lead to wider acceptance of autosegmentation in routine clinical practice. The authors conclude that currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment. Modern hardware platforms including GPUs allow most of the autosegmentation tasks to be done in a range of a few minutes. In the nearest future, improvements in CT-based autosegmentation tools will be achieved through standardization of imaging and contouring protocols. In the longer term, the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology. PMID:24784366

Control and Automation of Fluid Flow, Mass Transfer and Chemical Reactions in Microscale Segmented Flow

NASA Astrophysics Data System (ADS)

Abolhasani, Milad

Flowing trains of uniformly sized bubbles/droplets (i.e., segmented flows) and the associated mass transfer enhancement over their single-phase counterparts have been studied extensively during the past fifty years. Although the scaling behaviour of segmented flow formation is increasingly well understood, the predictive adjustment of the desired flow characteristics that influence the mixing and residence times, remains a challenge. Currently, a time consuming, slow and often inconsistent manual manipulation of experimental conditions is required to address this task. In my thesis, I have overcome the above-mentioned challenges and developed an experimental strategy that for the first time provided predictive control over segmented flows in a hands-off manner. A computer-controlled platform that consisted of a real-time image processing module within an integral controller, a silicon-based microreactor and automated fluid delivery technique was designed, implemented and validated. In a first part of my thesis I utilized this approach for the automated screening of physical mass transfer and solubility characteristics of carbon dioxide (CO2) in a physical solvent at a well-defined temperature and pressure and a throughput of 12 conditions per hour. Second, by applying the segmented flow approach to a recently discovered CO2 chemical absorbent, frustrated Lewis pairs (FLPs), I determined the thermodynamic characteristics of the CO2-FLP reaction. Finally, the segmented flow approach was employed for characterization and investigation of CO2-governed liquid-liquid phase separation process. The second part of my thesis utilized the segmented flow platform for the preparation and shape control of high quality colloidal nanomaterials (e.g., CdSe/CdS) via the automated control of residence times up to approximately 5 minutes. By introducing a novel oscillatory segmented flow concept, I was able to further extend the residence time limitation to 24 hours. A case study of a slow candidate reaction, the etching of gold nanorods during up to five hours, served to illustrate the utility of oscillatory segmented flows in assessing the shape evolution of colloidal nanomaterials on-chip via continuous optical interrogation at only one sensing location. The developed cruise control strategy will enable plug'n play operation of segmented flows in applications that include flow chemistry, material synthesis and in-flow analysis and screening.

Consumer perceptions of the sale of tobacco products in pharmacies and grocery stores among U.S. adults.

PubMed

Patwardhan, Pallavi; McMillen, Robert; Winickoff, Jonathan P

2013-07-09

Pharmacy-based tobacco sales are a rapidly increasing segment of the U.S. retail tobacco market. Growing evidence links easy access to tobacco retail outlets such as pharmacies to increased tobacco use. This mixed-mode survey was the first to employ a nationally representative sample of consumers (n = 3057) to explore their opinions on sale of tobacco products in pharmacies and grocery stores. The majority reported that sale of tobacco products should be either 'allowed if products hidden from view' (29.9%, 25.6%) or 'not allowed at all' (24.0%, 31.3%) in grocery stores and pharmacies, respectively. Significantly fewer smokers, compared to non-smokers, reported agreement on point-of-sale restrictions on sales of tobacco products (grocery stores: 27.1% vs. 59.6%, p < .01; pharmacy: 32.8% vs. 62.0%, p < .01). Opinions also varied significantly by demographic characteristics and factors such as presence of a child in the household and urban/rural location of residence. Overall, a majority of consumers surveyed either supported banning sales of tobacco in grocery stores and pharmacies or allowing sales only if the products are hidden from direct view. Both policy changes would represent a departure from the status quo. Consistent with the views of practicing pharmacists and professional pharmacy organizations, consumers are also largely supportive of more restrictive policies.

Consumer perceptions of the sale of tobacco products in pharmacies and grocery stores among U.S. adults

PubMed Central

2013-01-01

Background Pharmacy-based tobacco sales are a rapidly increasing segment of the U.S. retail tobacco market. Growing evidence links easy access to tobacco retail outlets such as pharmacies to increased tobacco use. This mixed-mode survey was the first to employ a nationally representative sample of consumers (n = 3057) to explore their opinions on sale of tobacco products in pharmacies and grocery stores. Results The majority reported that sale of tobacco products should be either ‘allowed if products hidden from view’ (29.9%, 25.6%) or ‘not allowed at all’ (24.0%, 31.3%) in grocery stores and pharmacies, respectively. Significantly fewer smokers, compared to non-smokers, reported agreement on point-of-sale restrictions on sales of tobacco products (grocery stores: 27.1% vs. 59.6%, p < .01; pharmacy: 32.8% vs. 62.0%, p < .01). Opinions also varied significantly by demographic characteristics and factors such as presence of a child in the household and urban/rural location of residence. Conclusions Overall, a majority of consumers surveyed either supported banning sales of tobacco in grocery stores and pharmacies or allowing sales only if the products are hidden from direct view. Both policy changes would represent a departure from the status quo. Consistent with the views of practicing pharmacists and professional pharmacy organizations, consumers are also largely supportive of more restrictive policies. PMID:23837647

The Consumer Motivation Scale: A detailed review of item generation, exploration, confirmation, and validation procedures.

PubMed

Barbopoulos, I; Johansson, L-O

2017-08-01

This data article offers a detailed description of analyses pertaining to the development of the Consumer Motivation Scale (CMS), from item generation and the extraction of factors, to confirmation of the factor structure and validation of the emergent dimensions. The established goal structure - consisting of the sub-goals Value for Money, Quality, Safety, Stimulation, Comfort, Ethics, and Social Acceptance - is shown to be related to a variety of consumption behaviors in different contexts and for different products, and should thereby prove useful in standard marketing research, as well as in the development of tailored marketing strategies, and the segmentation of consumer groups, settings, brands, and products.

3D multi-scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images.

PubMed

Li, Xiaomeng; Dou, Qi; Chen, Hao; Fu, Chi-Wing; Qi, Xiaojuan; Belavý, Daniel L; Armbrecht, Gabriele; Felsenberg, Dieter; Zheng, Guoyan; Heng, Pheng-Ann

2018-04-01

Intervertebral discs (IVDs) are small joints that lie between adjacent vertebrae. The localization and segmentation of IVDs are important for spine disease diagnosis and measurement quantification. However, manual annotation is time-consuming and error-prone with limited reproducibility, particularly for volumetric data. In this work, our goal is to develop an automatic and accurate method based on fully convolutional networks (FCN) for the localization and segmentation of IVDs from multi-modality 3D MR data. Compared with single modality data, multi-modality MR images provide complementary contextual information, which contributes to better recognition performance. However, how to effectively integrate such multi-modality information to generate accurate segmentation results remains to be further explored. In this paper, we present a novel multi-scale and modality dropout learning framework to locate and segment IVDs from four-modality MR images. First, we design a 3D multi-scale context fully convolutional network, which processes the input data in multiple scales of context and then merges the high-level features to enhance the representation capability of the network for handling the scale variation of anatomical structures. Second, to harness the complementary information from different modalities, we present a random modality voxel dropout strategy which alleviates the co-adaption issue and increases the discriminative capability of the network. Our method achieved the 1st place in the MICCAI challenge on automatic localization and segmentation of IVDs from multi-modality MR images, with a mean segmentation Dice coefficient of 91.2% and a mean localization error of 0.62 mm. We further conduct extensive experiments on the extended dataset to validate our method. We demonstrate that the proposed modality dropout strategy with multi-modality images as contextual information improved the segmentation accuracy significantly. Furthermore, experiments conducted on extended data collected from two different time points demonstrate the efficacy of our method on tracking the morphological changes in a longitudinal study. Copyright © 2018 Elsevier B.V. All rights reserved.

Assessing the viability of situationally driven segmentation opportunities in the health care market.

PubMed

Gehrt, K C; Pinto, M B

1993-01-01

The impact of situational factors has typically been investigated in the context of goods marketing. Very few studies have investigated the influence of situational factors on services marketing. This study demonstrates the importance of situational influence on services marketing by delineating a consumer-based, situationally characterized competitive market structure for health care services. The competitive structure of the health care market is delineated in terms of the similarity/substitutability of the three-factor, situational characterizations of ten health care alternatives. The general marketing implications of the market-structure delineation procedure and the health care-specific implications of the findings are discussed.

[Use of social marketing in population health programs (literature review)].

PubMed

Kholmogorova, G T; Gladysheva, N V

1991-01-01

At present health education programmes abroad make wide use of social marketing strategy. Unlike commercial marketing whose purpose is competition and struggle for the expansion of commodity markets, social marketing is aimed at disseminating certain ideas or introducing certain practices, using largely the technological base and strategy of commercial marketing. The authors give 8 fundamental principles of social marketing (consumer orientation, the theory of barter, the analysis of audience and segmentation, special surveys to detect the orientation of population, the choice of channels for information transmission application of "marketing mixture", control of ongoing programme and marketing management). Application fields of social marketing in public health are discussed.

Social marketing: application to medical education.

PubMed

David, S P; Greer, D S

2001-01-16

Medical education is often a frustrating endeavor, particularly when it attempts to change practice behavior. Traditional lecture-based educational methods are limited in their ability to sustain concentration and interest and to promote learner adherence to best-practice guidelines. Marketing techniques have been very effective in changing consumer behavior and physician behavior. However, the techniques of social marketing-goal identification, audience segmentation, and market research-have not been harnessed and applied to medical education. Social marketing can be applied to medical education in the effort to go beyond inoculation of learners with information and actually change behaviors. The tremendous potential of social marketing for medical education should be pilot-tested and systematically evaluated.

Fully automatic registration and segmentation of first-pass myocardial perfusion MR image sequences.

PubMed

Gupta, Vikas; Hendriks, Emile A; Milles, Julien; van der Geest, Rob J; Jerosch-Herold, Michael; Reiber, Johan H C; Lelieveldt, Boudewijn P F

2010-11-01

Derivation of diagnostically relevant parameters from first-pass myocardial perfusion magnetic resonance images involves the tedious and time-consuming manual segmentation of the myocardium in a large number of images. To reduce the manual interaction and expedite the perfusion analysis, we propose an automatic registration and segmentation method for the derivation of perfusion linked parameters. A complete automation was accomplished by first registering misaligned images using a method based on independent component analysis, and then using the registered data to automatically segment the myocardium with active appearance models. We used 18 perfusion studies (100 images per study) for validation in which the automatically obtained (AO) contours were compared with expert drawn contours on the basis of point-to-curve error, Dice index, and relative perfusion upslope in the myocardium. Visual inspection revealed successful segmentation in 15 out of 18 studies. Comparison of the AO contours with expert drawn contours yielded 2.23 ± 0.53 mm and 0.91 ± 0.02 as point-to-curve error and Dice index, respectively. The average difference between manually and automatically obtained relative upslope parameters was found to be statistically insignificant (P = .37). Moreover, the analysis time per slice was reduced from 20 minutes (manual) to 1.5 minutes (automatic). We proposed an automatic method that significantly reduced the time required for analysis of first-pass cardiac magnetic resonance perfusion images. The robustness and accuracy of the proposed method were demonstrated by the high spatial correspondence and statistically insignificant difference in perfusion parameters, when AO contours were compared with expert drawn contours. Copyright © 2010 AUR. Published by Elsevier Inc. All rights reserved.

An automatic quantification system for MS lesions with integrated DICOM structured reporting (DICOM-SR) for implementation within a clinical environment

NASA Astrophysics Data System (ADS)

Jacobs, Colin; Ma, Kevin; Moin, Paymann; Liu, Brent

2010-03-01

Multiple Sclerosis (MS) is a common neurological disease affecting the central nervous system characterized by pathologic changes including demyelination and axonal injury. MR imaging has become the most important tool to evaluate the disease progression of MS which is characterized by the occurrence of white matter lesions. Currently, radiologists evaluate and assess the multiple sclerosis lesions manually by estimating the lesion volume and amount of lesions. This process is extremely time-consuming and sensitive to intra- and inter-observer variability. Therefore, there is a need for automatic segmentation of the MS lesions followed by lesion quantification. We have developed a fully automatic segmentation algorithm to identify the MS lesions. The segmentation algorithm is accelerated by parallel computing using Graphics Processing Units (GPU) for practical implementation into a clinical environment. Subsequently, characterized quantification of the lesions is performed. The quantification results, which include lesion volume and amount of lesions, are stored in a structured report together with the lesion location in the brain to establish a standardized representation of the disease progression of the patient. The development of this structured report in collaboration with radiologists aims to facilitate outcome analysis and treatment assessment of the disease and will be standardized based on DICOM-SR. The results can be distributed to other DICOM-compliant clinical systems that support DICOM-SR such as PACS. In addition, the implementation of a fully automatic segmentation and quantification system together with a method for storing, distributing, and visualizing key imaging and informatics data in DICOM-SR for MS lesions improves the clinical workflow of radiologists and visualizations of the lesion segmentations and will provide 3-D insight into the distribution of lesions in the brain.

Essays in applied microeconomics

NASA Astrophysics Data System (ADS)

Wang, Xiaoting

In this dissertation I use Microeconomic theory to study firms' behavior. Chapter One introduces the motivations and main findings of this dissertation. Chapter Two studies the issue of information provision through advertisement when markets are segmented and consumers' price information is incomplete. Firms compete in prices and advertising strategies for consumers with transportation costs. High advertising costs contribute to market segmentation. Low advertising costs promote price competition among firms and improves consumer welfare. Chapter Three also investigates market power as a result of consumers' switching costs. A potential entrant can offer a new product bundled with an existing product to compensate consumers for their switching cost. If the primary market is competitive, bundling simply plays the role of price discrimination, and it does not dominate unbundled sales in the process of entry. If the entrant has market power in the primary market, then bundling also plays the role of leveraging market power and it dominates unbundled sales. The market for electric power generation has been opened to competition in recent years. Chapter Four looks at issues involved in the deregulated electricity market. By comparing the performance of the competitive market with the social optimum, we identify the conditions under which market equilibrium generates socially efficient levels of electric power. Chapter Two to Four investigate the strategic behavior among firms. Chapter Five studies the interaction between firms and unemployed workers in a frictional labor market. We set up an asymmetric job auction model, where two types of workers apply for two types of job openings by bidding in auctions and firms hire the applicant offering them the most profits. The job auction model internalizes the determination of the share of surplus from a match, therefore endogenously generates incentives for an efficient division of the matching surplus. Microeconomic foundation for competitive auctions is also discussed in this chapter.

KIDMONEY: Children as Big Business.

ERIC Educational Resources Information Center

Reese, Shelly

1996-01-01

Discusses how marketers are targeting children as a consumer segment. Highlights include advertising budgets and media, how children spend their money, the more influential role of the child in the family, in-school marketing, controversial advertising on Channel One, marketing on the Internet, and parental control. (AEF)

Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets

PubMed Central

Jeong, Won-Ki; Beyer, Johanna; Hadwiger, Markus; Vazquez, Amelio; Pfister, Hanspeter; Whitaker, Ross T.

2011-01-01

Recent advances in scanning technology provide high resolution EM (Electron Microscopy) datasets that allow neuroscientists to reconstruct complex neural connections in a nervous system. However, due to the enormous size and complexity of the resulting data, segmentation and visualization of neural processes in EM data is usually a difficult and very time-consuming task. In this paper, we present NeuroTrace, a novel EM volume segmentation and visualization system that consists of two parts: a semi-automatic multiphase level set segmentation with 3D tracking for reconstruction of neural processes, and a specialized volume rendering approach for visualization of EM volumes. It employs view-dependent on-demand filtering and evaluation of a local histogram edge metric, as well as on-the-fly interpolation and ray-casting of implicit surfaces for segmented neural structures. Both methods are implemented on the GPU for interactive performance. NeuroTrace is designed to be scalable to large datasets and data-parallel hardware architectures. A comparison of NeuroTrace with a commonly used manual EM segmentation tool shows that our interactive workflow is faster and easier to use for the reconstruction of complex neural processes. PMID:19834227

Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm.

PubMed

Ghane, Narjes; Vard, Alireza; Talebi, Ardeshir; Nematollahy, Pardis

2017-01-01

Recognition of white blood cells (WBCs) is the first step to diagnose some particular diseases such as acquired immune deficiency syndrome, leukemia, and other blood-related diseases that are usually done by pathologists using an optical microscope. This process is time-consuming, extremely tedious, and expensive and needs experienced experts in this field. Thus, a computer-aided diagnosis system that assists pathologists in the diagnostic process can be so effective. Segmentation of WBCs is usually a first step in developing a computer-aided diagnosis system. The main purpose of this paper is to segment WBCs from microscopic images. For this purpose, we present a novel combination of thresholding, k-means clustering, and modified watershed algorithms in three stages including (1) segmentation of WBCs from a microscopic image, (2) extraction of nuclei from cell's image, and (3) separation of overlapping cells and nuclei. The evaluation results of the proposed method show that similarity measures, precision, and sensitivity respectively were 92.07, 96.07, and 94.30% for nucleus segmentation and 92.93, 97.41, and 93.78% for cell segmentation. In addition, statistical analysis presents high similarity between manual segmentation and the results obtained by the proposed method.

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

PubMed Central

Rudyanto, Rina D.; Kerkstra, Sjoerd; van Rikxoort, Eva M.; Fetita, Catalin; Brillet, Pierre-Yves; Lefevre, Christophe; Xue, Wenzhe; Zhu, Xiangjun; Liang, Jianming; Öksüz, İlkay; Ünay, Devrim; Kadipaşaogandcaron;lu, Kamuran; Estépar, Raúl San José; Ross, James C.; Washko, George R.; Prieto, Juan-Carlos; Hoyos, Marcela Hernández; Orkisz, Maciej; Meine, Hans; Hüllebrand, Markus; Stöcker, Christina; Mir, Fernando Lopez; Naranjo, Valery; Villanueva, Eliseo; Staring, Marius; Xiao, Changyan; Stoel, Berend C.; Fabijanska, Anna; Smistad, Erik; Elster, Anne C.; Lindseth, Frank; Foruzan, Amir Hossein; Kiros, Ryan; Popuri, Karteek; Cobzas, Dana; Jimenez-Carretero, Daniel; Santos, Andres; Ledesma-Carbayo, Maria J.; Helmberger, Michael; Urschler, Martin; Pienn, Michael; Bosboom, Dennis G.H.; Campo, Arantza; Prokop, Mathias; de Jong, Pim A.; Ortiz-de-Solorzano, Carlos; Muñoz-Barrutia, Arrate; van Ginneken, Bram

2016-01-01

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases. PMID:25113321

Trained and consumer panel evaluation of sparkling wines sweetened to brut or demi sec residual sugar levels with three different sugars.

PubMed

McMahon, Kenneth M; Diako, Charles; Aplin, Jesse; Mattinson, D Scott; Culver, Caleb; Ross, Carolyn F

2017-09-01

The dosage liquid, added at the final stage of sparkling wine production, imparts residual sweetness to the wine. No study has yet analyzed the influence of dosage composition on the final wine's sensory profile or consumer acceptance. In this study, dosage composition was altered through the addition of different sugar types (ST; fructose, glucose, or sucrose) to produce seven sparkling wines of varying residual sugar levels (RSL), including no sugar added, brut (5.3-8.4gST/L) or demi sec (34.9-37.8gST/L). As evaluated by a trained panel (n=9), the interaction between ST and RSL influenced the perception of caramelized/vanilla/honey (CVH) flavor, sweet taste, and sour taste attributes (p<0.05). Demi sec wines displayed lower intensities of green flavor, yeasty flavor, and sour taste compared to the no sugar added wine (p<0.05). Consumers (n=126) also evaluated the sparkling wines and ST, RSL, and their interaction influenced consumer acceptance of different attributes, as well as the perception of the "refreshing" aspect of the wine (p<0.05). Overall consumer acceptance of sparkling wines was highly correlated (r 2 ≤0.88) to CVH, floral, and fruity flavors, as well as sweet taste and creamy mouthfeel. External preference mapping revealed two clusters of consumers. Both consumer clusters liked wines sweetened with fructose, but Cluster 1 liked the demi sec sparkling wine sweetened with fructose (32.8g/L fructose) while Cluster 2 preferred the brut wine sweetened with fructose (8.4g/L fructose). These results suggest that consumer preference for sparkling wine was segmented based on sweetness preference. The results of this study offer winemakers knowledge about the influence of dosage composition on the sensory profile of sparkling wine. Copyright © 2017 Elsevier Ltd. All rights reserved.

Consumer segmentation based on the level and structure of fruit and vegetable intake: an empirical evidence for US adults from the National Health and Nutrition Examination Survey (NHANES) 2005-2006.

PubMed

Demydas, Tetyana

2011-06-01

To identify consumption patterns of fruit and vegetables within a representative sample of US adults with a focus on degree of produce processing and to explore sociodemographic, lifestyle and nutritional profiles associated with these patterns. Cross-sectional analysis. Fruit and vegetable (F&V) consumption data were collected using two non-consecutive 24 h recalls. For the purpose of the study, F&V intakes were aggregated into seven subgroups indicating degree of processing, which afterwards were used as inputs into cluster analysis. The 2005-2006 National Health and Nutrition Examination Survey. The sample consisted of 2444 adults aged 20-59 years. Total average F&V intake of the adults was below the recommended level. Thereby, 20 % of the respondents consumed fruit only in the form of juice. Three F&V consumption patterns were identified: 'low-intake F&V consumers' (74 % of respondents), 'consumers of healthier F&V options' (13 %) and 'intensive fruit juice consumers' (13 %). These groups differed markedly in terms of their sociodemographic, lifestyle and health characteristics, such as gender, age, race/ethnicity, education, smoking, weight status, etc. Differences in nutrient profiles were also found, with the 'consumers of healthier F&V options' showing better nutritional quality compared with other clusters. Only a small share of US adults combines high F&V intakes with healthier F&V options that lead to a better nutritional profile. This raises discussion about a need to deliver more specific F&V promotion messages, including advice on healthier preparation methods, especially for the specific population groups.

Functional and organic eggs as an alternative to conventional production: a conjoint analysis of consumers' preferences.

PubMed

Mesías, Francisco J; Martínez-Carrasco, Federico; Martínez, José M; Gaspar, Paula

2011-02-01

In the current context of growing consumer demand for foodstuffs that are healthy and safe and that are obtained in a manner respectful to the welfare of animals, the analysis of consumer preferences towards attributes of this type takes on particular importance. These trends are especially clear in the case of the consumption of eggs because of their strong negative association with cholesterol levels and their extremely intensive systems of production. The introduction of variants that are more in harmony with current consumer demands represents an interesting market alternative. The present study was aimed at investigating the preferences of Spanish consumers for these alternative types of egg that are entering the market. The survey was conducted with 361 consumers from October 2007 to March 2008. The conjoint analysis allowed us to estimate the relative importance of the main attributes that affect consumer preferences for eggs and to distinguish segments of consumers with similar preference profiles. It was found that price is the most important attribute determining consumer preferences, followed by the hens' feed and their rearing conditions. It was also found that only some groups of consumers are willing to pay the premium necessary for alternative methods of production. 2010 Society of Chemical Industry.

A Fast Method for the Segmentation of Synaptic Junctions and Mitochondria in Serial Electron Microscopic Images of the Brain.

PubMed

Márquez Neila, Pablo; Baumela, Luis; González-Soriano, Juncal; Rodríguez, Jose-Rodrigo; DeFelipe, Javier; Merchán-Pérez, Ángel

2016-04-01

Recent electron microscopy (EM) imaging techniques permit the automatic acquisition of a large number of serial sections from brain samples. Manual segmentation of these images is tedious, time-consuming and requires a high degree of user expertise. Therefore, there is considerable interest in developing automatic segmentation methods. However, currently available methods are computationally demanding in terms of computer time and memory usage, and to work properly many of them require image stacks to be isotropic, that is, voxels must have the same size in the X, Y and Z axes. We present a method that works with anisotropic voxels and that is computationally efficient allowing the segmentation of large image stacks. Our approach involves anisotropy-aware regularization via conditional random field inference and surface smoothing techniques to improve the segmentation and visualization. We have focused on the segmentation of mitochondria and synaptic junctions in EM stacks from the cerebral cortex, and have compared the results to those obtained by other methods. Our method is faster than other methods with similar segmentation results. Our image regularization procedure introduces high-level knowledge about the structure of labels. We have also reduced memory requirements with the introduction of energy optimization in overlapping partitions, which permits the regularization of very large image stacks. Finally, the surface smoothing step improves the appearance of three-dimensional renderings of the segmented volumes.

Automatic cerebrospinal fluid segmentation in non-contrast CT images using a 3D convolutional network

NASA Astrophysics Data System (ADS)

Patel, Ajay; van de Leemput, Sil C.; Prokop, Mathias; van Ginneken, Bram; Manniesing, Rashindra

2017-03-01

Segmentation of anatomical structures is fundamental in the development of computer aided diagnosis systems for cerebral pathologies. Manual annotations are laborious, time consuming and subject to human error and observer variability. Accurate quantification of cerebrospinal fluid (CSF) can be employed as a morphometric measure for diagnosis and patient outcome prediction. However, segmenting CSF in non-contrast CT images is complicated by low soft tissue contrast and image noise. In this paper we propose a state-of-the-art method using a multi-scale three-dimensional (3D) fully convolutional neural network (CNN) to automatically segment all CSF within the cranial cavity. The method is trained on a small dataset comprised of four manually annotated cerebral CT images. Quantitative evaluation of a separate test dataset of four images shows a mean Dice similarity coefficient of 0.87 +/- 0.01 and mean absolute volume difference of 4.77 +/- 2.70 %. The average prediction time was 68 seconds. Our method allows for fast and fully automated 3D segmentation of cerebral CSF in non-contrast CT, and shows promising results despite a limited amount of training data.

Test-retest reliability of automated whole body and compartmental muscle volume measurements on a wide bore 3T MR system.

PubMed

Thomas, Marianna S; Newman, David; Leinhard, Olof Dahlqvist; Kasmai, Bahman; Greenwood, Richard; Malcolm, Paul N; Karlsson, Anette; Rosander, Johannes; Borga, Magnus; Toms, Andoni P

2014-09-01

To measure the test-retest reproducibility of an automated system for quantifying whole body and compartmental muscle volumes using wide bore 3 T MRI. Thirty volunteers stratified by body mass index underwent whole body 3 T MRI, two-point Dixon sequences, on two separate occasions. Water-fat separation was performed, with automated segmentation of whole body, torso, upper and lower leg volumes, and manually segmented lower leg muscle volumes. Mean automated total body muscle volume was 19·32 L (SD9·1) and 19·28 L (SD9·12) for first and second acquisitions (Intraclass correlation coefficient (ICC) = 1·0, 95% level of agreement -0·32-0·2 L). ICC for all automated test-retest muscle volumes were almost perfect (0·99-1·0) with 95% levels of agreement 1.8-6.6% of mean volume. Automated muscle volume measurements correlate closely with manual quantification (right lower leg: manual 1·68 L (2SD0·6) compared to automated 1·64 L (2SD 0·6), left lower leg: manual 1·69 L (2SD 0·64) compared to automated 1·63 L (SD0·61), correlation coefficients for automated and manual segmentation were 0·94-0·96). Fully automated whole body and compartmental muscle volume quantification can be achieved rapidly on a 3 T wide bore system with very low margins of error, excellent test-retest reliability and excellent correlation to manual segmentation in the lower leg. Sarcopaenia is an important reversible complication of a number of diseases. Manual quantification of muscle volume is time-consuming and expensive. Muscles can be imaged using in and out of phase MRI. Automated atlas-based segmentation can identify muscle groups. Automated muscle volume segmentation is reproducible and can replace manual measurements.

Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation

PubMed Central

Parmar, Chintan; Blezek, Daniel; Estepar, Raul San Jose; Pieper, Steve; Kim, John; Aerts, Hugo J. W. L.

2017-01-01

Purpose Accurate segmentation of lung nodules is crucial in the development of imaging biomarkers for predicting malignancy of the nodules. Manual segmentation is time consuming and affected by inter-observer variability. We evaluated the robustness and accuracy of a publically available semiautomatic segmentation algorithm that is implemented in the 3D Slicer Chest Imaging Platform (CIP) and compared it with the performance of manual segmentation. Methods CT images of 354 manually segmented nodules were downloaded from the LIDC database. Four radiologists performed the manual segmentation and assessed various nodule characteristics. The semiautomatic CIP segmentation was initialized using the centroid of the manual segmentations, thereby generating four contours for each nodule. The robustness of both segmentation methods was assessed using the region of uncertainty (δ) and Dice similarity index (DSI). The robustness of the segmentation methods was compared using the Wilcoxon-signed rank test (pWilcoxon<0.05). The Dice similarity index (DSIAgree) between the manual and CIP segmentations was computed to estimate the accuracy of the semiautomatic contours. Results The median computational time of the CIP segmentation was 10 s. The median CIP and manually segmented volumes were 477 ml and 309 ml, respectively. CIP segmentations were significantly more robust than manual segmentations (median δCIP = 14ml, median dsiCIP = 99% vs. median δmanual = 222ml, median dsimanual = 82%) with pWilcoxon~10−16. The agreement between CIP and manual segmentations had a median DSIAgree of 60%. While 13% (47/354) of the nodules did not require any manual adjustment, minor to substantial manual adjustments were needed for 87% (305/354) of the nodules. CIP segmentations were observed to perform poorly (median DSIAgree≈50%) for non-/sub-solid nodules with subtle appearances and poorly defined boundaries. Conclusion Semi-automatic CIP segmentation can potentially reduce the physician workload for 13% of nodules owing to its computational efficiency and superior stability compared to manual segmentation. Although manual adjustment is needed for many cases, CIP segmentation provides a preliminary contour for physicians as a starting point. PMID:28594880

78 FR 3895 - Agency Information Collection Activities: Announcement of Board Approval Under Delegated...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-17

... maintain their ALLL. Although Financial Accounting Standards Board (FASB) Accounting Standards Update No... investment amounts by impairment measurement method for only three segments: consumer credit cards, all other... recorded investment amounts by impairment measurement method for five loan categories: commercial real...

Thermal Profiling of Residential Energy Use

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albert, A; Rajagopal, R

This work describes a methodology for informing targeted demand-response (DR) and marketing programs that focus on the temperature-sensitive part of residential electricity demand. Our methodology uses data that is becoming readily available at utility companies-hourly energy consumption readings collected from "smart" electricity meters, as well as hourly temperature readings. To decompose individual consumption into a thermal-sensitive part and a base load (non-thermally-sensitive), we propose a model of temperature response that is based on thermal regimes, i.e., unobserved decisions of consumers to use their heating or cooling appliances. We use this model to extract useful benchmarks that compose thermal profiles ofmore » individual users, i.e., terse characterizations of the statistics of these users' temperature-sensitive consumption. We present example profiles generated using our model on real consumers, and show its performance on a large sample of residential users. This knowledge may, in turn, inform the DR program by allowing scarce operational and marketing budgets to be spent on the right users-those whose influencing will yield highest energy reductions-at the right time. We show that such segmentation and targeting of users may offer savings exceeding 100% of a random strategy.« less

Communication choices of the uninsured: implications for health marketing.

PubMed

Dutta, Mohan Jyoti; King, Andy J

2008-01-01

According to published scholarship on health services usage, an increasing number of Americans do not have health insurance coverage. The strong relationship between insurance coverage and health services utilization highlights the importance of reaching out to the uninsured via prevention campaigns and communication messages. This article examines the communication choices of the uninsured, documenting that the uninsured are more likely to consume entertainment-based television and are less likely to read, watch, and listen to information-based media. It further documents the positive relationship between interpersonal communication, community participation, and health insurance coverage. The entertainment-heavy media consumption patterns of the uninsured suggests the relevance of developing health marketing strategies that consider entertainment programming as an avenue for reaching out to this underserved segment of the population.

Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.

PubMed

Men, Kuo; Dai, Jianrong; Li, Yexiong

2017-12-01

Delineation of the clinical target volume (CTV) and organs at risk (OARs) is very important for radiotherapy but is time-consuming and prone to inter-observer variation. Here, we proposed a novel deep dilated convolutional neural network (DDCNN)-based method for fast and consistent auto-segmentation of these structures. Our DDCNN method was an end-to-end architecture enabling fast training and testing. Specifically, it employed a novel multiple-scale convolutional architecture to extract multiple-scale context features in the early layers, which contain the original information on fine texture and boundaries and which are very useful for accurate auto-segmentation. In addition, it enlarged the receptive fields of dilated convolutions at the end of networks to capture complementary context features. Then, it replaced the fully connected layers with fully convolutional layers to achieve pixel-wise segmentation. We used data from 278 patients with rectal cancer for evaluation. The CTV and OARs were delineated and validated by senior radiation oncologists in the planning computed tomography (CT) images. A total of 218 patients chosen randomly were used for training, and the remaining 60 for validation. The Dice similarity coefficient (DSC) was used to measure segmentation accuracy. Performance was evaluated on segmentation of the CTV and OARs. In addition, the performance of DDCNN was compared with that of U-Net. The proposed DDCNN method outperformed the U-Net for all segmentations, and the average DSC value of DDCNN was 3.8% higher than that of U-Net. Mean DSC values of DDCNN were 87.7% for the CTV, 93.4% for the bladder, 92.1% for the left femoral head, 92.3% for the right femoral head, 65.3% for the intestine, and 61.8% for the colon. The test time was 45 s per patient for segmentation of all the CTV, bladder, left and right femoral heads, colon, and intestine. We also assessed our approaches and results with those in the literature: our system showed superior performance and faster speed. These data suggest that DDCNN can be used to segment the CTV and OARs accurately and efficiently. It was invariant to the body size, body shape, and age of the patients. DDCNN could improve the consistency of contouring and streamline radiotherapy workflows. © 2017 American Association of Physicists in Medicine.

[Market of medical services provided to patients with sexually transmitted diseases].

PubMed

Martynenko, A V

2001-01-01

Data are presented from an investigation designed to study market of medical services delivered to patients with sexually transmitted diseases (STD). A model of the purchaser's behaviour of consumers of medical services is developed, decisive factors affecting the choice of a medical institution when applying for a profile medical advice are determined. Submitted in the paper is also an algorythm of analysis of expediency of segmentation of market of medical services delivered to STD patients. The most optimal principles of market segmentation include the following--economic (solvency), territorial (place of residence), social (belonging to one or another stratum of society).

Demographics and beef preferences affect consumer motivation for purchasing fresh beef steaks and roasts.

PubMed

Reicks, A L; Brooks, J C; Garmyn, A J; Thompson, L D; Lyford, C L; Miller, M F

2011-04-01

Surveys completed by 1370 consumers determined the motivational factors affecting consumer purchasing decisions for fresh beef steaks and roasts in three regions in the United States. Females placed greater importance on tenderness, ease of preparation, and nutritional value of steaks and roasts when compared to males. Age influenced tenderness, product consistency, and nutritional value of steaks, but influenced flavor, product consistency, and nutritional value of roasts. Consumers felt juiciness, nutritional value, and natural products were less important in determining their purchasing choices of steaks and roasts as their level of education increased. The preferred degree of doneness of steaks influenced the value placed on six of the nine purchasing motivators. Beef preferences and demographics influenced consumer purchasing decisions for fresh beef steaks and roasts. Results from this study can be used to help identify factors to positively influence purchasing decisions within targeted market segments. © 2010 The American Meat Science Association. Published by Elsevier Ltd. All rights reserved.

Heterogeneity and nonlinearity in consumers’ preferences: An application to the olive oil shopping behavior in Chile

PubMed Central

Romo-Muñoz, Rodrigo Alejandro; Cabas-Monje, Juan Hernán; Garrido-Henrríquez, Héctor Manuel

2017-01-01

In relatively unknown products, consumers use prices as a quality reference. Under such circumstances, the utility function can be non-negative for a specific price range and generate an inverted U-shaped function. The extra virgin olive oil market in Chile is a good example. Although domestic production and consumption have increased significantly in the last few years, consumer knowledge of this product is still limited. The objective of this study was to analyze Chilean consumer preferences and willingness to pay for extra virgin olive oil attributes. Consumers were segmented taking into account purchasing frequency. A Random Parameter Logit model was estimated for preference heterogeneity. Results indicate that the utility function is nonlinear allowing us to differentiate between two regimes. In the first regime, olive oil behaves as a conspicuous good, that is, higher utility is assigned to higher prices and consumers prefer foreign products in smaller containers. Under the second regime, Chilean olive oil in larger containers is preferred. PMID:28892516

Involving consumers in product design through collaboration: the case of online role-playing games.

PubMed

Yeh, Shu-Yu

2010-12-01

The release of software attributes to users by software designers for the creation of user-designed forms is regarded as a producer-consumer collaboration, leading consumers to expend significant effort on a specific product. This article identifies such software/product attributes within online role-playing games and then explores how consumers' prior experience affects the evaluation of such attributes. In this article, product attributes comprise customized, content, and interactive externality-sensitive and complementary externality-sensitive attributes, with the value of each attribute being greater for experts than for novices. In Study 1, data were collected and analyzed for the purpose of identifying such features in online role-playing games. The results can also be generalized to convergent products, such as TV games that have been redesigned as online games or mobile games found in Study 2. For the introduction of a convergent product to be successful, our research suggests that the potential market-segment focus should be on knowledgeable consumers who accept such products more readily.

My Green Car: Painting Motor City Green (Ep. 2) – DOE Lab-Corps Video Series

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saxena, Samveg; Shah, Nihar; Hansen, Dana

The Lab’s MyGreenCar team kicks off its customer discovery process in Detroit with a business boot camp designed for scientists developing energy-related technologies. Customer interviews lead to late night discussions and insights on less-than-receptive consumers. Back in Berkeley, the team decides to fine tune targeted customer segments. What makes a new technology compelling enough to transition out of the lab and become a consumer product? That’s the question Berkeley Lab researchers Samveg Saxena, Nihar Shah, and Dana Hansen plus industry mentor Russell Carrington set out to answer for MyGreenCar, an app providing personalized fuel economy or electric vehicle range estimatesmore » for consumers researching new cars. DOE’s Lab-Corps program offered the technology team some answers. The EERE-funded program, based on the National Science Foundation’s I-Corps™ model for entrepreneurial training, provides tools and training to move energy-related inventions to the marketplace. During Lab-Corp’s intensive six-week session, technology teams interview 100 customer and value chain members to discover which potential products based on their technologies will have significant market pull. A six video series follows the MyGreenCar team’s Lab-Corps experience, from pre-training preparation with the Lab’s Innovation and Partnerships Office through the ups and downs of the customer discovery process. Will the app make it to the marketplace? You’ll just have to watch.« less

Investigating the use of hair to assess polybrominated diphenyl ether exposure retrospectively.

PubMed

Carnevale, Amanda; Aleksa, Katarina; Goodyer, Cynthia G; Koren, Gideon

2014-04-01

Polybrominated diphenyl ethers (PBDEs) are chemicals that are added to a variety of consumer products as flame-retardants and have been classified as emerging endocrine disruptors. They are persistent and have been detected in humans. Previous studies have suggested that hair is a suitable matrix for examining human exposure to organic pollutants such as PBDEs. It is believed that the majority of exposure is from our indoor environment. The aim of this study was to investigate the changes in PBDE patterns and levels along the hair shaft, by using segmental analysis to retrospectively assess long-term exposure over a 1-year period. Questionnaires and hair samples from 65 women were collected at the Hospital for Sick Children, in Toronto, as part of a larger study. To assess long-term stability, hair samples were separated into 4- and 3-cm segments representing a 1-year period. Hair segments were analyzed for levels of 8 PBDE congeners, BDE-28, BDE-47, BDE-99, BDE-100, BDE-153, BDE-154, BDE-183, and BDE-209 on gas chromatography-mass spectrometry (MS). A Friedman test was used to detect the differences in exposure among segments, and factors such as dietary habits, hair care routine, and site of residence were investigated to determine if they might affect hair levels. A significant increase (P < 0.0001) in total PBDEs was seen among segments moving from proximal (root end) to distal along the hair shaft (median in pg/mg): first (33.3), second (43.0), third (61.6), and fourth (75.5) segments. Significantly lower levels of PBDEs were observed in artificially colored hair samples (P = 0.032), and a significant increase in PBDE levels was observed in women who consumed meat on a daily basis as opposed to weekly consumption (P = 0.040). The increase in PBDEs along the hair shaft suggests that hair PBDEs may be influenced by diet and artificial coloring. More work is needed to validate the use of PBDEs in hair as a biomarker of long-term exposure.

Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography.

PubMed

Yong, Yan Ling; Tan, Li Kuo; McLaughlin, Robert A; Chee, Kok Han; Liew, Yih Miin

2017-12-01

Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback scans is challenging and time consuming. We propose a linear-regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space. Benchmarked against gold-standard manual segmentation, our proposed algorithm achieves average locational accuracy of the vessel wall of 22 microns, and 0.985 and 0.970 in Dice coefficient and Jaccard similarity index, respectively. The average absolute error of luminal area estimation is 1.38%. The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

The wellness movement: imperatives for health care marketers.

PubMed

Bloch, P H

1984-01-01

This paper examines the impact of the growing national health consciousness on the delivery of health care services. The health-involved consumer is first profiled and implications for health care marketing strategy are then identified. Suggestions are also made regarding the tailoring of health services to the health-involved segment.

47 CFR 68.354 - Numbering and labeling requirements for terminal equipment.

Code of Federal Regulations, 2010 CFR

2010-10-01

... no competitive advantage for any entity or segment of the industry. (e) FCC numbering and labeling...) COMMON CARRIER SERVICES (CONTINUED) CONNECTION OF TERMINAL EQUIPMENT TO THE TELEPHONE NETWORK Conditions.... Customs Service to carry out their functions, and for consumers to easily identify the responsible party...

Relationships between sensory evaluations of beef tenderness, shear force measurements and consumer characteristics.

PubMed

Van Wezemael, Lynn; De Smet, Stefaan; Ueland, Øydis; Verbeke, Wim

2014-07-01

The supply of tender beef is an important challenge for the beef industry. Knowledge about the profile of consumers who are more optimistic or more accurate in their tenderness evaluations is important for product development and beef marketing purposes. Central location tests of beef steaks were performed in Norway and Belgium (n=218). Instrumental and sensorial tenderness of three muscles from Belgian Blue and Norwegian Red cattle was reported. Consumers who are optimistically evaluating tenderness were found to be more often male, less food neophobic, more positive towards beef healthiness, and showed fewer concerns about beef safety. No clear profile emerged for consumers who assessed tenderness similar to shear force measurements, which suggests that tenderness is mainly evaluated subjectively. The results imply a window of opportunities in tenderness improvements, and allow targeting a market segment which is less critical towards beef tenderness. © 2013 Elsevier Ltd. All rights reserved.

Identification of sensory attributes that drive consumer liking of commercial orange juice products in Korea.

PubMed

Kim, Mina K; Lee, Young-Jin; Kwak, Han Sub; Kang, Myung-woo

2013-09-01

Orange juice is a well-accepted fruit juice, and its consumption increases steadily. Many studies have been conducted to understand the sensory characteristics of orange juice throughout its varying processing steps. Sensory language and consumer likings of food can be influenced by culture. The objective of this study is to evaluate the sensory characteristics of commercially available orange juices in Korea and identify drivers of liking for orange juices in Korea. A quantitative descriptive analysis was conducted using a trained panel (n = 10) to evaluate 7 orange juice samples in triplicates, followed by consumer acceptance tests (n = 103). Univariate and multivariate statistical analyses were conducted for data analysis. The sensory characteristics of commercially available orange juice were documented and grouped: group 1 samples were characterized by high in natural citrus flavors such as orange peel, orange flesh, citrus fruit, and grape fruit, whereas group 2 samples were characterized by processed orange-like flavors such as over-ripe, cooked-orange, and yogurt. Regardless of orange flavor types, a high intensity of orange flavor in orange juice was identified as a driver of liking for orange juices in Korea. Three distinct clusters were segmented by varying sensory attributes that were evaluated by likes and dislikes. Overall, many similarities were noticed between Korean market segment and global orange juice market. By knowing the drivers of liking and understanding the distinct consumer clusters present in the Korean orange juice market, the orange juice industry could improve the strategic marketing of its products in Korea. © 2013 Institute of Food Technologists®

Automated tissue segmentation of MR brain images in the presence of white matter lesions.

PubMed

Valverde, Sergi; Oliver, Arnau; Roura, Eloy; González-Villà, Sandra; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Rovira, Àlex; Lladó, Xavier

2017-01-01

Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community. Copyright © 2016 Elsevier B.V. All rights reserved.

Smart Meter Driven Segmentation: What Your Consumption Says About You

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albert, A; Rajagopal, R

With the rollout of smart metering infrastructure at scale, demand-response (DR) programs may now be tailored based on users' consumption patterns as mined from sensed data. For issuing DR events it is key to understand the inter-temporal consumption dynamics as to appropriately segment the user population. We propose to infer occupancy states from consumption time series data using a hidden Markov model framework. Occupancy is characterized in this model by 1) magnitude, 2) duration, and 3) variability. We show that users may be grouped according to their consumption patterns into groups that exhibit qualitatively different dynamics that may be exploitedmore » for program enrollment purposes. We investigate empirically the information that residential energy consumers' temporal energy demand patterns characterized by these three dimensions may convey about their demographic, household, and appliance stock characteristics. Our analysis shows that temporal patterns in the user's consumption data can predict with good accuracy certain user characteristics. We use this framework to argue that there is a large degree of individual predictability in user consumption at a population level.« less

Using focus groups in the consumer research phase of a social marketing program to promote moderate-intensity physical activity and walking trail use in Sumter County, South Carolina.

PubMed

Burroughs, Ericka; Peck, Lara E; Sharpe, Patricia A; Granner, Michelle L; Bryant, Carol A; Fields, Regina

2006-01-01

The use of social marketing approaches in public health practice is increasing. Using marketing concepts such as the "four Ps" (product, price, place, and promotion), social marketing borrows from the principles of commercial marketing but promotes beneficial health behaviors. Consumer research is used to segment the population and develop a strategy based on those marketing concepts. In a community-based participatory research study, 17 focus groups were used in consumer research to develop a social marketing program to promote walking and other moderate-intensity physical activities. Two phases of focus groups were conducted. Phase 1 groups, which included both men and women, were asked to respond to questions that would guide the development of a social marketing program based on social marketing concepts. Phase 1 also determined the intervention's target audience, which was irregularly active women aged 35 to 54. Phase 2 groups, composed of members of the target audience, were asked to further define the product and discuss specific promotion strategies. Phase 1 participants determined that the program product, or target behavior, should be walking. In addition, they identified price, place, and promotion strategies. Phase 2 participants determined that moderate-intensity physical activity is best promoted using the term exercise and offered suggestions for marketing walking, or exercise, to the target audience. There have been few published studies of social marketing campaigns to promote physical activity. In this study, focus groups were key to understanding the target audience in a way that would not have been accomplished with quantitative data alone. The group discussions generated important insights into values and motivations that affect consumers' decisions to adopt a product or behavior. The focus group results guided the development of a social marketing program to promote physical activity in the target audience in Sumter County, South Carolina.

Dialog detection in narrative video by shot and face analysis

NASA Astrophysics Data System (ADS)

Kroon, B.; Nesvadba, J.; Hanjalic, A.

2007-01-01

The proliferation of captured personal and broadcast content in personal consumer archives necessitates comfortable access to stored audiovisual content. Intuitive retrieval and navigation solutions require however a semantic level that cannot be reached by generic multimedia content analysis alone. A fusion with film grammar rules can help to boost the reliability significantly. The current paper describes the fusion of low-level content analysis cues including face parameters and inter-shot similarities to segment commercial content into film grammar rule-based entities and subsequently classify those sequences into so-called shot reverse shots, i.e. dialog sequences. Moreover shot reverse shot specific mid-level cues are analyzed augmenting the shot reverse shot information with dialog specific descriptions.

The role of psychological determinants and demographic factors in consumer demand for farm-to-fork traceability systems.

PubMed

Myae, Aye Chan; Goddard, Ellen; Aubeeluck, Ashwina

2011-01-01

Traceability systems are an important tool (1) for tracking, monitoring, and managing product flows through the supply chain for better efficiency and profitability of suppliers, and (2) to improve consumer confidence in the face of serious food safety incidents. After the global bovine spongiform encephalopathy (BSE) crisis affected producers, consumers, trade, and the health status of animals and humans, new systems to help confirm the status of cattle products along the supply chain from farm to fork were implemented in many countries (Trautman et al. 2008 ). In this study, people's overall food safety beliefs are explored with the main objective of measuring the link between their food safety beliefs and their attitudes toward traceability. A comparison is made among English-speaking Canadians, French-speaking Canadians, and Japanese consumers. In the study, an Internet-based survey was used to collect data from nationally representative samples of the population in Canada-English (1275), Canada-French (343), and Japanese (1940) in the summer of 2009. Respondents' interests in traceability systems are clearly linked to their sense that the industry is primarily responsible for any food safety outbreaks. Moreover, it is clear that certain segments of the population in all samples feel strongly about the importance of farm to fork traceability in beef; thus, policymakers may wish to consider extending traceability beyond the point of slaughter as a way of encouraging beef sales in Canada.

Connecting plug-in vehicles with green electricity through consumer demand

NASA Astrophysics Data System (ADS)

Axsen, Jonn; Kurani, Kenneth S.

2013-03-01

The environmental benefits of plug-in electric vehicles (PEVs) increase if the vehicles are powered by electricity from ‘green’ sources such as solar, wind or small-scale hydroelectricity. Here, we explore the potential to build a market that pairs consumer purchases of PEVs with purchases of green electricity. We implement a web-based survey with three US samples defined by vehicle purchases: conventional new vehicle buyers (n = 1064), hybrid vehicle buyers (n = 364) and PEV buyers (n = 74). Respondents state their interest in a PEV as their next vehicle, in purchasing green electricity in one of three ways, i.e., monthly subscription, two-year lease or solar panel purchase, and in combining the two products. Although we find that a link between PEVs and green electricity is not presently strong in the consciousness of most consumers, the combination is attractive to some consumers when presented. Across all three respondent segments, pairing a PEV with a green electricity program increased interest in PEVs—with a 23% demand increase among buyers of conventional vehicles. Overall, about one-third of respondents presently value the combination of a PEV with green electricity; the proportion is much higher among previous HEV and PEV buyers. Respondents’ reported motives for interest in both products and their combination include financial savings (particularly among conventional buyers), concerns about air pollution and the environment, and interest in new technology (particularly among PEV buyers). The results provide guidance regarding policy and marketing strategies to advance PEVs and green electricity demand.

Interactive tele-radiological segmentation systems for treatment and diagnosis.

PubMed

Zimeras, S; Gortzis, L G

2012-01-01

Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where participants are separated by geographic, time, social and cultural barriers. The shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future. It is therefore crucial to develop a general information exchange e-medical system to enables its users to perform online and offline medical consultations through diagnosis. During the medical diagnosis, image analysis techniques combined with doctor's opinions could be useful for final medical decisions. Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools), manual segmentation of 2D and 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. The main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems.

Knowledge discovery from patients' behavior via clustering-classification algorithms based on weighted eRFM and CLV model: An empirical study in public health care services.

PubMed

Zare Hosseini, Zeinab; Mohammadzadeh, Mahdi

2016-01-01

The rapid growing of information technology (IT) motivates and makes competitive advantages in health care industry. Nowadays, many hospitals try to build a successful customer relationship management (CRM) to recognize target and potential patients, increase patient loyalty and satisfaction and finally maximize their profitability. Many hospitals have large data warehouses containing customer demographic and transactions information. Data mining techniques can be used to analyze this data and discover hidden knowledge of customers. This research develops an extended RFM model, namely RFML (added parameter: Length) based on health care services for a public sector hospital in Iran with the idea that there is contrast between patient and customer loyalty, to estimate customer life time value (CLV) for each patient. We used Two-step and K-means algorithms as clustering methods and Decision tree (CHAID) as classification technique to segment the patients to find out target, potential and loyal customers in order to implement strengthen CRM. Two approaches are used for classification: first, the result of clustering is considered as Decision attribute in classification process and second, the result of segmentation based on CLV value of patients (estimated by RFML) is considered as Decision attribute. Finally the results of CHAID algorithm show the significant hidden rules and identify existing patterns of hospital consumers.

Knowledge discovery from patients’ behavior via clustering-classification algorithms based on weighted eRFM and CLV model: An empirical study in public health care services

PubMed Central

Zare Hosseini, Zeinab; Mohammadzadeh, Mahdi

2016-01-01

The rapid growing of information technology (IT) motivates and makes competitive advantages in health care industry. Nowadays, many hospitals try to build a successful customer relationship management (CRM) to recognize target and potential patients, increase patient loyalty and satisfaction and finally maximize their profitability. Many hospitals have large data warehouses containing customer demographic and transactions information. Data mining techniques can be used to analyze this data and discover hidden knowledge of customers. This research develops an extended RFM model, namely RFML (added parameter: Length) based on health care services for a public sector hospital in Iran with the idea that there is contrast between patient and customer loyalty, to estimate customer life time value (CLV) for each patient. We used Two-step and K-means algorithms as clustering methods and Decision tree (CHAID) as classification technique to segment the patients to find out target, potential and loyal customers in order to implement strengthen CRM. Two approaches are used for classification: first, the result of clustering is considered as Decision attribute in classification process and second, the result of segmentation based on CLV value of patients (estimated by RFML) is considered as Decision attribute. Finally the results of CHAID algorithm show the significant hidden rules and identify existing patterns of hospital consumers. PMID:27610177

Parallelization of Finite Element Analysis Codes Using Heterogeneous Distributed Computing

NASA Technical Reports Server (NTRS)

Ozguner, Fusun

1996-01-01

Performance gains in computer design are quickly consumed as users seek to analyze larger problems to a higher degree of accuracy. Innovative computational methods, such as parallel and distributed computing, seek to multiply the power of existing hardware technology to satisfy the computational demands of large applications. In the early stages of this project, experiments were performed using two large, coarse-grained applications, CSTEM and METCAN. These applications were parallelized on an Intel iPSC/860 hypercube. It was found that the overall speedup was very low, due to large, inherently sequential code segments present in the applications. The overall execution time T(sub par), of the application is dependent on these sequential segments. If these segments make up a significant fraction of the overall code, the application will have a poor speedup measure.

Efficient terrestrial laser scan segmentation exploiting data structure

NASA Astrophysics Data System (ADS)

Mahmoudabadi, Hamid; Olsen, Michael J.; Todorovic, Sinisa

2016-09-01

New technologies such as lidar enable the rapid collection of massive datasets to model a 3D scene as a point cloud. However, while hardware technology continues to advance, processing 3D point clouds into informative models remains complex and time consuming. A common approach to increase processing efficiently is to segment the point cloud into smaller sections. This paper proposes a novel approach for point cloud segmentation using computer vision algorithms to analyze panoramic representations of individual laser scans. These panoramas can be quickly created using an inherent neighborhood structure that is established during the scanning process, which scans at fixed angular increments in a cylindrical or spherical coordinate system. In the proposed approach, a selected image segmentation algorithm is applied on several input layers exploiting this angular structure including laser intensity, range, normal vectors, and color information. These segments are then mapped back to the 3D point cloud so that modeling can be completed more efficiently. This approach does not depend on pre-defined mathematical models and consequently setting parameters for them. Unlike common geometrical point cloud segmentation methods, the proposed method employs the colorimetric and intensity data as another source of information. The proposed algorithm is demonstrated on several datasets encompassing variety of scenes and objects. Results show a very high perceptual (visual) level of segmentation and thereby the feasibility of the proposed algorithm. The proposed method is also more efficient compared to Random Sample Consensus (RANSAC), which is a common approach for point cloud segmentation.

Fully automated segmentation of callus by micro-CT compared to biomechanics.

PubMed

Bissinger, Oliver; Götz, Carolin; Wolff, Klaus-Dietrich; Hapfelmeier, Alexander; Prodinger, Peter Michael; Tischer, Thomas

2017-07-11

A high percentage of closed femur fractures have slight comminution. Using micro-CT (μCT), multiple fragment segmentation is much more difficult than segmentation of unfractured or osteotomied bone. Manual or semi-automated segmentation has been performed to date. However, such segmentation is extremely laborious, time-consuming and error-prone. Our aim was to therefore apply a fully automated segmentation algorithm to determine μCT parameters and examine their association with biomechanics. The femura of 64 rats taken after randomised inhibitory or neutral medication, in terms of the effect on fracture healing, and controls were closed fractured after a Kirschner wire was inserted. After 21 days, μCT and biomechanical parameters were determined by a fully automated method and correlated (Pearson's correlation). The fully automated segmentation algorithm automatically detected bone and simultaneously separated cortical bone from callus without requiring ROI selection for each single bony structure. We found an association of structural callus parameters obtained by μCT to the biomechanical properties. However, results were only explicable by additionally considering the callus location. A large number of slightly comminuted fractures in combination with therapies that influence the callus qualitatively and/or quantitatively considerably affects the association between μCT and biomechanics. In the future, contrast-enhanced μCT imaging of the callus cartilage might provide more information to improve the non-destructive and non-invasive prediction of callus mechanical properties. As studies evaluating such important drugs increase, fully automated segmentation appears to be clinically important.

Automated morphological analysis of bone marrow cells in microscopic images for diagnosis of leukemia: nucleus-plasma separation and cell classification using a hierarchical tree model of hematopoesis

NASA Astrophysics Data System (ADS)

Krappe, Sebastian; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2016-03-01

The morphological differentiation of bone marrow is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually under the use of bright field microscopy. This is a time-consuming, subjective, tedious and error-prone process. Furthermore, repeated examinations of a slide may yield intra- and inter-observer variances. For that reason a computer assisted diagnosis system for bone marrow differentiation is pursued. In this work we focus (a) on a new method for the separation of nucleus and plasma parts and (b) on a knowledge-based hierarchical tree classifier for the differentiation of bone marrow cells in 16 different classes. Classification trees are easily interpretable and understandable and provide a classification together with an explanation. Using classification trees, expert knowledge (i.e. knowledge about similar classes and cell lines in the tree model of hematopoiesis) is integrated in the structure of the tree. The proposed segmentation method is evaluated with more than 10,000 manually segmented cells. For the evaluation of the proposed hierarchical classifier more than 140,000 automatically segmented bone marrow cells are used. Future automated solutions for the morphological analysis of bone marrow smears could potentially apply such an approach for the pre-classification of bone marrow cells and thereby shortening the examination time.

Extended Multiscale Image Segmentation for Castellated Wall Management

NASA Astrophysics Data System (ADS)

Sakamoto, M.; Tsuguchi, M.; Chhatkuli, S.; Satoh, T.

2018-05-01

Castellated walls are positioned as tangible cultural heritage, which require regular maintenance to preserve their original state. For the demolition and repair work of the castellated wall, it is necessary to identify the individual stones constituting the wall. However, conventional approaches using laser scanning or integrated circuits (IC) tags were very time-consuming and cumbersome. Therefore, we herein propose an efficient approach for castellated wall management based on an extended multiscale image segmentation technique. In this approach, individual stone polygons are extracted from the castellated wall image and are associated with a stone management database. First, to improve the performance of the extraction of individual stone polygons having a convex shape, we developed a new shape criterion named convex hull fitness in the image segmentation process and confirmed its effectiveness. Next, we discussed the stone management database and its beneficial utilization in the repair work of castellated walls. Subsequently, we proposed irregular-shape indexes that are helpful for evaluating the stone shape and the stability of the stone arrangement state in castellated walls. Finally, we demonstrated an application of the proposed method for a typical castellated wall in Japan. Consequently, we confirmed that the stone polygons can be extracted with an acceptable level. Further, the condition of the shapes and the layout of the stones could be visually judged with the proposed irregular-shape indexes.

Sleep versus wake classification from heart rate variability using computational intelligence: consideration of rejection in classification models.

PubMed

Lewicke, Aaron; Sazonov, Edward; Corwin, Michael J; Neuman, Michael; Schuckers, Stephanie

2008-01-01

Reliability of classification performance is important for many biomedical applications. A classification model which considers reliability in the development of the model such that unreliable segments are rejected would be useful, particularly, in large biomedical data sets. This approach is demonstrated in the development of a technique to reliably determine sleep and wake using only the electrocardiogram (ECG) of infants. Typically, sleep state scoring is a time consuming task in which sleep states are manually derived from many physiological signals. The method was tested with simultaneous 8-h ECG and polysomnogram (PSG) determined sleep scores from 190 infants enrolled in the collaborative home infant monitoring evaluation (CHIME) study. Learning vector quantization (LVQ) neural network, multilayer perceptron (MLP) neural network, and support vector machines (SVMs) are tested as the classifiers. After systematic rejection of difficult to classify segments, the models can achieve 85%-87% correct classification while rejecting only 30% of the data. This corresponds to a Kappa statistic of 0.65-0.68. With rejection, accuracy improves by about 8% over a model without rejection. Additionally, the impact of the PSG scored indeterminate state epochs is analyzed. The advantages of a reliable sleep/wake classifier based only on ECG include high accuracy, simplicity of use, and low intrusiveness. Reliability of the classification can be built directly in the model, such that unreliable segments are rejected.

Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning.

PubMed

Looney, Pádraig; Stevenson, Gordon N; Nicolaides, Kypros H; Plasencia, Walter; Molloholli, Malid; Natsis, Stavros; Collins, Sally L

2018-06-07

We present a new technique to fully automate the segmentation of an organ from 3D ultrasound (3D-US) volumes, using the placenta as the target organ. Image analysis tools to estimate organ volume do exist but are too time consuming and operator dependant. Fully automating the segmentation process would potentially allow the use of placental volume to screen for increased risk of pregnancy complications. The placenta was segmented from 2,393 first trimester 3D-US volumes using a semiautomated technique. This was quality controlled by three operators to produce the "ground-truth" data set. A fully convolutional neural network (OxNNet) was trained using this ground-truth data set to automatically segment the placenta. OxNNet delivered state-of-the-art automatic segmentation. The effect of training set size on the performance of OxNNet demonstrated the need for large data sets. The clinical utility of placental volume was tested by looking at predictions of small-for-gestational-age babies at term. The receiver-operating characteristics curves demonstrated almost identical results between OxNNet and the ground-truth). Our results demonstrated good similarity to the ground-truth and almost identical clinical results for the prediction of SGA.

How to Quantify Penile Corpus Cavernosum Structures with Histomorphometry: Comparison of Two Methods

PubMed Central

Felix-Patrício, Bruno; De Souza, Diogo Benchimol; Gregório, Bianca Martins; Costa, Waldemar Silva; Sampaio, Francisco José

2015-01-01

The use of morphometrical tools in biomedical research permits the accurate comparison of specimens subjected to different conditions, and the surface density of structures is commonly used for this purpose. The traditional point-counting method is reliable but time-consuming, with computer-aided methods being proposed as an alternative. The aim of this study was to compare the surface density data of penile corpus cavernosum trabecular smooth muscle in different groups of rats, measured by two observers using the point-counting or color-based segmentation method. Ten normotensive and 10 hypertensive male rats were used in this study. Rat penises were processed to obtain smooth muscle immunostained histological slices and photomicrographs captured for analysis. The smooth muscle surface density was measured in both groups by two different observers by the point-counting method and by the color-based segmentation method. Hypertensive rats showed an increase in smooth muscle surface density by the two methods, and no difference was found between the results of the two observers. However, surface density values were higher by the point-counting method. The use of either method did not influence the final interpretation of the results, and both proved to have adequate reproducibility. However, as differences were found between the two methods, results obtained by either method should not be compared. PMID:26413547

How to Quantify Penile Corpus Cavernosum Structures with Histomorphometry: Comparison of Two Methods.

PubMed

Felix-Patrício, Bruno; De Souza, Diogo Benchimol; Gregório, Bianca Martins; Costa, Waldemar Silva; Sampaio, Francisco José

2015-01-01

The use of morphometrical tools in biomedical research permits the accurate comparison of specimens subjected to different conditions, and the surface density of structures is commonly used for this purpose. The traditional point-counting method is reliable but time-consuming, with computer-aided methods being proposed as an alternative. The aim of this study was to compare the surface density data of penile corpus cavernosum trabecular smooth muscle in different groups of rats, measured by two observers using the point-counting or color-based segmentation method. Ten normotensive and 10 hypertensive male rats were used in this study. Rat penises were processed to obtain smooth muscle immunostained histological slices and photomicrographs captured for analysis. The smooth muscle surface density was measured in both groups by two different observers by the point-counting method and by the color-based segmentation method. Hypertensive rats showed an increase in smooth muscle surface density by the two methods, and no difference was found between the results of the two observers. However, surface density values were higher by the point-counting method. The use of either method did not influence the final interpretation of the results, and both proved to have adequate reproducibility. However, as differences were found between the two methods, results obtained by either method should not be compared.

Identifying the ideal profile of French yogurts for different clusters of consumers.

PubMed

Masson, M; Saint-Eve, A; Delarue, J; Blumenthal, D

2016-05-01

Identifying the sensory properties that affect consumer preferences for food products is an important feature of product development. Different methods, such as external preference mapping or partial least squares regression, are used to establish relationships between sensory data and consumer preferences and to identify sensory attributes that drive consumer preferences, by highlighting optimum products. Plain French yogurts were evaluated by a sensory profiling method performed by 12 trained judges. In parallel, 180 consumers were asked to score their overall liking and complete a cognitive restraint questionnaire. After hierarchical cluster analysis on the liking scores, preference mapping using a quadratic regression model was performed. Five clusters of consumers were identified as a function of different preference patterns. Contrary to our expectations, fat levels were not discriminating. For each cluster, the results of preference mapping enabled the identification of optimum products. A comparison of the 5 sensory profiles revealed numerous differences between key sensory attributes. For example, one consumer cluster had a strong preference for products perceived as very thick, grainy, but with a less flowing texture, less sticky, whey presence and color, in contrast to other clusters. In addition, each segment of consumers was characterized according to the results of the cognitive restraint questionnaire. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Marketing Animal-Friendly Products: Addressing the Consumer Social Dilemma with Reinforcement Positioning Strategies.

PubMed

van Riemsdijk, Lenka; Ingenbleek, Paul T M; van Trijp, Hans C M; van der Veen, Gerrita

2017-12-14

This article presents a conceptual framework that aims to encourage consumer animal-friendly product choice by introducing positioning strategies for animal-friendly products. These strategies reinforce the animal welfare with different types of consumption values and can therefore reduce consumers' social dilemma, which is a major barrier to animal-friendly consumer choices. The article suggests how animal-friendly products can use various types of consumption values (functional, sensory, emotional, social, epistemic and situational) to create an attractive position relative to their competitors. It also explains why some consumer segments, such as those with a specific thinking style, may experience a stronger effect of some strategies, giving directions on how to approach different types of consumers. Finally, building on research asserting that animal welfare is a credence product attribute, the article proposes moderating effects of two factors that help consumers to evaluate the credibility of animal welfare claims, namely corporate social responsibility strategy and the role of stakeholders. Here it concludes that companies selling animal-friendly products need to be aware of the impact of their overall strategy on the effectiveness of positioning strategies for individual products and that, to gain consumer trust, they may need to collaborate with relevant stakeholders, such as media or animal-interest organizations.

Long-range airplane study: The consumer looks at SST travel

NASA Technical Reports Server (NTRS)

Landes, K. H.; Matter, J. A.

1980-01-01

The attitudes of long-range air travelers toward several basic air travel decisions, were surveyed. Of interest were tradeoffs involving time versus comfort and time versus cost as they pertain to supersonic versus conventional wide-body aircraft on overseas routes. The market focused upon was the segment of air travelers most likely to make that type of tradeoff decision: those having flown overseas routes for business or personal reasons in the recent past. The information generated is intended to provide quantifiable insight into consumer demand for supersonic as compared to wide-body aircraft alternatives for long-range overseas air travel.

Automatic multi-organ segmentation using learning-based segmentation and level set optimization.

PubMed

Kohlberger, Timo; Sofka, Michal; Zhang, Jingdan; Birkbeck, Neil; Wetzl, Jens; Kaftan, Jens; Declerck, Jérôme; Zhou, S Kevin

2011-01-01

We present a novel generic segmentation system for the fully automatic multi-organ segmentation from CT medical images. Thereby we combine the advantages of learning-based approaches on point cloud-based shape representation, such a speed, robustness, point correspondences, with those of PDE-optimization-based level set approaches, such as high accuracy and the straightforward prevention of segment overlaps. In a benchmark on 10-100 annotated datasets for the liver, the lungs, and the kidneys we show that the proposed system yields segmentation accuracies of 1.17-2.89 mm average surface errors. Thereby the level set segmentation (which is initialized by the learning-based segmentations) contributes with an 20%-40% increase in accuracy.

Automated measurement of uptake in cerebellum, liver, and aortic arch in full-body FDG PET/CT scans.

PubMed

Bauer, Christian; Sun, Shanhui; Sun, Wenqing; Otis, Justin; Wallace, Audrey; Smith, Brian J; Sunderland, John J; Graham, Michael M; Sonka, Milan; Buatti, John M; Beichel, Reinhard R

2012-06-01

The purpose of this work was to develop and validate fully automated methods for uptake measurement of cerebellum, liver, and aortic arch in full-body PET/CT scans. Such measurements are of interest in the context of uptake normalization for quantitative assessment of metabolic activity and/or automated image quality control. Cerebellum, liver, and aortic arch regions were segmented with different automated approaches. Cerebella were segmented in PET volumes by means of a robust active shape model (ASM) based method. For liver segmentation, a largest possible hyperellipsoid was fitted to the liver in PET scans. The aortic arch was first segmented in CT images of a PET/CT scan by a tubular structure analysis approach, and the segmented result was then mapped to the corresponding PET scan. For each of the segmented structures, the average standardized uptake value (SUV) was calculated. To generate an independent reference standard for method validation, expert image analysts were asked to segment several cross sections of each of the three structures in 134 F-18 fluorodeoxyglucose (FDG) PET/CT scans. For each case, the true average SUV was estimated by utilizing statistical models and served as the independent reference standard. For automated aorta and liver SUV measurements, no statistically significant scale or shift differences were observed between automated results and the independent standard. In the case of the cerebellum, the scale and shift were not significantly different, if measured in the same cross sections that were utilized for generating the reference. In contrast, automated results were scaled 5% lower on average although not shifted, if FDG uptake was calculated from the whole segmented cerebellum volume. The estimated reduction in total SUV measurement error ranged between 54.7% and 99.2%, and the reduction was found to be statistically significant for cerebellum and aortic arch. With the proposed methods, the authors have demonstrated that automated SUV uptake measurements in cerebellum, liver, and aortic arch agree with expert-defined independent standards. The proposed methods were found to be accurate and showed less intra- and interobserver variability, compared to manual analysis. The approach provides an alternative to manual uptake quantification, which is time-consuming. Such an approach will be important for application of quantitative PET imaging to large scale clinical trials. © 2012 American Association of Physicists in Medicine.

Retinal layer segmentation of macular OCT images using boundary classification

PubMed Central

Lang, Andrew; Carass, Aaron; Hauser, Matthew; Sotirchos, Elias S.; Calabresi, Peter A.; Ying, Howard S.; Prince, Jerry L.

2013-01-01

Optical coherence tomography (OCT) has proven to be an essential imaging modality for ophthalmology and is proving to be very important in neurology. OCT enables high resolution imaging of the retina, both at the optic nerve head and the macula. Macular retinal layer thicknesses provide useful diagnostic information and have been shown to correlate well with measures of disease severity in several diseases. Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology. In this work, we build a random forest classifier to segment eight retinal layers in macular cube images acquired by OCT. The random forest classifier learns the boundary pixels between layers, producing an accurate probability map for each boundary, which is then processed to finalize the boundaries. Using this algorithm, we can accurately segment the entire retina contained in the macular cube to an accuracy of at least 4.3 microns for any of the nine boundaries. Experiments were carried out on both healthy and multiple sclerosis subjects, with no difference in the accuracy of our algorithm found between the groups. PMID:23847738

Marketing Education for the next Four Billion: Challenges and Innovations

ERIC Educational Resources Information Center

Rosa, Jose Antonio

2012-01-01

This article argues for a third transformation in marketing pedagogy, one made necessary by the emergence of subsistence consumers as a high-growth market segment. Continued double-digit growth in buying power and consumption among the world's poor appear certain, provided that the subsistence merchants serving such markets are effective. Ensuring…

Dissent and the College Student in Revolt.

ERIC Educational Resources Information Center

Blocker, Clyde E.

To the familiar list that usually includes the war in Viet Nam, racial discrimination, capitalistic exploitation of the consumer, a breakdown in family structure, political corruption, the weakening position of organized religion, and a shift in the values of a large segment of society as supposed causes of campus unrest, the author adds the…

Helping Parents of Young Children with Disabilities Become Consumers of Early Intervention: A Marketing Approach.

ERIC Educational Resources Information Center

Fugate, Douglas L.; Fugate, Janet M.

1995-01-01

This article suggests the use of marketing techniques to disseminate information products to parents of young children with disabilities. A marketing plan might include the following steps: determination of market needs, market segmentation and target marketing, marketing goals and objectives, marketing strategy, marketing mix tactics, and control…

Inter-relationships of Salmonella status of flock and grow-out environment at sequential segments in broiler production and processing

USDA-ARS?s Scientific Manuscript database

Effective Salmonella control in broilers is important from the standpoint of both consumer protection and industry viability. We investigated associations between Salmonella recovery from different sample types collected at sequential stages of one grow-out from the broiler flock and production env...

How much segmentation is needed in the health care marketplace? An exploratory study of HMO and non-HMO customers.

PubMed

Tootelian, Dennis H; Mikhailitchenko, Andrey; Holst, Cindy; Gaedeke, Ralph M

2016-01-01

The health care landscape has changed dramatically. Consumers now seek plans whose benefits better fit their health care needs and desires for access to providers. This exploratory survey of more than 1,000 HMO and non-HMO customers found significant differences with respect to their selection processes for health plans and providers, and their expectations regarding access to and communication with health care providers. While there are some similarities in factors affecting choice, segmentation strategies are necessary to maximize the appeal of a plan, satisfy customers in the selection of physicians, and meet their expectations regarding access to those physicians.

Multilevel wireless capsule endoscopy video segmentation

NASA Astrophysics Data System (ADS)

Hwang, Sae; Celebi, M. Emre

2010-03-01

Wireless Capsule Endoscopy (WCE) is a relatively new technology (FDA approved in 2002) allowing doctors to view most of the small intestine. WCE transmits more than 50,000 video frames per examination and the visual inspection of the resulting video is a highly time-consuming task even for the experienced gastroenterologist. Typically, a medical clinician spends one or two hours to analyze a WCE video. To reduce the assessment time, it is critical to develop a technique to automatically discriminate digestive organs and shots each of which consists of the same or similar shots. In this paper a multi-level WCE video segmentation methodology is presented to reduce the examination time.

Biodegradation Of thermoplastic polyurethanes from vegetable oils

USDA-ARS?s Scientific Manuscript database

Thermoplastic urethanes based on polyricinoleic acid soft segments and MDI/BD hard segments with varied soft segment concentration were prepared. Soft segment concentration was varied fro, 40 to 70 wt %. Biodegradation was studied by respirometry. Segmented polyurethanes with soft segments based ...

Looking beyond borders: integrating best practices in benefit-risk analysis into the field of food and nutrition.

PubMed

Tijhuis, M J; Pohjola, M V; Gunnlaugsdóttir, H; Kalogeras, N; Leino, O; Luteijn, J M; Magnússon, S H; Odekerken-Schröder, G; Poto, M; Tuomisto, J T; Ueland, O; White, B C; Holm, F; Verhagen, H

2012-01-01

An integrated benefit-risk analysis aims to give guidance in decision situations where benefits do not clearly prevail over risks, and explicit weighing of benefits and risks is thus indicated. The BEPRARIBEAN project aims to advance benefit-risk analysis in the area of food and nutrition by learning from other fields. This paper constitutes the final stage of the project, in which commonalities and differences in benefit-risk analysis are identified between the Food and Nutrition field and other fields, namely Medicines, Food Microbiology, Environmental Health, Economics and Marketing-Finance, and Consumer Perception. From this, ways forward are characterized for benefit-risk analysis in Food and Nutrition. Integrated benefit-risk analysis in Food and Nutrition may advance in the following ways: Increased engagement and communication between assessors, managers, and stakeholders; more pragmatic problem-oriented framing of assessment; accepting some risk; pre- and post-market analysis; explicit communication of the assessment purpose, input and output; more human (dose-response) data and more efficient use of human data; segmenting populations based on physiology; explicit consideration of value judgments in assessment; integration of multiple benefits and risks from multiple domains; explicit recognition of the impact of consumer beliefs, opinions, views, perceptions, and attitudes on behaviour; and segmenting populations based on behaviour; the opportunities proposed here do not provide ultimate solutions; rather, they define a collection of issues to be taken account of in developing methods, tools, practices and policies, as well as refining the regulatory context, for benefit-risk analysis in Food and Nutrition and other fields. Thus, these opportunities will now need to be explored further and incorporated into benefit-risk practice and policy. If accepted, incorporation of these opportunities will also involve a paradigm shift in Food and Nutrition benefit-risk analysis towards conceiving the analysis as a process of creating shared knowledge among all stakeholders. Copyright © 2011 Elsevier Ltd. All rights reserved.

Will the US economy recover in 2010? A minimal spanning tree study

NASA Astrophysics Data System (ADS)

Zhang, Yiting; Lee, Gladys Hui Ting; Wong, Jian Cheng; Kok, Jun Liang; Prusty, Manamohan; Cheong, Siew Ann

2011-06-01

We calculated the cross correlations between the half-hourly times series of the ten Dow Jones US economic sectors over the period February 2000 to August 2008, the two-year intervals 2002-2003, 2004-2005, 2008-2009, and also over 11 segments within the present financial crisis, to construct minimal spanning trees (MSTs) of the US economy at the sector level. In all MSTs, a core-fringe structure is found, with consumer goods, consumer services, and the industrials consistently making up the core, and basic materials, oil & gas, healthcare, telecommunications, and utilities residing predominantly on the fringe. More importantly, we find that the MSTs can be classified into two distinct, statistically robust, topologies: (i) star-like, with the industrials at the center, associated with low-volatility economic growth; and (ii) chain-like, associated with high-volatility economic crisis. Finally, we present statistical evidence, based on the emergence of a star-like MST in Sep 2009, and the MST staying robustly star-like throughout the Greek Debt Crisis, that the US economy is on track to a recovery.

Applications of the marketing perspective in nutrition education.

PubMed

Fleming, P L

1987-09-01

The marketing paradigm is based on the premise of exchange of value, that is, value received for value given. The role of the nutrition educator as a marketer is to facilitate exchanges of value with consumers. To carry out this role, a strong orientation to the consumer, what she or he wants and needs and is willing to "pay," guides the development of the nutrition education mission, objectives, and strategies. The marketing paradigm calls for a marketing information system that includes internal record keeping, marketing intelligence gathering, and marketing research. The information is used in the marketing audit, which identifies organizational strengths and weaknesses and marketplace opportunities and barriers. Marketing objectives are formulated, and strategies for segmenting, positioning, and developing the marketing mix follow. These are translated in the marketing plan to an action plan, a budget, and profit and loss projections. Use of the marketing paradigm in nutrition education is not a panacea for organizational ills and marketplace problems. Instead, the paradigm raises issues to which nutrition educators must bring their expertise, commitment, ingenuity, and creativity.

How tobacco companies have used package quantity for consumer targeting.

PubMed

Persoskie, Alexander; Donaldson, Elisabeth A; Ryant, Chase

2018-05-31

Package quantity refers to the number of cigarettes or amount of other tobacco product in a package. Many countries restrict minimum cigarette package quantities to avoid low-cost packs that may lower barriers to youth smoking. We reviewed Truth Tobacco Industry Documents to understand tobacco companies' rationales for introducing new package quantities, including companies' expectations and research regarding how package quantity may influence consumer behaviour. A snowball sampling method (phase 1), a static search string (phase 2) and a follow-up snowball search (phase 3) identified 216 documents, mostly from the 1980s and 1990s, concerning cigarettes (200), roll-your-own tobacco (9), smokeless tobacco (6) and 'smokeless cigarettes' (1). Companies introduced small and large packages to motivate brand-switching and continued use among current users when faced with low market share or threats such as tax-induced price increases or competitors' use of price promotions. Companies developed and evaluated package quantities for specific brands and consumer segments. Large packages offered value-for-money and matched long-term, heavy users' consumption rates. Small packages were cheaper, matched consumption rates of newer and lighter users, and increased products' novelty, ease of carrying and perceived freshness. Some users also preferred small packages as a way to try to limit consumption or quit. Industry documents speculated about many potential effects of package quantity on appeal and use, depending on brand and consumer segment. The search was non-exhaustive, and we could not assess the quality of much of the research or other information on which the documents relied. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers.

PubMed

Prinyakupt, Jaroonrut; Pluempitiwiriyawej, Charnchai

2015-06-30

Blood smear microscopic images are routinely investigated by haematologists to diagnose most blood diseases. However, the task is quite tedious and time consuming. An automatic detection and classification of white blood cells within such images can accelerate the process tremendously. In this paper we propose a system to locate white blood cells within microscopic blood smear images, segment them into nucleus and cytoplasm regions, extract suitable features and finally, classify them into five types: basophil, eosinophil, neutrophil, lymphocyte and monocyte. Two sets of blood smear images were used in this study's experiments. Dataset 1, collected from Rangsit University, were normal peripheral blood slides under light microscope with 100× magnification; 555 images with 601 white blood cells were captured by a Nikon DS-Fi2 high-definition color camera and saved in JPG format of size 960 × 1,280 pixels at 15 pixels per 1 μm resolution. In dataset 2, 477 cropped white blood cell images were downloaded from CellaVision.com. They are in JPG format of size 360 × 363 pixels. The resolution is estimated to be 10 pixels per 1 μm. The proposed system comprises a pre-processing step, nucleus segmentation, cell segmentation, feature extraction, feature selection and classification. The main concept of the segmentation algorithm employed uses white blood cell's morphological properties and the calibrated size of a real cell relative to image resolution. The segmentation process combined thresholding, morphological operation and ellipse curve fitting. Consequently, several features were extracted from the segmented nucleus and cytoplasm regions. Prominent features were then chosen by a greedy search algorithm called sequential forward selection. Finally, with a set of selected prominent features, both linear and naïve Bayes classifiers were applied for performance comparison. This system was tested on normal peripheral blood smear slide images from two datasets. Two sets of comparison were performed: segmentation and classification. The automatically segmented results were compared to the ones obtained manually by a haematologist. It was found that the proposed method is consistent and coherent in both datasets, with dice similarity of 98.9 and 91.6% for average segmented nucleus and cell regions, respectively. Furthermore, the overall correction rate in the classification phase is about 98 and 94% for linear and naïve Bayes models, respectively. The proposed system, based on normal white blood cell morphology and its characteristics, was applied to two different datasets. The results of the calibrated segmentation process on both datasets are fast, robust, efficient and coherent. Meanwhile, the classification of normal white blood cells into five types shows high sensitivity in both linear and naïve Bayes models, with slightly better results in the linear classifier.

Consumers’ Perceptions of Preconception Health

PubMed Central

Squiers, Linda; Mitchell, Elizabeth W.; Levis, Denise M.; Lynch, Molly; Dolina, Suzanne; Margolis, Marjorie; Scales, Monica; Kish-Doto, Julia

2015-01-01

Purpose To inform the development of a preconception health (PCH) social marketing plan, we conducted qualitative research with prospective consumers. Approach We present formative findings based on the four Ps of social marketing: product, price, promotion, and place. Setting We conducted focus groups with 10 groups of women in Atlanta, Georgia, in fall 2010. Participants We classified women aged 18 to 44 into five groups based on their pregnancy plans, and then further segmented the groups based on socioeconomic status for a total of 10 groups. Method The focus group guide was designed to elicit participants’ responses about the product, price, promotion, and placement of PCH. We used NVivo 9 software to analyze focus group data. Results Women planning a pregnancy in the future had different perspectives on PCH as a product than women not planning a pregnancy. Barriers to PCH included lack of social support, addiction, and lack of awareness about PCH. Participants preferred to think of PCH behaviors as “promoting” a healthy baby rather than preventing an unhealthy birth outcome. Many women in the focus groups preferred to hear PCH messages from a health care provider, among other channels. Conclusion The results from this research will inform the development of a social marketing plan for PCH and the development of concepts that will be tested with consumers to determine their viability for use in a national campaign. PMID:23286658

Exploring American and Italian consumer preferences for Californian and Italian red wines.

PubMed

Torri, Luisa; Noble, Ann Curtis; Heymann, Hildegarde

2013-06-01

To increase the market share of Californian wines in other countries, wine preferences need to be explored in potential markets. This work studied the preferences of American and Italian consumers for red wines produced in California and Italy, focusing on wines made from the same varieties in each location. Descriptive analysis and consumer preference tests were performed. Americans scored each of the Californian wines significantly higher in preference than the Italian wines. In contrast, the Italian consumer preference scores for many Italian and Californian wines overlapped. By external preference mapping of the American consumer segments, the ideal flavour of one cluster was closest to the Californian Zinfandel, Merlot and Syrah, which had the 'most balanced' flavour profiles. Another cluster of Italians also preferred the Californian wines. In addition, one Italian cluster was driven by a dislike of the leather, band-aid and medicinal aromas of the Italian Merlot and Refosco. The results provided information that can contribute to wine marketing research necessary for successfully exporting Californian red wines to Italy and vice versa. © 2012 Society of Chemical Industry.

The Supply Chain's Role in Improving Animal Welfare.

PubMed

Harvey, David; Hubbard, Carmen

2013-08-14

Supply chains are already incorporating citizen/consumer demands for improved animal welfare, especially through product differentiation and the associated segmentation of markets. Nonetheless, the ability of the chain to deliver high(er) levels and standards of animal welfare is subject to two critical conditions: (a) the innovative and adaptive capacity of the chain to respond to society's demands; (b) the extent to which consumers actually purchase animal-friendly products. Despite a substantial literature reporting estimates of willingness to pay (WTP) for animal welfare, there is a belief that in practice people vote for substantially more and better animal welfare as citizens than they are willing to pay for as consumers. This citizen-consumer gap has significant consequences on the supply chain, although there is limited literature on the capacity and willingness of supply chains to deliver what the consumer wants and is willing to pay for. This paper outlines an economic analysis of supply chain delivery of improved standards for farm animal welfare in the EU and illustrates the possible consequences of improving animal welfare standards for the supply chain using a prototype belief network analysis.

A Multiatlas Segmentation Using Graph Cuts with Applications to Liver Segmentation in CT Scans

PubMed Central

2014-01-01

An atlas-based segmentation approach is presented that combines low-level operations, an affine probabilistic atlas, and a multiatlas-based segmentation. The proposed combination provides highly accurate segmentation due to registrations and atlas selections based on the regions of interest (ROIs) and coarse segmentations. Our approach shares the following common elements between the probabilistic atlas and multiatlas segmentation: (a) the spatial normalisation and (b) the segmentation method, which is based on minimising a discrete energy function using graph cuts. The method is evaluated for the segmentation of the liver in computed tomography (CT) images. Low-level operations define a ROI around the liver from an abdominal CT. We generate a probabilistic atlas using an affine registration based on geometry moments from manually labelled data. Next, a coarse segmentation of the liver is obtained from the probabilistic atlas with low computational effort. Then, a multiatlas segmentation approach improves the accuracy of the segmentation. Both the atlas selections and the nonrigid registrations of the multiatlas approach use a binary mask defined by coarse segmentation. We experimentally demonstrate that this approach performs better than atlas selections and nonrigid registrations in the entire ROI. The segmentation results are comparable to those obtained by human experts and to other recently published results. PMID:25276219

Automatic identification of inertial sensor placement on human body segments during walking

PubMed Central

2013-01-01

Background Current inertial motion capture systems are rarely used in biomedical applications. The attachment and connection of the sensors with cables is often a complex and time consuming task. Moreover, it is prone to errors, because each sensor has to be attached to a predefined body segment. By using wireless inertial sensors and automatic identification of their positions on the human body, the complexity of the set-up can be reduced and incorrect attachments are avoided. We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is attached is identified automatically. Methods Walking data was recorded from ten healthy subjects using an Xsens MVN Biomech system with full-body configuration (17 inertial sensors). Subjects were asked to walk for about 6 seconds at normal walking speed (about 5 km/h). After rotating the sensor data to a global coordinate frame with x-axis in walking direction, y-axis pointing left and z-axis vertical, RMS, mean, and correlation coefficient features were extracted from x-, y- and z-components and magnitudes of the accelerations, angular velocities and angular accelerations. As a classifier, a decision tree based on the C4.5 algorithm was developed using Weka (Waikato Environment for Knowledge Analysis). Results and conclusions After testing the algorithm with 10-fold cross-validation using 31 walking trials (involving 527 sensors), 514 sensors were correctly classified (97.5%). When a decision tree for a lower body plus trunk configuration (8 inertial sensors) was trained and tested using 10-fold cross-validation, 100% of the sensors were correctly identified. This decision tree was also tested on walking trials of 7 patients (17 walking trials) after anterior cruciate ligament reconstruction, which also resulted in 100% correct identification, thus illustrating the robustness of the method. PMID:23517757

Automatic identification of inertial sensor placement on human body segments during walking.

PubMed

Weenk, Dirk; van Beijnum, Bert-Jan F; Baten, Chris T M; Hermens, Hermie J; Veltink, Peter H

2013-03-21

Current inertial motion capture systems are rarely used in biomedical applications. The attachment and connection of the sensors with cables is often a complex and time consuming task. Moreover, it is prone to errors, because each sensor has to be attached to a predefined body segment. By using wireless inertial sensors and automatic identification of their positions on the human body, the complexity of the set-up can be reduced and incorrect attachments are avoided.We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is attached is identified automatically. Walking data was recorded from ten healthy subjects using an Xsens MVN Biomech system with full-body configuration (17 inertial sensors). Subjects were asked to walk for about 6 seconds at normal walking speed (about 5 km/h). After rotating the sensor data to a global coordinate frame with x-axis in walking direction, y-axis pointing left and z-axis vertical, RMS, mean, and correlation coefficient features were extracted from x-, y- and z-components and magnitudes of the accelerations, angular velocities and angular accelerations. As a classifier, a decision tree based on the C4.5 algorithm was developed using Weka (Waikato Environment for Knowledge Analysis). After testing the algorithm with 10-fold cross-validation using 31 walking trials (involving 527 sensors), 514 sensors were correctly classified (97.5%). When a decision tree for a lower body plus trunk configuration (8 inertial sensors) was trained and tested using 10-fold cross-validation, 100% of the sensors were correctly identified. This decision tree was also tested on walking trials of 7 patients (17 walking trials) after anterior cruciate ligament reconstruction, which also resulted in 100% correct identification, thus illustrating the robustness of the method.

Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients.

PubMed

Dolz, J; Kirişli, H A; Fechter, T; Karnitzki, S; Oehlke, O; Nestle, U; Vermandel, M; Massoptier, L

2016-05-01

Accurate delineation of organs at risk (OARs) on computed tomography (CT) image is required for radiation treatment planning (RTP). Manual delineation of OARs being time consuming and prone to high interobserver variability, many (semi-) automatic methods have been proposed. However, most of them are specific to a particular OAR. Here, an interactive computer-assisted system able to segment various OARs required for thoracic radiation therapy is introduced. Segmentation information (foreground and background seeds) is interactively added by the user in any of the three main orthogonal views of the CT volume and is subsequently propagated within the whole volume. The proposed method is based on the combination of watershed transformation and graph-cuts algorithm, which is used as a powerful optimization technique to minimize the energy function. The OARs considered for thoracic radiation therapy are the lungs, spinal cord, trachea, proximal bronchus tree, heart, and esophagus. The method was evaluated on multivendor CT datasets of 30 patients. Two radiation oncologists participated in the study and manual delineations from the original RTP were used as ground truth for evaluation. Delineation of the OARs obtained with the minimally interactive approach was approved to be usable for RTP in nearly 90% of the cases, excluding the esophagus, which segmentation was mostly rejected, thus leading to a gain of time ranging from 50% to 80% in RTP. Considering exclusively accepted cases, overall OARs, a Dice similarity coefficient higher than 0.7 and a Hausdorff distance below 10 mm with respect to the ground truth were achieved. In addition, the interobserver analysis did not highlight any statistically significant difference, at the exception of the segmentation of the heart, in terms of Hausdorff distance and volume difference. An interactive, accurate, fast, and easy-to-use computer-assisted system able to segment various OARs required for thoracic radiation therapy has been presented and clinically evaluated. The introduction of the proposed system in clinical routine may offer valuable new option to radiation oncologists in performing RTP.

Nearest patch matching for color image segmentation supporting neural network classification in pulmonary tuberculosis identification

NASA Astrophysics Data System (ADS)

Rulaningtyas, Riries; Suksmono, Andriyan B.; Mengko, Tati L. R.; Saptawati, Putri

2016-03-01

Pulmonary tuberculosis is a deadly infectious disease which occurs in many countries in Asia and Africa. In Indonesia, many people with tuberculosis disease are examined in the community health center. Examination of pulmonary tuberculosis is done through sputum smear with Ziehl - Neelsen staining using conventional light microscope. The results of Ziehl - Neelsen staining will give effect to the appearance of tuberculosis (TB) bacteria in red color and sputum background in blue color. The first examination is to detect the presence of TB bacteria from its color, then from the morphology of the TB bacteria itself. The results of Ziehl - Neelsen staining in sputum smear give the complex color images, so that the clinicians have difficulty when doing slide examination manually because it is time consuming and needs highly training to detect the presence of TB bacteria accurately. The clinicians have heavy workload to examine many sputum smear slides from the patients. To assist the clinicians when reading the sputum smear slide, this research built computer aided diagnose with color image segmentation, feature extraction, and classification method. This research used K-means clustering with patch technique to segment digital sputum smear images which separated the TB bacteria images from the background images. This segmentation method gave the good accuracy 97.68%. Then, feature extraction based on geometrical shape of TB bacteria was applied to this research. The last step, this research used neural network with back propagation method to classify TB bacteria and non TB bacteria images in sputum slides. The classification result of neural network back propagation are learning time (42.69±0.02) second, the number of epoch 5000, error rate of learning 15%, learning accuracy (98.58±0.01)%, and test accuracy (96.54±0.02)%.

Mini-DNA barcode in identification of the ornamental fish: A case study from Northeast India.

PubMed

Dhar, Bishal; Ghosh, Sankar Kumar

2017-09-05

The ornamental fishes were exported under the trade names or generic names, thus creating problems in species identification. In this regard, DNA barcoding could effectively elucidate the actual species status. However, the problem arises if the specimen is having taxonomic disputes, falsified by trade/generic names, etc., On the other hand, barcoding the archival museum specimens would be of greater benefit to address such issues as it would create firm, error-free reference database for rapid identification of any species. This can be achieved only by generating short sequences as DNA from chemically preserved are mostly degraded. Here we aimed to identify a short stretch of informative sites within the full-length barcode segment, capable of delineating diverse group of ornamental fish species, commonly traded from NE India. We analyzed 287 full-length barcode sequences from the major fish orders and compared the interspecific K2P distance with nucleotide substitutions patterns and found a strong correlation of interspecies distance with transversions (0.95, p<0.001). We, therefore, proposed a short stretch of 171bp (transversion rich) segment as mini-barcode. The proposed segment was compared with the full-length barcodes and found to delineate the species effectively. Successful PCR amplification and sequencing of the 171bp segment using designed primers for different orders validated it as mini-barcodes for ornamental fishes. Thus, our findings would be helpful in strengthening the global database with the sequence of archived fish species as well as an effective identification tool of the traded ornamental fish species, as a less time consuming, cost effective field-based application. Copyright © 2017 Elsevier B.V. All rights reserved.

Gap-free segmentation of vascular networks with automatic image processing pipeline.

PubMed

Hsu, Chih-Yang; Ghaffari, Mahsa; Alaraj, Ali; Flannery, Michael; Zhou, Xiaohong Joe; Linninger, Andreas

2017-03-01

Current image processing techniques capture large vessels reliably but often fail to preserve connectivity in bifurcations and small vessels. Imaging artifacts and noise can create gaps and discontinuity of intensity that hinders segmentation of vascular trees. However, topological analysis of vascular trees require proper connectivity without gaps, loops or dangling segments. Proper tree connectivity is also important for high quality rendering of surface meshes for scientific visualization or 3D printing. We present a fully automated vessel enhancement pipeline with automated parameter settings for vessel enhancement of tree-like structures from customary imaging sources, including 3D rotational angiography, magnetic resonance angiography, magnetic resonance venography, and computed tomography angiography. The output of the filter pipeline is a vessel-enhanced image which is ideal for generating anatomical consistent network representations of the cerebral angioarchitecture for further topological or statistical analysis. The filter pipeline combined with computational modeling can potentially improve computer-aided diagnosis of cerebrovascular diseases by delivering biometrics and anatomy of the vasculature. It may serve as the first step in fully automatic epidemiological analysis of large clinical datasets. The automatic analysis would enable rigorous statistical comparison of biometrics in subject-specific vascular trees. The robust and accurate image segmentation using a validated filter pipeline would also eliminate operator dependency that has been observed in manual segmentation. Moreover, manual segmentation is time prohibitive given that vascular trees have more than thousands of segments and bifurcations so that interactive segmentation consumes excessive human resources. Subject-specific trees are a first step toward patient-specific hemodynamic simulations for assessing treatment outcomes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Incorporating priors on expert performance parameters for segmentation validation and label fusion: a maximum a posteriori STAPLE

PubMed Central

Commowick, Olivier; Warfield, Simon K

2010-01-01

In order to evaluate the quality of segmentations of an image and assess intra- and inter-expert variability in segmentation performance, an Expectation Maximization (EM) algorithm for Simultaneous Truth And Performance Level Estimation (STAPLE) was recently developed. This algorithm, originally presented for segmentation validation, has since been used for many applications, such as atlas construction and decision fusion. However, the manual delineation of structures of interest is a very time consuming and burdensome task. Further, as the time required and burden of manual delineation increase, the accuracy of the delineation is decreased. Therefore, it may be desirable to ask the experts to delineate only a reduced number of structures or the segmentation of all structures by all experts may simply not be achieved. Fusion from data with some structures not segmented by each expert should be carried out in a manner that accounts for the missing information. In other applications, locally inconsistent segmentations may drive the STAPLE algorithm into an undesirable local optimum, leading to misclassifications or misleading experts performance parameters. We present a new algorithm that allows fusion with partial delineation and which can avoid convergence to undesirable local optima in the presence of strongly inconsistent segmentations. The algorithm extends STAPLE by incorporating prior probabilities for the expert performance parameters. This is achieved through a Maximum A Posteriori formulation, where the prior probabilities for the performance parameters are modeled by a beta distribution. We demonstrate that this new algorithm enables dramatically improved fusion from data with partial delineation by each expert in comparison to fusion with STAPLE. PMID:20879379

Incorporating priors on expert performance parameters for segmentation validation and label fusion: a maximum a posteriori STAPLE.

PubMed

Commowick, Olivier; Warfield, Simon K

2010-01-01

In order to evaluate the quality of segmentations of an image and assess intra- and inter-expert variability in segmentation performance, an Expectation Maximization (EM) algorithm for Simultaneous Truth And Performance Level Estimation (STAPLE) was recently developed. This algorithm, originally presented for segmentation validation, has since been used for many applications, such as atlas construction and decision fusion. However, the manual delineation of structures of interest is a very time consuming and burdensome task. Further, as the time required and burden of manual delineation increase, the accuracy of the delineation is decreased. Therefore, it may be desirable to ask the experts to delineate only a reduced number of structures or the segmentation of all structures by all experts may simply not be achieved. Fusion from data with some structures not segmented by each expert should be carried out in a manner that accounts for the missing information. In other applications, locally inconsistent segmentations may drive the STAPLE algorithm into an undesirable local optimum, leading to misclassifications or misleading experts performance parameters. We present a new algorithm that allows fusion with partial delineation and which can avoid convergence to undesirable local optima in the presence of strongly inconsistent segmentations. The algorithm extends STAPLE by incorporating prior probabilities for the expert performance parameters. This is achieved through a Maximum A Posteriori formulation, where the prior probabilities for the performance parameters are modeled by a beta distribution. We demonstrate that this new algorithm enables dramatically improved fusion from data with partial delineation by each expert in comparison to fusion with STAPLE.

Social marketing: approach to cultural and contextual relevance in a community-based physical activity intervention

PubMed Central

Keller, Colleen; Vega-López, Sonia; Ainsworth, Barbara; Nagle-Williams, Allison; Records, Kathie; Permana, Paska; Coonrod, Dean

2014-01-01

We report the social marketing strategies used for the design, recruitment and retention of participants in a community-based physical activity (PA) intervention, Madres para la Salud (Mothers for Health). The study example used to illustrate the use of social marketing is a 48-week prescribed walking program, Madres para la Salud (Mothers for Health), which tests a social support intervention to explore the effectiveness of a culturally specific program using ‘bouts’ of PA to effect the changes in body fat, fat tissue inflammation and postpartum depression symptoms in sedentary Hispanic women. Using the guidelines from the National Benchmark Criteria, we developed intervention, recruitment and retention strategies that reflect efforts to draw on community values, traditions and customs in intervention design, through partnership with community members. Most of the women enrolled in Madres para la Salud were born in Mexico, largely never or unemployed and resided among the highest crime neighborhoods with poor access to resources. We developed recruitment and retention strategies that characterized social marketing strategies that employed a culturally relevant, consumer driven and problem-specific design. Cost and benefit of program participation, consumer-derived motivation and segmentation strategies considered the development transition of the young Latinas as well as cultural and neighborhood barriers that impacted retention are described. PMID:23002252

Social marketing: approach to cultural and contextual relevance in a community-based physical activity intervention.

PubMed

Keller, Colleen; Vega-López, Sonia; Ainsworth, Barbara; Nagle-Williams, Allison; Records, Kathie; Permana, Paska; Coonrod, Dean

2014-03-01

We report the social marketing strategies used for the design, recruitment and retention of participants in a community-based physical activity (PA) intervention, Madres para la Salud (Mothers for Health). The study example used to illustrate the use of social marketing is a 48-week prescribed walking program, Madres para la Salud (Mothers for Health), which tests a social support intervention to explore the effectiveness of a culturally specific program using 'bouts' of PA to effect the changes in body fat, fat tissue inflammation and postpartum depression symptoms in sedentary Hispanic women. Using the guidelines from the National Benchmark Criteria, we developed intervention, recruitment and retention strategies that reflect efforts to draw on community values, traditions and customs in intervention design, through partnership with community members. Most of the women enrolled in Madres para la Salud were born in Mexico, largely never or unemployed and resided among the highest crime neighborhoods with poor access to resources. We developed recruitment and retention strategies that characterized social marketing strategies that employed a culturally relevant, consumer driven and problem-specific design. Cost and benefit of program participation, consumer-derived motivation and segmentation strategies considered the development transition of the young Latinas as well as cultural and neighborhood barriers that impacted retention are described.

Remote sensing image segmentation based on Hadoop cloud platform

NASA Astrophysics Data System (ADS)

Li, Jie; Zhu, Lingling; Cao, Fubin

2018-01-01

To solve the problem that the remote sensing image segmentation speed is slow and the real-time performance is poor, this paper studies the method of remote sensing image segmentation based on Hadoop platform. On the basis of analyzing the structural characteristics of Hadoop cloud platform and its component MapReduce programming, this paper proposes a method of image segmentation based on the combination of OpenCV and Hadoop cloud platform. Firstly, the MapReduce image processing model of Hadoop cloud platform is designed, the input and output of image are customized and the segmentation method of the data file is rewritten. Then the Mean Shift image segmentation algorithm is implemented. Finally, this paper makes a segmentation experiment on remote sensing image, and uses MATLAB to realize the Mean Shift image segmentation algorithm to compare the same image segmentation experiment. The experimental results show that under the premise of ensuring good effect, the segmentation rate of remote sensing image segmentation based on Hadoop cloud Platform has been greatly improved compared with the single MATLAB image segmentation, and there is a great improvement in the effectiveness of image segmentation.

My Green Car: Painting Motor City Green (Ep. 2) – DOE Lab-Corps Video Series

ScienceCinema

Saxena, Samveg; Shah, Nihar; Hansen, Dana

2018-06-12

The Lab’s MyGreenCar team kicks off its customer discovery process in Detroit with a business boot camp designed for scientists developing energy-related technologies. Customer interviews lead to late night discussions and insights on less-than-receptive consumers. Back in Berkeley, the team decides to fine tune targeted customer segments. What makes a new technology compelling enough to transition out of the lab and become a consumer product? That’s the question Berkeley Lab researchers Samveg Saxena, Nihar Shah, and Dana Hansen plus industry mentor Russell Carrington set out to answer for MyGreenCar, an app providing personalized fuel economy or electric vehicle range estimates for consumers researching new cars. DOE’s Lab-Corps program offered the technology team some answers. The EERE-funded program, based on the National Science Foundation’s I-Corps™ model for entrepreneurial training, provides tools and training to move energy-related inventions to the marketplace. During Lab-Corp’s intensive six-week session, technology teams interview 100 customer and value chain members to discover which potential products based on their technologies will have significant market pull. A six video series follows the MyGreenCar team’s Lab-Corps experience, from pre-training preparation with the Lab’s Innovation and Partnerships Office through the ups and downs of the customer discovery process. Will the app make it to the marketplace? You’ll just have to watch.

Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.

PubMed

Hu, Peijun; Wu, Fa; Peng, Jialin; Bao, Yuanyuan; Chen, Feng; Kong, Dexing

2017-03-01

Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images. The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model. Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency. A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.

Object-Based Classification of Ikonos Imagery for Mapping Large-Scale Vegetation Communities in Urban Areas.

PubMed

Mathieu, Renaud; Aryal, Jagannath; Chong, Albert K

2007-11-20

Effective assessment of biodiversity in cities requires detailed vegetation maps.To date, most remote sensing of urban vegetation has focused on thematically coarse landcover products. Detailed habitat maps are created by manual interpretation of aerialphotographs, but this is time consuming and costly at large scale. To address this issue, wetested the effectiveness of object-based classifications that use automated imagesegmentation to extract meaningful ground features from imagery. We applied thesetechniques to very high resolution multispectral Ikonos images to produce vegetationcommunity maps in Dunedin City, New Zealand. An Ikonos image was orthorectified and amulti-scale segmentation algorithm used to produce a hierarchical network of image objects.The upper level included four coarse strata: industrial/commercial (commercial buildings),residential (houses and backyard private gardens), vegetation (vegetation patches larger than0.8/1ha), and water. We focused on the vegetation stratum that was segmented at moredetailed level to extract and classify fifteen classes of vegetation communities. The firstclassification yielded a moderate overall classification accuracy (64%, κ = 0.52), which ledus to consider a simplified classification with ten vegetation classes. The overallclassification accuracy from the simplified classification was 77% with a κ value close tothe excellent range (κ = 0.74). These results compared favourably with similar studies inother environments. We conclude that this approach does not provide maps as detailed as those produced by manually interpreting aerial photographs, but it can still extract ecologically significant classes. It is an efficient way to generate accurate and detailed maps in significantly shorter time. The final map accuracy could be improved by integrating segmentation, automated and manual classification in the mapping process, especially when considering important vegetation classes with limited spectral contrast.

Automatic segmentation of 4D cardiac MR images for extraction of ventricular chambers using a spatio-temporal approach

NASA Astrophysics Data System (ADS)

Atehortúa, Angélica; Zuluaga, Maria A.; Ourselin, Sébastien; Giraldo, Diana; Romero, Eduardo

2016-03-01

An accurate ventricular function quantification is important to support evaluation, diagnosis and prognosis of several cardiac pathologies. However, expert heart delineation, specifically for the right ventricle, is a time consuming task with high inter-and-intra observer variability. A fully automatic 3D+time heart segmentation framework is herein proposed for short-axis-cardiac MRI sequences. This approach estimates the heart using exclusively information from the sequence itself without tuning any parameters. The proposed framework uses a coarse-to-fine approach, which starts by localizing the heart via spatio-temporal analysis, followed by a segmentation of the basal heart that is then propagated to the apex by using a non-rigid-registration strategy. The obtained volume is then refined by estimating the ventricular muscle by locally searching a prior endocardium- pericardium intensity pattern. The proposed framework was applied to 48 patients datasets supplied by the organizers of the MICCAI 2012 Right Ventricle segmentation challenge. Results show the robustness, efficiency and competitiveness of the proposed method both in terms of accuracy and computational load.

Tracking the Evolution of Cerebral Gadolinium-Enhancing Lesions to Persistent T1 Black Holes in Multiple Sclerosis: Validation of a Semiautomated Pipeline.

PubMed

Andermatt, Simon; Papadopoulou, Athina; Radue, Ernst-Wilhelm; Sprenger, Till; Cattin, Philippe

2017-09-01

Some gadolinium-enhancing multiple sclerosis (MS) lesions remain T1-hypointense over months ("persistent black holes, BHs") and represent areas of pronounced tissue loss. A reduced conversion of enhancing lesions to persistent BHs could suggest a favorable effect of a medication on tissue repair. However, the individual tracking of enhancing lesions can be very time-consuming in large clinical trials. We created a semiautomated workflow for tracking the evolution of individual MS lesions, to calculate the proportion of enhancing lesions becoming persistent BHs at follow-up. Our workflow automatically coregisters, compares, and detects overlaps between lesion masks at different time points. We tested the algorithm in a data set of Magnetic Resonance images (1.5 and 3T; spin-echo T1-sequences) from a phase 3 clinical trial (n = 1,272), in which all enhancing lesions and all BHs had been previously segmented at baseline and year 2. The algorithm analyzed the segmentation masks in a longitudinal fashion to determine which enhancing lesions at baseline turned into BHs at year 2. Images of 50 patients (192 enhancing lesions) were also reviewed by an experienced MRI rater, blinded to the algorithm results. In this MRI data set, there were no cases that could not be processed by the algorithm. At year 2, 417 lesions were classified as persistent BHs (417/1,613 = 25.9%). The agreement between the rater and the algorithm was > 98%. Due to the semiautomated procedure, this algorithm can be of great value in the analysis of large clinical trials, when a rater-based analysis would be time-consuming. Copyright © 2017 by the American Society of Neuroimaging.

Automatic segmentation for detecting uterine fibroid regions treated with MR-guided high intensity focused ultrasound (MR-HIFU).

PubMed

Antila, Kari; Nieminen, Heikki J; Sequeiros, Roberto Blanco; Ehnholm, Gösta

2014-07-01

Up to 25% of women suffer from uterine fibroids (UF) that cause infertility, pain, and discomfort. MR-guided high intensity focused ultrasound (MR-HIFU) is an emerging technique for noninvasive, computer-guided thermal ablation of UFs. The volume of induced necrosis is a predictor of the success of the treatment. However, accurate volume assessment by hand can be time consuming, and quick tools produce biased results. Therefore, fast and reliable tools are required in order to estimate the technical treatment outcome during the therapy event so as to predict symptom relief. A novel technique has been developed for the segmentation and volume assessment of the treated region. Conventional algorithms typically require user interaction ora priori knowledge of the target. The developed algorithm exploits the treatment plan, the coordinates of the intended ablation, for fully automatic segmentation with no user input. A good similarity to an expert-segmented manual reference was achieved (Dice similarity coefficient = 0.880 ± 0.074). The average automatic segmentation time was 1.6 ± 0.7 min per patient against an order of tens of minutes when done manually. The results suggest that the segmentation algorithm developed, requiring no user-input, provides a feasible and practical approach for the automatic evaluation of the boundary and volume of the HIFU-treated region.

Content analysis of false and misleading claims in television advertising for prescription and nonprescription drugs.

PubMed

Faerber, Adrienne E; Kreling, David H

2014-01-01

False and misleading advertising for drugs can harm consumers and the healthcare system, and previous research has demonstrated that physician-targeted drug advertisements may be misleading. However, there is a dearth of research comparing consumer-targeted drug advertising to evidence to evaluate whether misleading or false information is being presented in these ads. To compare claims in consumer-targeted television drug advertising to evidence, in order to evaluate the frequency of false or misleading television drug advertising targeted to consumers. A content analysis of a cross-section of television advertisements for prescription and nonprescription drugs aired from 2008 through 2010. We analyzed commercial segments containing prescription and nonprescription drug advertisements randomly selected from the Vanderbilt Television News Archive, a census of national news broadcasts. For each advertisement, the most-emphasized claim in each ad was identified based on claim iteration, mode of communication, duration and placement. This claim was then compared to evidence by trained coders, and categorized as being objectively true, potentially misleading, or false. Potentially misleading claims omitted important information, exaggerated information, made lifestyle associations, or expressed opinions. False claims were factually false or unsubstantiated. Of the most emphasized claims in prescription (n = 84) and nonprescription (n = 84) drug advertisements, 33 % were objectively true, 57 % were potentially misleading and 10 % were false. In prescription drug ads, there were more objectively true claims (43 %) and fewer false claims (2 %) than in nonprescription drug ads (23 % objectively true, 7 % false). There were similar numbers of potentially misleading claims in prescription (55 %) and nonprescription (61 %) drug ads. Potentially misleading claims are prevalent throughout consumer-targeted prescription and nonprescription drug advertising on television. These results are in conflict with proponents who argue the social value of drug advertising is found in informing consumers about drugs.

Worlds apart. Consumer acceptance of functional foods and beverages in Germany and China.

PubMed

Siegrist, Michael; Shi, Jing; Giusto, Alice; Hartmann, Christina

2015-09-01

This study examined consumers' willingness to buy functional foods. Data were collected from an Internet survey in Germany (n = 502) and China (n = 443). The results showed that consumers in China were much more willing to buy functional foods, compared with their German counterparts. A substantial segment of the German consumers indicated lower willingness to buy functional foods, compared with the same foods without additional health benefits. The findings further showed that in both countries, the participants with higher health motivation and more trust in the food industry reported higher willingness to buy functional foods than the participants with lower health motivation and less trust in the industry. Food neophobia had a negative impact on acceptance of functional foods in the Chinese sample. No such association was observed for the German sample. The results suggest that cultural factors play a significant role in the acceptance of functional foods; therefore, caution should be exercised in generalizing research findings from Western countries to others. Copyright © 2015 Elsevier Ltd. All rights reserved.

Analyzing the Sensitivity of Hydrogen Vehicle Sales to Consumers' Preferences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greene, David L; Lin, Zhenhong; Dong, Jing

2013-01-01

The success of hydrogen vehicles will depend on consumer behavior as well as technology, energy prices and public policy. This study examines the sensitivity of the future market shares of hydrogen-powered vehicles to alternative assumptions about consumers preferences. The Market Acceptance of Advanced Automotive Technologies model was used to project future market shares. The model has 1,458 market segments, differentiated by travel behavior, geography, and tolerance to risk, among other factors, and it estimates market shares for twenty advanced power-train technologies. The market potential of hydrogen vehicles is most sensitive to the improvement of drive train technology, especially cost reduction.more » The long-run market success of hydrogen vehicles is less sensitive to the price elasticity of vehicle choice, how consumers evaluate future fuel costs, the importance of fuel availability and limited driving range. The importance of these factors will likely be greater in the early years following initial commercialization of hydrogen vehicles.« less

Relationship between involvement and functional milk desserts intention to purchase. Influence on attitude towards packaging characteristics.

PubMed

Ares, Gastón; Besio, Mariángela; Giménez, Ana; Deliza, Rosires

2010-10-01

Consumers perceive functional foods as member of the particular food category to which they belong. In this context, apart from health and sensory characteristics, non-sensory factors such as packaging might have a key role on determining consumers' purchase decisions regarding functional foods. The aims of the present work were to study the influence of different package attributes on consumer willingness to purchase regular and functional chocolate milk desserts; and to assess if the influence of these attributes was affected by consumers' level of involvement with the product. A conjoint analysis task was carried out with 107 regular milk desserts consumers, who were asked to score their willingness to purchase of 16 milk dessert package concepts varying in five features of the package, and to complete a personal involvement inventory questionnaire. Consumers' level of involvement with the product affected their interest in the evaluated products and their reaction towards the considered conjoint variables, suggesting that it could be a useful segmentation tool during food development. Package colour and the presence of a picture on the label were the variables with the highest relative importance, regardless of consumers' involvement with the product. The importance of these variables was higher than the type of dessert indicating that packaging may play an important role in consumers' perception and purchase intention of functional foods.

Semi-automated brain tumor and edema segmentation using MRI.

PubMed

Xie, Kai; Yang, Jie; Zhang, Z G; Zhu, Y M

2005-10-01

Manual segmentation of brain tumors from magnetic resonance images is a challenging and time-consuming task. A semi-automated method has been developed for brain tumor and edema segmentation that will provide objective, reproducible segmentations that are close to the manual results. Additionally, the method segments non-enhancing brain tumor and edema from healthy tissues in magnetic resonance images. In this study, a semi-automated method was developed for brain tumor and edema segmentation and volume measurement using magnetic resonance imaging (MRI). Some novel algorithms for tumor segmentation from MRI were integrated in this medical diagnosis system. We exploit a hybrid level set (HLS) segmentation method driven by region and boundary information simultaneously, region information serves as a propagation force which is robust and boundary information serves as a stopping functional which is accurate. Ten different patients with brain tumors of different size, shape and location were selected, a total of 246 axial tumor-containing slices obtained from 10 patients were used to evaluate the effectiveness of segmentation methods. This method was applied to 10 non-enhancing brain tumors and satisfactory results were achieved. Two quantitative measures for tumor segmentation quality estimation, namely, correspondence ratio (CR) and percent matching (PM), were performed. For the segmentation of brain tumor, the volume total PM varies from 79.12 to 93.25% with the mean of 85.67+/-4.38% while the volume total CR varies from 0.74 to 0.91 with the mean of 0.84+/-0.07. For the segmentation of edema, the volume total PM varies from 72.86 to 87.29% with the mean of 79.54+/-4.18% while the volume total CR varies from 0.69 to 0.85 with the mean of 0.79+/-0.08. The HLS segmentation method perform better than the classical level sets (LS) segmentation method in PM and CR. The results of this research may have potential applications, both as a staging procedure and a method of evaluating tumor response during treatment, this method can be used as a clinical image analysis tool for doctors or radiologists.

High speed printing with polygon scan heads

NASA Astrophysics Data System (ADS)

Stutz, Glenn

2016-03-01

To reduce and in many cases eliminate the costs associated with high volume printing of consumer and industrial products, this paper investigates and validates the use of the new generation of high speed pulse on demand (POD) lasers in concert with high speed (HS) polygon scan heads (PSH). Associated costs include consumables such as printing ink and nozzles, provisioning labor, maintenance and repair expense as well as reduction of printing lines due to high through put. Targets that are applicable and investigated include direct printing on plastics, printing on paper/cardboard as well as printing on labels. Market segments would include consumer products (CPG), medical and pharmaceutical products, universal ID (UID), and industrial products. In regards to the POD lasers employed, the wavelengths include UV(355nm), Green (532nm) and IR (1064nm) operating within the repetition range of 180 to 250 KHz.

3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set.

PubMed

Popuri, Karteek; Cobzas, Dana; Murtha, Albert; Jägersand, Martin

2012-07-01

Brain tumor segmentation is a required step before any radiation treatment or surgery. When performed manually, segmentation is time consuming and prone to human errors. Therefore, there have been significant efforts to automate the process. But, automatic tumor segmentation from MRI data is a particularly challenging task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. In our work, we propose an automatic brain tumor segmentation method that addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multidimensional feature set. Then, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this work is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned region statistics in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters from the normal brain region to be in the tumor region. This leads to a better disambiguation of the tumor from brain tissue. We evaluated the performance of our automatic segmentation method on 15 real MRI scans of brain tumor patients, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Validation with the expert segmentation labels yielded encouraging results: Jaccard (58%), Precision (81%), Recall (67%), Hausdorff distance (24 mm). Using priors on the brain/tumor appearance, our proposed automatic 3D variational segmentation method was able to better disambiguate the tumor from the surrounding tissue.

Preference Mapping of Soymilk with Different U.S. Consumers.

PubMed

Lawrence, S E; Lopetcharat, K; Drake, M A

2016-02-01

This study determined and compared drivers of liking for unflavored soymilk with different U.S. consumer groups. A highly trained panel documented appearance, mouthfeel and flavor attributes of 26 commercial soymilks. Twelve representative soymilks were then selected for evaluation by consumers from 3 age/cultural categories (n = 75 each category; Caucasian/African American females aged 18 to 30 y; Asian females aged 18 to 30 y; Caucasian/African American females aged 40 to 64 y). Consumers evaluated overall liking and liking and intensity of specific attributes. Results were evaluated by analysis of variance, followed by internal and external preference mapping. Age had no effect on overall liking, while ethnicity did (Caucasian/African American compared with Asian; P < 0.05). Caucasians/African Americans differentiated soymilks more than Asians and assigned a wider range of liking scores than Asians (2.1 to 7.2 compared with 4.0 to 6.1). Three consumer clusters were identified. Sweet taste with vanilla/vanillin and sweet aromatic flavors and higher viscosity were preferred by most consumers and differences between consumer clusters were primarily in drivers of dislike. Drivers of dislike were not identified for Cluster 1 consumers while Clusters 2 and 3 consumers (n = 84, n = 80) disliked beany, green/grassy and meaty/brothy flavors and astringency. Cluster 3 (n = 80) consumers scored all soymilks higher in liking (P < 0.05) than Cluster 2 consumers, and were willing to overlook disliked attributes with the addition of sweet taste, whereas the Cluster 2 consumers were not. These findings can be utilized to produce soymilks with attributes that are well liked by target consumers and to tailor attributes for segments of the population that have not yet been accommodated. © 2015 Institute of Food Technologists®

The effectiveness of policy on consumer choices for private road passenger transport emissions reductions in six major economies

NASA Astrophysics Data System (ADS)

Mercure, J.-F.; Lam, A.

2015-06-01

The effectiveness of fiscal policy to influence vehicle purchases for emissions reductions in private passenger road transport depends on its ability to incentivise consumers to make choices oriented towards lower emissions vehicles. However, car purchase choices are known to be strongly socially determined, and this sector is highly diverse due to significant socio-economic differences between consumer groups. Here, we present a comprehensive dataset and analysis of the structure of the 2012 private passenger vehicle fleet-years in six major economies across the World (UK, USA, China, India, Japan and Brazil) in terms of price, engine size and emissions distributions. We argue that choices and aggregate elasticities of substitution can be predicted using this data, enabling us to evaluate the effectiveness of potential fiscal and technological change policies on fleet-year emissions reductions. We provide tools to do so based on the distributive structure of prices and emissions in segments of a diverse market, both for conventional as well as unconventional engine technologies. We find that markets differ significantly between nations, and that correlations between engine sizes, emissions and prices exist strongly in some markets and not strongly in others. We furthermore find that markets for unconventional engine technologies have patchy coverages of varying levels. These findings are interpreted in terms of policy strategy.

Bioenergetics models to estimate numbers of larval lampreys consumed by smallmouth bass in Elk Creek, Oregon

USGS Publications Warehouse

Schultz, Luke; Heck, Michael; Kowalski, Brandon M; Eagles-Smith, Collin A.; Coates, Kelly C.; Dunham, Jason B.

2017-01-01

Nonnative fishes have been increasingly implicated in the decline of native fishes in the Pacific Northwest. Smallmouth Bass Micropterus dolomieu were introduced into the Umpqua River in southwest Oregon in the early 1960s. The spread of Smallmouth Bass throughout the basin coincided with a decline in counts of upstream-migrating Pacific Lampreys Entosphenus tridentatus. This suggested the potential for ecological interactions between Smallmouth Bass and Pacific Lampreys, as well as freshwater-resident Western Brook Lampreys Lampetra richardsoni. To evaluate the potential effects of Smallmouth Bass on lampreys, we sampled diets of Smallmouth Bass and used bioenergetics models to estimate consumption of larval lampreys in a segment of Elk Creek, a tributary to the lower Umpqua River. We captured 303 unique Smallmouth Bass (mean: 197 mm and 136 g) via angling in July and September. We combined information on Smallmouth Bass diet and energy density with other variables (temperature, body size, growth, prey energy density) in a bioenergetics model to estimate consumption of larval lampreys. Larval lampreys were found in 6.2% of diet samples, and model estimates indicated that the Smallmouth Bass we captured consumed 925 larval lampreys in this 2-month study period. When extrapolated to a population estimate of Smallmouth Bass in this segment, we estimated 1,911 larval lampreys were consumed between July and September. Although the precision of these estimates was low, this magnitude of consumption suggests that Smallmouth Bass may negatively affect larval lamprey populations.

A new user-assisted segmentation and tracking technique for an object-based video editing system

NASA Astrophysics Data System (ADS)

Yu, Hong Y.; Hong, Sung-Hoon; Lee, Mike M.; Choi, Jae-Gark

2004-03-01

This paper presents a semi-automatic segmentation method which can be used to generate video object plane (VOP) for object based coding scheme and multimedia authoring environment. Semi-automatic segmentation can be considered as a user-assisted segmentation technique. A user can initially mark objects of interest around the object boundaries and then the user-guided and selected objects are continuously separated from the unselected areas through time evolution in the image sequences. The proposed segmentation method consists of two processing steps: partially manual intra-frame segmentation and fully automatic inter-frame segmentation. The intra-frame segmentation incorporates user-assistance to define the meaningful complete visual object of interest to be segmentation and decides precise object boundary. The inter-frame segmentation involves boundary and region tracking to obtain temporal coherence of moving object based on the object boundary information of previous frame. The proposed method shows stable efficient results that could be suitable for many digital video applications such as multimedia contents authoring, content based coding and indexing. Based on these results, we have developed objects based video editing system with several convenient editing functions.

Learning to segment mouse embryo cells

NASA Astrophysics Data System (ADS)

León, Juan; Pardo, Alejandro; Arbeláez, Pablo

2017-11-01

Recent advances in microscopy enable the capture of temporal sequences during cell development stages. However, the study of such sequences is a complex task and time consuming task. In this paper we propose an automatic strategy to adders the problem of semantic and instance segmentation of mouse embryos using NYU's Mouse Embryo Tracking Database. We obtain our instance proposals as refined predictions from the generalized hough transform, using prior knowledge of the embryo's locations and their current cell stage. We use two main approaches to learn the priors: Hand crafted features and automatic learned features. Our strategy increases the baseline jaccard index from 0.12 up to 0.24 using hand crafted features and 0.28 by using automatic learned ones.

The evaluation of multi-structure, multi-atlas pelvic anatomy features in a prostate MR lymphography CAD system

NASA Astrophysics Data System (ADS)

Meijs, M.; Debats, O.; Huisman, H.

2015-03-01

In prostate cancer, the detection of metastatic lymph nodes indicates progression from localized disease to metastasized cancer. The detection of positive lymph nodes is, however, a complex and time consuming task for experienced radiologists. Assistance of a two-stage Computer-Aided Detection (CAD) system in MR Lymphography (MRL) is not yet feasible due to the large number of false positives in the first stage of the system. By introducing a multi-structure, multi-atlas segmentation, using an affine transformation followed by a B-spline transformation for registration, the organ location is given by a mean density probability map. The atlas segmentation is semi-automatically drawn with ITK-SNAP, using Active Contour Segmentation. Each anatomic structure is identified by a label number. Registration is performed using Elastix, using Mutual Information and an Adaptive Stochastic Gradient optimization. The dataset consists of the MRL scans of ten patients, with lymph nodes manually annotated in consensus by two expert readers. The feature map of the CAD system consists of the Multi-Atlas and various other features (e.g. Normalized Intensity and multi-scale Blobness). The voxel-based Gentleboost classifier is evaluated using ROC analysis with cross validation. We show in a set of 10 studies that adding multi-structure, multi-atlas anatomical structure likelihood features improves the quality of the lymph node voxel likelihood map. Multiple structure anatomy maps may thus make MRL CAD more feasible.

Thrombus segmentation by texture dynamics from microscopic image sequences

NASA Astrophysics Data System (ADS)

Brieu, Nicolas; Serbanovic-Canic, Jovana; Cvejic, Ana; Stemple, Derek; Ouwehand, Willem; Navab, Nassir; Groher, Martin

2010-03-01

The genetic factors of thrombosis are commonly explored by microscopically imaging the coagulation of blood cells induced by injuring a vessel of mice or of zebrafish mutants. The latter species is particularly interesting since skin transparency permits to non-invasively acquire microscopic images of the scene with a CCD camera and to estimate the parameters characterizing the thrombus development. These parameters are currently determined by manual outlining, which is both error prone and extremely time consuming. Even though a technique for automatic thrombus extraction would be highly valuable for gene analysts, little work can be found, which is mainly due to very low image contrast and spurious structures. In this work, we propose to semi-automatically segment the thrombus over time from microscopic image sequences of wild-type zebrafish larvae. To compensate the lack of valuable spatial information, our main idea consists of exploiting the temporal information by modeling the variations of the pixel intensities over successive temporal windows with a linear Markov-based dynamic texture formalization. We then derive an image from the estimated model parameters, which represents the probability of a pixel to belong to the thrombus. We employ this probability image to accurately estimate the thrombus position via an active contour segmentation incorporating also prior and spatial information of the underlying intensity images. The performance of our approach is tested on three microscopic image sequences. We show that the thrombus is accurately tracked over time in each sequence if the respective parameters controlling prior influence and contour stiffness are correctly chosen.

Automatic detection of axillary lymphadenopathy on CT scans of untreated chronic lymphocytic leukemia patients

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Hua, Jeremy; Chellappa, Vivek; Petrick, Nicholas; Sahiner, Berkman; Farooqui, Mohammed; Marti, Gerald; Wiestner, Adrian; Summers, Ronald M.

2012-03-01

Patients with chronic lymphocytic leukemia (CLL) have an increased frequency of axillary lymphadenopathy. Pretreatment CT scans can be used to upstage patients at the time of presentation and post-treatment CT scans can reduce the number of complete responses. In the current clinical workflow, the detection and diagnosis of lymph nodes is usually performed manually by examining all slices of CT images, which can be time consuming and highly dependent on the observer's experience. A system for automatic lymph node detection and measurement is desired. We propose a computer aided detection (CAD) system for axillary lymph nodes on CT scans in CLL patients. The lung is first automatically segmented and the patient's body in lung region is extracted to set the search region for lymph nodes. Multi-scale Hessian based blob detection is then applied to detect potential lymph nodes within the search region. Next, the detected potential candidates are segmented by fast level set method. Finally, features are calculated from the segmented candidates and support vector machine (SVM) classification is utilized for false positive reduction. Two blobness features, Frangi's and Li's, are tested and their free-response receiver operating characteristic (FROC) curves are generated to assess system performance. We applied our detection system to 12 patients with 168 axillary lymph nodes measuring greater than 10 mm. All lymph nodes are manually labeled as ground truth. The system achieved sensitivities of 81% and 85% at 2 false positives per patient for Frangi's and Li's blobness, respectively.

Safe electrode trajectory planning in SEEG via MIP-based vessel segmentation

NASA Astrophysics Data System (ADS)

Scorza, Davide; Moccia, Sara; De Luca, Giuseppe; Plaino, Lisa; Cardinale, Francesco; Mattos, Leonardo S.; Kabongo, Luis; De Momi, Elena

2017-03-01

Stereo-ElectroEncephaloGraphy (SEEG) is a surgical procedure that allows brain exploration of patients affected by focal epilepsy by placing intra-cerebral multi-lead electrodes. The electrode trajectory planning is challenging and time consuming. Various constraints have to be taken into account simultaneously, such as absence of vessels at the electrode Entry Point (EP), where bleeding is more likely to occur. In this paper, we propose a novel framework to help clinicians in defining a safe trajectory and focus our attention on EP. For each electrode, a Maximum Intensity Projection (MIP) image was obtained from Computer Tomography Angiography (CTA) slices of the brain first centimeter measured along the electrode trajectory. A Gaussian Mixture Model (GMM), modified to include neighborhood prior through Markov Random Fields (GMM-MRF), is used to robustly segment vessels and deal with the noisy nature of MIP images. Results are compared with simple GMM and manual global Thresholding (Th) by computing sensitivity, specificity, accuracy and Dice similarity index against manual segmentation performed under the supervision of an expert surgeon. In this work we present a novel framework which can be easily integrated into manual and automatic planner to help surgeon during the planning phase. GMM-MRF qualitatively showed better performance over GMM in reproducing the connected nature of brain vessels also in presence of noise and image intensity drops typical of MIP images. With respect Th, it is a completely automatic method and it is not influenced by inter-subject variability.

ACM-based automatic liver segmentation from 3-D CT images by combining multiple atlases and improved mean-shift techniques.

PubMed

Ji, Hongwei; He, Jiangping; Yang, Xin; Deklerck, Rudi; Cornelis, Jan

2013-05-01

In this paper, we present an autocontext model(ACM)-based automatic liver segmentation algorithm, which combines ACM, multiatlases, and mean-shift techniques to segment liver from 3-D CT images. Our algorithm is a learning-based method and can be divided into two stages. At the first stage, i.e., the training stage, ACM is performed to learn a sequence of classifiers in each atlas space (based on each atlas and other aligned atlases). With the use of multiple atlases, multiple sequences of ACM-based classifiers are obtained. At the second stage, i.e., the segmentation stage, the test image will be segmented in each atlas space by applying each sequence of ACM-based classifiers. The final segmentation result will be obtained by fusing segmentation results from all atlas spaces via a multiclassifier fusion technique. Specially, in order to speed up segmentation, given a test image, we first use an improved mean-shift algorithm to perform over-segmentation and then implement the region-based image labeling instead of the original inefficient pixel-based image labeling. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that the average volume overlap error and the average surface distance achieved by our method are 8.3% and 1.5 m, respectively, which are comparable to the results reported in the existing state-of-the-art work on liver segmentation.

Automated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes

PubMed Central

de Sisternes, Luis; Jonna, Gowtham; Moss, Jason; Marmor, Michael F.; Leng, Theodore; Rubin, Daniel L.

2017-01-01

This work introduces and evaluates an automated intra-retinal segmentation method for spectral-domain optical coherence (SD-OCT) retinal images. While quantitative assessment of retinal features in SD-OCT data is important, manual segmentation is extremely time-consuming and subjective. We address challenges that have hindered prior automated methods, including poor performance with diseased retinas relative to healthy retinas, and data smoothing that obscures image features such as small retinal drusen. Our novel segmentation approach is based on the iterative adaptation of a weighted median process, wherein a three-dimensional weighting function is defined according to image intensity and gradient properties, and a set of smoothness constraints and pre-defined rules are considered. We compared the segmentation results for 9 segmented outlines associated with intra-retinal boundaries to those drawn by hand by two retinal specialists and to those produced by an independent state-of-the-art automated software tool in a set of 42 clinical images (from 14 patients). These images were obtained with a Zeiss Cirrus SD-OCT system, including healthy, early or intermediate AMD, and advanced AMD eyes. As a qualitative evaluation of accuracy, a highly experienced third independent reader blindly rated the quality of the outlines produced by each method. The accuracy and image detail of our method was superior in healthy and early or intermediate AMD eyes (98.15% and 97.78% of results not needing substantial editing) to the automated method we compared against. While the performance was not as good in advanced AMD (68.89%), it was still better than the manual outlines or the comparison method (which failed in such cases). We also tested our method’s performance on images acquired with a different SD-OCT manufacturer, collected from a large publicly available data set (114 healthy and 255 AMD eyes), and compared the data quantitatively to reference standard markings of the internal limiting membrane and inner boundary of retinal pigment epithelium, producing a mean unsigned positioning error of 6.04 ± 7.83µm (mean under 2 pixels). Our automated method should be applicable to data from different OCT manufacturers and offers detailed layer segmentations in healthy and AMD eyes. PMID:28663874

Social marketing and MARTIN: tools for organizing, analyzing, and interpreting qualitative data.

PubMed

Higgins, J W

1998-11-01

The purpose of this article is to discuss how the computer software program MARTIN and social marketing concepts (understanding the consumer perspective, exchange, marketing mix, and segmentation) were used as organizational, analytical, and interpretive tools for qualitative data. The qualitative data are from a case study on citizen participation in a health reform policy in British Columbia. The concept of broad-based public participation is a fundamental element of health promotion and citizenship. There is a gap, however, between the promise and reality of citizen participation in health promotion. Emerging from the analysis was an understanding of the societal circumstances that inhibited or fostered participation. This article describes how the code-based, theory-building attributes of the MARTIN software facilitated a new conceptualization of participatory citizenship and generated new insights into understanding why some people participate and others do not.

Automated volumetry for unilateral hippocampal sclerosis detection in patients with temporal lobe epilepsy.

PubMed

Martins, Cristina; Moreira da Silva, Nadia; Silva, Guilherme; Rozanski, Verena E; Silva Cunha, Joao Paulo

2016-08-01

Hippocampal sclerosis (HS) is the most common cause of temporal lobe epilepsy (TLE) and can be identified in magnetic resonance imaging as hippocampal atrophy and subsequent volume loss. Detecting this kind of abnormalities through simple radiological assessment could be difficult, even for experienced radiologists. For that reason, hippocampal volumetry is generally used to support this kind of diagnosis. Manual volumetry is the traditional approach but it is time consuming and requires the physician to be familiar with neuroimaging software tools. In this paper, we propose an automated method, written as a script that uses FSL-FIRST, to perform hippocampal segmentation and compute an index to quantify hippocampi asymmetry (HAI). We compared the automated detection of HS (left or right) based on the HAI with the agreement of two experts in a group of 19 patients and 15 controls, achieving 84.2% sensitivity, 86.7% specificity and a Cohen's kappa coefficient of 0.704. The proposed method is integrated in the "Advanced Brain Imaging Lab" (ABrIL) cloud neurocomputing platform. The automated procedure is 77% (on average) faster to compute vs. the manual volumetry segmentation performed by an experienced physician.

Web-based Factors Affecting Online Purchasing Behaviour

NASA Astrophysics Data System (ADS)

Ariff, Mohd Shoki Md; Sze Yan, Ng; Zakuan, Norhayati; Zaidi Bahari, Ahamad; Jusoh, Ahmad

2013-06-01

The growing use of internet and online purchasing among young consumers in Malaysia provides a huge prospect in e-commerce market, specifically for B2C segment. In this market, if E-marketers know the web-based factors affecting online buyers' behaviour, and the effect of these factors on behaviour of online consumers, then they can develop their marketing strategies to convert potential customers into active one, while retaining existing online customers. Review of previous studies related to the online purchasing behaviour in B2C market has point out that the conceptualization and empirical validation of the online purchasing behaviour of Information and Communication Technology (ICT) literate users, or ICT professional, in Malaysia has not been clearly addressed. This paper focuses on (i) web-based factors which online buyers (ICT professional) keep in mind while shopping online; and (ii) the effect of web-based factors on online purchasing behaviour. Based on the extensive literature review, a conceptual framework of 24 items of five factors was constructed to determine web-based factors affecting online purchasing behaviour of ICT professional. Analysis of data was performed based on the 310 questionnaires, which were collected using a stratified random sampling method, from ICT undergraduate students in a public university in Malaysia. The Exploratory factor analysis performed showed that five factors affecting online purchase behaviour are Information Quality, Fulfilment/Reliability/Customer Service, Website Design, Quick and Details, and Privacy/Security. The result of Multiple Regression Analysis indicated that Information Quality, Quick and Details, and Privacy/Security affect positively online purchase behaviour. The results provide a usable model for measuring web-based factors affecting buyers' online purchase behaviour in B2C market, as well as for online shopping companies to focus on the factors that will increase customers' online purchase.

Estimating heat tolerance of plants by ion leakage: a new method based on gradual heating.

PubMed

Ilík, Petr; Špundová, Martina; Šicner, Michal; Melkovičová, Helena; Kučerová, Zuzana; Krchňák, Pavel; Fürst, Tomáš; Večeřová, Kristýna; Panzarová, Klára; Benediktyová, Zuzana; Trtílek, Martin

2018-05-01

Heat tolerance of plants related to cell membrane thermostability is commonly estimated via the measurement of ion leakage from plant segments after defined heat treatment. To compare heat tolerance of various plants, it is crucial to select suitable heating conditions. This selection is time-consuming and optimizing the conditions for all investigated plants may even be impossible. Another problem of the method is its tendency to overestimate basal heat tolerance. Here we present an improved ion leakage method, which does not suffer from these drawbacks. It is based on gradual heating of plant segments in a water bath or algal suspensions from room temperature up to 70-75°C. The electrical conductivity of the bath/suspension, which is measured continuously during heating, abruptly increases at a certain temperature T COND (within 55-70°C). The T COND value can be taken as a measure of cell membrane thermostability, representing the heat tolerance of plants/organisms. Higher T COND corresponds to higher heat tolerance (basal or acquired) connected to higher thermostability of the cell membrane, as evidenced by the common ion leakage method. The new method also enables determination of the thermostability of photochemical reactions in photosynthetic samples via the simultaneous measurement of Chl fluorescence. © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.

Automatic corpus callosum segmentation for standardized MR brain scanning

NASA Astrophysics Data System (ADS)

Xu, Qing; Chen, Hong; Zhang, Li; Novak, Carol L.

2007-03-01

Magnetic Resonance (MR) brain scanning is often planned manually with the goal of aligning the imaging plane with key anatomic landmarks. The planning is time-consuming and subject to inter- and intra- operator variability. An automatic and standardized planning of brain scans is highly useful for clinical applications, and for maximum utility should work on patients of all ages. In this study, we propose a method for fully automatic planning that utilizes the landmarks from two orthogonal images to define the geometry of the third scanning plane. The corpus callosum (CC) is segmented in sagittal images by an active shape model (ASM), and the result is further improved by weighting the boundary movement with confidence scores and incorporating region based refinement. Based on the extracted contour of the CC, several important landmarks are located and then combined with landmarks from the coronal or transverse plane to define the geometry of the third plane. Our automatic method is tested on 54 MR images from 24 patients and 3 healthy volunteers, with ages ranging from 4 months to 70 years old. The average accuracy with respect to two manually labeled points on the CC is 3.54 mm and 4.19 mm, and differed by an average of 2.48 degrees from the orientation of the line connecting them, demonstrating that our method is sufficiently accurate for clinical use.

Biomechanical Model for Computing Deformations for Whole-Body Image Registration: A Meshless Approach

PubMed Central

Li, Mao; Miller, Karol; Joldes, Grand Roman; Kikinis, Ron; Wittek, Adam

2016-01-01

Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2-D models and computing single organ deformations. In this study, 3-D comprehensive patient-specific non-linear biomechanical models implemented using Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithms are applied to predict a 3-D deformation field for whole-body image registration. Unlike a conventional approach which requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the Fuzzy C-Means (FCM) algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge-based Hausdorff distance. The Hausdorff distance-based evaluation determines that our meshless models led to successful registration of the vast majority of the image features. PMID:26791945

A software tool for advanced MRgFUS prostate therapy planning and follow up

NASA Astrophysics Data System (ADS)

van Straaten, Dörte; Hoogenboom, Martijn; van Amerongen, Martinus J.; Weiler, Florian; Issawi, Jumana Al; Günther, Matthias; Fütterer, Jurgen; Jenne, Jürgen W.

2017-03-01

US guided HIFU/FUS ablation for the therapy of prostate cancer is a clinical established method, while MR guided HIFU/FUS applications for prostate recently started clinical evaluation. Even if MRI examination is an excellent diagnostic tool for prostate cancer, it is a time consuming procedure and not practicable within an MRgFUS therapy session. The aim of our ongoing work is to develop software to support therapy planning and post-therapy follow-up for MRgFUS on localized prostate cancer, based on multi-parametric MR protocols. The clinical workflow of diagnosis, therapy and follow-up of MR guided FUS on prostate cancer was deeply analyzed. Based on this, the image processing workflow was designed and all necessary components, e.g. GUI, viewer, registration tools etc. were defined and implemented. The software bases on MeVisLab with several implemented C++ modules for the image processing tasks. The developed software, called LTC (Local Therapy Control) will register and visualize automatically all images (T1w, T2w, DWI etc.) and ADC or perfusion maps gained from the diagnostic MRI session. This maximum of diagnostic information helps to segment all necessary ROIs, e.g. the tumor, for therapy planning. Final therapy planning will be performed based on these segmentation data in the following MRgFUS therapy session. In addition, the developed software should help to evaluate the therapy success, by synchronization and display of pre-therapeutic, therapy and follow-up image data including the therapy plan and thermal dose information. In this ongoing project, the first stand-alone prototype was completed and will be clinically evaluated.

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging

NASA Astrophysics Data System (ADS)

Martin, Spencer; Brophy, Mark; Palma, David; Louie, Alexander V.; Yu, Edward; Yaremko, Brian; Ahmad, Belal; Barron, John L.; Beauchemin, Steven S.; Rodrigues, George; Gaede, Stewart

2015-02-01

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development.

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging.

PubMed

Martin, Spencer; Brophy, Mark; Palma, David; Louie, Alexander V; Yu, Edward; Yaremko, Brian; Ahmad, Belal; Barron, John L; Beauchemin, Steven S; Rodrigues, George; Gaede, Stewart

2015-02-21

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development.

A computational model for simulating solute transport and oxygen consumption along the nephrons

PubMed Central

Vallon, Volker; Edwards, Aurélie

2016-01-01

The goal of this study was to investigate water and solute transport, with a focus on sodium transport (TNa) and metabolism along individual nephron segments under differing physiological and pathophysiological conditions. To accomplish this goal, we developed a computational model of solute transport and oxygen consumption (QO2) along different nephron populations of a rat kidney. The model represents detailed epithelial and paracellular transport processes along both the superficial and juxtamedullary nephrons, with the loop of Henle of each model nephron extending to differing depths of the inner medulla. We used the model to assess how changes in TNa may alter QO2 in different nephron segments and how shifting the TNa sites alters overall kidney QO2. Under baseline conditions, the model predicted a whole kidney TNa/QO2, which denotes the number of moles of Na+ reabsorbed per moles of O2 consumed, of ∼15, with TNa efficiency predicted to be significantly greater in cortical nephron segments than in medullary segments. The TNa/QO2 ratio was generally similar among the superficial and juxtamedullary nephron segments, except for the proximal tubule, where TNa/QO2 was ∼20% higher in superficial nephrons, due to the larger luminal flow along the juxtamedullary proximal tubules and the resulting higher, flow-induced transcellular transport. Moreover, the model predicted that an increase in single-nephron glomerular filtration rate does not significantly affect TNa/QO2 in the proximal tubules but generally increases TNa/QO2 along downstream segments. The latter result can be attributed to the generally higher luminal [Na+], which raises paracellular TNa. Consequently, vulnerable medullary segments, such as the S3 segment and medullary thick ascending limb, may be relatively protected from flow-induced increases in QO2 under pathophysiological conditions. PMID:27707705

Long-term prognostic value of a comprehensive assessment of cardiac magnetic resonance indexes after an ST-segment elevation myocardial infarction.

PubMed

Merlos, Pilar; López-Lereu, Maria P; Monmeneu, Jose V; Sanchis, Juan; Núñez, Julio; Bonanad, Clara; Valero, Ernesto; Miñana, Gema; Chaustre, Fabián; Gómez, Cristina; Oltra, Ricardo; Palacios, Lorena; Bosch, Maria J; Navarro, Vicente; Llácer, Angel; Chorro, Francisco J; Bodí, Vicente

2013-08-01

A variety of cardiac magnetic resonance indexes predict mid-term prognosis in ST-segment elevation myocardial infarction patients. The extent of transmural necrosis permits simple and accurate prediction of systolic recovery. However, its long-term prognostic value beyond a comprehensive clinical and cardiac magnetic resonance evaluation is unknown. We hypothesized that a simple semiquantitative assessment of the extent of transmural necrosis is the best resonance index to predict long-term outcome soon after a first ST-segment elevation myocardial infarction. One week after a first ST-segment elevation myocardial infarction we carried out a comprehensive quantification of several resonance parameters in 206 consecutive patients. A semiquantitative assessment (altered number of segments in the 17-segment model) of edema, baseline and post-dobutamine wall motion abnormalities, first pass perfusion, microvascular obstruction, and the extent of transmural necrosis was also performed. During follow-up (median 51 months), 29 patients suffered a major adverse cardiac event (8 cardiac deaths, 11 nonfatal myocardial infarctions, and 10 readmissions for heart failure). Major cardiac events were associated with more severely altered quantitative and semiquantitative resonance indexes. After a comprehensive multivariate adjustment, the extent of transmural necrosis was the only resonance index independently related to the major cardiac event rate (hazard ratio=1.34 [1.19-1.51] per each additional segment displaying>50% transmural necrosis, P<.001). A simple and non-time consuming semiquantitative analysis of the extent of transmural necrosis is the most powerful cardiac magnetic resonance index to predict long-term outcome soon after a first ST-segment elevation myocardial infarction. Copyright © 2013 Sociedad Española de Cardiología. Published by Elsevier Espana. All rights reserved.

Glioblastoma Segmentation: Comparison of Three Different Software Packages.

PubMed

Fyllingen, Even Hovig; Stensjøen, Anne Line; Berntsen, Erik Magnus; Solheim, Ole; Reinertsen, Ingerid

2016-01-01

To facilitate a more widespread use of volumetric tumor segmentation in clinical studies, there is an urgent need for reliable, user-friendly segmentation software. The aim of this study was therefore to compare three different software packages for semi-automatic brain tumor segmentation of glioblastoma; namely BrainVoyagerTM QX, ITK-Snap and 3D Slicer, and to make data available for future reference. Pre-operative, contrast enhanced T1-weighted 1.5 or 3 Tesla Magnetic Resonance Imaging (MRI) scans were obtained in 20 consecutive patients who underwent surgery for glioblastoma. MRI scans were segmented twice in each software package by two investigators. Intra-rater, inter-rater and between-software agreement was compared by using differences of means with 95% limits of agreement (LoA), Dice's similarity coefficients (DSC) and Hausdorff distance (HD). Time expenditure of segmentations was measured using a stopwatch. Eighteen tumors were included in the analyses. Inter-rater agreement was highest for BrainVoyager with difference of means of 0.19 mL and 95% LoA from -2.42 mL to 2.81 mL. Between-software agreement and 95% LoA were very similar for the different software packages. Intra-rater, inter-rater and between-software DSC were ≥ 0.93 in all analyses. Time expenditure was approximately 41 min per segmentation in BrainVoyager, and 18 min per segmentation in both 3D Slicer and ITK-Snap. Our main findings were that there is a high agreement within and between the software packages in terms of small intra-rater, inter-rater and between-software differences of means and high Dice's similarity coefficients. Time expenditure was highest for BrainVoyager, but all software packages were relatively time-consuming, which may limit usability in an everyday clinical setting.

Rigid shape matching by segmentation averaging.

PubMed

Wang, Hongzhi; Oliensis, John

2010-04-01

We use segmentations to match images by shape. The new matching technique does not require point-to-point edge correspondence and is robust to small shape variations and spatial shifts. To address the unreliability of segmentations computed bottom-up, we give a closed form approximation to an average over all segmentations. Our method has many extensions, yielding new algorithms for tracking, object detection, segmentation, and edge-preserving smoothing. For segmentation, instead of a maximum a posteriori approach, we compute the "central" segmentation minimizing the average distance to all segmentations of an image. For smoothing, instead of smoothing images based on local structures, we smooth based on the global optimal image structures. Our methods for segmentation, smoothing, and object detection perform competitively, and we also show promising results in shape-based tracking.

Styling and Design in Multi-Segmented Market Strategies: The Case of the Italian Knitwear Sector

ERIC Educational Resources Information Center

Marcone, Maria Rosaria

2013-01-01

This study analyses Italian knitwear manufacturers that operate both in international consumer and in business markets and assesses the different forms of competition that exist between them. The purpose of the research is to analyse the different positioning of these firms within the chain of production that they belong to. The firms that deal…

A procedure for the measurement of Oxygen Consumption Rates (OCRs) in red wines and some observations about the influence of wine initial chemical composition.

PubMed

Marrufo-Curtido, Almudena; Carrascón, Vanesa; Bueno, Mónica; Ferreira, Vicente; Escudero, Ana

2018-05-15

The rates at which wine consumes oxygen are important technological parameters for whose measurement there are not accepted procedures. In this work, volumes of 8 wines are contacted with controlled volumes of air in air-tight tubes containing oxygen-sensors and are further agitated at 25 °C until O 2 consumption is complete. Three exposure levels of O 2 were used: low (10 mg/L) and medium or high (18 or 32 mg/L plus the required amount to oxidize all wine SO 2 ). In each oxygen level, 2-4 independent segments following pseudo-first order kinetics were identified, plus an initial segment at which wine consumed O 2 very fast. Overall, multivariate data techniques identify six different Oxygen-Consumption-Rates (OCRs) as required to completely define wine O 2 consumption. Except the last one, all could be modeled from the wine initial chemical composition. Total acetaldehyde, Mn, Cu/Fe, blue and red pigments and gallic acid seem to be essential to determine these OCRs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multi-object model-based multi-atlas segmentation for rodent brains using dense discrete correspondences

NASA Astrophysics Data System (ADS)

Lee, Joohwi; Kim, Sun Hyung; Styner, Martin

2016-03-01

The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra- subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool.

Comparison of different deep learning approaches for parotid gland segmentation from CT images

NASA Astrophysics Data System (ADS)

Hänsch, Annika; Schwier, Michael; Gass, Tobias; Morgas, Tomasz; Haas, Benjamin; Klein, Jan; Hahn, Horst K.

2018-02-01

The segmentation of target structures and organs at risk is a crucial and very time-consuming step in radiotherapy planning. Good automatic methods can significantly reduce the time clinicians have to spend on this task. Due to its variability in shape and often low contrast to surrounding structures, segmentation of the parotid gland is especially challenging. Motivated by the recent success of deep learning, we study different deep learning approaches for parotid gland segmentation. Particularly, we compare 2D, 2D ensemble and 3D U-Net approaches and find that the 2D U-Net ensemble yields the best results with a mean Dice score of 0.817 on our test data. The ensemble approach reduces false positives without the need for an automatic region of interest detection. We also apply our trained 2D U-Net ensemble to segment the test data of the 2015 MICCAI head and neck auto-segmentation challenge. With a mean Dice score of 0.861, our classifier exceeds the highest mean score in the challenge. This shows that the method generalizes well onto data from independent sites. Since appropriate reference annotations are essential for training but often difficult and expensive to obtain, it is important to know how many samples are needed to properly train a neural network. We evaluate the classifier performance after training with differently sized training sets (50-450) and find that 250 cases (without using extensive data augmentation) are sufficient to obtain good results with the 2D ensemble. Adding more samples does not significantly improve the Dice score of the segmentations.

Segmentation quality evaluation using region-based precision and recall measures for remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Xueliang; Feng, Xuezhi; Xiao, Pengfeng; He, Guangjun; Zhu, Liujun

2015-04-01

Segmentation of remote sensing images is a critical step in geographic object-based image analysis. Evaluating the performance of segmentation algorithms is essential to identify effective segmentation methods and optimize their parameters. In this study, we propose region-based precision and recall measures and use them to compare two image partitions for the purpose of evaluating segmentation quality. The two measures are calculated based on region overlapping and presented as a point or a curve in a precision-recall space, which can indicate segmentation quality in both geometric and arithmetic respects. Furthermore, the precision and recall measures are combined by using four different methods. We examine and compare the effectiveness of the combined indicators through geometric illustration, in an effort to reveal segmentation quality clearly and capture the trade-off between the two measures. In the experiments, we adopted the multiresolution segmentation (MRS) method for evaluation. The proposed measures are compared with four existing discrepancy measures to further confirm their capabilities. Finally, we suggest using a combination of the region-based precision-recall curve and the F-measure for supervised segmentation evaluation.