Picturing obesity: analyzing the social epidemiology of obesity conveyed through US news media images.

Science.gov (United States)

Gollust, Sarah E; Eboh, Ijeoma; Barry, Colleen L

2012-05-01

News media coverage can affect how Americans view health policy issues. While previous research has investigated the text content of news media coverage of obesity, these studies have tended to ignore the photographs and other images that accompany obesity-related news coverage. Images can convey important messages about which groups in society are more or less affected by a health problem, and, in turn, shape public understanding about the social epidemiology of that condition. In this study, we analyzed the images of overweight and obese individuals in Time and Newsweek coverage over a 25-year period (1984-2009), and compared these depictions, which we characterize as representing the "news media epidemiology" of obesity, to data describing the true national prevalence of obesity within key populations of interest over this period. Data collected included descriptive features of news stories and accompanying images, and demographic characteristics of individuals portrayed in images. Over the 25-year period, we found that news magazines increasingly depicted non-whites as overweight and obese, and showed overweight and obese individuals less often performing stereotypical behaviors. Even with increasing representation of non-whites over time, news magazines still underrepresented African Americans and Latinos. In addition, the elderly were starkly underrepresented in images of the overweight and obese compared to actual prevalence rates. Research in other policy arenas has linked media depictions of the populations affected by social problems with public support for policies to combat them. Further research is needed to understand how news media depictions can affect public stigma toward overweight and obese individuals and public support for obesity prevention efforts. Copyright © 2012 Elsevier Ltd. All rights reserved.

Hemorrhage detection in MRI brain images using images features

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Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

2013-11-01

The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

Sustainable Corporate Social Media Marketing Based on Message Structural Features: Firm Size Plays a Significant Role as a Moderator

Directory of Open Access Journals (Sweden)

Moon Young Kang

2018-04-01

Full Text Available Social media has been receiving attention as a cost-effective tool to build corporate brand image and to enrich customer relationships. This phenomenon calls for more attention to developing a model that measures the impact of structural features, used in corporate social media messages. Based on communication science, this study proposes a model to measure the impact of three essential message structural features (interactivity, formality, and immediacy in corporate social media on customers’ purchase intentions, mediated by brand attitude and corporate trust. Especially, social media platforms are believed to provide a good marketing platform for small and medium enterprises (SMEs by providing access to huge audiences at a very low cost. The findings from this study based on a structural equation model suggest that brand attitude and corporate trust have larger impacts on purchase intention for SMEs than large firms. This implies that SMEs with little to no presence in the market should pay more attention to building corporate trust and brand attitude for their sustainable growth.

Comparative effects of Facebook and conventional media on body image dissatisfaction.

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Cohen, Rachel; Blaszczynski, Alex

2015-01-01

Appearance comparison has consistently been shown to engender body image dissatisfaction. To date, most studies have demonstrated this relationship between appearance comparison and body image dissatisfaction in the context of conventional media images depicting the thin-ideal. Social comparison theory posits that people are more likely to compare themselves to similar others. Since social media forums such as Facebook involve one's peers, the current study aimed to determine whether the relationship between appearance comparison and body image dissatisfaction would be stronger for those exposed to social media images, compared to conventional media images. A sample of 193 female first year university students were randomly allocated to view a series of either Facebook or conventional media thin-ideal images. Participants completed questionnaires assessing pre- and post- image exposure measures of thin-ideal internalisation, appearance comparison, self-esteem, Facebook use and eating disorder risk. Type of exposure was not found to moderate the relationship between appearance comparison and changes in body image dissatisfaction. When analysed according to exposure type, appearance comparison only significantly predicted body image dissatisfaction change for those exposed to Facebook, but not conventional media. Facebook use was found to predict higher baseline body image dissatisfaction and was associated with higher eating disorder risk. The findings suggest the importance of extending the body image dissatisfaction literature by taking into account emerging social media formats. It is recommended that interventions for body image dissatisfaction and eating disorders consider appearance comparison processes elicited by thin-ideal content on social media forums, such as Facebook, in addition to conventional media.

Talking Back to the Media Ideal: The Development and Validation of the Critical Processing of Beauty Images Scale

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Engeln-Maddox, Renee; Miller, Steven A.

2008-01-01

This article details the development of the Critical Processing of Beauty Images Scale (CPBI) and studies demonstrating the psychometric soundness of this measure. The CPBI measures women's tendency to engage in critical processing of media images featuring idealized female beauty. Three subscales were identified using exploratory factor analysis…

Feature-Based Retinal Image Registration Using D-Saddle Feature

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Roziana Ramli

2017-01-01

Full Text Available Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%, Harris-PIIFD (4%, H-M (16%, and Saddle (16%. Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.

Quantitative imaging features: extension of the oncology medical image database

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Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

2015-03-01

Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

Media-portrayed idealized images, self-objectification, and eating behavior.

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Monro, Fiona J; Huon, Gail F

2006-11-01

This study examined the effects of media-portrayed idealized images on young women's eating behavior. The study compared the effects for high and low self-objectifiers. 72 female university students participated in this experiment. Six magazine advertisements featuring idealized female models were used as the experimental stimuli, and the same six advertisements with the idealized body digitally removed became the control stimuli. Eating behavior was examined using a classic taste test that involved both sweet and savory food. Participants' restraint status was assessed. We found that total food intake after exposure was the same in the body present and absent conditions. There were also no differences between high and low self-objectifiers' total food intake. However, for the total amount of food consumed and for sweet food there were significant group by condition interaction effects. High self-objectifiers ate more food in the body present than the body absent condition. In contrast, low self-objectifiers ate more food in the body absent than in the body present condition. Restraint status was not found to moderate the relationship between exposure to idealized images the amount of food consumed. Our results indicate that exposure to media-portrayed idealized images can lead to changes in eating behavior and highlight the complexity of the association between idealized image exposure and eating behavior. These results are discussed in terms of their implications for the prevention of dieting-related disorders.

Body image, eating disorders, and the media.

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Hogan, Marjorie J; Strasburger, Victor C

2008-12-01

Adolescence is a time of tremendous change in physical appearance. Many adolescents report dissatisfaction with their body shape and size. Forming one's body image is a complex process, influenced by family, peers, and media messages. Increasing evidence shows that the combination of ubiquitous ads for foods and emphasis on female beauty and thinness in both advertising and programming leads to confusion and dissatisfaction for many young people. Sociocultural factors, specifically media exposure, play an important role in the development of disordered body image. Of significant concern, studies have revealed a link between media exposure and the likelihood of having symptoms of disordered eating or a frank eating disorder. Pediatricians and other adults must work to promote media education and make media healthier for young people. More research is needed to identify the most vulnerable children and adolescents.

Image segmentation-based robust feature extraction for color image watermarking

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Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

2018-04-01

This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

Images in Social Media

DEFF Research Database (Denmark)

Ørnager, Susanne; Lund, Haakon

This book focuses on methodologies, organization and communication of digital image collection research that utilize social media content. (“Image” is here understood as cultural, conventional and commercial - stock photos - representations.) The lecture offer expert views that provide different...... fake news, image manipulation, mobile photos etc. these issues are very complex and need a publication of their own. This book should primarily be useful for students in library and information science, psychology, and computer science....

Economic Image Media Text: Definition of the Concept and its Content

Directory of Open Access Journals (Sweden)

Anastasia V. Kudrina

2017-12-01

Full Text Available The article describes the concept of economic image media text and its significance for participants of economic relations. The image factor is one of the ways to gain a place in the economic market, therefore media resources - journalism, advertising and PR are actively used by businessmen and companies with the widest range of pragmatic goals. The concept of the political image and the political image media text as a means of its implementation are known both among media users and among researchers. In spite of the fact that economic relations are closely connected with political ones, and economic factors often determine political processes, practically nothing is known about the economic image media text in the circle of media researchers.

INTEGRATION OF IMAGE-DERIVED AND POS-DERIVED FEATURES FOR IMAGE BLUR DETECTION

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T.-A. Teo

2016-06-01

Full Text Available The image quality plays an important role for Unmanned Aerial Vehicle (UAV’s applications. The small fixed wings UAV is suffering from the image blur due to the crosswind and the turbulence. Position and Orientation System (POS, which provides the position and orientation information, is installed onto an UAV to enable acquisition of UAV trajectory. It can be used to calculate the positional and angular velocities when the camera shutter is open. This study proposes a POS-assisted method to detect the blur image. The major steps include feature extraction, blur image detection and verification. In feature extraction, this study extracts different features from images and POS. The image-derived features include mean and standard deviation of image gradient. For POS-derived features, we modify the traditional degree-of-linear-blur (blinear method to degree-of-motion-blur (bmotion based on the collinear condition equations and POS parameters. Besides, POS parameters such as positional and angular velocities are also adopted as POS-derived features. In blur detection, this study uses Support Vector Machines (SVM classifier and extracted features (i.e. image information, POS data, blinear and bmotion to separate blur and sharp UAV images. The experiment utilizes SenseFly eBee UAV system. The number of image is 129. In blur image detection, we use the proposed degree-of-motion-blur and other image features to classify the blur image and sharp images. The classification result shows that the overall accuracy using image features is only 56%. The integration of image-derived and POS-derived features have improved the overall accuracy from 56% to 76% in blur detection. Besides, this study indicates that the performance of the proposed degree-of-motion-blur is better than the traditional degree-of-linear-blur.

The impact of thin idealized media images on body satisfaction: does body appreciation protect women from negative effects?

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Halliwell, Emma

2013-09-01

This article examines whether positive body image can protect women from negative media exposure effects. University women (N=112) were randomly allocated to view advertisements featuring ultra-thin models or control images. Women who reported high levels of body appreciation did not report negative media exposure effects. Furthermore, the protective role of body appreciation was also evident among women known to be vulnerable to media exposure. Women high on thin-ideal internalization and low on body appreciation reported appearance-discrepancies that were more salient and larger when they viewed models compared to the control group. However, women high on thin-ideal internalization and also high on body appreciation rated appearance-discrepancies as less important and no difference in size than the control group. The results support the notion that positive body image protects women from negative environmental appearance messages and suggests that promoting positive body image may be an effective intervention strategy. Copyright © 2013 Elsevier Ltd. All rights reserved.

Wilson’s disease: Atypical imaging features

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Venugopalan Y Vishnu

2016-10-01

Full Text Available Wilson’s disease is a genetic movement disorder with characteristic clinical and imaging features. We report a 17- year-old boy who presented with sialorrhea, hypophonic speech, paraparesis with repeated falls and recurrent seizures along with cognitive decline. He had bilateral Kayser Flescher rings. Other than the typical features of Wilson’s disease in cranial MRI, there were extensive white matter signal abnormalities (T2 and FLAIR hyperintensities and gyriform contrast enhancement which are rare imaging features in Wilson's disease. A high index of suspicion is required to diagnose Wilson’s disease when atypical imaging features are present.

Pathology Image-Sharing on Social Media: Recommendations for Protecting Privacy While Motivating Education.

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Crane, Genevieve M; Gardner, Jerad M

2016-08-01

There is a rising interest in the use of social media by pathologists. However, the use of pathology images on social media has been debated, particularly gross examination, autopsy, and dermatologic condition photographs. The immediacy of the interactions, increased interest from patients and patient groups, and fewer barriers to public discussion raise additional considerations to ensure patient privacy is protected. Yet these very features all add to the power of social media for educating other physicians and the nonmedical public about disease and for creating better understanding of the important role of pathologists in patient care. The professional and societal benefits are overwhelmingly positive, and we believe the potential for harm is minimal provided common sense and routine patient privacy principles are utilized. We lay out ethical and practical guidelines for pathologists who use social media professionally. © 2016 American Medical Association. All Rights Reserved.

Body image, media, and eating disorders.

Science.gov (United States)

Derenne, Jennifer L; Beresin, Eugene V

2006-01-01

Eating disorders, including obesity, are a major public health problem today. Throughout history, body image has been determined by various factors, including politics and media. Exposure to mass media (television, movies, magazines, Internet) is correlated with obesity and negative body image, which may lead to disordered eating. The authors attempt to explain the historical context of the problem and explore potential avenues for change. The authors review changes in ideal female body type throughout history, comment on current attitudes toward shape and weight in both men and women, and outline interventions aimed at increasing healthy habits and fostering self-esteem in youth. Throughout history, the ideal of beauty has been difficult to achieve and has been shaped by social context. Current mass media is ubiquitous and powerful, leading to increased body dissatisfaction among both men and women. Parents need to limit children's exposure to media, promote healthy eating and moderate physical activity, and encourage participation in activities that increase mastery and self-esteem. Funding for high-quality, visible advertising campaigns promoting healthy life styles may increase awareness.

Unsupervised feature learning for autonomous rock image classification

Science.gov (United States)

Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

2017-09-01

Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

Infrared image enhancement with learned features

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Fan, Zunlin; Bi, Duyan; Ding, Wenshan

2017-11-01

Due to the variation of imaging environment and limitations of infrared imaging sensors, infrared images usually have some drawbacks: low contrast, few details and indistinct edges. Hence, to promote the applications of infrared imaging technology, it is essential to improve the qualities of infrared images. To enhance image details and edges adaptively, we propose an infrared image enhancement method under the proposed image enhancement scheme. On the one hand, on the assumption of high-quality image taking more evident structure singularities than low-quality images, we propose an image enhancement scheme that depends on the extractions of structure features. On the other hand, different from the current image enhancement algorithms based on deep learning networks that try to train and build the end-to-end mappings on improving image quality, we analyze the significance of first layer in Stacked Sparse Denoising Auto-encoder and propose a novel feature extraction for the proposed image enhancement scheme. Experiment results prove that the novel feature extraction is free from some artifacts on the edges such as blocking artifacts, ;gradient reversal;, and pseudo contours. Compared with other enhancement methods, the proposed method achieves the best performance in infrared image enhancement.

Impact of today's media on university student's body image in Pakistan: a conservative, developing country's perspective.

Science.gov (United States)

Khan, Amad N; Khalid, Salema; Khan, Hussain I; Jabeen, Mehnaz

2011-05-24

Living in a world greatly controlled by mass media makes it impossible to escape its pervading influence. As media in Pakistan has been free in the true sense of the word for only a few years, its impact on individuals is yet to be assessed. Our study aims to be the first to look at the effect media has on the body image of university students in a conservative, developing country like Pakistan. Also, we introduced the novel concept of body image dissatisfaction as being both negative and positive. A cross-sectional study was conducted among 7 private universities over a period of two weeks in the city of Karachi, Pakistan's largest and most populous city. Convenience sampling was used to select both male and female undergraduate students aged between 18 and 25 and a sample size of 783 was calculated. Of the 784 final respondents, 376 (48%) were males and 408 (52%) females. The mean age of males was 20.77 (+/- 1.85) years and females was 20.38 (+/- 1.63) years. Out of these, 358 (45.6%) respondents had a positive BID (body image dissatisfaction) score while 426 (54.4%) had a negative BID score. Of the respondents who had positive BID scores, 93 (24.7%) were male and 265 (65.0%) were female. Of the respondents with a negative BID score, 283 (75.3%) were male and 143 (35.0%) were female. The results for BID vs. media exposure were similar in both high and low peer pressure groups. Low media exposure meant positive BID scores and vice versa in both groups (p media exposure and negative body image dissatisfaction. Finally, we looked at the association between gender and image dissatisfaction. Again a statistically significant association was found between positive body image dissatisfaction and female gender and negative body image dissatisfaction and male gender (p media to have an overall negative effect on individuals' body image. A striking feature of our study, however, was the finding that negative body image dissatisfaction was found to be more prevalent in

Associations between Chinese/Asian versus Western mass media influences and body image disturbances of young Chinese women.

Science.gov (United States)

Jackson, Todd; Jiang, Chengcheng; Chen, Hong

2016-06-01

In this study, we evaluated associations of experiences with mass media imported from Western nations such as the United States versus mass media from China and other Asian countries with eating and body image disturbances of young Chinese women. Participating women (N=456) completed self-report measures of disordered eating, specific sources of appearance dissatisfaction (fatness, facial features, stature), and Western versus Chinese/Asian mass media influences. The sample was significantly more likely to report perceived pressure from, comparisons with, and preferences for physical appearance depictions in Chinese/Asian mass media than Western media. Chinese/Asian media influences also combined for more unique variance in prediction models for all disturbances except stature concerns. While experiences with Western media were related to disturbances as well, the overall impact of Chinese/Asian media influences was more prominent. Copyright © 2016 Elsevier Ltd. All rights reserved.

Imaging features of aggressive angiomyxoma

International Nuclear Information System (INIS)

Jeyadevan, N.N.; Sohaib, S.A.A.; Thomas, J.M.; Jeyarajah, A.; Shepherd, J.H.; Fisher, C.

2003-01-01

AIM: To describe the imaging features of aggressive angiomyxoma in a rare benign mesenchymal tumour most frequently arising from the perineum in young female patients. MATERIALS AND METHODS: We reviewed the computed tomography (CT) and magnetic resonance (MR) imaging features of patients with aggressive angiomyxoma who were referred to our hospital. The imaging features were correlated with clinical information and pathology in all patients. RESULTS: Four CT and five MR studies were available for five patients (all women, mean age 39, range 24-55). Three patients had recurrent tumour at follow-up. CT and MR imaging demonstrated a well-defined mass-displacing adjacent structures. The tumour was of low attenuation relative to muscle on CT. On MR, the tumour was isointense relative to muscle on T1-weighted image, hyperintense on T2-weighted image and enhanced avidly after gadolinium contrast with a characteristic 'swirled' internal pattern. MR imaging demonstrates the extent of the tumour and its relation to the pelvic floor. Recurrent tumour has a similar appearance to the primary lesion. CONCLUSION: The MR appearances of aggressive angiomyxomas are characteristic, and the diagnosis should be considered in any young woman presenting with a well-defined mass arising from the perineum. Jeyadevan, N. N. etal. (2003). Clinical Radiology58, 157--162

Image Processing and Features Extraction of Fingerprint Images ...

African Journals Online (AJOL)

To demonstrate the importance of the image processing of fingerprint images prior to image enrolment or comparison, the set of fingerprint images in databases (a) and (b) of the FVC (Fingerprint Verification Competition) 2000 database were analyzed using a features extraction algorithm. This paper presents the results of ...

Feature hashing for fast image retrieval

Science.gov (United States)

Yan, Lingyu; Fu, Jiarun; Zhang, Hongxin; Yuan, Lu; Xu, Hui

2018-03-01

Currently, researches on content based image retrieval mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very timeconsuming and unscalable. Hence, we need to pay much attention to the efficiency of image retrieval. In this paper, we propose a feature hashing method for image retrieval which not only generates compact fingerprint for image representation, but also prevents huge semantic loss during the process of hashing. To generate the fingerprint, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.

Feature-based Alignment of Volumetric Multi-modal Images

Science.gov (United States)

Toews, Matthew; Zöllei, Lilla; Wells, William M.

2014-01-01

This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis.

Science.gov (United States)

Myburgh, Hermanus C; van Zijl, Willemien H; Swanepoel, DeWet; Hellström, Sten; Laurent, Claude

2016-03-01

Otitis media is one of the most common childhood diseases worldwide, but because of lack of doctors and health personnel in developing countries it is often misdiagnosed or not diagnosed at all. This may lead to serious, and life-threatening complications. There is, thus a need for an automated computer based image-analyzing system that could assist in making accurate otitis media diagnoses anywhere. A method for automated diagnosis of otitis media is proposed. The method uses image-processing techniques to classify otitis media. The system is trained using high quality pre-assessed images of tympanic membranes, captured by digital video-otoscopes, and classifies undiagnosed images into five otitis media categories based on predefined signs. Several verification tests analyzed the classification capability of the method. An accuracy of 80.6% was achieved for images taken with commercial video-otoscopes, while an accuracy of 78.7% was achieved for images captured on-site with a low cost custom-made video-otoscope. The high accuracy of the proposed otitis media classification system compares well with the classification accuracy of general practitioners and pediatricians (~64% to 80%) using traditional otoscopes, and therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved populations.

Liver imaging with MDCT and high concentration contrast media

International Nuclear Information System (INIS)

Spielmann, Audrey L.

2003-01-01

Liver imaging has advanced greatly over the last 10 years with helical CT capability and more recently the addition of multidetector-row CT (MDCT). Multidetector CT technology facilitates imaging at faster speeds with improved image quality and less breathing artifact [Abdom. Imaging 25 (2000) 643]. Exquisite three-dimensional data sets can be obtained with thin collimation providing improved lesion detection, multiplanar imaging, and the ability to perform CT angiography of the liver and mesenteric vessels. New challenges arise with this advance in technology including safety considerations. The radiation dose to the patient has increased with MDCT and this is compounded by the ability to perform multi-phase liver imaging. Furthermore, issues of contrast media administration require reconsideration including optimal timing and rate of administration, the total volume of contrast needed and the ideal iodine concentration of the contrast media. Recently, the use of high concentration contrast media (HCCM) has been explored and study results to date will be reviewed

Image feature detectors and descriptors foundations and applications

CERN Document Server

Hassaballah, Mahmoud

2016-01-01

This book provides readers with a selection of high-quality chapters that cover both theoretical concepts and practical applications of image feature detectors and descriptors. It serves as reference for researchers and practitioners by featuring survey chapters and research contributions on image feature detectors and descriptors. Additionally, it emphasizes several keywords in both theoretical and practical aspects of image feature extraction. The keywords include acceleration of feature detection and extraction, hardware implantations, image segmentation, evolutionary algorithm, ordinal measures, as well as visual speech recognition. .

The Mass and Individual Terror in the Mirror of the Soviet and Russian Cinema (the Feature Films of the Sound Period and Media Literacy Education

Directory of Open Access Journals (Sweden)

Alexander Fedorov

2015-12-01

Full Text Available This article gives the way for hermeneutic analysis of the topic of the mass and individual terror in the mirror of the Soviet and Russian cinema (the feature films of the sound period. The hermeneutical analysis suggests media text comprehension through comparison with historical, cultural tradition and reality; penetration of its logic; through comparison of media images in historical and cultural context by combining historical, hermeneutical analysis of the structural, plot, ethical, ideological, iconographic / visual, media stereotypes and analysis of media text characters. An analysis of this kind of media texts, in our opinion, is particularly important for media literacy education of future historians, culture and art historians, sociologists, psychologists and educators.

W-transform method for feature-oriented multiresolution image retrieval

Energy Technology Data Exchange (ETDEWEB)

Kwong, M.K.; Lin, B. [Argonne National Lab., IL (United States). Mathematics and Computer Science Div.

1995-07-01

Image database management is important in the development of multimedia technology. Since an enormous amount of digital images is likely to be generated within the next few decades in order to integrate computers, television, VCR, cables, telephone and various imaging devices. Effective image indexing and retrieval systems are urgently needed so that images can be easily organized, searched, transmitted, and presented. Here, the authors present a local-feature-oriented image indexing and retrieval method based on Kwong, and Tang`s W-transform. Multiresolution histogram comparison is an effective method for content-based image indexing and retrieval. However, most recent approaches perform multiresolution analysis for whole images but do not exploit the local features present in the images. Since W-transform is featured by its ability to handle images of arbitrary size, with no periodicity assumptions, it provides a natural tool for analyzing local image features and building indexing systems based on such features. In this approach, the histograms of the local features of images are used in the indexing, system. The system not only can retrieve images that are similar or identical to the query images but also can retrieve images that contain features specified in the query images, even if the retrieved images as a whole might be very different from the query images. The local-feature-oriented method also provides a speed advantage over the global multiresolution histogram comparison method. The feature-oriented approach is expected to be applicable in managing large-scale image systems such as video databases and medical image databases.

Decomposition of Diffuse Reflectance Images - Features for Monitoring Structure in Turbid Media

DEFF Research Database (Denmark)

Skytte, Jacob Lercke; Nielsen, Otto Højager Attermann; Andersen, Ulf

2013-01-01

Light scattering in turbid media can be related to the microstructure of media. Thus, light scattering can potentially be used for process control of products where the structure is a key component. However process control requires robust and sensitive input data to function properly. In this study...

The role of electronic media in shaping the country's image

Directory of Open Access Journals (Sweden)

Azel Zhanibek

2011-07-01

Full Text Available This article discusses the influence of electronic media in the country to external image of Kazakhstan. Special attention is paid to the experience of various countries in promoting the country's image by electronic media.

Field application of feature-enhanced imaging

International Nuclear Information System (INIS)

Mucciardi, A.N.

1988-01-01

One of the more challenging ultrasonic inspection problems is bimetallic weld inspection or, in general, dissimilar metal welds. These types of welds involve complicated geometries and various mixtures of materials. Attempts to address this problem with imaging alone have fallen short of desired goals. The probable reason for this is the lack of information supplied by imaging systems, which are limited to amplitude and time displays. Having RF information available for analysis greatly enhances the information obtainable from dissimilar metal welds and, coupled with the spatial map generated by an imaging system, can significantly improve the reliability of dissimilar metal weld inspections. Ultra Image and TestPro are, respectively, an imaging system and a feature-based signal analysis system. The purpose of this project is to integrate these two systems to produce a feature-enhanced imaging system. This means that a software link is established between Ultra Image and the PC-based TestPro system so that the user of the combined system can perform all the usual imaging functions and also have available a wide variety of RF signal analysis functions. The analysis functions include waveform feature-based pattern recognition as well as artificial intelligence/expert system techniques

A comparison of image features for registering LWIR and visual images

CSIR Research Space (South Africa)

Cronje, J

2012-11-01

Full Text Available This paper presents a comparison of several established and recent image feature-descriptors to register long wave infra-red images in the 8–14 m band to visual band images. The feature descriptors were chosen to include robust algorithms, SURF...

Superpixel-Based Feature for Aerial Image Scene Recognition

Directory of Open Access Journals (Sweden)

Hongguang Li

2018-01-01

Full Text Available Image scene recognition is a core technology for many aerial remote sensing applications. Different landforms are inputted as different scenes in aerial imaging, and all landform information is regarded as valuable for aerial image scene recognition. However, the conventional features of the Bag-of-Words model are designed using local points or other related information and thus are unable to fully describe landform areas. This limitation cannot be ignored when the aim is to ensure accurate aerial scene recognition. A novel superpixel-based feature is proposed in this study to characterize aerial image scenes. Then, based on the proposed feature, a scene recognition method of the Bag-of-Words model for aerial imaging is designed. The proposed superpixel-based feature that utilizes landform information establishes top-task superpixel extraction of landforms to bottom-task expression of feature vectors. This characterization technique comprises the following steps: simple linear iterative clustering based superpixel segmentation, adaptive filter bank construction, Lie group-based feature quantification, and visual saliency model-based feature weighting. Experiments of image scene recognition are carried out using real image data captured by an unmanned aerial vehicle (UAV. The recognition accuracy of the proposed superpixel-based feature is 95.1%, which is higher than those of scene recognition algorithms based on other local features.

The media's impact on body image: Implications for prevention and treatment

OpenAIRE

Shaw, J.; Waller, G.

1995-01-01

Recent research has demonstrated that media images of “ideal” female models have an impact upon women's body image, leading to dissatisfaction and perceptual distortion. The evidence for this link between media presentation and body image distortion is reviewed, and theoretical models are advanced to explain the link. In particular, women's use of social comparison in establishing their self-concept seems to be an important psychological construct in understanding the impact of the media upon...

Solving jigsaw puzzles using image features

DEFF Research Database (Denmark)

Nielsen, Ture R.; Drewsen, Peter; Hansen, Klaus

2008-01-01

In this article, we describe a method for automatic solving of the jigsaw puzzle problem based on using image features instead of the shape of the pieces. The image features are used for obtaining an accurate measure for edge similarity to be used in a new edge matching algorithm. The algorithm i...

Feature Evaluation for Building Facade Images - AN Empirical Study

Science.gov (United States)

Yang, M. Y.; Förstner, W.; Chai, D.

2012-08-01

The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.

Imaging features of kaposiform lymphangiomatosis

International Nuclear Information System (INIS)

Goyal, Pradeep; Alomari, Ahmad I.; Shaikh, Raja; Chaudry, Gulraiz; Kozakewich, Harry P.; Perez-Atayde, Antonio R.; Trenor, Cameron C.; Fishman, Steven J.; Greene, Arin K.

2016-01-01

Kaposiform lymphangiomatosis is a rare, aggressive lymphatic disorder. The imaging and presenting features of kaposiform lymphangiomatosis can overlap with those of central conducting lymphatic anomaly and generalized lymphatic anomaly. To analyze the imaging findings of kaposiform lymphangiomatosis disorder and highlight features most suggestive of this diagnosis. We retrospectively identified and characterized 20 children and young adults with histopathological diagnosis of kaposiform lymphangiomatosis and radiologic imaging referred to the vascular anomalies center between 1995 and 2015. The median age at onset was 6.5 years (range 3 months to 27 years). The most common presenting features were respiratory compromise (dyspnea, cough, chest pain; 55.5%), swelling/mass (25%), bleeding (15%) and fracture (5%). The thoracic cavity was involved in all patients; all patients had mediastinal involvement followed by lung parenchymal disease (90%) and pleural (85%) and pericardial (50%) effusions. The most common extra-thoracic sites of disease were the retroperitoneum (80%), bone (60%), abdominal viscera (55%) and muscles (45%). There was characteristic enhancing and infiltrative soft-tissue thickening in the mediastinum and retroperitoneum extending along the lymphatic distribution. Kaposiform lymphangiomatosis has overlapping imaging features with central conducting lymphatic anomaly and generalized lymphatic anomaly. Presence of mediastinal or retroperitoneal enhancing and infiltrative soft-tissue disease along the lymphatic distribution, hemorrhagic effusions and moderate thrombocytopenia (50-100,000/μl) should favor diagnosis of kaposiform lymphangiomatosis. (orig.)

The Media Representations of Police Image

Directory of Open Access Journals (Sweden)

Wayne W. L. Chan

2015-09-01

Full Text Available The Hong Kong Police Force has undergone one of its biggest challenges in the Occupy Movement that emerged in the last year. Despite the sheer complexity of the police roles, we know little about its representations in the media coverage, and even less about the extent to which the imagery impacts of police acting as peacekeepers would have been made upon the images of police acting as crime fighters. Against this background, this empirical research aims to investigate the police image and its relation to the police’s specifically categorized duties in Hong Kong. The content analysis of local newspaper accounts is used as the research method. It is found that there would be generally negative media representations of police in the order-maintenance work whereas the police images in the crime-fighting duties could still remain positive. The reasons for these findings and their implications for the conceptions of the police role are discussed.

Image fusion using sparse overcomplete feature dictionaries

Science.gov (United States)

Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

2015-10-06

Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

An Effective Combined Feature For Web Based Image Retrieval

Directory of Open Access Journals (Sweden)

H.M.R.B Herath

2015-08-01

Full Text Available Abstract Technology advances as well as the emergence of large scale multimedia applications and the revolution of the World Wide Web has changed the world into a digital age. Anybody can use their mobile phone to take a photo at any time anywhere and upload that image to ever growing image databases. Development of effective techniques for visual and multimedia retrieval systems is one of the most challenging and important directions of the future research. This paper proposes an effective combined feature for web based image retrieval. Frequently used colour and texture features are explored in order to develop a combined feature for this purpose. Widely used three colour features Colour moments Colour coherence vector and Colour Correlogram and three texture features Grey Level Co-occurrence matrix Tamura features and Gabor filter were analyzed for their performance. Precision and Recall were used to evaluate the performance of each of these techniques. By comparing precision and recall values the methods that performed best were taken and combined to form a hybrid feature. The developed combined feature was evaluated by developing a web based CBIR system. A web crawler was used to first crawl through Web sites and images found in those sites are downloaded and the combined feature representation technique was used to extract image features. The test results indicated that this web system can be used to index web images with the combined feature representation schema and to find similar images. Random image retrievals using the web system shows that the combined feature can be used to retrieve images belonging to the general image domain. Accuracy of the retrieval can be noted high for natural images like outdoor scenes images of flowers etc. Also images which have a similar colour and texture distribution were retrieved as similar even though the images were belonging to deferent semantic categories. This can be ideal for an artist who wants

Microscopic imaging through turbid media Monte Carlo modeling and applications

CERN Document Server

Gu, Min; Deng, Xiaoyuan

2015-01-01

This book provides a systematic introduction to the principles of microscopic imaging through tissue-like turbid media in terms of Monte-Carlo simulation. It describes various gating mechanisms based on the physical differences between the unscattered and scattered photons and method for microscopic image reconstruction, using the concept of the effective point spread function. Imaging an object embedded in a turbid medium is a challenging problem in physics as well as in biophotonics. A turbid medium surrounding an object under inspection causes multiple scattering, which degrades the contrast, resolution and signal-to-noise ratio. Biological tissues are typically turbid media. Microscopic imaging through a tissue-like turbid medium can provide higher resolution than transillumination imaging in which no objective is used. This book serves as a valuable reference for engineers and scientists working on microscopy of tissue turbid media.

A kernel-based multi-feature image representation for histopathology image classification

International Nuclear Information System (INIS)

Moreno J; Caicedo J Gonzalez F

2010-01-01

This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of latent semantic analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, support vector machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that; the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

A KERNEL-BASED MULTI-FEATURE IMAGE REPRESENTATION FOR HISTOPATHOLOGY IMAGE CLASSIFICATION

Directory of Open Access Journals (Sweden)

J Carlos Moreno

2010-09-01

Full Text Available This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of Latent Semantic Analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, Support Vector Machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that, the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

Lithuania’s Images in Russian Mass Media after the Annexation of Crimea

Directory of Open Access Journals (Sweden)

Pugačiauskas Virgilijus

2017-12-01

Full Text Available One of the distinctive features of Russia’s confrontation with the West over the 2014-2016 period is the intensification of Russian propaganda both in foreign countries and within the state. Lithuania, whose relations with a major neighbour were not normalized, and which openly supported Ukraine’s position, attracted the additional attention of Russian mass media in which an incitement to anti- Lithuanian moods was bolstered. In this case, it is endeavoured to generally describe how the mass media (television and newspapers played a role in contriving a social construct and ascertain the Lithuanian quantitative characteristics which are presented in Russian mass media. Referring to the analysis, one can distinguish three prevailing negative images of Lithuania - that is, Russophobic and anti-Russian; a falsifier of history; and a failing and non-influential state. These images, being consistently and purposefully exploited in Russian information space, almost with no alternative sources, turned into undeniable truth for the majority of Russian citizens. This provides the Kremlin with vast possibilities of manipulation in constructing the tactics and strategy of geopolitical instability. On the other hand, one should not forget that such a negative picture of Lithuania serves as a way in which Russian society justifies Putin’s political system and demonstrates its superiority over the values of the Western world.

Analysis of RTM extended images for VTI media

KAUST Repository

Li, Vladimir; Tsvankin, Ilya; Alkhalifah, Tariq Ali

2015-01-01

velocity analysis remain generally valid in the extended image space for complex media. The dependence of RMO on errors in the anisotropy parameters provides essential insights for anisotropic wavefield tomography using extended images.

The Effects of Immigration and Media Influence on Body Image Among Pakistani Men.

Science.gov (United States)

Saghir, Sheeba; Hyland, Lynda

2017-07-01

This study examined the role of media influence and immigration on body image among Pakistani men. Attitudes toward the body were compared between those living in Pakistan ( n = 56) and those who had immigrated to the United Arab Emirates ( n = 58). Results of a factorial analysis of variance demonstrated a significant main effect of immigrant status. Pakistani men living in the United Arab Emirates displayed poorer body image than those in the Pakistan sample. Results also indicated a second main effect of media influence.Those highly influenced by the media displayed poorer body image. No interaction effect was observed between immigrant status and media influence on body image. These findings suggest that media influence and immigration are among important risk factors for the development of negative body image among non-Western men. Interventions designed to address the negative effects of the media and immigration may be effective at reducing body image disorders and other related health problems in this population.

Impact of today's media on university student's body image in Pakistan: a conservative, developing country's perspective

Directory of Open Access Journals (Sweden)

Khan Hussain I

2011-05-01

Full Text Available Abstract Background Living in a world greatly controlled by mass media makes it impossible to escape its pervading influence. As media in Pakistan has been free in the true sense of the word for only a few years, its impact on individuals is yet to be assessed. Our study aims to be the first to look at the effect media has on the body image of university students in a conservative, developing country like Pakistan. Also, we introduced the novel concept of body image dissatisfaction as being both negative and positive. Methods A cross-sectional study was conducted among 7 private universities over a period of two weeks in the city of Karachi, Pakistan's largest and most populous city. Convenience sampling was used to select both male and female undergraduate students aged between 18 and 25 and a sample size of 783 was calculated. Results Of the 784 final respondents, 376 (48% were males and 408 (52% females. The mean age of males was 20.77 (+/- 1.85 years and females was 20.38 (+/- 1.63 years. Out of these, 358 (45.6% respondents had a positive BID (body image dissatisfaction score while 426 (54.4% had a negative BID score. Of the respondents who had positive BID scores, 93 (24.7% were male and 265 (65.0% were female. Of the respondents with a negative BID score, 283 (75.3% were male and 143 (35.0% were female. The results for BID vs. media exposure were similar in both high and low peer pressure groups. Low media exposure meant positive BID scores and vice versa in both groups (p Conclusions Our study confirmed the tendency of the media to have an overall negative effect on individuals' body image. A striking feature of our study, however, was the finding that negative body image dissatisfaction was found to be more prevalent in males as compared to females. Likewise, positive BID scores were more prevalent amongst females.

Radiologic image communication with fiberoptic media

International Nuclear Information System (INIS)

Huang, H.K.; Stewart, B.K.; Loloyan, M.; Tecotzky, R.

1990-01-01

Copper wires and coaxial cables are conventional media for transmitting radiologic images. The high impedance of these cables limits the speed of transmission, the bandwidth of the image, and the distance between nodes. This paper investigates characteristics of radiologic image communication with fiber optics as the medium. The model S L = F (B, D, M, C, W, TR) describes the signal loss S L of the image as a function (F) of the image bandwidth (B), the distance between two nodes (D), the mode of the fiber used (M), the connector type (C), the wavelength (W), and the characteristics of the optical transmitter and receiver pair (TR)

Breast image feature learning with adaptive deconvolutional networks

Science.gov (United States)

Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.

2012-03-01

Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

Smart Images Search based on Visual Features Fusion

International Nuclear Information System (INIS)

Saad, M.H.

2013-01-01

Image search engines attempt to give fast and accurate access to the wide range of the huge amount images available on the Internet. There have been a number of efforts to build search engines based on the image content to enhance search results. Content-Based Image Retrieval (CBIR) systems have achieved a great interest since multimedia files, such as images and videos, have dramatically entered our lives throughout the last decade. CBIR allows automatically extracting target images according to objective visual contents of the image itself, for example its shapes, colors and textures to provide more accurate ranking of the results. The recent approaches of CBIR differ in terms of which image features are extracted to be used as image descriptors for matching process. This thesis proposes improvements of the efficiency and accuracy of CBIR systems by integrating different types of image features. This framework addresses efficient retrieval of images in large image collections. A comparative study between recent CBIR techniques is provided. According to this study; image features need to be integrated to provide more accurate description of image content and better image retrieval accuracy. In this context, this thesis presents new image retrieval approaches that provide more accurate retrieval accuracy than previous approaches. The first proposed image retrieval system uses color, texture and shape descriptors to form the global features vector. This approach integrates the yc b c r color histogram as a color descriptor, the modified Fourier descriptor as a shape descriptor and modified Edge Histogram as a texture descriptor in order to enhance the retrieval results. The second proposed approach integrates the global features vector, which is used in the first approach, with the SURF salient point technique as local feature. The nearest neighbor matching algorithm with a proposed similarity measure is applied to determine the final image rank. The second approach

Social media literacy protects against the negative impact of exposure to appearance ideal social media images in young adult women but not men.

Science.gov (United States)

Tamplin, Natalie C; McLean, Siân A; Paxton, Susan J

2018-05-25

Frequent exposure to appearance ideal social media is associated with body dissatisfaction. We hypothesised that commercial and peer social media literacy would protect against the negative impact of exposure to social media appearance ideal images on young adults' body image. The study was presented as an investigation of alcohol promotion on social media. Participants were 187 women (M age  = 24.6, SD = 3.7) and 187 men (M age  = 22.8, SD = 3.9) who viewed gender-matched alcohol-related appearance ideal social media images or control images containing alcohol only. Social media literacy was assessed prior to image exposure and body satisfaction measured before and after exposure. A negative effect of ideal image exposure on body satisfaction was observed in both women and men. In women only, commercial-social media literacy moderated the negative effect of exposure, independent of internalization or body comparison. Inclusion of social media literacy skills in prevention interventions is supported. Copyright © 2018 Elsevier Ltd. All rights reserved.

Value of MR contrast media in image-guided body interventions.

Science.gov (United States)

Saeed, Maythem; Wilson, Mark

2012-01-28

In the past few years, there have been multiple advances in magnetic resonance (MR) instrumentation, in vivo devices, real-time imaging sequences and interventional procedures with new therapies. More recently, interventionists have started to use minimally invasive image-guided procedures and local therapies, which reduce the pain from conventional surgery and increase drug effectiveness, respectively. Local therapy also reduces the systemic dose and eliminates the toxic side effects of some drugs to other organs. The success of MR-guided procedures depends on visualization of the targets in 3D and precise deployment of ablation catheters, local therapies and devices. MR contrast media provide a wealth of tissue contrast and allows 3D and 4D image acquisitions. After the development of fast imaging sequences, the clinical applications of MR contrast media have been substantially expanded to include pre- during- and post-interventions. Prior to intervention, MR contrast media have the potential to localize and delineate pathologic tissues of vital organs, such as the brain, heart, breast, kidney, prostate, liver and uterus. They also offer other options such as labeling therapeutic agents or cells. During intervention, these agents have the capability to map blood vessels and enhance the contrast between the endovascular guidewire/catheters/devices, blood and tissues as well as direct therapies to the target. Furthermore, labeling therapeutic agents or cells aids in visualizing their delivery sites and tracking their tissue distribution. After intervention, MR contrast media have been used for assessing the efficacy of ablation and therapies. It should be noted that most image-guided procedures are under preclinical research and development. It can be concluded that MR contrast media have great value in preclinical and some clinical interventional procedures. Future applications of MR contrast media in image-guided procedures depend on their safety, tolerability

Multispectral Image Feature Points

Directory of Open Access Journals (Sweden)

Cristhian Aguilera

2012-09-01

Full Text Available This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.

Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

Science.gov (United States)

Kim, Deok-Hwan

As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

Self-esteem threat combined with exposure to thin media images leads to body image compensatory self-enhancement.

Science.gov (United States)

Jarry, Josée L; Kossert, Amy L

2007-03-01

This study examined the effect of a self-esteem threat combined with exposure to thin images on body image (BI) satisfaction and investment. Female participants (N=94) received a self-esteem threat consisting of false failure feedback or received false success feedback on an intellectual task allegedly highly predictive of academic and professional success. They then viewed media images featuring thin models or products. After viewing thin models, women who had received failure feedback declared themselves more satisfied about their appearance and less invested in it than did women who had received success feedback. These results suggest that exposure to the thin ideal may inspire women experiencing self-esteem threats to use appearance as an alternative source of worth, thus maintaining their global esteem through BI compensatory self-enhancement. Potential long-term implications of this strategy, such as a paradoxical increase in BI investment and the development of eating pathology, are discussed.

Social comparisons with media images are cognitively inefficient even for women who say they feel pressure from the media.

Science.gov (United States)

Want, Stephen C; Saiphoo, Alyssa

2017-03-01

The present study investigated whether social comparisons with media images are cognitively efficient (demanding minimal mental effort) or cognitively effortful processes, in a sample of female undergraduate students (N=151) who reported feeling pressure from the media regarding their appearance. Two groups were shown 12 images of thin and attractive female models. One group was asked to memorize a complex 8-digit number during exposure to the images (Cognitively Busy condition), while the other memorized a much simpler number (Free View condition). A third group (Control condition) viewed images without people. Participants in the Free View condition demonstrated significantly increased negative mood and lowered appearance satisfaction from before to after exposure, while participants in the Cognitively Busy and Control conditions did not. We argue that these results suggest social comparisons with media images are at least somewhat cognitively effortful even among women who say they feel pressure from the media. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Image of the White Movement in the Russian Feature Cinema at the Present Stage (1992–2016

Directory of Open Access Journals (Sweden)

Alexander Fedorov

2016-04-01

Full Text Available This article gives the way for hermeneutic analysis of the topic of the White movement in the mirror of the Russian cinema at the present stage (1992-2016. The hermeneutical analysis suggests media text comprehension through comparison with historical, cultural tradition and reality; penetration of its logic; through comparison of media images in historical and cultural context by combining historical, hermeneutical analysis of the structural, plot, ethical, ideological, iconographic / visual, media stereotypes and analysis of media text characters. An analysis of this kind of media texts, in our opinion, is particularly important for media literacy education of future historians, culture and art historians, sociologists, psychologists and educators.Thus, the comparative analysis of plot schemes, characters, and ideology of the Russian feature films of 1992-2016, in varying degrees of affecting the subject of the White movement, leads to the conclusion about the essential similarity of their media stereotypes. Content analysis of screen media texts of 1992-2016 on the topic related to the White movement allows generally to submit their basic narrative schemes.

Speckle suppression via sparse representation for wide-field imaging through turbid media.

Science.gov (United States)

Jang, Hwanchol; Yoon, Changhyeong; Chung, Euiheon; Choi, Wonshik; Lee, Heung-No

2014-06-30

Speckle suppression is one of the most important tasks in the image transmission through turbid media. Insufficient speckle suppression requires an additional procedure such as temporal ensemble averaging over multiple exposures. In this paper, we consider the image recovery process based on the so-called transmission matrix (TM) of turbid media for the image transmission through the media. We show that the speckle left unremoved in the TM-based image recovery can be suppressed effectively via sparse representation (SR). SR is a relatively new signal reconstruction framework which works well even for ill-conditioned problems. This is the first study to show the benefit of using the SR as compared to the phase conjugation (PC) a de facto standard method to date for TM-based imaging through turbid media including a live cell through tissue slice.

Feature Detector and Descriptor for Medical Images

Science.gov (United States)

Sargent, Dusty; Chen, Chao-I.; Tsai, Chang-Ming; Wang, Yuan-Fang; Koppel, Daniel

2009-02-01

The ability to detect and match features across multiple views of a scene is a crucial first step in many computer vision algorithms for dynamic scene analysis. State-of-the-art methods such as SIFT and SURF perform successfully when applied to typical images taken by a digital camera or camcorder. However, these methods often fail to generate an acceptable number of features when applied to medical images, because such images usually contain large homogeneous regions with little color and intensity variation. As a result, tasks like image registration and 3D structure recovery become difficult or impossible in the medical domain. This paper presents a scale, rotation and color/illumination invariant feature detector and descriptor for medical applications. The method incorporates elements of SIFT and SURF while optimizing their performance on medical data. Based on experiments with various types of medical images, we combined, adjusted, and built on methods and parameter settings employed in both algorithms. An approximate Hessian based detector is used to locate scale invariant keypoints and a dominant orientation is assigned to each keypoint using a gradient orientation histogram, providing rotation invariance. Finally, keypoints are described with an orientation-normalized distribution of gradient responses at the assigned scale, and the feature vector is normalized for contrast invariance. Experiments show that the algorithm detects and matches far more features than SIFT and SURF on medical images, with similar error levels.

Picture a Protest: Analyzing Media Images Tweeted From Ferguson

OpenAIRE

Holly S. Cowart; Lynsey M. Saunders; Ginger E. Blackstone

2016-01-01

This content analysis examines media depiction of events in Ferguson, Missouri, following the shooting of the unarmed teenager Michael Brown by a police officer. Using images from the Twitter feeds of nine major media outlets in the month following the shooting, it identifies themes present in those images. Descriptive statistics reveal differences in the roles of people who appear to be White and those who appear to be Black. The two groups are rarely pictured together. The visual narrative ...

Adapting Local Features for Face Detection in Thermal Image

Directory of Open Access Journals (Sweden)

Chao Ma

2017-11-01

Full Text Available A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses. We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.

Features Selection for Skin Micro-Image Symptomatic Recognition

Institute of Scientific and Technical Information of China (English)

HUYue-li; CAOJia-lin; ZHAOQian; FENGXu

2004-01-01

Automatic recognition of skin micro-image symptom is important in skin diagnosis and treatment. Feature selection is to improve the classification performance of skin micro-image symptom.This paper proposes a hybrid approach based on the support vector machine (SVM) technique and genetic algorithm (GA) to select an optimum feature subset from the feature group extracted from the skin micro-images. An adaptive GA is introduced for maintaining the convergence rate. With the proposed method, the average cross validation accuracy is increased from 88.25% using all features to 96.92% using only selected features provided by a classifier for classification of 5 classes of skin symptoms. The experimental results are satisfactory.

Features Selection for Skin Micro-Image Symptomatic Recognition

Institute of Scientific and Technical Information of China (English)

HU Yue-li; CAO Jia-lin; ZHAO Qian; FENG Xu

2004-01-01

Automatic recognition of skin micro-image symptom is important in skin diagnosis and treatment. Feature selection is to improve the classification performance of skin micro-image symptom.This paper proposes a hybrid approach based on the support vector machine (SVM) technique and genetic algorithm (GA) to select an optimum feature subset from the feature group extracted from the skin micro-images. An adaptive GA is introduced for maintaining the convergence rate. With the proposed method, the average cross validation accuracy is increased from 88.25% using all features to 96.92 % using only selected features provided by a classifier for classification of 5 classes of skin symptoms. The experimental results are satisfactory.

FEATURE EVALUATION FOR BUILDING FACADE IMAGES – AN EMPIRICAL STUDY

Directory of Open Access Journals (Sweden)

M. Y. Yang

2012-08-01

Full Text Available The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.

Body Image as Strategy for Engagement in Social Media

Directory of Open Access Journals (Sweden)

Tarcisio Torres Silva

2015-06-01

This work intends to analyze not only how communication technologies have contributed to the emergence of such events but also how image production can be interpreted in such environments. Since the use of social media in protests caught the attention of broadcasting media in 2009 during demonstrations in Iran, a strong connection can be noticed between the content circulating through digital communication technologies and the body. For images produced during the Arab Spring, the same is observed with a series of strategies connecting body image and social mobilization. Our intention is to contribute to the debate of political images, considering the way they have been produced in contemporary society, which deals with a complex environment composed of communication technologies, social organization, and the body itself.

The analysis of image feature robustness using cometcloud

Directory of Open Access Journals (Sweden)

Xin Qi

2012-01-01

Full Text Available The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval.

Color and neighbor edge directional difference feature for image retrieval

Institute of Scientific and Technical Information of China (English)

Chaobing Huang; Shengsheng Yu; Jingli Zhou; Hongwei Lu

2005-01-01

@@ A novel image feature termed neighbor edge directional difference unit histogram is proposed, in which the neighbor edge directional difference unit is defined and computed for every pixel in the image, and is used to generate the neighbor edge directional difference unit histogram. This histogram and color histogram are used as feature indexes to retrieve color image. The feature is invariant to image scaling and translation and has more powerful descriptive for the natural color images. Experimental results show that the feature can achieve better retrieval performance than other color-spatial features.

High speed color imaging through scattering media with a large field of view

Science.gov (United States)

Zhuang, Huichang; He, Hexiang; Xie, Xiangsheng; Zhou, Jianying

2016-09-01

Optical imaging through complex media has many important applications. Although research progresses have been made to recover optical image through various turbid media, the widespread application of the technology is hampered by the recovery speed, requirement on specific illumination, poor image quality and limited field of view. Here we demonstrate that above-mentioned drawbacks can be essentially overcome. The realization of high speed color imaging through turbid media is successfully carried out by taking into account the media memory effect, the point spread function, the exit pupil of the optical system, and the optimized signal to noise ratio. By retrieving selected speckles with enlarged field of view, high quality image is recovered with a responding speed only determined by the frame rates of the image capturing devices. The immediate application of the technique is expected to register static and dynamic imaging under human skin to recover information with a wearable device.

Comparative effects of Facebook and conventional media on body image dissatisfaction

OpenAIRE

Cohen, Rachel; Blaszczynski, Alex

2015-01-01

Background Appearance comparison has consistently been shown to engender body image dissatisfaction. To date, most studies have demonstrated this relationship between appearance comparison and body image dissatisfaction in the context of conventional media images depicting the thin-ideal. Social comparison theory posits that people are more likely to compare themselves to similar others. Since social media forums such as Facebook involve one?s peers, the current study aimed to determine wheth...

Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection.

Science.gov (United States)

Li, Baopu; Meng, Max Q-H

2012-05-01

Tumor in digestive tract is a common disease and wireless capsule endoscopy (WCE) is a relatively new technology to examine diseases for digestive tract especially for small intestine. This paper addresses the problem of automatic recognition of tumor for WCE images. Candidate color texture feature that integrates uniform local binary pattern and wavelet is proposed to characterize WCE images. The proposed features are invariant to illumination change and describe multiresolution characteristics of WCE images. Two feature selection approaches based on support vector machine, sequential forward floating selection and recursive feature elimination, are further employed to refine the proposed features for improving the detection accuracy. Extensive experiments validate that the proposed computer-aided diagnosis system achieves a promising tumor recognition accuracy of 92.4% in WCE images on our collected data.

Histological image classification using biologically interpretable shape-based features

International Nuclear Information System (INIS)

Kothari, Sonal; Phan, John H; Young, Andrew N; Wang, May D

2013-01-01

Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions

Determination of the Image Complexity Feature in Pattern Recognition

Directory of Open Access Journals (Sweden)

Veacheslav L. Perju

2003-11-01

Full Text Available The new image complexity informative feature is proposed. The experimental estimation of the image complexity is carried out. There are elaborated two optical-electronic processors for image complexity calculation. The determination of the necessary number of the image's digitization elements depending on the image complexity was carried out. The accuracy of the image complexity feature calculation was made.

Fast optical recording media based on semiconductor nanostructures for image recording and processing

International Nuclear Information System (INIS)

Kasherininov, P. G.; Tomasov, A. A.

2008-01-01

Fast optical recording media based on semiconductor nanostructures (CdTe, GaAs) for image recording and processing with a speed to 10 6 cycle/s (which exceeds the speed of known recording media based on metal-insulator-semiconductor-(liquid crystal) (MIS-LC) structures by two to three orders of magnitude), a photosensitivity of 10 -2 V/cm 2 , and a spatial resolution of 5-10 (line pairs)/mm are developed. Operating principles of nanostructures as fast optical recording media and methods for reading images recorded in such media are described. Fast optical processors for recording images in incoherent light based on CdTe crystal nanostructures are implemented. The possibility of their application to fabricate image correlators is shown.

[The specific microbiological and clinical features of acute otitis media].

Science.gov (United States)

Gurov, A V; Levina, Yu V; Guseva, A L; Elchueva, Z G; Efimova, S P; Gordienko, M V

The objective of the present study was to elucidate the specific features of the clinical course of acute otitis media as well as the peculiarities of the vestibular function and the microbial paysage associated with this pathological condition under the present-day conditions. The study included 135 patients presenting with acute otitis media (AOM) at different stages of the disease. The discharge obtained from the tympanic cavity of all the patients was examined with the use of polymerase chain reaction in real time, audiological and vestibulogical methods. The distinctive features of acute otitis medium associated with Streptococcus pneumoniae infection were found to be the intense pain syndrome with the symptoms of intoxication, well apparent inflammatory changes in the tympanic membrane as revealed by otoscopy, the increased frequency of sensorineural impairment of hearing, and the characteristic type B tympanometric curve. Typical of AOM associated with Haemophilus influenza infection are the mild pain syndrome, weak changes in the tympanic membrane as revealed by otoscopy, conductive hearing loss, and the type C tympanometric curve.

IKLAN POLITIK ERA IMAGE DAN KEKUASAAN MEDIA

Directory of Open Access Journals (Sweden)

Bedjo Riyanto

2004-01-01

Full Text Available The direct election of the president and vice president is succeed democratitation process. People has their own sovereignity to decide their own choice. Shifting mass mobilitation from political machine party to the power of mass media machine is the consequences of it. In the age of image recently%2C press and television broadcast have become a deciding factor of candidate winning. Who takes the power by force of this factor%2C is the winner. The victory of Susilo Bambang Yudhoyono and Jusuf Kalla to be president and vice president in general election September 20th 2004 is the evident of the thesis. Abstract in Bahasa Indonesia : political ad%2C the power of mass media%2C image building%2C candidate winning.

Narrated truths: the image of psychiatry in the media.

Science.gov (United States)

Nesseler, Thomas

2011-11-01

In recent years, we have witnessed an increase in media attention on the subject of mental illness, which mass media frequently portray as a new phenomenon affecting large sections of the population. Reports about people suffering from mental disorders and on psychiatric or psychotherapeutic clinics, however, are often characterised by their emphasis on stereotypes and one-sided invariably negative attributes both in the choice of wording and the images used. This paper is an attempt to elucidate this apparent contradiction from both a narrative and a socio-historical perspective. In view of the development of modern moving image formats and storytelling techniques, it seeks to identify possible ways of harnessing the media to present a more considered and differentiated picture of psychiatric disorders and mental illnesses. Professionally moderated discussion forums based on social media techniques are to serve just as well as stories that take account of the narrative universals such as reward, success and human relations.

Picture a Protest: Analyzing Media Images Tweeted From Ferguson

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Holly S. Cowart

2016-10-01

Full Text Available This content analysis examines media depiction of events in Ferguson, Missouri, following the shooting of the unarmed teenager Michael Brown by a police officer. Using images from the Twitter feeds of nine major media outlets in the month following the shooting, it identifies themes present in those images. Descriptive statistics reveal differences in the roles of people who appear to be White and those who appear to be Black. The two groups are rarely pictured together. The visual narrative presented on Twitter depicts two distinct sides. Police are consistently shown prepared for conflict, but rarely are protesters in images with police. The implications of these findings are explored through the theoretical viewpoint of agenda setting.

Application of eigen value expansion to feature extraction from MRI images

International Nuclear Information System (INIS)

Kinosada, Yasutomi; Takeda, Kan; Nakagawa, Tsuyoshi

1991-01-01

The eigen value expansion technique was utilized for feature extraction of magnetic resonance (MR) images. The eigen value expansion is an orthonormal transformation method which decomposes a set of images into some statistically uncorrelated images. The technique was applied to MR images obtained with various imaging parameters at the same anatomical site. It generated one mean image and another set of images called bases for the images. Each basis corresponds to a feature in the images. A basis is, therefore, utilized for the feature extraction from MR images and a weighted sum of bases is also used for the feature enhancement. Furthermore, any MR image with specific feature can be obtained from a linear combination of the mean image and all of the bases. Images of hemorrhaged brain with a spin echo sequence and a series of cinematic cerebro spinal fluid flow images with ECG gated gradient refocused echo sequence were employed to estimate the ability of the feature extraction and the contrast enhancement. Results showed us that proposed application of an eigen value expansion technique to the feature extraction of MR images is good enough to clinical use and superior to other feature extraction methods such as producing a calculated MR image with a given TR and TE or the matched-filter method in processing speed and reproducibility of results. (author)

Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features

Science.gov (United States)

Mousavi Kahaki, Seyed Mostafa; Nordin, Md Jan; Ashtari, Amir H.; J. Zahra, Sophia

2016-01-01

An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics—such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient—are insufficient for achieving adequate results under different image deformations. Thus, new descriptor’s similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence. PMID:26985996

Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features.

Directory of Open Access Journals (Sweden)

Seyed Mostafa Mousavi Kahaki

Full Text Available An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics--such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient--are insufficient for achieving adequate results under different image deformations. Thus, new descriptor's similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence.

THE IMAGE OF BALI TOURISM IN SOCIAL NETWORKING MEDIA

Directory of Open Access Journals (Sweden)

Ni Gusti Ayu Dewi Paramita Arisandi

2016-03-01

Full Text Available Tourism is growing very rapidly with the development of technology, especially information technology .Through information technology people can easily get access to information about various destinations and tourist attractions as well as hotel by online media communication. Therefore, this study was trying to use social media such as facebook and twitter, where both social media were widely used by companies engaged in tourism in the world as well as in Bali. Internet consists of several websites, but the most effective one to conduct the research on tourism such as webseries 2.0 or commonly called as social networking media. Social medias - Facebook and Twitter - both were wildly used by the companies running in the sector of tourism in Bali, such as Hotels, Villas, Restaurants, Spas, Travel Agents, Airlines, and Tourist Attraction Managements, to advertised their products and services in their respective field. Through those social media, tourists will be able to access the comments or reviews about the companies. The problem raised in this research is how Bali’s tourism data in social networking media were used and considered, and how was the image of Bali’s tourism in their point of viewbased on the research. The method used here was descriptive qualitative method which describes this phenomenon descriptively by analyzing the obtained comments on social networking media such as facebook and twitter, and then classified in the form of comments positive, negative, and unidentified comment. Afterwards comments were re-analyzed through 4A Approach which consist of Attraction, amenities, Ancillary, and Accessibility. Data which has been obtained re-analyzed via the data obtained from the homepage of (TripAdvisor and Agoda.com. In addition, to strengthen the research data obtained from the homepage of TripAdvisor and Agoda.com , then it has been elaborated on the point rating system which indicatedthat the company were registered in both of

THE EFFECT OF IMAGE ENHANCEMENT METHODS DURING FEATURE DETECTION AND MATCHING OF THERMAL IMAGES

Directory of Open Access Journals (Sweden)

O. Akcay

2017-05-01

Full Text Available A successful image matching is essential to provide an automatic photogrammetric process accurately. Feature detection, extraction and matching algorithms have performed on the high resolution images perfectly. However, images of cameras, which are equipped with low-resolution thermal sensors are problematic with the current algorithms. In this paper, some digital image processing techniques were applied to the low-resolution images taken with Optris PI 450 382 x 288 pixel optical resolution lightweight thermal camera to increase extraction and matching performance. Image enhancement methods that adjust low quality digital thermal images, were used to produce more suitable images for detection and extraction. Three main digital image process techniques: histogram equalization, high pass and low pass filters were considered to increase the signal-to-noise ratio, sharpen image, remove noise, respectively. Later on, the pre-processed images were evaluated using current image detection and feature extraction methods Maximally Stable Extremal Regions (MSER and Speeded Up Robust Features (SURF algorithms. Obtained results showed that some enhancement methods increased number of extracted features and decreased blunder errors during image matching. Consequently, the effects of different pre-process techniques were compared in the paper.

The Effect of Media on Body Image in Pregnant and Postpartum Women.

Science.gov (United States)

Coyne, Sarah M; Liechty, Toni; Collier, Kevin M; Sharp, Aubrey D; Davis, Emilie J; Kroff, Savannah L

2018-07-01

Much research has found that exposure to certain types of media portrayals of women can be related to body image concerns among women. The current paper focuses on the impact of certain messages on pregnant and postpartum women. These women are rarely examined in a media research context but are particularly vulnerable to body image concerns. This experimental study involved 192 pregnant or postpartum women who read a magazine containing glamorized media portrayals of pregnant/postpartum women or a control magazine. Pregnant women reported lower body image after only five minutes of exposure to the magazine with pregnant/postpartum women compared to the control group. There was no immediate effect on postpartum women. Implications for the media industry, health professionals, and women are discussed.

Biomedical imaging modality classification using combined visual features and textual terms.

Science.gov (United States)

Han, Xian-Hua; Chen, Yen-Wei

2011-01-01

We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

Comparison of the image quality of intravenous urograms using low-osmolar contrast media

International Nuclear Information System (INIS)

Kaye, B.; Howard, J.; Foord, K.D.; Cumberland, D.C.

1988-01-01

Almost equivalent, intravenous iodine doses of the three new low-osmolar contrast media, ioxaglate (Hexabrix), iopamidol (Niopam) and iohexol (Omnipaque) have been compared for image quality on the intravenous urogram. Generally good radiographic images were obtained. Iohexol gave better results for the nephrogram and pelvicalyceal distension compared with the other contrast media, but only the nephrogram results were statistically significant. Pyelographic density and ureteric distension and density were similar with all three contrast media. In patients where low-osmolality contrast media need to be used for intravenous urography, we suggest that iohexol gives the best radiographic images. Other factors, such as cost and the relative incidence of side-effects of the low-osmolar contrast media also need to be taken into consideration. (author)

ASCI 2010 contrast media guideline for cardiac imaging: a report of the Asian Society of Cardiovascular Imaging cardiac computed tomography and cardiac magnetic resonance imaging guideline working group

Science.gov (United States)

Kitagawa, Kakuya; Tsai, I-Chen; Chan, Carmen; Yu, Wei; Yong, Hwan Seok; Choi, Byoung Wook

2010-01-01

The use of contrast media for cardiac imaging becomes increasing as the widespread of cardiac CT and cardiac MR. A radiologist needs to carefully consider the indication and the injection protocol of contrast media to be used as well as the possibility of adverse effect. There are several guidelines for contrast media in western countries. However, these are focusing the adverse effect of contrast media. The Asian Society of Cardiovascular Imaging, the only society dedicated to cardiovascular imaging in Asia, formed a Working Group and created a guideline, which summarizes the integrated knowledge of contrast media for cardiac imaging. In cardiac imaging, coronary artery evaluation is feasible by non-contrast MR angiography, which can be an alternative examination in high risk patients for the use of iodine contrast media. Furthermore, the body habitus of Asian patients is usually smaller than that of their western counterparts. This necessitates modifications in the injection protocol and in the formula for calculation of estimated glomerular filtration rate. This guideline provided fundamental information for the use of contrast media for Asian patients in cardiac imaging. PMID:20931289

A visual perceptual descriptor with depth feature for image retrieval

Science.gov (United States)

Wang, Tianyang; Qin, Zhengrui

2017-07-01

This paper proposes a visual perceptual descriptor (VPD) and a new approach to extract perceptual depth feature for 2D image retrieval. VPD mimics human visual system, which can easily distinguish regions that have different textures, whereas for regions which have similar textures, color features are needed for further differentiation. We apply VPD on the gradient direction map of an image, capture texture-similar regions to generate a VPD map. We then impose the VPD map on a quantized color map and extract color features only from the overlapped regions. To reflect the nature of perceptual distance in single 2D image, we propose and extract the perceptual depth feature by computing the nuclear norm of the sparse depth map of an image. Extracted color features and the perceptual depth feature are both incorporated to a feature vector, we utilize this vector to represent an image and measure similarity. We observe that the proposed VPD + depth method achieves a promising result, and extensive experiments prove that it outperforms other typical methods on 2D image retrieval.

Analysis of RTM extended images for VTI media

KAUST Repository

Li, Vladimir

2015-08-19

Extended images obtained from reverse-time migration (RTM) contain information about the accuracy of the velocity field and subsurface illumination at different incidence angles. Here, we evaluate the influence of errors in the anisotropy parameters on the shape of the residual moveout (RMO) in P-wave RTM extended images for VTI (transversely isotropic with a vertical symmetry axis) media. Considering the actual spatial distribution of the zero-dip NMO velocity (Vnmo), which could be approximately estimated by conventional techniques, we analyze the extended images obtained with distorted fields of the parameters η and δ. Differential semblance optimization (DSO) and stack-power estimates are employed to study the sensitivity of focusing to the anisotropy parameters. The results show that the signature of η is dip-dependent, whereas errors in δ cause defocusing only if that parameter is laterally varying. Hence, earlier results regarding the influence of η and δ on reflection moveout and migration velocity analysis remain generally valid in the extended image space for complex media. The dependence of RMO on errors in the anisotropy parameters provides essential insights for anisotropic wavefield tomography using extended images.

Image mosaicking based on feature points using color-invariant values

Science.gov (United States)

Lee, Dong-Chang; Kwon, Oh-Seol; Ko, Kyung-Woo; Lee, Ho-Young; Ha, Yeong-Ho

2008-02-01

In the field of computer vision, image mosaicking is achieved using image features, such as textures, colors, and shapes between corresponding images, or local descriptors representing neighborhoods of feature points extracted from corresponding images. However, image mosaicking based on feature points has attracted more recent attention due to the simplicity of the geometric transformation, regardless of distortion and differences in intensity generated by camera motion in consecutive images. Yet, since most feature-point matching algorithms extract feature points using gray values, identifying corresponding points becomes difficult in the case of changing illumination and images with a similar intensity. Accordingly, to solve these problems, this paper proposes a method of image mosaicking based on feature points using color information of images. Essentially, the digital values acquired from a real digital color camera are converted to values of a virtual camera with distinct narrow bands. Values based on the surface reflectance and invariant to the chromaticity of various illuminations are then derived from the virtual camera values and defined as color-invariant values invariant to changing illuminations. The validity of these color-invariant values is verified in a test using a Macbeth Color-Checker under simulated illuminations. The test also compares the proposed method using the color-invariant values with the conventional SIFT algorithm. The accuracy of the matching between the feature points extracted using the proposed method is increased, while image mosaicking using color information is also achieved.

Analysis of RTM extended images for VTI media

KAUST Repository

Li, Vladimir

2016-04-28

Extended images obtained from reverse time migration (RTM) contain information about the accuracy of the velocity field and subsurface illumination at different incidence angles. Here, we evaluate the influence of errors in the anisotropy parameters on the shape of the residual moveout (RMO) in P-wave RTM extended images for VTI (transversely isotropic with a vertical symmetry axis) media. Using the actual spatial distribution of the zero-dip NMO velocity (Vnmo), which could be approximately estimated by conventional techniques, we analyze the extended images obtained with distorted fields of the parameters η and δ. Differential semblance optimization (DSO) and stack-power estimates are employed to study the sensitivity of focusing to the anisotropy parameters. We also build angle gathers to facilitate interpretation of the shape of RMO in the extended images. The results show that the signature of η is dip-dependent, whereas errors in δ cause defocusing only if that parameter is laterally varying. Hence, earlier results regarding the influence of η and δ on reflection moveout and migration velocity analysis remain generally valid in the extended image space for complex media. The dependence of RMO on errors in the anisotropy parameters provides essential insights for anisotropic wavefield tomography using extended images.

Analysis of RTM extended images for VTI media

KAUST Repository

Li, Vladimir; Tsvankin, Ilya; Alkhalifah, Tariq Ali

2016-01-01

Extended images obtained from reverse time migration (RTM) contain information about the accuracy of the velocity field and subsurface illumination at different incidence angles. Here, we evaluate the influence of errors in the anisotropy parameters on the shape of the residual moveout (RMO) in P-wave RTM extended images for VTI (transversely isotropic with a vertical symmetry axis) media. Using the actual spatial distribution of the zero-dip NMO velocity (Vnmo), which could be approximately estimated by conventional techniques, we analyze the extended images obtained with distorted fields of the parameters η and δ. Differential semblance optimization (DSO) and stack-power estimates are employed to study the sensitivity of focusing to the anisotropy parameters. We also build angle gathers to facilitate interpretation of the shape of RMO in the extended images. The results show that the signature of η is dip-dependent, whereas errors in δ cause defocusing only if that parameter is laterally varying. Hence, earlier results regarding the influence of η and δ on reflection moveout and migration velocity analysis remain generally valid in the extended image space for complex media. The dependence of RMO on errors in the anisotropy parameters provides essential insights for anisotropic wavefield tomography using extended images.

Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

Science.gov (United States)

Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

2018-04-01

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

Imaging Teachers: In Fact and in the Mass Media.

Science.gov (United States)

Reyes, Xae Alicia; Rios, Diana I.

2003-01-01

The impact of mass media on public images of teachers and students is considered in a dialogue between two educational and mass media researchers. Stereotypes in films, such as teacher-savior and student-failure, and abundant reports about Latino dropout rates and low academic achievement impact teachers and the public, who accept negative images…

Consumer opinion on social policy approaches to promoting positive body image: Airbrushed media images and disclaimer labels.

Science.gov (United States)

Paraskeva, Nicole; Lewis-Smith, Helena; Diedrichs, Phillippa C

2017-02-01

Disclaimer labels on airbrushed media images have generated political attention and advocacy as a social policy approach to promoting positive body image. Experimental research suggests that labelling is ineffective and consumers' viewpoints have been overlooked. A mixed-method study explored British consumers' ( N = 1555, aged 11-78 years) opinions on body image and social policy approaches. Thematic analysis indicated scepticism about the effectiveness of labelling images. Quantitatively, adults, although not adolescents, reported that labelling was unlikely to improve body image. Appearance diversity in media and reorienting social norms from appearance to function and health were perceived as effective strategies. Social policy and research implications are discussed.

Hyperspectral image classifier based on beach spectral feature

International Nuclear Information System (INIS)

Liang, Zhang; Lianru, Gao; Bing, Zhang

2014-01-01

The seashore, especially coral bank, is sensitive to human activities and environmental changes. A multispectral image, with coarse spectral resolution, is inadaptable for identify subtle spectral distinctions between various beaches. To the contrary, hyperspectral image with narrow and consecutive channels increases our capability to retrieve minor spectral features which is suit for identification and classification of surface materials on the shore. Herein, this paper used airborne hyperspectral data, in addition to ground spectral data to study the beaches in Qingdao. The image data first went through image pretreatment to deal with the disturbance of noise, radiation inconsistence and distortion. In succession, the reflection spectrum, the derivative spectrum and the spectral absorption features of the beach surface were inspected in search of diagnostic features. Hence, spectra indices specific for the unique environment of seashore were developed. According to expert decisions based on image spectrums, the beaches are ultimately classified into sand beach, rock beach, vegetation beach, mud beach, bare land and water. In situ surveying reflection spectrum from GER1500 field spectrometer validated the classification production. In conclusion, the classification approach under expert decision based on feature spectrum is proved to be feasible for beaches

Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms

Directory of Open Access Journals (Sweden)

Xian-Hua Han

2011-01-01

extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

Diffusion tensor image registration using hybrid connectivity and tensor features.

Science.gov (United States)

Wang, Qian; Yap, Pew-Thian; Wu, Guorong; Shen, Dinggang

2014-07-01

Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. Copyright © 2013 Wiley Periodicals, Inc.

MR imaging features of craniodiaphyseal dysplasia

Energy Technology Data Exchange (ETDEWEB)

Marden, Franklin A. [Mallinckrodt Institute of Radiology, Washington University Medical Center, 510 South Kingshighway Blvd., MO 63110, St. Louis (United States); Department of Radiology, St. Louis Children' s Hospital, Children' s Place, MO 63110, St. Louis (United States); Wippold, Franz J. [Mallinckrodt Institute of Radiology, Washington University Medical Center, 510 South Kingshighway Blvd., MO 63110, St. Louis (United States); Department of Radiology, St. Louis Children' s Hospital, Children' s Place, MO 63110, St. Louis (United States); Department of Radiology/Nuclear Medicine, F. Edward Hebert School of Medicine, Uniformed Services University of the Health Sciences, MD 20814, Bethesda (United States)

2004-02-01

We report the magnetic resonance (MR) imaging findings in a 4-year-old girl with characteristic radiographic and computed tomography (CT) features of craniodiaphyseal dysplasia. MR imaging exquisitely depicted cranial nerve compression, small foramen magnum, hydrocephalus, and other intracranial complications of this syndrome. A syrinx of the cervical spinal cord was demonstrated. We suggest that MR imaging become a routine component of the evaluation of these patients. (orig.)

The mass remote sensing image data management based on Oracle InterMedia

Science.gov (United States)

Zhao, Xi'an; Shi, Shaowei

2013-07-01

With the development of remote sensing technology, getting the image data more and more, how to apply and manage the mass image data safely and efficiently has become an urgent problem to be solved. According to the methods and characteristics of the mass remote sensing image data management and application, this paper puts forward to a new method that takes Oracle Call Interface and Oracle InterMedia to store the image data, and then takes this component to realize the system function modules. Finally, it successfully takes the VC and Oracle InterMedia component to realize the image data storage and management.

MR Imaging Features of Fibrocystic Change of the Breast

Science.gov (United States)

Chen, Jeon-Hor; Liu, Hui; Baek, Hyeon-Man; Nalcioglu, Orhan; Su, Min-Ying

2008-01-01

Purpose Studies specifically reporting MR imaging of fibrocystic change (FCC) of the breast are very few and its MR imaging features are not clearly known. The purpose of this study was to analyze the MR imaging features of FCC of the breast. Materials and Methods Thirty one patients of pathologically proved FCC of the breast were retrospectively reviewed. The MRI study was performed using a 1.5 T MR scanner with standard bilateral breast coil. The imaging protocol consisted of pre-contrast T1W imaging and dynamic contrast-enhanced axial T1W imaging. The MRI features were interpreted based on the morphologic and enhancement kinetic descriptors defined on ACR BIRADS-MRI lexicon. Results FCC of the breast had a wide spectrum of morphologic and kinetic features on MRI. Two types of FCC were found, including a more diffuse type of non-mass lesion (12/31, 39%) showing benign enhancement kinetic pattern with medium wash-in in early phase (9/10, 90%) and a focal mass type lesion (11/31, 35%) with enhancement kinetic usually showing rapid up-slope mimicking a breast cancer (8/11, 73%). Conclusion MRI is able to elaborate the diverse imaging features of fibrocystic change of the breast. Our result showed that FCC presenting as focal mass type lesion were usually over-diagnosed as malignancy. Understanding MR imaging of FCC is important to determine which cohort of patients should be followed up alone or receive aggressive management. PMID:18436406

Who owns the images? Image politics and media criticism in theater: a separation of powers

NARCIS (Netherlands)

Röttger, K.; Jackob, A.

2007-01-01

This article investigates the ownership of images analyzing not only the place and mode of image circulation, but also the question of who can access these images under what circumstances. It is not only concerned with the technical or electronic media that are often held responsible for the

Uniform competency-based local feature extraction for remote sensing images

Science.gov (United States)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

Robust Image Hashing Using Radon Transform and Invariant Features

Directory of Open Access Journals (Sweden)

Y.L. Liu

2016-09-01

Full Text Available A robust image hashing method based on radon transform and invariant features is proposed for image authentication, image retrieval, and image detection. Specifically, an input image is firstly converted into a counterpart with a normalized size. Then the invariant centroid algorithm is applied to obtain the invariant feature point and the surrounding circular area, and the radon transform is employed to acquire the mapping coefficient matrix of the area. Finally, the hashing sequence is generated by combining the feature vectors and the invariant moments calculated from the coefficient matrix. Experimental results show that this method not only can resist against the normal image processing operations, but also some geometric distortions. Comparisons of receiver operating characteristic (ROC curve indicate that the proposed method outperforms some existing methods in classification between perceptual robustness and discrimination.

Strategi Komunikasi Pemasaran dalam Membangun Brand Image pada Media Sosial Twitter @Pekanbaruco

OpenAIRE

Febriani, Mega

2014-01-01

Social media twitter is a gathering place for those people who want to shareinformation and a place to make new friends and interact online. Therefore, todaymany social media twitter accounts are present using the brand image as businessaccount because success of the twitter to promote products and services. Amidst themany other competitors that carries the same brand image, @PekanbaruCo managein building a brand image as a business accounts evidenced by an increase infollowers and clients. T...

Feature representation of RGB-D images using joint spatial-depth feature pooling

DEFF Research Database (Denmark)

Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

2016-01-01

Recent development in depth imaging technology makes acquisition of depth information easier. With the additional depth cue, RGB-D cameras can provide effective support for many RGB-D perception tasks beyond traditional RGB information. However, current feature representation based on RGB-D image...

FEATURE MATCHING OF HISTORICAL IMAGES BASED ON GEOMETRY OF QUADRILATERALS

Directory of Open Access Journals (Sweden)

F. Maiwald

2018-05-01

Full Text Available This contribution shows an approach to match historical images from the photo library of the Saxon State and University Library Dresden (SLUB in the context of a historical three-dimensional city model of Dresden. In comparison to recent images, historical photography provides diverse factors which make an automatical image analysis (feature detection, feature matching and relative orientation of images difficult. Due to e.g. film grain, dust particles or the digitalization process, historical images are often covered by noise interfering with the image signal needed for a robust feature matching. The presented approach uses quadrilaterals in image space as these are commonly available in man-made structures and façade images (windows, stones, claddings. It is explained how to generally detect quadrilaterals in images. Consequently, the properties of the quadrilaterals as well as the relationship to neighbouring quadrilaterals are used for the description and matching of feature points. The results show that most of the matches are robust and correct but still small in numbers.

Contrast Media for X-ray and Magnetic Resonance Imaging: Development, Current Status and Future Perspectives.

Science.gov (United States)

Frenzel, Thomas; Lawaczeck, Rüdiger; Taupitz, Matthias; Jost, Gregor; Lohrke, Jessica; Sieber, Martin A; Pietsch, Hubertus

2015-09-01

Over the last 120 years, the extensive advances in medical imaging allowed enhanced diagnosis and therapy of many diseases and thereby improved the quality of life of many patient generations. From the beginning, all technical solutions and imaging procedures were combined with dedicated pharmaceutical developments of contrast media, to further enhance the visualization of morphology and physiology. This symbiosis of imaging hardware and contrast media development was of high importance for the development of modern clinical radiology. Today, all available clinically approved contrast media fulfill the highest requirements for clinical safety and efficacy. All new concepts to increase the efficacy of contrast media have also to consider the high clinical safety standards and cost of goods of current marketed contrast media. Nevertheless, diagnostic imaging will contribute significantly to the progresses in medicine, and new contrast media developments are mandatory to address the medical needs of the future.

Hand and wrist arthritis of Behcet disease: Imaging features

International Nuclear Information System (INIS)

Sugawara, Shunsuke; Ehara, Shigeru; Hitachi, Shin; Sugimoto, Hideharu

2010-01-01

Background: Reports on arthritis in Behcet disease are relatively scarce, and imaging features vary. Purpose: To document the various imaging features of articular disorders of the hand and wrist in Behcet disease. Material and Methods: Four patients, four women aged 26 to 65 years, fulfilling the diagnostic criteria of Behcet disease, with imaging findings of hand and wrist arthritis, were seen in two institutions. Radiography and magnetic resonance (MR) imaging were studied to elucidate the pattern and distribution. Results: Both non-erosive arthritis and erosive arthritis of different features were noted: one with non-erosive synovitis of the wrist, one with wrist synovitis with minimal erosion, and two with erosive arthritis of the distal interphalangeal joint. Conclusion: Imaging manifestations of arthritis of Behcet disease vary, and may be similar to other seronegative arthritides

Image Retrieval based on Integration between Color and Geometric Moment Features

International Nuclear Information System (INIS)

Saad, M.H.; Saleh, H.I.; Konbor, H.; Ashour, M.

2012-01-01

Content based image retrieval is the retrieval of images based on visual features such as colour, texture and shape. .the Current approaches to CBIR differ in terms of which image features are extracted; recent work deals with combination of distances or scores from different and usually independent representations in an attempt to induce high level semantics from the low level descriptors of the images. content-based image retrieval has many application areas such as, education, commerce, military, searching, commerce, and biomedicine and Web image classification. This paper proposes a new image retrieval system, which uses color and geometric moment feature to form the feature vectors. Bhattacharyya distance and histogram intersection are used to perform feature matching. This framework integrates the color histogram which represents the global feature and geometric moment as local descriptor to enhance the retrieval results. The proposed technique is proper for precisely retrieving images even in deformation cases such as geometric deformations and noise. It is tested on a standard the results shows that a combination of our approach as a local image descriptor with other global descriptors outperforms other approaches.

Special features of informative photography in Spanish online media

Directory of Open Access Journals (Sweden)

Lic. María Isabel Villa

2008-01-01

Full Text Available This paper shows the most relevant conclusions of the research: “The informative image in online media: newspaper, radio and television”. Firstly, the author presents the theory and methodology which has been employed. Secondly, she describes how they were put into practice on a sample collected in 2004. This sample consisted of 311 images from the following newspapers online editions: La Vanguardia, El País and El Mundo; as from the radio stations Cadena Ser, Onda Cero, COPE, as well as from the TV channels Antena 3 and Telecinco. Finally, the paper explores how the object of study has changed and developed until 2008 and its possible lines of research. One of the main conclusions is that the modular design of the pages damages the visual message. Moreover, The presentation is homogeneous and the edition of the images is sharp to adjust them to fixed sizes. In addition to this, photographers have become anonymous, whereas news agencies have become the main suppliers of images.

The sharing of radiological images by professional mixed martial arts fighters on social media.

Science.gov (United States)

Rahmani, George; Joyce, Cormac W; McCarthy, Peter

2017-06-01

Mixed martial arts is a sport that has recently enjoyed a significant increase in popularity. This rise in popularity has catapulted many of these "cage fighters" into stardom and many regularly use social media to reach out to their fans. An interesting result of this interaction on social media is that athletes are sharing images of their radiological examinations when they sustain an injury. To review instances where mixed martial arts fighters shared images of their radiological examinations on social media and in what context they were shared. An Internet search was performed using the Google search engine. Search terms included "MMA," "mixed martial arts," "injury," "scan," "X-ray," "fracture," and "break." Articles which discussed injuries to MMA fighters were examined and those in which the fighter themselves shared a radiological image of their injury on social media were identified. During our search, we identified 20 MMA fighters that had shared radiological images of their injuries on social media. There were 15 different types of injury, with a fracture of the mid-shaft of the ulna being the most common. The most popular social media platform was Twitter. The most common imaging modality X-ray (71%). The majority of injuries were sustained during competition (81%) and 35% of these fights resulted in a win for the fighter. Professional mixed martial artists are sharing radiological images of their injuries on social media. This may be in an attempt to connect with fans and raise their profile among other fighters.

Feature Importance for Human Epithelial (HEp-2 Cell Image Classification

Directory of Open Access Journals (Sweden)

Vibha Gupta

2018-02-01

Full Text Available Indirect Immuno-Fluorescence (IIF microscopy imaging of human epithelial (HEp-2 cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD systems, based on image-based classification, can help in terms of time, effort, and reliability of diagnosis. Such approaches are based on extracting some representative features from the images. This work explores the selection of the most distinctive features for HEp-2 cell images using various feature selection (FS methods. Considering that there is no single universally optimal feature selection technique, we also propose hybridization of one class of FS methods (filter methods. Furthermore, the notion of variable importance for ranking features, provided by another type of approaches (embedded methods such as Random forest, Random uniform forest is exploited to select a good subset of features from a large set, such that addition of new features does not increase classification accuracy. In this work, we have also, with great consideration, designed class-specific features to capture morphological visual traits of the cell patterns. We perform various experiments and discussions to demonstrate the effectiveness of FS methods along with proposed and a standard feature set. We achieve state-of-the-art performance even with small number of features, obtained after the feature selection.

Self-imaging of partially coherent light in graded-index media.

Science.gov (United States)

Ponomarenko, Sergey A

2015-02-15

We demonstrate that partially coherent light beams of arbitrary intensity and spectral degree of coherence profiles can self-image in linear graded-index media. The results can be applicable to imaging with noisy spatial or temporal light sources.

Imaging in scattering media using correlation image sensors and sparse convolutional coding

KAUST Repository

Heide, Felix; Xiao, Lei; Kolb, Andreas; Hullin, Matthias B.; Heidrich, Wolfgang

2014-01-01

Correlation image sensors have recently become popular low-cost devices for time-of-flight, or range cameras. They usually operate under the assumption of a single light path contributing to each pixel. We show that a more thorough analysis of the sensor data from correlation sensors can be used can be used to analyze the light transport in much more complex environments, including applications for imaging through scattering and turbid media. The key of our method is a new convolutional sparse coding approach for recovering transient (light-in-flight) images from correlation image sensors. This approach is enabled by an analysis of sparsity in complex transient images, and the derivation of a new physically-motivated model for transient images with drastically improved sparsity.

Imaging in scattering media using correlation image sensors and sparse convolutional coding

KAUST Repository

Heide, Felix

2014-10-17

Correlation image sensors have recently become popular low-cost devices for time-of-flight, or range cameras. They usually operate under the assumption of a single light path contributing to each pixel. We show that a more thorough analysis of the sensor data from correlation sensors can be used can be used to analyze the light transport in much more complex environments, including applications for imaging through scattering and turbid media. The key of our method is a new convolutional sparse coding approach for recovering transient (light-in-flight) images from correlation image sensors. This approach is enabled by an analysis of sparsity in complex transient images, and the derivation of a new physically-motivated model for transient images with drastically improved sparsity.

Renal angiomyoadenomatous tumour: Imaging features

Science.gov (United States)

Sahni, V. Anik; Hirsch, Michelle S.; Silverman, Stuart G.

2012-01-01

Renal angiomyoadenomatous tumour is a rare, recently described neoplasm with a distinctive histological appearance. Although reported in the pathology literature, to our knowledge, no prior reports have described its imaging appearance. We describe the computed tomography and magnetic resonance imaging features of an incidentally detected renal angiomyoadenomatous tumour that appeared as a well-marginated, solid T2-hypointense enhancing mass, in a 50-year-old woman. It is indistinguishable from a variety of benign and malignant renal neoplasms. PMID:23093565

Generating description with multi-feature fusion and saliency maps of image

Science.gov (United States)

Liu, Lisha; Ding, Yuxuan; Tian, Chunna; Yuan, Bo

2018-04-01

Generating description for an image can be regard as visual understanding. It is across artificial intelligence, machine learning, natural language processing and many other areas. In this paper, we present a model that generates description for images based on RNN (recurrent neural network) with object attention and multi-feature of images. The deep recurrent neural networks have excellent performance in machine translation, so we use it to generate natural sentence description for images. The proposed method uses single CNN (convolution neural network) that is trained on ImageNet to extract image features. But we think it can not adequately contain the content in images, it may only focus on the object area of image. So we add scene information to image feature using CNN which is trained on Places205. Experiments show that model with multi-feature extracted by two CNNs perform better than which with a single feature. In addition, we make saliency weights on images to emphasize the salient objects in images. We evaluate our model on MSCOCO based on public metrics, and the results show that our model performs better than several state-of-the-art methods.

Exploring adolescent views of body image: the influence of media.

Science.gov (United States)

Spurr, Shelley; Berry, Lois; Walker, Keith

2013-01-01

The purpose of this article is to present findings from two parallel qualitative studies that used focus groups to explore adolescent views of psychological wellness and healthy bodies. Nine focus groups were held with 46 adolescents aged 16-19 years from two Mid-Western Canadian high schools. Both studies were designed with an interpretive humanist perspective and then a 6-step thematic approach was used to analyze the data. Common themes emerging in the focus group discussions in both studies included the negative impact of media on adolescent body image and pressure to conform to the Western views of physical appearance. These findings illustrate the need for nurses to understand the influence of the media on adolescents' views of their body image and to incorporate protocols for assessment, education, and counseling of adolescents on the healthy usage of media into their pediatric clinical practice. Through consistent participation in the development and implementation of health policies, nurses play a critical role in supporting adolescents to develop healthy views of body image.

Special feature on imaging systems and techniques

Science.gov (United States)

Yang, Wuqiang; Giakos, George

2013-07-01

The IEEE International Conference on Imaging Systems and Techniques (IST'2012) was held in Manchester, UK, on 16-17 July 2012. The participants came from 26 countries or regions: Austria, Brazil, Canada, China, Denmark, France, Germany, Greece, India, Iran, Iraq, Italy, Japan, Korea, Latvia, Malaysia, Norway, Poland, Portugal, Sweden, Switzerland, Taiwan, Tunisia, UAE, UK and USA. The technical program of the conference consisted of a series of scientific and technical sessions, exploring physical principles, engineering and applications of new imaging systems and techniques, as reflected by the diversity of the submitted papers. Following a rigorous review process, a total of 123 papers were accepted, and they were organized into 30 oral presentation sessions and a poster session. In addition, six invited keynotes were arranged. The conference not only provided the participants with a unique opportunity to exchange ideas and disseminate research outcomes but also paved a way to establish global collaboration. Following the IST'2012, a total of 55 papers, which were technically extended substantially from their versions in the conference proceeding, were submitted as regular papers to this special feature of Measurement Science and Technology . Following a rigorous reviewing process, 25 papers have been finally accepted for publication in this special feature and they are organized into three categories: (1) industrial tomography, (2) imaging systems and techniques and (3) image processing. These papers not only present the latest developments in the field of imaging systems and techniques but also offer potential solutions to existing problems. We hope that this special feature provides a good reference for researchers who are active in the field and will serve as a catalyst to trigger further research. It has been our great pleasure to be the guest editors of this special feature. We would like to thank the authors for their contributions, without which it would

Correlation between image quality of CT scan and amount of intravenous contrast media

International Nuclear Information System (INIS)

Yoon, Dae Young; Choi, Dae Seob; Kim, Seung Hyup; Han, Joon Koo; Choi, Byung Ihn; Im, Jung Gi; Han, Moon Hee; Chang, Kee Hyun; Kim, Jong Hyo; Han, Man Chung

1993-01-01

A blind, comparative clinical study was performed prospectively to examine the correlation between image quality of CT scan in terms of contrast enhancement effect and amount of intravenous contrast media. A total of 357 patients were randomized into two groups. Ionic high-osmolality contrast media (68% meglumine ioglicate) was administered intravenously as 100 ml bolus in one group and as 50 ml bolus in the other group. Statistically significant differences of image quality were found in CT scans of the brain, head and neck, chest and abdomen (p 0.05). We suggest that amount of contrast media may be reduced in pelvis CT without significant degradation of image quality

Social media mining and visualization for point-of-interest recommendation

Institute of Scientific and Technical Information of China (English)

Ren Xingyi; Song Meina; E Haihong; Song Junde

2017-01-01

With the rapid growth of location-based social networks (LBSNs),point-of-interest (POI) recommendation has become an important research problem.As one of the most representative social media platforms,Twitter provides various real-life information for POI recommendation in real time.Despite that POI recommendation has been actively studied,tweet images have not been well utilized for this research problem.State-of-the-art visual features like convolutional neural network (CNN) features have shown significant performance gains over the traditional bag-of-visual-words in unveiling the image's semantics.Unfortunately,they have not been employed for POI recommendation from social websites.Hence,how to make the most of tweet images to improve the performance of POI recommendation and visualization remains open.In this paper,we thoroughly study the impact of tweet images on POI recommendation for different POI categories using various visual features.A novel topic model called social media Twitter-latent Dirichlet allocation (SM-TwitterLDA) which jointly models five Twitter features,(i.e.,text,image,location,timestamp and hashtag) is designed to discover POIs from the sheer amount of tweets.Moreover,each POI is visualized by representative images selected on three predefined criteria.Extensive experiments have been conducted on a real-life tweet dataset to verify the effectiveness of our method.

Prototype Theory Based Feature Representation for PolSAR Images

OpenAIRE

Huang Xiaojing; Yang Xiangli; Huang Pingping; Yang Wen

2016-01-01

This study presents a new feature representation approach for Polarimetric Synthetic Aperture Radar (PolSAR) image based on prototype theory. First, multiple prototype sets are generated using prototype theory. Then, regularized logistic regression is used to predict similarities between a test sample and each prototype set. Finally, the PolSAR image feature representation is obtained by ensemble projection. Experimental results of an unsupervised classification of PolSAR images show that our...

HDR IMAGING FOR FEATURE DETECTION ON DETAILED ARCHITECTURAL SCENES

Directory of Open Access Journals (Sweden)

G. Kontogianni

2015-02-01

Full Text Available 3D reconstruction relies on accurate detection, extraction, description and matching of image features. This is even truer for complex architectural scenes that pose needs for 3D models of high quality, without any loss of detail in geometry or color. Illumination conditions influence the radiometric quality of images, as standard sensors cannot depict properly a wide range of intensities in the same scene. Indeed, overexposed or underexposed pixels cause irreplaceable information loss and degrade digital representation. Images taken under extreme lighting environments may be thus prohibitive for feature detection/extraction and consequently for matching and 3D reconstruction. High Dynamic Range (HDR images could be helpful for these operators because they broaden the limits of illumination range that Standard or Low Dynamic Range (SDR/LDR images can capture and increase in this way the amount of details contained in the image. Experimental results of this study prove this assumption as they examine state of the art feature detectors applied both on standard dynamic range and HDR images.

Textural features for image classification

Science.gov (United States)

Haralick, R. M.; Dinstein, I.; Shanmugam, K.

1973-01-01

Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.

Science.gov (United States)

Nikfarjam, Azadeh; Sarker, Abeed; O'Connor, Karen; Ginn, Rachel; Gonzalez, Graciela

2015-05-01

Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Radiomic features analysis in computed tomography images of lung nodule classification.

Directory of Open Access Journals (Sweden)

Chia-Hung Chen

Full Text Available Radiomics, which extract large amount of quantification image features from diagnostic medical images had been widely used for prognostication, treatment response prediction and cancer detection. The treatment options for lung nodules depend on their diagnosis, benign or malignant. Conventionally, lung nodule diagnosis is based on invasive biopsy. Recently, radiomics features, a non-invasive method based on clinical images, have shown high potential in lesion classification, treatment outcome prediction.Lung nodule classification using radiomics based on Computed Tomography (CT image data was investigated and a 4-feature signature was introduced for lung nodule classification. Retrospectively, 72 patients with 75 pulmonary nodules were collected. Radiomics feature extraction was performed on non-enhanced CT images with contours which were delineated by an experienced radiation oncologist.Among the 750 image features in each case, 76 features were found to have significant differences between benign and malignant lesions. A radiomics signature was composed of the best 4 features which included Laws_LSL_min, Laws_SLL_energy, Laws_SSL_skewness and Laws_EEL_uniformity. The accuracy using the signature in benign or malignant classification was 84% with the sensitivity of 92.85% and the specificity of 72.73%.The classification signature based on radiomics features demonstrated very good accuracy and high potential in clinical application.

Prostate cancer multi-feature analysis using trans-rectal ultrasound images

International Nuclear Information System (INIS)

Mohamed, S S; Salama, M M A; Kamel, M; El-Saadany, E F; Rizkalla, K; Chin, J

2005-01-01

This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yeilding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN. (note)

Image Mosaic Method Based on SIFT Features of Line Segment

Directory of Open Access Journals (Sweden)

Jun Zhu

2014-01-01

Full Text Available This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling.

SU-E-J-237: Image Feature Based DRR and Portal Image Registration

Energy Technology Data Exchange (ETDEWEB)

Wang, X; Chang, J [NY Weill Cornell Medical Ctr, NY (United States)

2014-06-01

Purpose: Two-dimensional (2D) matching of the kV X-ray and digitally reconstructed radiography (DRR) images is an important setup technique for image-guided radiotherapy (IGRT). In our clinics, mutual information based methods are used for this purpose on commercial linear accelerators, but with often needs for manual corrections. This work proved the feasibility that feature based image transform can be used to register kV and DRR images. Methods: The scale invariant feature transform (SIFT) method was implemented to detect the matching image details (or key points) between the kV and DRR images. These key points represent high image intensity gradients, and thus the scale invariant features. Due to the poor image contrast from our kV image, direct application of the SIFT method yielded many detection errors. To assist the finding of key points, the center coordinates of the kV and DRR images were read from the DICOM header, and the two groups of key points with similar relative positions to their corresponding centers were paired up. Using these points, a rigid transform (with scaling, horizontal and vertical shifts) was estimated. We also artificially introduced vertical and horizontal shifts to test the accuracy of our registration method on anterior-posterior (AP) and lateral pelvic images. Results: The results provided a satisfactory overlay of the transformed kV onto the DRR image. The introduced vs. detected shifts were fit into a linear regression. In the AP image experiments, linear regression analysis showed a slope of 1.15 and 0.98 with an R2 of 0.89 and 0.99 for the horizontal and vertical shifts, respectively. The results are 1.2 and 1.3 with R2 of 0.72 and 0.82 for the lateral image shifts. Conclusion: This work provided an alternative technique for kV to DRR alignment. Further improvements in the estimation accuracy and image contrast tolerance are underway.

Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images.

Science.gov (United States)

Rakotomamonjy, Alain; Petitjean, Caroline; Salaün, Mathieu; Thiberville, Luc

2014-06-01

To assess the feasibility of lung cancer diagnosis using fibered confocal fluorescence microscopy (FCFM) imaging technique and scattering features for pattern recognition. FCFM imaging technique is a new medical imaging technique for which interest has yet to be established for diagnosis. This paper addresses the problem of lung cancer detection using FCFM images and, as a first contribution, assesses the feasibility of computer-aided diagnosis through these images. Towards this aim, we have built a pattern recognition scheme which involves a feature extraction stage and a classification stage. The second contribution relies on the features used for discrimination. Indeed, we have employed the so-called scattering transform for extracting discriminative features, which are robust to small deformations in the images. We have also compared and combined these features with classical yet powerful features like local binary patterns (LBP) and their variants denoted as local quinary patterns (LQP). We show that scattering features yielded to better recognition performances than classical features like LBP and their LQP variants for the FCFM image classification problems. Another finding is that LBP-based and scattering-based features provide complementary discriminative information and, in some situations, we empirically establish that performance can be improved when jointly using LBP, LQP and scattering features. In this work we analyze the joint capability of FCFM images and scattering features for lung cancer diagnosis. The proposed method achieves a good recognition rate for such a diagnosis problem. It also performs well when used in conjunction with other features for other classical medical imaging classification problems. Copyright © 2014 Elsevier B.V. All rights reserved.

Feature extraction for magnetic domain images of magneto-optical recording films using gradient feature segmentation

International Nuclear Information System (INIS)

Quanqing, Zhu.; Xinsai, Wang; Xuecheng, Zou; Haihua, Li; Xiaofei, Yang

2002-01-01

In this paper, we present a method to realize feature extraction on low contrast magnetic domain images of magneto-optical recording films. The method is based on the following three steps: first, Lee-filtering method is adopted to realize pre-filtering and noise reduction; this is followed by gradient feature segmentation, which separates the object area from the background area; finally the common linking method is adopted and the characteristic parameters of magnetic domain are calculated. We describe these steps with particular emphasis on the gradient feature segmentation. The results show that this method has advantages over other traditional ones for feature extraction of low contrast images

Blind image quality assessment based on aesthetic and statistical quality-aware features

Science.gov (United States)

Jenadeleh, Mohsen; Masaeli, Mohammad Masood; Moghaddam, Mohsen Ebrahimi

2017-07-01

The main goal of image quality assessment (IQA) methods is the emulation of human perceptual image quality judgments. Therefore, the correlation between objective scores of these methods with human perceptual scores is considered as their performance metric. Human judgment of the image quality implicitly includes many factors when assessing perceptual image qualities such as aesthetics, semantics, context, and various types of visual distortions. The main idea of this paper is to use a host of features that are commonly employed in image aesthetics assessment in order to improve blind image quality assessment (BIQA) methods accuracy. We propose an approach that enriches the features of BIQA methods by integrating a host of aesthetics image features with the features of natural image statistics derived from multiple domains. The proposed features have been used for augmenting five different state-of-the-art BIQA methods, which use statistical natural scene statistics features. Experiments were performed on seven benchmark image quality databases. The experimental results showed significant improvement of the accuracy of the methods.

3D Image Display Courses for Information Media Students.

Science.gov (United States)

Yanaka, Kazuhisa; Yamanouchi, Toshiaki

2016-01-01

Three-dimensional displays are used extensively in movies and games. These displays are also essential in mixed reality, where virtual and real spaces overlap. Therefore, engineers and creators should be trained to master 3D display technologies. For this reason, the Department of Information Media at the Kanagawa Institute of Technology has launched two 3D image display courses specifically designed for students who aim to become information media engineers and creators.

Seismic Full Waveform Modeling & Imaging in Attenuating Media

Science.gov (United States)

Guo, Peng

Seismic attenuation strongly affects seismic waveforms by amplitude loss and velocity dispersion. Without proper inclusion of Q parameters, errors can be introduced for seismic full waveform modeling and imaging. Three different (Carcione's, Robertsson's, and the generalized Robertsson's) isotropic viscoelastic wave equations based on the generalized standard linear solid (GSLS) are evaluated. The second-order displacement equations are derived, and used to demonstrate that, with the same stress relaxation times, these viscoelastic formulations are equivalent. By introducing separate memory variables for P and S relaxation functions, Robertsson's formulation is generalized to allow different P and S wave stress relaxation times, which improves the physical consistency of the Qp and Qs modelled in the seismograms.The three formulations have comparable computational cost. 3D seismic finite-difference forward modeling is applied to anisotropic viscoelastic media. The viscoelastic T-matrix (a dynamic effective medium theory) relates frequency-dependent anisotropic attenuation and velocity to reservoir properties in fractured HTI media, based on the meso-scale fluid flow attenuation mechanism. The seismic signatures resulting from changing viscoelastic reservoir properties are easily visible. Analysis of 3D viscoelastic seismograms suggests that anisotropic attenuation is a potential tool for reservoir characterization. To compensate the Q effects during reverse-time migration (RTM) in viscoacoustic and viscoelastic media, amplitudes need to be compensated during wave propagation; the propagation velocity of the Q-compensated wavefield needs to be the same as in the attenuating wavefield, to restore the phase information. Both amplitude and phase can be compensated when the velocity dispersion and the amplitude loss are decoupled. For wave equations based on the GSLS, because Q effects are coupled in the memory variables, Q-compensated wavefield propagates faster than

Convolutional neural network features based change detection in satellite images

Science.gov (United States)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

Texture Feature Analysis for Different Resolution Level of Kidney Ultrasound Images

Science.gov (United States)

Kairuddin, Wan Nur Hafsha Wan; Mahmud, Wan Mahani Hafizah Wan

2017-08-01

Image feature extraction is a technique to identify the characteristic of the image. The objective of this work is to discover the texture features that best describe a tissue characteristic of a healthy kidney from ultrasound (US) image. Three ultrasound machines that have different specifications are used in order to get a different quality (different resolution) of the image. Initially, the acquired images are pre-processed to de-noise the speckle to ensure the image preserve the pixels in a region of interest (ROI) for further extraction. Gaussian Low- pass Filter is chosen as the filtering method in this work. 150 of enhanced images then are segmented by creating a foreground and background of image where the mask is created to eliminate some unwanted intensity values. Statistical based texture features method is used namely Intensity Histogram (IH), Gray-Level Co-Occurance Matrix (GLCM) and Gray-level run-length matrix (GLRLM).This method is depends on the spatial distribution of intensity values or gray levels in the kidney region. By using One-Way ANOVA in SPSS, the result indicated that three features (Contrast, Difference Variance and Inverse Difference Moment Normalized) from GLCM are not statistically significant; this concludes that these three features describe a healthy kidney characteristics regardless of the ultrasound image quality.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

Directory of Open Access Journals (Sweden)

Yuanshen Zhao

2016-01-01

Full Text Available Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion.

Science.gov (United States)

Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

2016-01-29

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.

The Impact of an Educational Intervention to Protect Women against the Influence of Media Images

Science.gov (United States)

Ogden, Jane; Smith, Lauren; Nolan, Helen; Moroney, Rachel; Lynch, Hannah

2011-01-01

Purpose: Media images of unrealistic beauty have been identified as a determinant of women's body dissatisfaction. This experimental study aims to explore whether the negative impact of such images could be reduced by a one-time educational intervention consisting of a presentation and discussion, teaching women to be critical of media images.…

Memory-effect based deconvolution microscopy for super-resolution imaging through scattering media

Science.gov (United States)

Edrei, Eitan; Scarcelli, Giuliano

2016-09-01

High-resolution imaging through turbid media is a fundamental challenge of optical sciences that has attracted a lot of attention in recent years for its wide range of potential applications. Here, we demonstrate that the resolution of imaging systems looking behind a highly scattering medium can be improved below the diffraction-limit. To achieve this, we demonstrate a novel microscopy technique enabled by the optical memory effect that uses a deconvolution image processing and thus it does not require iterative focusing, scanning or phase retrieval procedures. We show that this newly established ability of direct imaging through turbid media provides fundamental and practical advantages such as three-dimensional refocusing and unambiguous object reconstruction.

Classification of radiolarian images with hand-crafted and deep features

Science.gov (United States)

Keçeli, Ali Seydi; Kaya, Aydın; Keçeli, Seda Uzunçimen

2017-12-01

Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.

Neighbors Based Discriminative Feature Difference Learning for Kinship Verification

DEFF Research Database (Denmark)

Duan, Xiaodong; Tan, Zheng-Hua

2015-01-01

In this paper, we present a discriminative feature difference learning method for facial image based kinship verification. To transform feature difference of an image pair to be discriminative for kinship verification, a linear transformation matrix for feature difference between an image pair...... than the commonly used feature concatenation, leading to a low complexity. Furthermore, there is no positive semi-definitive constrain on the transformation matrix while there is in metric learning methods, leading to an easy solution for the transformation matrix. Experimental results on two public...... databases show that the proposed method combined with a SVM classification method outperforms or is comparable to state-of-the-art kinship verification methods. © Springer International Publishing AG, Part of Springer Science+Business Media...

Body image, eating disorders, and the relationship to adolescent media use.

Science.gov (United States)

Benowitz-Fredericks, Carson A; Garcia, Kaylor; Massey, Meredith; Vasagar, Brintha; Borzekowski, Dina L G

2012-06-01

Historically and currently, media messages around body shape and size emphasize the importance of being below-average weight for women and hypermuscular for men. The media messages around physical appearance are not realistic for most and lead to body dissatisfaction for most adolescents. Interventions designed to mitigate the influence of negative media messages on adolescents' body image are presented; however, most have shown limited success. Copyright © 2012 Elsevier Inc. All rights reserved.

Positive media portrayals of obese persons: impact on attitudes and image preferences.

Science.gov (United States)

Pearl, Rebecca L; Puhl, Rebecca M; Brownell, Kelly D

2012-11-01

The purpose of this research was to assess the impact of nonstereotypical, positive media portrayals of obese persons on biased attitudes, as well as propose a change in media practices that could reduce public weight bias and consequent negative health outcomes for those who experience weight stigma. Two online experiments were conducted in which participants viewed either a stigmatizing or a positive photograph of an obese model. In Experiment 1 (N = 146), participants viewed a photograph of either a Caucasian or African American obese woman; in Experiment 2 (N = 145), participants viewed either a Caucasian male or female obese model. Multiple linear regression models were used to analyze outcomes for social distance attitudes toward the obese models depicted in the images, in addition to other negative attitudes and image preferences. Participants who viewed the stigmatizing images endorsed stronger social distance attitudes and more negative attitudes toward obese persons than participants who viewed the positive images, and there was a stronger preference for the positive images than the stigmatizing images. These results were consistent regardless of the race or gender of the obese model pictured. The findings indicate that more positive media portrayals of obese individuals may help reduce weight stigma and its associated negative health outcomes.

SU-D-BRA-04: Computerized Framework for Marker-Less Localization of Anatomical Feature Points in Range Images Based On Differential Geometry Features for Image-Guided Radiation Therapy

International Nuclear Information System (INIS)

Soufi, M; Arimura, H; Toyofuku, F; Nakamura, K; Hirose, T; Umezu, Y; Shioyama, Y

2016-01-01

Purpose: To propose a computerized framework for localization of anatomical feature points on the patient surface in infrared-ray based range images by using differential geometry (curvature) features. Methods: The general concept was to reconstruct the patient surface by using a mathematical modeling technique for the computation of differential geometry features that characterize the local shapes of the patient surfaces. A region of interest (ROI) was firstly extracted based on a template matching technique applied on amplitude (grayscale) images. The extracted ROI was preprocessed for reducing temporal and spatial noises by using Kalman and bilateral filters, respectively. Next, a smooth patient surface was reconstructed by using a non-uniform rational basis spline (NURBS) model. Finally, differential geometry features, i.e. the shape index and curvedness features were computed for localizing the anatomical feature points. The proposed framework was trained for optimizing shape index and curvedness thresholds and tested on range images of an anthropomorphic head phantom. The range images were acquired by an infrared ray-based time-of-flight (TOF) camera. The localization accuracy was evaluated by measuring the mean of minimum Euclidean distances (MMED) between reference (ground truth) points and the feature points localized by the proposed framework. The evaluation was performed for points localized on convex regions (e.g. apex of nose) and concave regions (e.g. nasofacial sulcus). Results: The proposed framework has localized anatomical feature points on convex and concave anatomical landmarks with MMEDs of 1.91±0.50 mm and 3.70±0.92 mm, respectively. A statistically significant difference was obtained between the feature points on the convex and concave regions (P<0.001). Conclusion: Our study has shown the feasibility of differential geometry features for localization of anatomical feature points on the patient surface in range images. The proposed

SU-D-BRA-04: Computerized Framework for Marker-Less Localization of Anatomical Feature Points in Range Images Based On Differential Geometry Features for Image-Guided Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Soufi, M; Arimura, H; Toyofuku, F [Kyushu University, Fukuoka, Fukuoka (Japan); Nakamura, K [Hamamatsu University School of Medicine, Hamamatsu, Shizuoka (Japan); Hirose, T; Umezu, Y [Kyushu University Hospital, Fukuoka, Fukuoka (Japan); Shioyama, Y [Saga Heavy Ion Medical Accelerator in Tosu, Tosu, Saga (Japan)

2016-06-15

Purpose: To propose a computerized framework for localization of anatomical feature points on the patient surface in infrared-ray based range images by using differential geometry (curvature) features. Methods: The general concept was to reconstruct the patient surface by using a mathematical modeling technique for the computation of differential geometry features that characterize the local shapes of the patient surfaces. A region of interest (ROI) was firstly extracted based on a template matching technique applied on amplitude (grayscale) images. The extracted ROI was preprocessed for reducing temporal and spatial noises by using Kalman and bilateral filters, respectively. Next, a smooth patient surface was reconstructed by using a non-uniform rational basis spline (NURBS) model. Finally, differential geometry features, i.e. the shape index and curvedness features were computed for localizing the anatomical feature points. The proposed framework was trained for optimizing shape index and curvedness thresholds and tested on range images of an anthropomorphic head phantom. The range images were acquired by an infrared ray-based time-of-flight (TOF) camera. The localization accuracy was evaluated by measuring the mean of minimum Euclidean distances (MMED) between reference (ground truth) points and the feature points localized by the proposed framework. The evaluation was performed for points localized on convex regions (e.g. apex of nose) and concave regions (e.g. nasofacial sulcus). Results: The proposed framework has localized anatomical feature points on convex and concave anatomical landmarks with MMEDs of 1.91±0.50 mm and 3.70±0.92 mm, respectively. A statistically significant difference was obtained between the feature points on the convex and concave regions (P<0.001). Conclusion: Our study has shown the feasibility of differential geometry features for localization of anatomical feature points on the patient surface in range images. The proposed

Effect of scatter media on small gamma camera imaging characteristics

International Nuclear Information System (INIS)

Ser, H. K.; Choi, Y.; Yim, K. C.

2001-01-01

Effect of scatter media materials and thickness, located between radioactivity and small gamma camera, on imaging characteristics was evaluated. The small gamma camera developed for breast imaging was consisted of collimator, NaI(TI) crystal (60x60x6 mm 3 ). PSPMT (position sensitive photomultiplier tube), NIMs and personal computer. Monte Carlo simulation was performed to evaluate the system sensitivity with different scatter media thickness (0∼8 cm) and materials (air and acrylie) with parallel hole collimator and diverging collimator. The sensitivity and spatial resolution was measured using the small gamma camera with the same condition applied to the simulation. Counts was decreased by 10% (air) and 54% (acrylic) with the parallel hole collimator and by 35% (air) and 63% (acrylic) with the diverging collimator. Spatial resolution was decreased as increasing the thickness of scatter media. This study substantiate the importance of a gamma camera positioning and the minimization of the distance between detector and target lesion in the clinical application of a gamma camera

The sub-wavelength imaging performance of disordered wire media

International Nuclear Information System (INIS)

Powell, David A.

2008-01-01

An analysis of the sub-wavelength imaging performance of disordered thin wire media is undertaken, in order to understand how its performance may be affected by manufacturing errors. The structure is found to be extremely robust to disorder which keeps the wires parallel. Variation in the orientation of the wires and their longitudinal position causes more significant degradation in the image quality, which is quantified numerically

Imaging features of musculoskeletal tuberculosis

International Nuclear Information System (INIS)

Vuyst, Dimitri De; Vanhoenacker, Filip; Bernaerts, Anja; Gielen, Jan; Schepper, Arthur M. de

2003-01-01

The purpose of this article is to review the imaging characteristics of musculoskeletal tuberculosis. Skeletal tuberculosis represents one-third of all cases of tuberculosis occurring in extrapulmonary sites. Hematogenous spread from a distant focus elsewhere in the body is the cornerstone in the understanding of imaging features of musculoskeletal tuberculosis. The most common presentations are tuberculous spondylitis, arthritis, osteomyelitis, and soft tissue involvement. The diagnostic value of the different imaging techniques, which include conventional radiography, CT, and MR imaging, are emphasized. Whereas conventional radiography is the mainstay in the diagnosis of tuberculous arthritis and osteomyelitis, MR imaging may detect associated bone marrow and soft tissue abnormalities. MR imaging is generally accepted as the imaging modality of choice for diagnosis, demonstration of the extent of the disease of tuberculous spondylitis, and soft tissue tuberculosis. Moreover, it may be very helpful in the differential diagnosis with pyogenic spondylodiscitis, as it may easily demonstrate anterior corner destruction, the relative preservation of the intervertebral disk, multilevel involvement with or without skip lesions, and a large soft tissue abscess, as these are all arguments in favor of a tuberculous spondylitis. On the other hand, CT is still superior in the demonstration of calcifications, which are found in chronic tuberculous abscesses. (orig.)

Hybrid wavefront sensing and image correction algorithm for imaging through turbulent media

Science.gov (United States)

Wu, Chensheng; Robertson Rzasa, John; Ko, Jonathan; Davis, Christopher C.

2017-09-01

It is well known that passive image correction of turbulence distortions often involves using geometry-dependent deconvolution algorithms. On the other hand, active imaging techniques using adaptive optic correction should use the distorted wavefront information for guidance. Our work shows that a hybrid hardware-software approach is possible to obtain accurate and highly detailed images through turbulent media. The processing algorithm also takes much fewer iteration steps in comparison with conventional image processing algorithms. In our proposed approach, a plenoptic sensor is used as a wavefront sensor to guide post-stage image correction on a high-definition zoomable camera. Conversely, we show that given the ground truth of the highly detailed image and the plenoptic imaging result, we can generate an accurate prediction of the blurred image on a traditional zoomable camera. Similarly, the ground truth combined with the blurred image from the zoomable camera would provide the wavefront conditions. In application, our hybrid approach can be used as an effective way to conduct object recognition in a turbulent environment where the target has been significantly distorted or is even unrecognizable.

Iris recognition based on key image feature extraction.

Science.gov (United States)

Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

2008-01-01

In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

Contemporary Urban Media Art – Images of Urgency

DEFF Research Database (Denmark)

Toft, Tanya Søndergaard

. Under the themes ‘intensity’, ‘intelligence’ and ‘immersion’ the dissertation examines some of the ways in which our experience is affected by ubiquitous technological mediation, especially in hi-tech urban contexts, and simultaneously how artistic interventions work towards a critical understanding......The dissertation examinesfrom a combined academic and curatorial perspective how urban media art – media aesthetic art forms situated in the urban context – may be considered contemporary in the understanding of art that departs from, responds to and co-exists with ‘time’ and temporal experience...... and exploitation of this new condition in our technological reality. I suggest that urban media art – as sensible constructions of temporal emergency images situated in the urban environment – potentially interfere with the temporal experiences we are offered in our communicative context. Based on this, we can...

Focal hepatic lesions with peripheral eosinophilia: imaging features of various disease

Energy Technology Data Exchange (ETDEWEB)

Seo, Joon Beom; Han, Joon Koo; Kim, Tae Kyoung; Choi, Byung Ihn; Han, Man Chung [Seoul National Univ. College of Medicine, Seoul (Korea, Republic of); Song, Chi Sung [Seoul City Boramae Hospital, Seoul (Korea, Republic of)

1999-01-01

Due to the recent advent of various imaging modalities such as ultrasonography, computed tomography and magnetic resonance imaging, as well as knowledge of the characteristic imaging features of hepatic lesions, radiologic examination plays a major role in the differential diagnosis of focal hepatic lesions. However, various 'nonspecific' or 'unusual' imaging features of focal hepatic lesions are occasionally encountered, and this makes correct diagnosis difficult. In such a situation, the presence of peripheral eosinophilia helps narrow the differential diagnoses. The aim of this pictorial essay is to describe the imaging features of various disease entities which cause focal hepatic lesions and peripheral eosinophilia.

Online Feature Selection for Classifying Emphysema in HRCT Images

Directory of Open Access Journals (Sweden)

M. Prasad

2008-06-01

Full Text Available Feature subset selection, applied as a pre- processing step to machine learning, is valuable in dimensionality reduction, eliminating irrelevant data and improving classifier performance. In the classic formulation of the feature selection problem, it is assumed that all the features are available at the beginning. However, in many real world problems, there are scenarios where not all features are present initially and must be integrated as they become available. In such scenarios, online feature selection provides an efficient way to sort through a large space of features. It is in this context that we introduce online feature selection for the classification of emphysema, a smoking related disease that appears as low attenuation regions in High Resolution Computer Tomography (HRCT images. The technique was successfully evaluated on 61 HRCT scans and compared with different online feature selection approaches, including hill climbing, best first search, grafting, and correlation-based feature selection. The results were also compared against ldensity maskr, a standard approach used for emphysema detection in medical image analysis.

The effect of experimental presentation of thin media images on body satisfaction: a meta-analytic review.

Science.gov (United States)

Groesz, Lisa M; Levine, Michael P; Murnen, Sarah K

2002-01-01

The effect of experimental manipulations of the thin beauty ideal, as portrayed in the mass media, on female body image was evaluated using meta-analysis. Data from 25 studies (43 effect sizes) were used to examine the main effect of mass media images of the slender ideal, as well as the moderating effects of pre-existing body image problems, the age of the participants, the number of stimulus presentations, and the type of research design. Body image was significantly more negative after viewing thin media images than after viewing images of either average size models, plus size models, or inanimate objects. This effect was stronger for between-subjects designs, participants less than 19 years of age, and for participants who are vulnerable to activation of a thinness schema. Results support the sociocultural perspective that mass media promulgate a slender ideal that elicits body dissatisfaction. Implications for prevention and research on social comparison processes are considered. Copyright 2002 by John Wiley & Sons, Inc.

Imaging biofilm in porous media using X-ray computed microtomography.

Science.gov (United States)

Davit, Y; Iltis, G; Debenest, G; Veran-Tissoires, S; Wildenschild, D; Gerino, M; Quintard, M

2011-04-01

In this study, a new technique for three-dimensional imaging of biofilm within porous media using X-ray computed microtomography is presented. Due to the similarity in X-ray absorption coefficients for the porous media (plastic), biofilm and aqueous phase, an X-ray contrast agent is required to image biofilm within the experimental matrix using X-ray computed tomography. The presented technique utilizes a medical suspension of barium sulphate to differentiate between the aqueous phase and the biofilm. Potassium iodide is added to the suspension to aid in delineation between the biofilm and the experimental porous medium. The iodide readily diffuses into the biofilm while the barium sulphate suspension remains in the aqueous phase. This allows for effective differentiation of the three phases within the experimental systems utilized in this study. The behaviour of the two contrast agents, in particular of the barium sulphate, is addressed by comparing two-dimensional images of biofilm within a pore network obtained by (1) optical visualization and (2) X-ray absorption radiography. We show that the contrast mixture provides contrast between the biofilm, the aqueous-phase and the solid-phase (beads). The imaging method is then applied to two three-dimensional packed-bead columns within which biofilm was grown. Examples of reconstructed images are provided to illustrate the effectiveness of the method. Limitations and applications of the technique are discussed. A key benefit, associated with the presented method, is that it captures a substantial amount of information regarding the topology of the pore-scale transport processes. For example, the quantification of changes in porous media effective parameters, such as dispersion or permeability, induced by biofilm growth, is possible using specific upscaling techniques and numerical analysis. We emphasize that the results presented here serve as a first test of this novel approach; issues with accurate segmentation of

Two-Level Evaluation on Sensor Interoperability of Features in Fingerprint Image Segmentation

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Ya-Shuo Li

2012-03-01

Full Text Available Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment images derived from other sensors. This degrades the segmentation performance. This paper empirically analyzes the sensor interoperability problem of segmentation feature, which refers to the feature’s ability to adapt to the raw fingerprints captured by different sensors. To address this issue, this paper presents a two-level feature evaluation method, including the first level feature evaluation based on segmentation error rate and the second level feature evaluation based on decision tree. The proposed method is performed on a number of fingerprint databases which are obtained from various sensors. Experimental results show that the proposed method can effectively evaluate the sensor interoperability of features, and the features with good evaluation results acquire better segmentation accuracies of images originating from different sensors.

Digital watermarking opportunities enabled by mobile media proliferation

Science.gov (United States)

Modro, Sierra; Sharma, Ravi K.

2009-02-01

Consumer usages of mobile devices and electronic media are changing. Mobile devices now include increased computational capabilities, mobile broadband access, better integrated sensors, and higher resolution screens. These enhanced features are driving increased consumption of media such as images, maps, e-books, audio, video, and games. As users become more accustomed to using mobile devices for media, opportunities arise for new digital watermarking usage models. For example, transient media, like images being displayed on screens, could be watermarked to provide a link between mobile devices. Applications based on these emerging usage models utilizing watermarking can provide richer user experiences and drive increased media consumption. We describe the enabling factors and highlight a few of the usage models and new opportunities. We also outline how the new opportunities are driving further innovation in watermarking technologies. We discuss challenges in market adoption of applications based on these usage models.

Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

Directory of Open Access Journals (Sweden)

Xi Wenfei

2017-07-01

Full Text Available Feature point extraction technology has become a research hotspot in the photogrammetry and computer vision. The commonly used point feature extraction operators are SIFT operator, Forstner operator, Harris operator and Moravec operator, etc. With the high spatial resolution characteristics, UAV image is different from the traditional aviation image. Based on these characteristics of the unmanned aerial vehicle (UAV, this paper uses several operators referred above to extract feature points from the building images, grassland images, shrubbery images, and vegetable greenhouses images. Through the practical case analysis, the performance, advantages, disadvantages and adaptability of each algorithm are compared and analyzed by considering their speed and accuracy. Finally, the suggestions of how to adapt different algorithms in diverse environment are proposed.

Mass media image of selected instruments of economic develepment

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Kruliš Ladislav

2016-07-01

Full Text Available The goal of this paper is twofold. Firstly, two instruments of economic development – investment incentives and cluster initiatives – were compared according to the frequency of their occurrence in selected mass media sources in the Czech Republic in the periods 2004-2005 and 2011-2012. Secondly, the mass media image of these two instruments of economic development was evaluated with respect to the frames deductively constructed from literature review. The findings pointed out a higher occurrence of the mass media articles/news dealing with investment incentives. These articles/news were, additionally, more controversial and covered a wider spectrum of frames. Politicians were a relatively more frequent type of actors who created the media message from the articles/news. On the contrary, the mass media articles/news concerning cluster initiatives typically created the frame of positive effects of clusters. The messages were told either by economic experts or by public authority representatives who were closely connected with cluster initiatives. Spatial origin of these messages was rather limited. The definitional vagueness, intangible and uncontroversial nature of cluster initiatives restrained their media appeal.

A Novel Technique for Shape Feature Extraction Using Content Based Image Retrieval

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Dhanoa Jaspreet Singh

2016-01-01

Full Text Available With the advent of technology and multimedia information, digital images are increasing very quickly. Various techniques are being developed to retrieve/search digital information or data contained in the image. Traditional Text Based Image Retrieval System is not plentiful. Since it is time consuming as it require manual image annotation. Also, the image annotation differs with different peoples. An alternate to this is Content Based Image Retrieval (CBIR system. It retrieves/search for image using its contents rather the text, keywords etc. A lot of exploration has been compassed in the range of Content Based Image Retrieval (CBIR with various feature extraction techniques. Shape is a significant image feature as it reflects the human perception. Moreover, Shape is quite simple to use by the user to define object in an image as compared to other features such as Color, texture etc. Over and above, if applied alone, no descriptor will give fruitful results. Further, by combining it with an improved classifier, one can use the positive features of both the descriptor and classifier. So, a tryout will be made to establish an algorithm for accurate feature (Shape extraction in Content Based Image Retrieval (CBIR. The main objectives of this project are: (a To propose an algorithm for shape feature extraction using CBIR, (b To evaluate the performance of proposed algorithm and (c To compare the proposed algorithm with state of art techniques.

Tunable output-frequency filter algorithm for imaging through scattering media under LED illumination

Science.gov (United States)

Zhou, Meiling; Singh, Alok Kumar; Pedrini, Giancarlo; Osten, Wolfgang; Min, Junwei; Yao, Baoli

2018-03-01

We present a tunable output-frequency filter (TOF) algorithm to reconstruct the object from noisy experimental data under low-power partially coherent illumination, such as LED, when imaging through scattering media. In the iterative algorithm, we employ Gaussian functions with different filter windows at different stages of iteration process to reduce corruption from experimental noise to search for a global minimum in the reconstruction. In comparison with the conventional iterative phase retrieval algorithm, we demonstrate that the proposed TOF algorithm achieves consistent and reliable reconstruction in the presence of experimental noise. Moreover, the spatial resolution and distinctive features are retained in the reconstruction since the filter is applied only to the region outside the object. The feasibility of the proposed method is proved by experimental results.

Features, events and processes evaluation catalogue for argillaceous media

International Nuclear Information System (INIS)

Mazurek, M.; Pearson, F.J.; Volckaert, G.; Bock, H.

2003-01-01

The OECD/NEA Working Group on the Characterisation, the Understanding and the Performance of Argillaceous Rocks as Repository Host Formations for the disposal of radioactive waste (known as the 'Clay Club') launched a project called FEPCAT (Features, Events and Processes Catalogue for argillaceous media) in late 1998. The present report provides the results of work performed by an expert group to develop a FEPs database related to argillaceous formations, whether soft or indurated. It describes the methodology used for the work performed, provides a list of relevant FEPs and summarises the knowledge on each of them. It also provides general conclusions and identifies priorities for future work. (authors)

Bones, body parts, and sex appeal: An analysis of #thinspiration images on popular social media.

Science.gov (United States)

Ghaznavi, Jannath; Taylor, Laramie D

2015-06-01

The present study extends research on thinspiration images, visual and/or textual images intended to inspire weight loss, from pro-eating disorder websites to popular photo-sharing social media websites. The article reports on a systematic content analysis of thinspiration images (N=300) on Twitter and Pinterest. Images tended to be sexually suggestive and objectifying with a focus on ultra-thin, bony, scantily-clad women. Results indicated that particular social media channels and labels (i.e., tags) were characterized by more segmented, bony content and greater social endorsement compared to others. In light of theories of media influence, results offer insight into the potentially harmful effects of exposure to sexually suggestive and objectifying content in large online communities on body image, quality of life, and mental health. Copyright © 2015 Elsevier Ltd. All rights reserved.

Image-based overlay and alignment metrology through optically opaque media with sub-surface probe microscopy

Science.gov (United States)

van Es, Maarten H.; Mohtashami, Abbas; Piras, Daniele; Sadeghian, Hamed

2018-03-01

Nondestructive subsurface nanoimaging through optically opaque media is considered to be extremely challenging and is essential for several semiconductor metrology applications including overlay and alignment and buried void and defect characterization. The current key challenge in overlay and alignment is the measurement of targets that are covered by optically opaque layers. Moreover, with the device dimensions moving to the smaller nodes and the issue of the so-called loading effect causing offsets between between targets and product features, it is increasingly desirable to perform alignment and overlay on product features or so-called on-cell overlay, which requires higher lateral resolution than optical methods can provide. Our recently developed technique known as SubSurface Ultrasonic Resonance Force Microscopy (SSURFM) has shown the capability for high-resolution imaging of structures below a surface based on (visco-)elasticity of the constituent materials and as such is a promising technique to perform overlay and alignment with high resolution in upcoming production nodes. In this paper, we describe the developed SSURFM technique and the experimental results on imaging buried features through various layers and the ability to detect objects with resolution below 10 nm. In summary, the experimental results show that the SSURFM is a potential solution for on-cell overlay and alignment as well as detecting buried defects or voids and generally metrology through optically opaque layers.

Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images

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Mei Yu

2012-01-01

Full Text Available The most critical step in grayscale medical image retrieval systems is feature extraction. Understanding the interrelatedness between the characteristics of lesion images and corresponding imaging features is crucial for image training, as well as for features extraction. A feature-extraction algorithm is developed based on different imaging properties of lesions and on the discrepancy in density between the lesions and their surrounding normal liver tissues in triple-phase contrast-enhanced computed tomographic (CT scans. The algorithm includes mainly two processes: (1 distance transformation, which is used to divide the lesion into distinct regions and represents the spatial structure distribution and (2 representation using bag of visual words (BoW based on regions. The evaluation of this system based on the proposed feature extraction algorithm shows excellent retrieval results for three types of liver lesions visible on triple-phase scans CT images. The results of the proposed feature extraction algorithm show that although single-phase scans achieve the average precision of 81.9%, 80.8%, and 70.2%, dual- and triple-phase scans achieve 86.3% and 88.0%.

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters.

Science.gov (United States)

Brynolfsson, Patrik; Nilsson, David; Torheim, Turid; Asklund, Thomas; Karlsson, Camilla Thellenberg; Trygg, Johan; Nyholm, Tufve; Garpebring, Anders

2017-06-22

In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

Latin Letters Recognition Using Optical Character Recognition to Convert Printed Media Into Digital Format

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Rio Anugrah

2017-12-01

Full Text Available Printed media is still popular now days society. Unfortunately, such media encountered several drawbacks. For example, this type of media consumes large storage that impact in high maintenance cost. To keep printed information more efficient and long-lasting, people usually convert it into digital format. In this paper, we built Optical Character Recognition (OCR system to enable automatic conversion the image containing the sentence in Latin characters into digital text-shaped information. This system consists of several interrelated stages including preprocessing, segmentation, feature extraction, classifier, model and recognition. In preprocessing, the median filter is used to clarify the image from noise and the Otsu’s function is used to binarize the image. It followed by character segmentation using connected component labeling. Artificial neural network (ANN is used for feature extraction to recognize the character. The result shows that this system enable to recognize the characters in the image whose success rate is influenced by the training of the system.

THE EFFECT OF SOCIAL MEDIA MARKETING ACTIVITIES ON BRAND AWARENESS, BRAND IMAGE AND BRAND LOYALTY

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Yusuf BİLGİN

2018-04-01

Full Text Available The aim of this research is to examine the effect of social media marketing activities on brand awareness, brand image and brand loyalty. In addition, it has been aimed to analyze the effect of brand awareness and brand image on brand loyalty in this research. The population of the research consists of the consumers who actively follow five brands with the highest social score according to the Marketing Turkey social media brand performance data on social media communication channels such as Facebook, Twitter and Instagram. In this research, quantitative method has been used and research data has been obtained via online questionnaires shared on social media from 547 brand followers with applying convenience sampling method. The obtained data have been analyzed by structural equation modeling (SEM. As a result of the analysis, social media marketing activities have been found as effective factors on brand image and brand loyalty, besides it has been determined that the most obvious effect seen on brand awareness. In addition, it has been found out that brand awareness and brand image have a significant effect on brand loyalty. Furthermore, in the research, it has been achieved that the brand awareness has a limited effect on the brand image.

Does the Internet function like magazines? An exploration of image-focused media, eating pathology, and body dissatisfaction.

Science.gov (United States)

Bair, Carrie E; Kelly, Nichole R; Serdar, Kasey L; Mazzeo, Suzanne E

2012-12-01

Research has identified a relation between exposure to thin-ideal magazine and television media images and eating disorder pathology. However, few studies have examined the potential influence of Internet media on eating disorder behaviors and attitudes. This study investigated associations among image-focused media exposure, body dissatisfaction, eating pathology and thin-ideal internalization in a sample of 421 female undergraduates. Undergraduate women spent significantly more time viewing online appearance-oriented media, rather than reading image-focused magazines. Appearance-oriented Internet and television use were associated with eating pathology. Moreover, the association between image-focused Internet use and BD was mediated by thin-ideal internalization. These findings are consistent with those of previous research, and highlight the vulnerability individuals high in thin-ideal internalization might have to media exposure. They also suggest that Internet media use is an important topic to attend to in eating disorders prevention and treatment. Copyright © 2012 Elsevier Ltd. All rights reserved.

SAR Image Classification Based on Its Texture Features

Institute of Scientific and Technical Information of China (English)

LI Pingxiang; FANG Shenghui

2003-01-01

SAR images not only have the characteristics of all-ay, all-eather, but also provide object information which is different from visible and infrared sensors. However, SAR images have some faults, such as more speckles and fewer bands. The authors conducted the experiments of texture statistics analysis on SAR image features in order to improve the accuracy of SAR image interpretation.It is found that the texture analysis is an effective method for improving the accuracy of the SAR image interpretation.

Getting the message: media images and stereotypes and their effect on Asian Americans.

Science.gov (United States)

Mok, T A

1998-01-01

Mass media sources such as television and movies arguably offer up little in the way of positive Asian/Asian American images or role models. This article contends that the media do not often portray the diversity that is inherent within the Asian American culture and that such a paucity of Asian images may greatly affect perceptions Asian Americans may hold both of their own racial group and of the larger society. This article examines both media images of Asians and Asian Americans and autobiographical information from Asian American literature to illustrate the potentially detrimental effects of being a person of color in a society that emphasizes a monoracial standard of beauty. Information gleaned from first-hand accounts from Asian Americans often points to the media as a potent source of information as to how attractiveness is defined and measured. This article concludes with a discussion of some brief case examples and ethical imperatives for mental health workers in terms of both self-awareness and education as well as considerations for culturally sensitive therapy.

Comparing writing style feature-based classification methods for estimating user reputations in social media.

Science.gov (United States)

Suh, Jong Hwan

2016-01-01

In recent years, the anonymous nature of the Internet has made it difficult to detect manipulated user reputations in social media, as well as to ensure the qualities of users and their posts. To deal with this, this study designs and examines an automatic approach that adopts writing style features to estimate user reputations in social media. Under varying ways of defining Good and Bad classes of user reputations based on the collected data, it evaluates the classification performance of the state-of-art methods: four writing style features, i.e. lexical, syntactic, structural, and content-specific, and eight classification techniques, i.e. four base learners-C4.5, Neural Network (NN), Support Vector Machine (SVM), and Naïve Bayes (NB)-and four Random Subspace (RS) ensemble methods based on the four base learners. When South Korea's Web forum, Daum Agora, was selected as a test bed, the experimental results show that the configuration of the full feature set containing content-specific features and RS-SVM combining RS and SVM gives the best accuracy for classification if the test bed poster reputations are segmented strictly into Good and Bad classes by portfolio approach. Pairwise t tests on accuracy confirm two expectations coming from the literature reviews: first, the feature set adding content-specific features outperform the others; second, ensemble learning methods are more viable than base learners. Moreover, among the four ways on defining the classes of user reputations, i.e. like, dislike, sum, and portfolio, the results show that the portfolio approach gives the highest accuracy.

Derivative-based scale invariant image feature detector with error resilience.

Science.gov (United States)

Mainali, Pradip; Lafruit, Gauthier; Tack, Klaas; Van Gool, Luc; Lauwereins, Rudy

2014-05-01

We present a novel scale-invariant image feature detection algorithm (D-SIFER) using a newly proposed scale-space optimal 10th-order Gaussian derivative (GDO-10) filter, which reaches the jointly optimal Heisenberg's uncertainty of its impulse response in scale and space simultaneously (i.e., we minimize the maximum of the two moments). The D-SIFER algorithm using this filter leads to an outstanding quality of image feature detection, with a factor of three quality improvement over state-of-the-art scale-invariant feature transform (SIFT) and speeded up robust features (SURF) methods that use the second-order Gaussian derivative filters. To reach low computational complexity, we also present a technique approximating the GDO-10 filters with a fixed-length implementation, which is independent of the scale. The final approximation error remains far below the noise margin, providing constant time, low cost, but nevertheless high-quality feature detection and registration capabilities. D-SIFER is validated on a real-life hyperspectral image registration application, precisely aligning up to hundreds of successive narrowband color images, despite their strong artifacts (blurring, low-light noise) typically occurring in such delicate optical system setups.

Quality Evaluation in Wireless Imaging Using Feature-Based Objective Metrics

OpenAIRE

Engelke, Ulrich; Zepernick, Hans-Jürgen

2007-01-01

This paper addresses the evaluation of image quality in the context of wireless systems using feature-based objective metrics. The considered metrics comprise of a weighted combination of feature values that are used to quantify the extend by which the related artifacts are present in a processed image. In view of imaging applications in mobile radio and wireless communication systems, reduced-reference objective quality metrics are investigated for quantifying user-perceived quality. The exa...

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

Science.gov (United States)

Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

2017-10-06

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

Imaging through scattering media by Fourier filtering and single-pixel detection

Science.gov (United States)

Jauregui-Sánchez, Y.; Clemente, P.; Lancis, J.; Tajahuerce, E.

2018-02-01

We present a novel imaging system that combines the principles of Fourier spatial filtering and single-pixel imaging in order to recover images of an object hidden behind a turbid medium by transillumination. We compare the performance of our single-pixel imaging setup with that of a conventional system. We conclude that the introduction of Fourier gating improves the contrast of images in both cases. Furthermore, we show that the combination of single-pixel imaging and Fourier spatial filtering techniques is particularly well adapted to provide images of objects transmitted through scattering media.

Feature extraction from mammographic images using fast marching methods

International Nuclear Information System (INIS)

Bottigli, U.; Golosio, B.

2002-01-01

Features extraction from medical images represents a fundamental step for shape recognition and diagnostic support. The present work faces the problem of the detection of large features, such as massive lesions and organ contours, from mammographic images. The regions of interest are often characterized by an average grayness intensity that is different from the surrounding. In most cases, however, the desired features cannot be extracted by simple gray level thresholding, because of image noise and non-uniform density of the surrounding tissue. In this work, edge detection is achieved through the fast marching method (Level Set Methods and Fast Marching Methods, Cambridge University Press, Cambridge, 1999), which is based on the theory of interface evolution. Starting from a seed point in the shape of interest, a front is generated which evolves according to an appropriate speed function. Such function is expressed in terms of geometric properties of the evolving interface and of image properties, and should become zero when the front reaches the desired boundary. Some examples of application of such method to mammographic images from the CALMA database (Nucl. Instr. and Meth. A 460 (2001) 107) are presented here and discussed

Imaging features of juxtacortical chondroma in children

International Nuclear Information System (INIS)

Miller, Stephen F.

2014-01-01

Juxtacortical chondroma is a rare benign bone lesion in children. Children usually present with a mildly painful mass, which prompts diagnostic imaging studies. The rarity of this condition often presents a diagnostic challenge. Correct diagnosis is crucial in guiding surgical management. To describe the characteristic imaging findings of juxtacortical chondroma in children. We identified all children who were diagnosed with juxtacortical chondroma between 1998 and 2012. A single experienced pediatric radiologist reviewed all diagnostic imaging studies, including plain radiographs, CT, MR and bone scans. Seven children (5 boys and 2 girls) with juxtacortical chondroma were identified, ranging in age from 6 years to 16 years (mean 12.3 years). Mild pain and a palpable mass were present in all seven children. Plain radiographs were available in 6/7, MR in 7/7, CT in 4/7 and skeletal scintigraphy in 5/7 children. Three lesions were located in the proximal humerus, with one each in the distal radius, distal femur, proximal tibia and scapula. Radiographic and CT features deemed highly suggestive of juxtacortical chondroma included cortical scalloping, underlying cortical sclerosis and overhanging margins. MRI features consistent with juxtacortical chondroma included isointensity to skeletal muscle on T1, marked hyperintensity on T2 and peripheral rim enhancement after contrast agent administration. One of seven lesions demonstrated intramedullary extension, and 2/7 showed adjacent soft-tissue edema. Juxtacortical chondroma is an uncommon benign lesion in children with characteristic features on plain radiographs, CT and MR. Recognition of these features is invaluable in guiding appropriate surgical management. (orig.)

Imaging features of juxtacortical chondroma in children

Energy Technology Data Exchange (ETDEWEB)

Miller, Stephen F. [St. Jude Children' s Research Hospital, Department of Radiological Sciences, Memphis, TN (United States)

2014-01-15

Juxtacortical chondroma is a rare benign bone lesion in children. Children usually present with a mildly painful mass, which prompts diagnostic imaging studies. The rarity of this condition often presents a diagnostic challenge. Correct diagnosis is crucial in guiding surgical management. To describe the characteristic imaging findings of juxtacortical chondroma in children. We identified all children who were diagnosed with juxtacortical chondroma between 1998 and 2012. A single experienced pediatric radiologist reviewed all diagnostic imaging studies, including plain radiographs, CT, MR and bone scans. Seven children (5 boys and 2 girls) with juxtacortical chondroma were identified, ranging in age from 6 years to 16 years (mean 12.3 years). Mild pain and a palpable mass were present in all seven children. Plain radiographs were available in 6/7, MR in 7/7, CT in 4/7 and skeletal scintigraphy in 5/7 children. Three lesions were located in the proximal humerus, with one each in the distal radius, distal femur, proximal tibia and scapula. Radiographic and CT features deemed highly suggestive of juxtacortical chondroma included cortical scalloping, underlying cortical sclerosis and overhanging margins. MRI features consistent with juxtacortical chondroma included isointensity to skeletal muscle on T1, marked hyperintensity on T2 and peripheral rim enhancement after contrast agent administration. One of seven lesions demonstrated intramedullary extension, and 2/7 showed adjacent soft-tissue edema. Juxtacortical chondroma is an uncommon benign lesion in children with characteristic features on plain radiographs, CT and MR. Recognition of these features is invaluable in guiding appropriate surgical management. (orig.)

COLOR IMAGE RETRIEVAL BASED ON FEATURE FUSION THROUGH MULTIPLE LINEAR REGRESSION ANALYSIS

Directory of Open Access Journals (Sweden)

K. Seetharaman

2015-08-01

Full Text Available This paper proposes a novel technique based on feature fusion using multiple linear regression analysis, and the least-square estimation method is employed to estimate the parameters. The given input query image is segmented into various regions according to the structure of the image. The color and texture features are extracted on each region of the query image, and the features are fused together using the multiple linear regression model. The estimated parameters of the model, which is modeled based on the features, are formed as a vector called a feature vector. The Canberra distance measure is adopted to compare the feature vectors of the query and target images. The F-measure is applied to evaluate the performance of the proposed technique. The obtained results expose that the proposed technique is comparable to the other existing techniques.

Relations among media influence, body image, eating concerns, and sexual orientation in men: A preliminary investigation.

Science.gov (United States)

Carper, Teresa L Marino; Negy, Charles; Tantleff-Dunn, Stacey

2010-09-01

The current study explored the relation between sexual orientation, media persuasion, and eating and body image concerns among 78 college men (39 gay; 39 straight). Participants completed measures of sexual orientation, eating disorder symptoms, appearance-related anxiety, perceived importance of physical attractiveness, perceptions of media influence, and media exposure. Gay men scored significantly higher on drive for thinness, body dissatisfaction, and body image-related anxiety than their straight counterparts. Additionally, perceptions of media influence were higher for gay men, and significantly mediated the relation between sexual orientation and eating and body image concerns. Sexual orientation also moderated the relation between perceived media influence and beliefs regarding the importance of physical attractiveness, as this relation was significant for gay men, but not straight men. The current findings suggest that gay men's increased vulnerability to media influence partially accounts for the relatively high rate of eating pathology observed in this population. Copyright © 2010 Elsevier Ltd. All rights reserved.

Imaging features of benign adrenal cysts

International Nuclear Information System (INIS)

Sanal, Hatice Tuba; Kocaoglu, Murat; Yildirim, Duzgun; Bulakbasi, Nail; Guvenc, Inanc; Tayfun, Cem; Ucoz, Taner

2006-01-01

Benign adrenal gland cysts (BACs) are rare lesions with a variable histological spectrum and may mimic not only each other but also malignant ones. We aimed to review imaging features of BACs which can be helpful in distinguishing each entity and determining the subsequent appropriate management

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

Science.gov (United States)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

Featured Image: Diamonds in a Meteorite

Science.gov (United States)

Kohler, Susanna

2018-04-01

This unique image which measures only 60 x 80 micrometers across reveals details in the Kapoeta meteorite, an 11-kg stone that fell in South Sudan in 1942. The sparkle in the image? A cluster of nanodiamonds discovered embedded in the stone in a recent study led by Yassir Abdu (University of Sharjah, United Arab Emirates). Abdu and collaborators showed that these nanodiamonds have similar spectral features to the interiors of dense interstellar clouds and they dont show any signs of shock features. This may suggest that the nanodiamonds were formed by condensation of nebular gases early in the history of the solar system. The diamonds were trapped in the surface material of the Kapoeta meteorites parent body, thought to be the asteroid Vesta. To read more about the authors study, check out the original article below.CitationYassir A. Abdu et al 2018 ApJL 856 L9. doi:10.3847/2041-8213/aab433

Vaginal Masses: Magnetic Resonance Imaging Features with Pathologic Correlation

International Nuclear Information System (INIS)

Elsayes, K.M.; Narra, V.R.; Dillman, J.R.; Velcheti, V.; Hameed, O.; Tongdee, R.; Menias, C.O.

2007-01-01

The detection of vaginal lesions has increased with the expanding use of cross-sectional imaging. Magnetic resonance imaging (MRI) - with its high-contrast resolution and multiplanar capabilities - is often useful for characterizing vaginal masses. Vaginal masses can be classified as congenital, inflammatory, cystic (benign), and neoplastic (benign or malignant) in etiology. Recognition of the typical MR imaging features of such lesions is important because it often determines the treatment approach and may obviate surgery. Finally, vaginal MR imaging can be used to evaluate post-treatment changes related to previous surgery and radiation therapy. In this article, we will review pertinent vaginal anatomy, vaginal and pelvic MRI technique, and the MRI features of a variety of vaginal lesions with pathological correlation

No-reference image quality assessment based on statistics of convolution feature maps

Science.gov (United States)

Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo

2018-04-01

We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.

Heineken in the House: Improving Online Media Reputation through Featuring a Sponsored Brand Community

NARCIS (Netherlands)

Vermeer, S.; Remmelswaal, P.; Jacobs, S.

2017-01-01

Nowadays, more and more organizations use social media to promote their sponsorships of big events. Heineken has created a major brand community by facilitating the Holland Heineken House during the Olympic Games. This study investigates to what extent featuring a sponsored brand community on social

Imaging professionals' views of social media and its implications.

Science.gov (United States)

Bagley, Jennifer E; DiGiacinto, Dora D; Hargraves, Kensi

2014-01-01

To help radiation sciences students and professionals understand the implications of and best practices for personal postings on social media Web sites. The authors conducted a survey to capture radiologic science professionals' opinions regarding trends related to using social media for employment, as well as for their personal use. The majority of imaging professionals are mindful of their privacy settings and believe their activity on social media sites reflects on them professionally. Participants in this study noted they maintain high privacy settings. In spite of this, both supervisors and nonsupervisors in this study held opinions about the use of social media in employment decisions that are inconsistent with what can occur in the workplace. Survey respondents did not believe there should be employment sanctions for behaviors that routinely are sanctioned in the workplace. An important message that has emerged from this research is that employees should not only adhere to the strictest privacy settings on their personal social media sites, but they also should be judicious in what they choose to share, with the understanding that nothing posted online is truly private. Supervisors and nonsupervisors should become familiar with their institutional policies regarding the use of social media in the workplace, and supervisors specifically should ensure that they follow institutional policy regarding the use of social media in employment decisions.

Confocal Imaging of porous media

Science.gov (United States)

Shah, S.; Crawshaw, D.; Boek, D.

2012-12-01

Carbonate rocks, which hold approximately 50% of the world's oil and gas reserves, have a very complicated and heterogeneous structure in comparison with sandstone reservoir rock. We present advances with different techniques to image, reconstruct, and characterize statistically the micro-geometry of carbonate pores. The main goal here is to develop a technique to obtain two dimensional and three dimensional images using Confocal Laser Scanning Microscopy. CLSM is used in epi-fluorescent imaging mode, allowing for the very high optical resolution of features well below 1μm size. Images of pore structures were captured using CLSM imaging where spaces in the carbonate samples were impregnated with a fluorescent, dyed epoxy-resin, and scanned in the x-y plane by a laser probe. We discuss the sample preparation in detail for Confocal Imaging to obtain sub-micron resolution images of heterogeneous carbonate rocks. We also discuss the technical and practical aspects of this imaging technique, including its advantages and limitation. We present several examples of this application, including studying pore geometry in carbonates, characterizing sub-resolution porosity in two dimensional images. We then describe approaches to extract statistical information about porosity using image processing and spatial correlation function. We have managed to obtain very low depth information in z -axis (~ 50μm) to develop three dimensional images of carbonate rocks with the current capabilities and limitation of CLSM technique. Hence, we have planned a novel technique to obtain higher depth information to obtain high three dimensional images with sub-micron resolution possible in the lateral and axial planes.

A blur-invariant local feature for motion blurred image matching

Science.gov (United States)

Tong, Qiang; Aoki, Terumasa

2017-07-01

Image matching between a blurred (caused by camera motion, out of focus, etc.) image and a non-blurred image is a critical task for many image/video applications. However, most of the existing local feature schemes fail to achieve this work. This paper presents a blur-invariant descriptor and a novel local feature scheme including the descriptor and the interest point detector based on moment symmetry - the authors' previous work. The descriptor is based on a new concept - center peak moment-like element (CPME) which is robust to blur and boundary effect. Then by constructing CPMEs, the descriptor is also distinctive and suitable for image matching. Experimental results show our scheme outperforms state of the art methods for blurred image matching

The image of the „women politicians” in the post-communist media

Directory of Open Access Journals (Sweden)

Ionela Băluţă

2013-02-01

Full Text Available The analysis of mass-media discourses is a privileged lenses for the study of legitimate and valorised political roles and identities that contribute to the construction of political space. The role of such discourses is even more relevant for building the public image of political women since gender stereotypes, spread and consolidated through media, are essential for establishing the categories of capital that are more efficient for a political career. Based on a four-month daily monitoring of two major newspapers, this study shows that during their second postcommunist decade, Romanian political women have been rather absent from the mass-media discourse, their image being either negligeable and neutralized by the masculine rules of the political game or placed within the range of the exotic and/or mondenity.

Mass-like extramedullary hematopoiesis: imaging features

Energy Technology Data Exchange (ETDEWEB)

Ginzel, Andrew W. [Synergy Radiology Associates, Houston, TX (United States); Kransdorf, Mark J.; Peterson, Jeffrey J.; Garner, Hillary W. [Mayo Clinic, Department of Radiology, Jacksonville, FL (United States); Murphey, Mark D. [American Institute for Radiologic Pathology, Silver Spring, MD (United States)

2012-08-15

To report the imaging appearances of mass-like extramedullary hematopoiesis (EMH), to identify those features that are sufficiently characteristic to allow a confident diagnosis, and to recognize the clinical conditions associated with EMH and the relative incidence of mass-like disease. We retrospectively identified 44 patients with EMH; 12 of which (27%) had focal mass-like lesions and formed the study group. The study group consisted of 6 male and 6 female subjects with a mean age of 58 years (range 13-80 years). All 12 patients underwent CT imaging and 3 of the 12 patients had undergone additional MR imaging. The imaging characteristics of the extramedullary hematopoiesis lesions in the study group were analyzed and recorded. The patient's clinical presentation, including any condition associated with extramedullary hematopoiesis, was also recorded. Ten of the 12 (83%) patients had one or more masses located along the axial skeleton. Of the 10 patients with axial masses, 9 (90%) had multiple masses and 7 (70%) demonstrated internal fat. Eight patients (80%) had paraspinal masses and 4 patients (40%) had presacral masses. Seven patients (70%) had splenomegaly. Eleven of the 12 patients had a clinical history available for review. A predisposing condition for extramedullary hematopoiesis was present in 10 patients and included various anemias (5 cases; 45%), myelofibrosis/myelodysplastic syndrome (4 cases; 36%), and marrow proliferative disorder (1 case; 9%). One patient had no known predisposing condition. Mass-like extramedullary hematopoiesis most commonly presents as multiple, fat-containing lesions localized to the axial skeleton. When these imaging features are identified, extramedullary hematopoiesis should be strongly considered, particularly when occurring in the setting of a predisposing medical condition. (orig.)

A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer

Directory of Open Access Journals (Sweden)

Pattichis Marios S

2007-11-01

Full Text Available Abstract Background In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i the distance from the tissue (panoramic vs close up, (ii difference in viewing angles and (iii color correction. Methods We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 × 576 pixels and 24 bits color for: (i a variety of testing targets from a color palette with a known color distribution, (ii different viewing angles, (iv two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i Statistical Features (SF, (ii Spatial Gray Level Dependence Matrices (SGLDM, and (iii Gray Level Difference Statistics (GLDS. All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. Results For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better

Imaging features of skeletal changes in children with Gaucher disease

International Nuclear Information System (INIS)

Zhang Ningning; Duan Xiaomin; Duan Yanlong

2011-01-01

Objective: To discuss the imaging features of skeletal changes in children with Gaucher disease on X-ray and MRI images. Methods: One hundred and nine children with Gaucher disease were enrolled in this study. They all received routine X-ray for spine with anterior-posterior (A-P) and lateral view and bilateral femurs with A-P view. Among them, 18 patients received X-ray for pelvic with A-P view, 14 patients received X-ray for left wrist with A-P view, and 14 patients received MRI scan for femur. The MRI scan included T 1 -weighted imaging, T 2 -weighted imaging and fat-suppressed T 2 -weighted imaging with short tau inversion recovery (STIR) sequence. The imaging features of the X-ray and MRI images were analyzed retrospectively. Results: The most common feature is osteoporosis, which presented in 91 cases (83.5%). Besides this, decreased density of metaphysis occurred in 86 cases (78.9%), erlenmeyer flask deformity of metaphysis occurred in 89 patients (81.7%), thinner cortex occurred in 69 cases (63.3%), osteolytic destruction occurred in. 31 cases (28.4%), pathological fractures occurred in 26 cases (23.9%), osteosclerosis occurred in 12 cases (11.0%). cystic degeneration of bone occurred in 16 cases (14.7%), and dislocation of the hip occurred in 4 cases. All 14 patients received MRI presented abnormal signals. Among them, 4 patients presented low signal intensity both on T 1 -weighted and T 2 -weighted images in bone marrow, the other ten presented high signal intensity mixed in low signal intensity areas on T 2 - weighted and fat-suppressed T 2 -weighted images. Conclusions: The imaging features of skeletal changes in children with Gaucher disease are of some characteristics, which could provide useful information for the clinical treatment. (authors)

SU-D-202-02: Quantitative Imaging: Correlation Between Image Feature Analysis and the Accuracy of Manually Drawn Contours On PET Images

Energy Technology Data Exchange (ETDEWEB)

Lamichhane, N; Johnson, P; Chinea, F; Patel, V; Yang, F [University of Miami, Miami, FL (United States)

2016-06-15

Purpose: To evaluate the correlation between image features and the accuracy of manually drawn target contours on synthetic PET images Methods: A digital PET phantom was used in combination with Monte Carlo simulation to create a set of 26 simulated PET images featuring a variety of tumor shapes and activity heterogeneity. These tumor volumes were used as a gold standard in comparisons with manual contours delineated by 10 radiation oncologist on the simulated PET images. Metrics used to evaluate segmentation accuracy included the dice coefficient, false positive dice, false negative dice, symmetric mean absolute surface distance, and absolute volumetric difference. Image features extracted from the simulated tumors consisted of volume, shape complexity, mean curvature, and intensity contrast along with five texture features derived from the gray-level neighborhood difference matrices including contrast, coarseness, busyness, strength, and complexity. Correlation between these features and contouring accuracy were examined. Results: Contour accuracy was reasonably well correlated with a variety of image features. Dice coefficient ranged from 0.7 to 0.90 and was correlated closely with contrast (r=0.43, p=0.02) and complexity (r=0.5, p<0.001). False negative dice ranged from 0.10 to 0.50 and was correlated closely with contrast (r=0.68, p<0.001) and complexity (r=0.66, p<0.001). Absolute volumetric difference ranged from 0.0002 to 0.67 and was correlated closely with coarseness (r=0.46, p=0.02) and complexity (r=0.49, p=0.008). Symmetric mean absolute difference ranged from 0.02 to 1 and was correlated closely with mean curvature (r=0.57, p=0.02) and contrast (r=0.6, p=0.001). Conclusion: The long term goal of this study is to assess whether contouring variability can be reduced by providing feedback to the practitioner based on image feature analysis. The results are encouraging and will be used to develop a statistical model which will enable a prediction of

Improved image retrieval based on fuzzy colour feature vector

Science.gov (United States)

Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

2013-03-01

One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

THE EFFECT OF SOCIAL MEDIA MARKETING ACTIVITIES ON BRAND AWARENESS, BRAND IMAGE AND BRAND LOYALTY

OpenAIRE

Yusuf BİLGİN

2018-01-01

The aim of this research is to examine the effect of social media marketing activities on brand awareness, brand image and brand loyalty. In addition, it has been aimed to analyze the effect of brand awareness and brand image on brand loyalty in this research. The population of the research consists of the consumers who actively follow five brands with the highest social score according to the Marketing Turkey social media brand performance data on social media communication channels such as ...

An image-processing methodology for extracting bloodstain pattern features.

Science.gov (United States)

Arthur, Ravishka M; Humburg, Philomena J; Hoogenboom, Jerry; Baiker, Martin; Taylor, Michael C; de Bruin, Karla G

2017-08-01

There is a growing trend in forensic science to develop methods to make forensic pattern comparison tasks more objective. This has generally involved the application of suitable image-processing methods to provide numerical data for identification or comparison. This paper outlines a unique image-processing methodology that can be utilised by analysts to generate reliable pattern data that will assist them in forming objective conclusions about a pattern. A range of features were defined and extracted from a laboratory-generated impact spatter pattern. These features were based in part on bloodstain properties commonly used in the analysis of spatter bloodstain patterns. The values of these features were consistent with properties reported qualitatively for such patterns. The image-processing method developed shows considerable promise as a way to establish measurable discriminating pattern criteria that are lacking in current bloodstain pattern taxonomies. Copyright © 2017 Elsevier B.V. All rights reserved.

Professional image creation by students on social media sites (in the light of empirical research

Directory of Open Access Journals (Sweden)

Stankiewicz Janina

2016-05-01

Full Text Available Internet has become an important way of our image creation, including the professional one. By participating in social media, it is possible to shape our own image not only in eyes of private persons, but also of potential employers. The aim of the article is to identify activities which were undertaken by young participants in labour market (students associated with creating their own professional image in social media. It was found that they usually concentrated on shaping private relationships, as well as the development of their knowledge of the subjects which were not connected with work or job and also on sharing that knowledge with other users of social media. In contrast, much less attention they paid on creating their own professional image and the development of their careers.

Reduced iodinated contrast media for abdominal imaging by dual-layer spectral detector computed tomography for patients with kidney disease

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Hirokazu Saito, MD

2018-04-01

Full Text Available Contrast-enhanced computed tomography using iodinated contrast media is useful for diagnosis of gastrointestinal diseases. However, contrast-induced nephropathy remains problematic for kidney diseases patients. Although current guidelines recommended the use of a minimal dose of contrast media necessary to obtain adequate images for diagnosis, obtaining adequate images with sufficient contrast enhancement is difficult with conventional computed tomography using reduced contrast media. Dual-layer spectral detector computed tomography enables the simultaneous acquisition of low- and high-energy data and the reconstruction of virtual monochromatic images ranging from 40 to 200 keV, retrospectively. Low-energy virtual monochromatic images can enhance the contrast of images, thereby facilitating reduced contrast media. In case 1, abdominal computed tomography angiography at 50 keV using 40% of the conventional dose of contrast media revealed the artery that was the source of diverticular bleeding in the ascending colon. In case 2, ischemia of the transverse colon was diagnosed by contrast-enhanced computed tomography and iodine-selective imaging using 40% of the conventional dose of contrast media. In case 3, advanced esophagogastric junctional cancer was staged and preoperative abdominal computed tomography angiography could be obtained with 30% of the conventional dose of contrast media. However, the texture of virtual monochromatic images may be a limitation at low energy. Keywords: Virtual monochromatic images, Contrast-induced nephropathy

Modality prediction of biomedical literature images using multimodal feature representation

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Pelka, Obioma

2016-08-01

Full Text Available This paper presents the modelling approaches performed to automatically predict the modality of images found in biomedical literature. Various state-of-the-art visual features such as Bag-of-Keypoints computed with dense SIFT descriptors, texture features and Joint Composite Descriptors were used for visual image representation. Text representation was obtained by vector quantisation on a Bag-of-Words dictionary generated using attribute importance derived from a χ-test. Computing the principal components separately on each feature, dimension reduction as well as computational load reduction was achieved. Various multiple feature fusions were adopted to supplement visual image information with corresponding text information. The improvement obtained when using multimodal features vs. visual or text features was detected, analysed and evaluated. Random Forest models with 100 to 500 deep trees grown by resampling, a multi class linear kernel SVM with C=0.05 and a late fusion of the two classifiers were used for modality prediction. A Random Forest classifier achieved a higher accuracy and computed Bag-of-Keypoints with dense SIFT descriptors proved to be a better approach than with Lowe SIFT.

The impact of cosmetic surgery media portrayals on body image and attitudes

OpenAIRE

Ashikali, Eleni-Marina

2014-01-01

The cosmetic surgery industry has rapidly expanded and Professional Associations for surgery in the UK and USA have expressed concern over the ways in which surgery is portrayed in the media. This thesis aimed to investigate how different portrayals of cosmetic surgery in the media impact women and adolescent girls’ body image and attitudes towards surgery. Moreover, it examined a number of moderating variables which may affect responses to such media.\\ud The first three studies examined the ...

Localized scleroderma: imaging features

International Nuclear Information System (INIS)

Liu, P.; Uziel, Y.; Chuang, S.; Silverman, E.; Krafchik, B.; Laxer, R.

1994-01-01

Localized scleroderma is distinct from the diffuse form of scleroderma and does not show Raynaud's phenomenon and visceral involvement. The imaging features in 23 patients ranging from 2 to 17 years of age (mean 11.1 years) were reviewed. Leg length discrepancy and muscle atrophy were the most common findings (five patients), with two patients also showing modelling deformity of the fibula. One patient with lower extremity involvement showed abnormal bone marrow signals on MR. Disabling joint contracture requiring orthopedic intervention was noted in one patient. In two patients with ''en coup de sabre'' facial deformity, CT and MR scans revealed intracranial calcifications and white matter abnormality in the ipsilateral frontal lobes, with one also showing migrational abnormality. In a third patient, CT revealed white matter abnormality in the ipsilateral parietal lobe. In one patient with progressive facial hemiatrophy, CT and MR scans showed the underlying hypoplastic left maxillary antrum and cheek. Imaging studies of areas of clinical concern revealed positive findings in half our patients. (orig.)

Combining low level features and visual attributes for VHR remote sensing image classification

Science.gov (United States)

Zhao, Fumin; Sun, Hao; Liu, Shuai; Zhou, Shilin

2015-12-01

Semantic classification of very high resolution (VHR) remote sensing images is of great importance for land use or land cover investigation. A large number of approaches exploiting different kinds of low level feature have been proposed in the literature. Engineers are often frustrated by their conclusions and a systematic assessment of various low level features for VHR remote sensing image classification is needed. In this work, we firstly perform an extensive evaluation of eight features including HOG, dense SIFT, SSIM, GIST, Geo color, LBP, Texton and Tiny images for classification of three public available datasets. Secondly, we propose to transfer ground level scene attributes to remote sensing images. Thirdly, we combine both low-level features and mid-level visual attributes to further improve the classification performance. Experimental results demonstrate that i) Dene SIFT and HOG features are more robust than other features for VHR scene image description. ii) Visual attribute competes with a combination of low level features. iii) Multiple feature combination achieves the best performance under different settings.

A unified framework for image retrieval using keyword and visual features.

Science.gov (United States)

Jing, Feng; Li, Mingling; Zhang, Hong-Jiang; Zhang, Bo

2005-07-01

In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.

"I don't need people to tell me I'm pretty on social media:" A qualitative study of social media and body image in early adolescent girls.

Science.gov (United States)

Burnette, C Blair; Kwitowski, Melissa A; Mazzeo, Suzanne E

2017-12-01

Social media appear to contribute to body dissatisfaction in adolescents, although few empirical studies exist. This study used six focus groups (total N=38) to explore relations between social media use and body image in early adolescent girls (ages 12-14). Thematic analysis identified patterns in the data. In this sample, social media use was high. Girls endorsed some appearance concerns and social comparison, particularly with peers. However, they displayed high media literacy, appreciation of differences, and confidence, strategies that appeared helpful in mitigating the potential negative association between social media exposure and body image. Girls reported these characteristics were nurtured by positive parental influence and a supportive school environment. Results support an ecological approach to the prevention of body dissatisfaction. Although peer influence strengthens throughout adolescence, current findings suggest that parents and the school environment are associated with girls' attitudes and behaviors regarding social media and body image. Copyright © 2017 Elsevier Ltd. All rights reserved.

Actions improving the image of a nurse in electronic media. Opinion of students at medical courses

Directory of Open Access Journals (Sweden)

Jakubowska Klaudia

2017-09-01

Full Text Available Aim. The aim of study was to define actions improving the image of nurses in electronic media. Material and method. 219 women and 44 men took part in a survey. They were the students of the following courses: nursing, medical rescue, obstetrics, medicine, dentistry, pharmaceutics, physiotherapy, public health. The studies were undertaken with use of own questionnaire in 2015. Results. Majority of respondents 64,6% (n=169 stated that improvement of image of their own profession belongs to the nurses, and only 35,4% (n=93 respondents indicated that the professional organizations of nurses and midwives have their impact on it. According to the students, the most crucial action that should be undertaken by professional organizations in order to improve the image of profession in electronic media was the improvement of wages and working conditions (72,2%, n=189 and better promotion of the profession in electronic media (73,8%, n=193. The nurses can influence the improvement of their image in media by taking care of the good opinion about the profession by setting good example (32%, n=84, and also by creating blogs, social forum, online information services, etc. (26,2%, n=69. Conclusions. According to the respondents, the image of a nurse in electronic media is shaped by the television and radio. The mentioned media tend to present nursing environment in a negative light. The data analysis shows that according to the respondents, the professional organizations of nurses and midwives and nurses themselves should be responsible for improvement of the situation. In order to improve the image, the nurses should promote professional achievements, change the stereotype used in shows and movies, and familiarize the public with the profession. The following branches of mass media should be used: internet websites, television and radio.

Face detection on distorted images using perceptual quality-aware features

Science.gov (United States)

Gunasekar, Suriya; Ghosh, Joydeep; Bovik, Alan C.

2014-02-01

We quantify the degradation in performance of a popular and effective face detector when human-perceived image quality is degraded by distortions due to additive white gaussian noise, gaussian blur or JPEG compression. It is observed that, within a certain range of perceived image quality, a modest increase in image quality can drastically improve face detection performance. These results can be used to guide resource or bandwidth allocation in a communication/delivery system that is associated with face detection tasks. A new face detector based on QualHOG features is also proposed that augments face-indicative HOG features with perceptual quality-aware spatial Natural Scene Statistics (NSS) features, yielding improved tolerance against image distortions. The new detector provides statistically significant improvements over a strong baseline on a large database of face images representing a wide range of distortions. To facilitate this study, we created a new Distorted Face Database, containing face and non-face patches from images impaired by a variety of common distortion types and levels. This new dataset is available for download and further experimentation at www.ideal.ece.utexas.edu/˜suriya/DFD/.

Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts.

Science.gov (United States)

Gómez-Adorno, Helena; Markov, Ilia; Sidorov, Grigori; Posadas-Durán, Juan-Pablo; Sanchez-Perez, Miguel A; Chanona-Hernandez, Liliana

2016-01-01

We introduce a lexical resource for preprocessing social media data. We show that a neural network-based feature representation is enhanced by using this resource. We conducted experiments on the PAN 2015 and PAN 2016 author profiling corpora and obtained better results when performing the data preprocessing using the developed lexical resource. The resource includes dictionaries of slang words, contractions, abbreviations, and emoticons commonly used in social media. Each of the dictionaries was built for the English, Spanish, Dutch, and Italian languages. The resource is freely available.

Efficient and robust model-to-image alignment using 3D scale-invariant features.

Science.gov (United States)

Toews, Matthew; Wells, William M

2013-04-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. Copyright © 2012 Elsevier B.V. All rights reserved.

Automatic plankton image classification combining multiple view features via multiple kernel learning.

Science.gov (United States)

Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

2017-12-28

Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system

Images with impact : The electoral consequences of party leader portrayal in the media

NARCIS (Netherlands)

Aaldering, L.

2018-01-01

This dissertation studies how the media portray party leaders in terms of their character traits, and when and to what extent these mediated leadership images have electoral consequences. The research focusses on the media portrayal of party leaders to establish leader effects, instead of leader

An improved feature extraction algorithm based on KAZE for multi-spectral image

Science.gov (United States)

Yang, Jianping; Li, Jun

2018-02-01

Multi-spectral image contains abundant spectral information, which is widely used in all fields like resource exploration, meteorological observation and modern military. Image preprocessing, such as image feature extraction and matching, is indispensable while dealing with multi-spectral remote sensing image. Although the feature matching algorithm based on linear scale such as SIFT and SURF performs strong on robustness, the local accuracy cannot be guaranteed. Therefore, this paper proposes an improved KAZE algorithm, which is based on nonlinear scale, to raise the number of feature and to enhance the matching rate by using the adjusted-cosine vector. The experiment result shows that the number of feature and the matching rate of the improved KAZE are remarkably than the original KAZE algorithm.

Multiplexing and de-multiplexing with scattering media for large field of view and multispectral imaging

Science.gov (United States)

Sahoo, Sujit Kumar; Tang, Dongliang; Dang, Cuong

2018-02-01

Large field of view multispectral imaging through scattering medium is a fundamental quest in optics community. It has gained special attention from researchers in recent years for its wide range of potential applications. However, the main bottlenecks of the current imaging systems are the requirements on specific illumination, poor image quality and limited field of view. In this work, we demonstrated a single-shot high-resolution colour-imaging through scattering media using a monochromatic camera. This novel imaging technique is enabled by the spatial, spectral decorrelation property and the optical memory effect of the scattering media. Moreover the use of deconvolution image processing further annihilate above-mentioned drawbacks arise due iterative refocusing, scanning or phase retrieval procedures.

Magnetic resonance imaging features of fibrocystic change of the breast.

Science.gov (United States)

Chen, Jeon-Hor; Liu, Hui; Baek, Hyeon-Man; Nalcioglu, Orhan; Su, Min-Ying

2008-11-01

Studies specifically reporting MRI of fibrocystic change (FCC) of the breast are very few and its MRI features are not clearly known. The purpose of this study was to analyze the MRI features of FCC of the breast. Thirty-one patients with pathologically proven FCC of the breast were retrospectively reviewed. The MRI study was performed using a 1.5-T MR scanner with standard bilateral breast coil. The imaging protocol consisted of pre-contrast T1-weighed imaging and dynamic contrast-enhanced axial T1-weighed imaging. The MRI features were interpreted based on the morphologic and enhancement kinetic descriptors defined on ACR BIRADS-MRI lexicon. FCC of the breast had a wide spectrum of morphologic and kinetic features on MRI. Two types of FCC were found, including a more diffuse type of nonmass lesion (12/31, 39%) showing benign enhancement kinetic pattern with medium wash-in in early phase (9/10, 90%) and a focal mass-type lesion (11/31, 35%) with enhancement kinetic usually showing rapid up-slope mimicking a breast cancer (8/11, 73%). MRI is able to elaborate the diverse imaging features of FCC of the breast. Our result showed that FCC presenting as a focal mass-type lesion was usually overdiagnosed as malignancy. Understanding MRI of FCC is important to determine which cohort of patients should be followed up alone or receive aggressive management.

Feature Extraction in Sequential Multimedia Images: with Applications in Satellite Images and On-line Videos

Science.gov (United States)

Liang, Yu-Li

Multimedia data is increasingly important in scientific discovery and people's daily lives. Content of massive multimedia is often diverse and noisy, and motion between frames is sometimes crucial in analyzing those data. Among all, still images and videos are commonly used formats. Images are compact in size but do not contain motion information. Videos record motion but are sometimes too big to be analyzed. Sequential images, which are a set of continuous images with low frame rate, stand out because they are smaller than videos and still maintain motion information. This thesis investigates features in different types of noisy sequential images, and the proposed solutions that intelligently combined multiple features to successfully retrieve visual information from on-line videos and cloudy satellite images. The first task is detecting supraglacial lakes above ice sheet in sequential satellite images. The dynamics of supraglacial lakes on the Greenland ice sheet deeply affect glacier movement, which is directly related to sea level rise and global environment change. Detecting lakes above ice is suffering from diverse image qualities and unexpected clouds. A new method is proposed to efficiently extract prominent lake candidates with irregular shapes, heterogeneous backgrounds, and in cloudy images. The proposed system fully automatize the procedure that track lakes with high accuracy. We further cooperated with geoscientists to examine the tracked lakes and found new scientific findings. The second one is detecting obscene content in on-line video chat services, such as Chatroulette, that randomly match pairs of users in video chat sessions. A big problem encountered in such systems is the presence of flashers and obscene content. Because of various obscene content and unstable qualities of videos capture by home web-camera, detecting misbehaving users is a highly challenging task. We propose SafeVchat, which is the first solution that achieves satisfactory

Tracking image features with PCA-SURF descriptors

CSIR Research Space (South Africa)

Pancham, A

2015-05-01

Full Text Available IAPR International Conference on Machine Vision Applications, May 18-22, 2015, Tokyo, JAPAN Tracking Image Features with PCA-SURF Descriptors Ardhisha Pancham CSIR, UKZN South Africa apancham@csir.co.za Daniel Withey CSIR South Africa...

A comparative study of image low level feature extraction algorithms

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M.M. El-gayar

2013-07-01

Full Text Available Feature extraction and matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods for assessing the performance of popular image matching algorithms are presented and rely on costly descriptors for detection and matching. Specifically, the method assesses the type of images under which each of the algorithms reviewed herein perform to its maximum or highest efficiency. The efficiency is measured in terms of the number of matches founds by the algorithm and the number of type I and type II errors encountered when the algorithm is tested against a specific pair of images. Current comparative studies asses the performance of the algorithms based on the results obtained in different criteria such as speed, sensitivity, occlusion, and others. This study addresses the limitations of the existing comparative tools and delivers a generalized criterion to determine beforehand the level of efficiency expected from a matching algorithm given the type of images evaluated. The algorithms and the respective images used within this work are divided into two groups: feature-based and texture-based. And from this broad classification only three of the most widely used algorithms are assessed: color histogram, FAST (Features from Accelerated Segment Test, SIFT (Scale Invariant Feature Transform, PCA-SIFT (Principal Component Analysis-SIFT, F-SIFT (fast-SIFT and SURF (speeded up robust features. The performance of the Fast-SIFT (F-SIFT feature detection methods are compared for scale changes, rotation, blur, illumination changes and affine transformations. All the experiments use repeatability measurement and the number of correct matches for the evaluation measurements. SIFT presents its stability in most situations although its slow. F-SIFT is the fastest one with good performance as the same as SURF, SIFT, PCA-SIFT show its advantages in rotation and illumination changes.

A study on utility of magnetic resonance imaging for female pelvic cavity using enteral MRI contrast media

International Nuclear Information System (INIS)

Kim, Ham Gyum

1997-01-01

For radiological test in soft tissue or neighboring part with same signal intensity, proper test method and equipment shall be selected as needed. In case of female pelvic cavity, ultrasonography or computed tomography alternatively used, but MRI can be more usefully applied to design treatment method or operation plan by improving the diagnostic accuracy and careful observation of lesion characteristics. Magnetic Resonance Imaging using recently developed Enteral MRI contrast media can acquire more diagnostic information than using only intravenous contrast media. Thus this study attempted to examine the utility of anatomic structure and diagnostic acquisition by imaging the female pelvic cavity using Enteral MRI contrast media. As a result of analyzing magnetic resonance imaging after administering Enteral MRI contrast media to pelvic cavity suspect patients, more diagnostic information media could be acquired than only using intravenous contrast. Especially, in the diagnosis of lesion position, shape, distinction from neighboring tissues it is thought that external Enteral MRI contrast media should be used

Modeling multiple visual words assignment for bag-of-features based medical image retrieval

KAUST Repository

Wang, Jim Jing-Yan

2012-01-01

In this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments\\' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.

Modeling multiple visual words assignment for bag-of-features based medical image retrieval

KAUST Repository

Wang, Jim Jing-Yan; Almasri, Islam

2012-01-01

In this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.

Perinatal clinical and imaging features of CLOVES syndrome

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Fernandez-Pineda, Israel [Virgen del Rocio Children' s Hospital, Department of Pediatric Surgery, Seville (Spain); Fajardo, Manuel [Virgen del Rocio Children' s Hospital, Department of Pediatric Radiology, Seville (Spain); Chaudry, Gulraiz; Alomari, Ahmad I. [Children' s Hospital Boston and Harvard Medical School, Division of Vascular and Interventional Radiology, Boston, MA (United States)

2010-08-15

We report a neonate with antenatal imaging features suggestive of CLOVES syndrome. Postnatal clinical and imaging findings confirmed the diagnosis, with the constellation of truncal overgrowth, cutaneous capillary malformation, lymphatic and musculoskeletal anomalies. The clinical, radiological and histopathological findings noted in this particular phenotype help differentiate it from other overgrowth syndromes with complex vascular anomalies. (orig.)

US or MR Imaging Features of Polypoid Endometriosis: A Case Report

International Nuclear Information System (INIS)

Park, Jae Il; Cho, Jae Ho; Kim, Geum Rae; Kim, Mi Jin

2009-01-01

Polypoid endometriosis is a rare variant of endometriosis that is pathologically similar to an endometrial polyp. This lesion is frequently mistaken for a solid neoplasm in clinical, radiological and pathological examinations. The clinical and pathological features of the lesion have been well described in the English literature. However, its imaging features have not been reported in the Korean literature. We describe ultrasound and magnetic resonance imaging features of pathologically-confirmed polypoid endometriosis

US or MR Imaging Features of Polypoid Endometriosis: A Case Report

Energy Technology Data Exchange (ETDEWEB)

Park, Jae Il; Cho, Jae Ho; Kim, Geum Rae; Kim, Mi Jin [Yeungnam University, Gyeongsan (Korea, Republic of)

2009-12-15

Polypoid endometriosis is a rare variant of endometriosis that is pathologically similar to an endometrial polyp. This lesion is frequently mistaken for a solid neoplasm in clinical, radiological and pathological examinations. The clinical and pathological features of the lesion have been well described in the English literature. However, its imaging features have not been reported in the Korean literature. We describe ultrasound and magnetic resonance imaging features of pathologically-confirmed polypoid endometriosis.

Imaging features of mycobacterium in patients with acquired immunodeficiency syndrome

International Nuclear Information System (INIS)

Yang Jun; Sun Yue; Wei Liangui; Xu Yunliang; Li Xingwang

2013-01-01

Objective: To analyze the imaging features of mycobacterium in AIDS patients. Methods: Twenty-three cases of mycobacterium tuberculosis and 13 patients of non-tuberculous mycobacteria were proved etiologically and included in this study. All patients underwent X-ray and CT examinations, imaging data were analyzed and compared. Results: The imaging findings of mycobacterium tuberculosis in AIDS patients included consolidation (n = 11), pleural effusion (n = 11), mediastinal lymphadenopathy (n = 11). Pulmonary lesions were always diffuse distribution, and 14 patients of extrapulmonary tuberculosis were found. Pulmonary lesions in non-tuberculous mycobacteria tend to be circumscribed. Conclusions: Non-tuberculous mycobacterial infection in AIDS patients is more common and usually combined with other infections. Imaging features are atypical. (authors)

Face recognition via sparse representation of SIFT feature on hexagonal-sampling image

Science.gov (United States)

Zhang, Daming; Zhang, Xueyong; Li, Lu; Liu, Huayong

2018-04-01

This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.

Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images

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Soheila Gheisari

2018-01-01

Full Text Available Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.

Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning

Directory of Open Access Journals (Sweden)

Sun Xun

2016-12-01

Full Text Available In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR images using multiple-feature fusion and ensemble learning. First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images. Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results. Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results. Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.

COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment

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Simonetta Paloscia

2017-01-01

Full Text Available In this work, X band images acquired by COSMO-SkyMed (CSK on alpine environment have been analyzed for investigating snow characteristics and their effect on backscattering variations. Preliminary results confirmed the capability of simultaneous optical and Synthetic Aperture Radar (SAR images (Landsat-8 and CSK in separating snow/no-snow areas and in detecting wet snow. The sensitivity of backscattering to snow depth has not always been confirmed, depending on snow characteristics related to the season. A model based on Dense Media Radiative Transfer theory (DMRT-QMS was applied for simulating the backscattering response on the X band from snow cover in different conditions of grain size, snow density and depth. By using DMRT-QMS and snow in-situ data collected on Cordevole basin in Italian Alps, the effect of grain size and snow density, beside snow depth and snow water equivalent, was pointed out, showing that the snow features affect the backscatter in different and sometimes opposite ways. Experimental values of backscattering were correctly simulated by using this model and selected intervals of ground parameters. The relationship between simulated and measured backscattering for the entire dataset shows slope >0.9, determination coefficient, R2 = 0.77, and root mean square error, RMSE = 1.1 dB, with p-value <0.05.

Magnetic resonance perfusion imaging without contrast media

International Nuclear Information System (INIS)

Martirosian, Petros; Graf, Hansjoerg; Schick, Fritz; Boss, Andreas; Schraml, Christina; Schwenzer, Nina F.; Claussen, Claus D.

2010-01-01

Principles of magnetic resonance imaging techniques providing perfusion-related contrast weighting without administration of contrast media are reported and analysed systematically. Especially common approaches to arterial spin labelling (ASL) perfusion imaging allowing quantitative assessment of specific perfusion rates are described in detail. The potential of ASL for perfusion imaging was tested in several types of tissue. After a systematic comparison of technical aspects of continuous and pulsed ASL techniques the standard kinetic model and tissue properties of influence to quantitative measurements of perfusion are reported. For the applications demonstrated in this paper a flow-sensitive alternating inversion recovery (FAIR) ASL perfusion preparation approach followed by true fast imaging with steady precession (true FISP) data recording was developed and implemented on whole-body scanners operating at 0.2, 1.5 and 3 T for quantitative perfusion measurement in various types of tissue. ASL imaging provides a non-invasive tool for assessment of tissue perfusion rates in vivo. Images recorded from kidney, lung, brain, salivary gland and thyroid gland provide a spatial resolution of a few millimetres and sufficient signal to noise ratio in perfusion maps after 2-5 min of examination time. Newly developed ASL techniques provide especially high image quality and quantitative perfusion maps in tissues with relatively high perfusion rates (as also present in many tumours). Averaging of acquisitions and image subtraction procedures are mandatory, leading to the necessity of synchronization of data recording to breathing in abdominal and thoracic organs. (orig.)

Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features

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Keranmu Xielifuguli

2014-01-01

Full Text Available People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity filtering compared with image similarity retrieval methods based on image features. We found that sensitivity filtering using color correlograms was suitable for the classification of preferred images, while sensitivity filtering using local binary patterns was suitable for the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a 90% success rate. Thus, we conclude that the proposed method is efficient for filtering unpleasant images.

3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes.

Science.gov (United States)

Zhong, Zichun; Guo, Xiaohu; Cai, Yiqi; Yang, Yin; Wang, Jing; Jia, Xun; Mao, Weihua

2016-01-01

By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT) scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs) are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs) of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes.

3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes

Directory of Open Access Journals (Sweden)

Zichun Zhong

2016-01-01

Full Text Available By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes.

Simultenious binary hash and features learning for image retrieval

Science.gov (United States)

Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

2016-05-01

Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

The Media: The Image of the Scientist is Bad

Science.gov (United States)

Maugh, Thomas H., II

1978-01-01

Many individuals are concerned with the erroneous image of science and scientists that is given to the public by the media. To improve the situation, it is suggested that individuals and organizations protest to movie studios and networks when inaccuracies appear and when scientists are portrayed in a denigrating manner. (Author/MA)

ISSUES ON DISABILITY ADVERTISING IMAGING IN MEDIA

Directory of Open Access Journals (Sweden)

Nebojsa Randjelovic

2012-09-01

Full Text Available A great many government organizations, charities, advocacy groups, consulting firms and media organizations are expending enormous amounts of resources because they all agree with the premise that media are a powerful educator for cultural values and attitudes. Social learning theory, cultivation and media dependency theory all support that premise, as does a body of work in the rehabilitation literature. In the small world of advertising the results of this study suggest that images of people with disabilities in advertising are a bigger part of the overall advertising environment than they were in 2001 despite the difficulties associated with contextual elements in an ad, and the public outrage/civil suits. But if we compare the appearance rate for AI advertising, as part of the total advertising environment, to the percentage of adults classified as disabled in the total U.S. population based, people with disabilities are very much under-represented (1.7% from this study as compared to 12% according to the 2009 Disability Compendium. If equal representation is the goal, we are not there yet. If acceptance of the use of disabled portrayals in general product advertising on the part of the advertising industry is the goal, then much progress has been made.

The fuzzy Hough Transform-feature extraction in medical images

International Nuclear Information System (INIS)

Philip, K.P.; Dove, E.L.; Stanford, W.; Chandran, K.B.; McPherson, D.D.; Gotteiner, N.L.

1994-01-01

Identification of anatomical features is a necessary step for medical image analysis. Automatic methods for feature identification using conventional pattern recognition techniques typically classify an object as a member of a predefined class of objects, but do not attempt to recover the exact or approximate shape of that object. For this reason, such techniques are usually not sufficient to identify the borders of organs when individual geometry varies in local detail, even though the general geometrical shape is similar. The authors present an algorithm that detects features in an image based on approximate geometrical models. The algorithm is based on the traditional and generalized Hough Transforms but includes notions from fuzzy set theory. The authors use the new algorithm to roughly estimate the actual locations of boundaries of an internal organ, and from this estimate, to determine a region of interest around the organ. Based on this rough estimate of the border location, and the derived region of interest, the authors find the final estimate of the true borders with other image processing techniques. The authors present results that demonstrate that the algorithm was successfully used to estimate the approximate location of the chest wall in humans, and of the left ventricular contours of a dog heart obtained from cine-computed tomographic images. The authors use this fuzzy Hough Transform algorithm as part of a larger procedures to automatically identify the myocardial contours of the heart. This algorithm may also allow for more rapid image processing and clinical decision making in other medical imaging applications

Localized scleroderma: imaging features

Energy Technology Data Exchange (ETDEWEB)

Liu, P. (Dept. of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON (Canada)); Uziel, Y. (Div. of Rheumatology, Hospital for Sick Children, Toronto, ON (Canada)); Chuang, S. (Dept. of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON (Canada)); Silverman, E. (Div. of Rheumatology, Hospital for Sick Children, Toronto, ON (Canada)); Krafchik, B. (Div. of Dermatology, Dept. of Pediatrics, Hospital for Sick Children, Toronto, ON (Canada)); Laxer, R. (Div. of Rheumatology, Hospital for Sick Children, Toronto, ON (Canada))

1994-06-01

Localized scleroderma is distinct from the diffuse form of scleroderma and does not show Raynaud's phenomenon and visceral involvement. The imaging features in 23 patients ranging from 2 to 17 years of age (mean 11.1 years) were reviewed. Leg length discrepancy and muscle atrophy were the most common findings (five patients), with two patients also showing modelling deformity of the fibula. One patient with lower extremity involvement showed abnormal bone marrow signals on MR. Disabling joint contracture requiring orthopedic intervention was noted in one patient. In two patients with ''en coup de sabre'' facial deformity, CT and MR scans revealed intracranial calcifications and white matter abnormality in the ipsilateral frontal lobes, with one also showing migrational abnormality. In a third patient, CT revealed white matter abnormality in the ipsilateral parietal lobe. In one patient with progressive facial hemiatrophy, CT and MR scans showed the underlying hypoplastic left maxillary antrum and cheek. Imaging studies of areas of clinical concern revealed positive findings in half our patients. (orig.)

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

Science.gov (United States)

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

Neural network-based feature point descriptors for registration of optical and SAR images

Science.gov (United States)

Abulkhanov, Dmitry; Konovalenko, Ivan; Nikolaev, Dmitry; Savchik, Alexey; Shvets, Evgeny; Sidorchuk, Dmitry

2018-04-01

Registration of images of different nature is an important technique used in image fusion, change detection, efficient information representation and other problems of computer vision. Solving this task using feature-based approaches is usually more complex than registration of several optical images because traditional feature descriptors (SIFT, SURF, etc.) perform poorly when images have different nature. In this paper we consider the problem of registration of SAR and optical images. We train neural network to build feature point descriptors and use RANSAC algorithm to align found matches. Experimental results are presented that confirm the method's effectiveness.

WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

Energy Technology Data Exchange (ETDEWEB)

Lee, J; Nishikawa, R [University of Pittsburgh, Pittsburgh, PA (United States); Reiser, I [The University of Chicago, Chicago, IL (United States); Boone, J [UC Davis Medical Center, Sacramento, CA (United States)

2015-06-15

Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification

Image features for misalignment correction in medical flat-detector CT

International Nuclear Information System (INIS)

Wicklein, Julia; Kunze, Holger; Kalender, Willi A.; Kyriakou, Yiannis

2012-01-01

Purpose: Misalignment artifacts are a serious problem in medical flat-detector computed tomography. Generally, the geometrical parameters, which are essential for reconstruction, are provided by preceding calibration routines. These procedures are time consuming and the later use of stored parameters is sensitive toward external impacts or patient movement. The method of choice in a clinical environment would be a markerless online-calibration procedure that allows flexible scan trajectories and simultaneously corrects misalignment and motion artifacts during the reconstruction process. Therefore, different image features were evaluated according to their capability of quantifying misalignment. Methods: Projections of the FORBILD head and thorax phantoms were simulated. Additionally, acquisitions of a head phantom and patient data were used for evaluation. For the reconstruction different sources and magnitudes of misalignment were introduced in the geometry description. The resulting volumes were analyzed by entropy (based on the gray-level histogram), total variation, Gabor filter texture features, Haralick co-occurrence features, and Tamura texture features. The feature results were compared to the back-projection mismatch of the disturbed geometry. Results: The evaluations demonstrate the ability of several well-established image features to classify misalignment. The authors elaborated the particular suitability of the gray-level histogram-based entropy on identifying misalignment artifacts, after applying an appropriate window level (bone window). Conclusions: Some of the proposed feature extraction algorithms show a strong correlation with the misalignment level. Especially, entropy-based methods showed very good correspondence, with the best of these being the type that uses the gray-level histogram for calculation. This makes it a suitable image feature for online-calibration.

Ultrasound-mediated Optical Imaging and Focusing in Scattering Media

Science.gov (United States)

Suzuki, Yuta

Because of its non-ionizing and molecular sensing nature, light has been an attractive tool in biomedicine. Scanning an optical focus allows not only high-resolution imaging but also manipulation and therapy. However, due to multiple photon scattering events, conventional optical focusing using an ordinary lens is limited to shallow depths of one transport mean free path (lt'), which corresponds to approximately 1 mm in human tissue. To overcome this limitation, ultrasonic modulation (or encoding ) of diffuse light inside scattering media has enabled us to develop both deep-tissue optical imaging and focusing techniques, namely, ultrasound-modulated optical tomography (UOT) and time-reversed ultrasonically encoded (TRUE) optical focusing. While UOT measures the power of the encoded light to obtain an image, TRUE focusing generates a time-reversed (or phase-conjugated) copy of the encoded light, using a phase-conjugate mirror to focus light inside scattering media beyond 1 lt'. However, despite extensive progress in both UOT and TRUE focusing, the low signal-to-noise ratio in encoded-light detection remains a challenge to meeting both the speed and depth requirements for in vivo applications. This dissertation describes technological advancements of both UOT and TRUE focusing, in terms of their signal detection sensitivities, operational depths, and operational speeds. The first part of this dissertation describes sensitivity improvements of encoded-light detection in UOT, achieved by using a large area (˜5 cm x 5 cm) photorefractive polymer. The photorefractive polymer allowed us to improve the detection etendue by more than 10 times that of previous detection schemes. It has enabled us to resolve absorbing objects embedded inside diffused media thicker than 80 lt', using moderate light power and short ultrasound pulses. The second part of this dissertation describes energy enhancement and fluorescent excitation using TRUE focusing in turbid media, using

The algorithm of fast image stitching based on multi-feature extraction

Science.gov (United States)

Yang, Chunde; Wu, Ge; Shi, Jing

2018-05-01

This paper proposed an improved image registration method combining Hu-based invariant moment contour information and feature points detection, aiming to solve the problems in traditional image stitching algorithm, such as time-consuming feature points extraction process, redundant invalid information overload and inefficiency. First, use the neighborhood of pixels to extract the contour information, employing the Hu invariant moment as similarity measure to extract SIFT feature points in those similar regions. Then replace the Euclidean distance with Hellinger kernel function to improve the initial matching efficiency and get less mismatching points, further, estimate affine transformation matrix between the images. Finally, local color mapping method is adopted to solve uneven exposure, using the improved multiresolution fusion algorithm to fuse the mosaic images and realize seamless stitching. Experimental results confirm high accuracy and efficiency of method proposed in this paper.

Improved Feature Detection in Fused Intensity-Range Images with Complex SIFT (ℂSIFT

Directory of Open Access Journals (Sweden)

Boris Jutzi

2011-09-01

Full Text Available The real and imaginary parts are proposed as an alternative to the usual Polar representation of complex-valued images. It is proven that the transformation from Polar to Cartesian representation contributes to decreased mutual information, and hence to greater distinctiveness. The Complex Scale-Invariant Feature Transform (ℂSIFT detects distinctive features in complex-valued images. An evaluation method for estimating the uniformity of feature distributions in complex-valued images derived from intensity-range images is proposed. In order to experimentally evaluate the proposed methodology on intensity-range images, three different kinds of active sensing systems were used: Range Imaging, Laser Scanning, and Structured Light Projection devices (PMD CamCube 2.0, Z+F IMAGER 5003, Microsoft Kinect.

Weight-adapted iodinated contrast media administration in abdomino-pelvic CT: Can image quality be maintained?

Science.gov (United States)

Perrin, E; Jackson, M; Grant, R; Lloyd, C; Chinaka, F; Goh, V

2018-02-01

In many centres, a fixed method of contrast-media administration is used for CT regardless of patient body habitus. The aim of this trial was to assess contrast enhancement of the aorta, portal vein, liver and spleen during abdomino-pelvic CT imaging using a weight-adapted contrast media protocol compared to the current fixed dose method. Thirty-nine oncology patients, who had previously undergone CT abdomino-pelvic imaging at the institution using a fixed contrast media dose, were prospectively imaged using a weight-adapted contrast media dose (1.4 ml/kg). The two sets of images were assessed for contrast enhancement levels (HU) at locations in the liver, aorta, portal vein and spleen during portal-venous enhancement phase. The t-test was used to compare the difference in results using a non-inferiority margin of 10 HU. When the contrast dose was tailored to patient weight, contrast enhancement levels were shown to be non-inferior to the fixed dose method (liver p contrast dose reduction of 165 ml using the weight-adapted method compared to the fixed dose method, with a mean cost per patient of £6.81 and £7.19 respectively. Using a weight-adapted method of contrast media administration was shown to be non-inferior to a fixed dose method of contrast media administration. Patients weighing 76 kg, or less, received a lower contrast dose which may have associated cost savings. A weight-adapted contrast media protocol should be implemented for portal-venous phase abdomino-pelvic CT for oncology patients with adequate renal function (>70 ml/min/1.73 m 2 ). Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Imaging features of foot osteoid osteoma

Energy Technology Data Exchange (ETDEWEB)

Shukla, Satyen; Clarke, Andrew W.; Saifuddin, Asif [Royal National Orthopaedic Hospital NHS Trust, Department of Radiology, Stanmore, Middlesex (United Kingdom)

2010-07-15

We performed a retrospective review of the imaging of nine patients with a diagnosis of foot osteoid osteoma (OO). Radiographs, computed tomography (CT) and magnetic resonance imaging (MRI) had been performed in all patients. Radiographic features evaluated were the identification of a nidus and cortical thickening. CT features noted were nidus location (affected bone - intramedullary, intracortical, subarticular) and nidus calcification. MRI features noted were the presence of an identifiable nidus, presence and grade of bone oedema and whether a joint effusion was identified. Of the nine patients, three were female and six male, with a mean age of 21 years (range 11-39 years). Classical symptoms of OO (night pain, relief with aspirin) were identified in five of eight (62.5%) cases (in one case, the medical records could not be retrieved). In five patients the lesion was located in the hindfoot (four calcaneus, one talus), while four were in the mid- or forefoot (two metatarsal and two phalangeal). Radiographs were normal in all patients with hindfoot OO. CT identified the nidus in all cases (89%) except one terminal phalanx lesion, while MRI demonstrated a nidus in six of nine cases (67%). The nidus was of predominantly intermediate signal intensity on T1-weighted (T1W) sequences, with intermediate to high signal intensity on T2-weighted (T2W) sequences. High-grade bone marrow oedema, limited to the affected bone and adjacent soft tissue oedema was identified in all cases. In a young patient with chronic hindfoot pain and a normal radiograph, MRI features suggestive of possible OO include extensive bone marrow oedema limited to one bone, with a possible nidus demonstrated in two-thirds of cases. The presence or absence of a nidus should be confirmed with high-resolution CT. (orig.)

Oriented Edge-Based Feature Descriptor for Multi-Sensor Image Alignment and Enhancement

Directory of Open Access Journals (Sweden)

Myung-Ho Ju

2013-10-01

Full Text Available In this paper, we present an efficient image alignment and enhancement method for multi-sensor images. The shape of the object captured in a multi-sensor images can be determined by comparing variability of contrast using corresponding edges across multi-sensor image. Using this cue, we construct a robust feature descriptor based on the magnitudes of the oriented edges. Our proposed method enables fast image alignment by identifying matching features in multi-sensor images. We enhance the aligned multi-sensor images through the fusion of the salient regions from each image. The results of stitching the multi-sensor images and their enhancement demonstrate that our proposed method can align and enhance multi-sensor images more efficiently than previous methods.

How Older People Think about Images of Aging in Advertising and the Media.

Science.gov (United States)

Bradley, Don E.; Longino, Charles F., Jr.

2001-01-01

A literature review documents distorted images of aging in mass media and advertising, including underrepresentation and stereotyping. Older consumers are dissatisfied with these images, and their growing purchasing power is forcing advertisers to make more effective appeals. (Contains 20 references.) (SK)

Automatic detection of diabetic retinopathy features in ultra-wide field retinal images

Science.gov (United States)

Levenkova, Anastasia; Sowmya, Arcot; Kalloniatis, Michael; Ly, Angelica; Ho, Arthur

2017-03-01

Diabetic retinopathy (DR) is a major cause of irreversible vision loss. DR screening relies on retinal clinical signs (features). Opportunities for computer-aided DR feature detection have emerged with the development of Ultra-WideField (UWF) digital scanning laser technology. UWF imaging covers 82% greater retinal area (200°), against 45° in conventional cameras3 , allowing more clinically relevant retinopathy to be detected4 . UWF images also provide a high resolution of 3078 x 2702 pixels. Currently DR screening uses 7 overlapping conventional fundus images, and the UWF images provide similar results1,4. However, in 40% of cases, more retinopathy was found outside the 7-field ETDRS) fields by UWF and in 10% of cases, retinopathy was reclassified as more severe4 . This is because UWF imaging allows examination of both the central retina and more peripheral regions, with the latter implicated in DR6 . We have developed an algorithm for automatic recognition of DR features, including bright (cotton wool spots and exudates) and dark lesions (microaneurysms and blot, dot and flame haemorrhages) in UWF images. The algorithm extracts features from grayscale (green "red-free" laser light) and colour-composite UWF images, including intensity, Histogram-of-Gradient and Local binary patterns. Pixel-based classification is performed with three different classifiers. The main contribution is the automatic detection of DR features in the peripheral retina. The method is evaluated by leave-one-out cross-validation on 25 UWF retinal images with 167 bright lesions, and 61 other images with 1089 dark lesions. The SVM classifier performs best with AUC of 94.4% / 95.31% for bright / dark lesions.

Three-dimensional multislice CT imaging of otitis media

International Nuclear Information System (INIS)

Suzuki, Miyako; Yoshikawa, Hiroshi; Hosokawa, Akira; Furukawa, Tomoyasu; Ichikawa, Ginichiro; Wada, Akihiro; Ando, Ichiro

2002-01-01

In recent years, the multislice CT system has come into practical use that enables table movement of half mm, resulting in a significant improvement in resolution. The use of this CT system enables to depict the entire auditory ossicles, including the stapes. 3D reconstruction was performed using helical CT data in 5 patients with chronic otitis media and 5 patients with cholesteatoma. An Aquilion Multi (Toshiba) multislice helical CT scanner and a Xtension (Toshiba) image workstation were used in this study. We demonstrated the 3D display with axial, coronal and sagittal images. Compared with the normal ears, it was necessary to set a higher threshold for the affected ears. It is important to select suitable threshold for demonstration of 3D images optimally. Bone destruction of the stapes was confirmed at surgery in 2 ears. The stapes was observed at 3D-CT imaging in other 18 ears. It was found that the 3D images of the ossicular destruction in ears with cholesteatoma were consistent with surgical findings. It is therefore concluded that 3D imaging of the middle ear using a multislice CT scanner is clinically useful. (author)

Three-dimensional multislice CT imaging of otitis media

Energy Technology Data Exchange (ETDEWEB)

Suzuki, Miyako [Yanagibasi Hospital, Tokyo (Japan); Yoshikawa, Hiroshi; Hosokawa, Akira; Furukawa, Tomoyasu; Ichikawa, Ginichiro [Juntendo Univ., Tokyo (Japan). School of Medicine; Wada, Akihiro; Ando, Ichiro [Juntendo Univ., Chiba (Japan). Urayasu Hospital

2002-07-01

In recent years, the multislice CT system has come into practical use that enables table movement of half mm, resulting in a significant improvement in resolution. The use of this CT system enables to depict the entire auditory ossicles, including the stapes. 3D reconstruction was performed using helical CT data in 5 patients with chronic otitis media and 5 patients with cholesteatoma. An Aquilion Multi (Toshiba) multislice helical CT scanner and a Xtension (Toshiba) image workstation were used in this study. We demonstrated the 3D display with axial, coronal and sagittal images. Compared with the normal ears, it was necessary to set a higher threshold for the affected ears. It is important to select suitable threshold for demonstration of 3D images optimally. Bone destruction of the stapes was confirmed at surgery in 2 ears. The stapes was observed at 3D-CT imaging in other 18 ears. It was found that the 3D images of the ossicular destruction in ears with cholesteatoma were consistent with surgical findings. It is therefore concluded that 3D imaging of the middle ear using a multislice CT scanner is clinically useful. (author)

Forty-five degree backscattering-mode nonlinear absorption imaging in turbid media.

Science.gov (United States)

Cui, Liping; Knox, Wayne H

2010-01-01

Two-color nonlinear absorption imaging has been previously demonstrated with endogenous contrast of hemoglobin and melanin in turbid media using transmission-mode detection and a dual-laser technology approach. For clinical applications, it would be generally preferable to use backscattering mode detection and a simpler single-laser technology. We demonstrate that imaging in backscattering mode in turbid media using nonlinear absorption can be obtained with as little as 1-mW average power per beam with a single laser source. Images have been achieved with a detector receiving backscattered light at a 45-deg angle relative to the incoming beams' direction. We obtain images of capillary tube phantoms with resolution as high as 20 microm and penetration depth up to 0.9 mm for a 300-microm tube at SNR approximately 1 in calibrated scattering solutions. Simulation results of the backscattering and detection process using nonimaging optics are demonstrated. A Monte Carlo-based method shows that the nonlinear signal drops exponentially as the depth increases, which agrees well with our experimental results. Simulation also shows that with our current detection method, only 2% of the signal is typically collected with a 5-mm-radius detector.

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

OpenAIRE

Gupta, Saurabh; Girshick, Ross; Arbeláez, Pablo; Malik, Jitendra

2014-01-01

In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an av...

Barrett's esophagus: clinical features, obesity, and imaging.

LENUS (Irish Health Repository)

Quigley, Eamonn M M

2011-09-01

The following includes commentaries on clinical features and imaging of Barrett\\'s esophagus (BE); the clinical factors that influence the development of BE; the influence of body fat distribution and central obesity; the role of adipocytokines and proinflammatory markers in carcinogenesis; the role of body mass index (BMI) in healing of Barrett\\'s epithelium; the role of surgery in prevention of carcinogenesis in BE; the importance of double-contrast esophagography and cross-sectional images of the esophagus; and the value of positron emission tomography\\/computed tomography.

Pengguna Media Interaktif Sebagai Kenyataan Maya: Studi Resepsi Khalayak Suarasurabaya.net Sebagai Media Interaktif

Directory of Open Access Journals (Sweden)

Ido Prijana Hadi

2011-07-01

Full Text Available My aim in writing this paper is to describe that in this fast-changing world, media in Indonesia has undergone a rapid transformation. Digital technology continues to reshape the mass media landscape using internet technology. Internet brings a technical communication revolution, a fundamental change takes place in the structure of connections, artificial memories and the reproduction of their content. Internet technology has made communication much easier and less expensive. It has attracted many people and has penetrated into people’s daily lives. The mass media also have accepted the internet. Almost all forms of traditional media (old media in local media, such as radio, television, and newspaper have extended their work into this new field. The internet and the World Wide Web have both significantly influenced modern journalism. In online media allows readers to enjoy browsing their product and service of contents, such as news feed, podcasts, desktop alert, news on mobile phones, PDA and others mobile devices. Online media offer not only text but also digital images , audio file, moving images (video, internet radio and internet t v. The interactive features of the internet seemingly imply that online media have more advantages than traditional media forms (old media. So, the internet have dramatically evolved become new media with characteristic multimedia, hypertext, interactivity, archives , and virtuality. The most important structural new media characteristic is the integration of telecommunications, data communications and mass communication in a single medium – it is the convergence. It should be pointed out that the trend toward digital is affecting the various media and brings the local media in East Java to become a global media, where breaking news from Surabaya or anywhere in East Java is transmitted to around the world in a matter of minutes . The research was carried out to find out how user reception on convergence media

Angle gathers in wave-equation imaging for transversely isotropic media

KAUST Repository

Alkhalifah, Tariq Ali; Fomel, Sergey B.

2010-01-01

In recent years, wave-equation imaged data are often presented in common-image angle-domain gathers as a decomposition in the scattering angle at the reflector, which provide a natural access to analysing migration velocities and amplitudes. In the case of anisotropic media, the importance of angle gathers is enhanced by the need to properly estimate multiple anisotropic parameters for a proper representation of the medium. We extract angle gathers for each downward-continuation step from converting offset-frequency planes into angle-frequency planes simultaneously with applying the imaging condition in a transversely isotropic with a vertical symmetry axis (VTI) medium. The analytic equations, though cumbersome, are exact within the framework of the acoustic approximation. They are also easily programmable and show that angle gather mapping in the case of anisotropic media differs from its isotropic counterpart, with the difference depending mainly on the strength of anisotropy. Synthetic examples demonstrate the importance of including anisotropy in the angle gather generation as mapping of the energy is negatively altered otherwise. In the case of a titled axis of symmetry (TTI), the same VTI formulation is applicable but requires a rotation of the wavenumbers. © 2010 European Association of Geoscientists & Engineers.

Angle gathers in wave-equation imaging for transversely isotropic media

KAUST Repository

Alkhalifah, Tariq Ali

2010-11-12

In recent years, wave-equation imaged data are often presented in common-image angle-domain gathers as a decomposition in the scattering angle at the reflector, which provide a natural access to analysing migration velocities and amplitudes. In the case of anisotropic media, the importance of angle gathers is enhanced by the need to properly estimate multiple anisotropic parameters for a proper representation of the medium. We extract angle gathers for each downward-continuation step from converting offset-frequency planes into angle-frequency planes simultaneously with applying the imaging condition in a transversely isotropic with a vertical symmetry axis (VTI) medium. The analytic equations, though cumbersome, are exact within the framework of the acoustic approximation. They are also easily programmable and show that angle gather mapping in the case of anisotropic media differs from its isotropic counterpart, with the difference depending mainly on the strength of anisotropy. Synthetic examples demonstrate the importance of including anisotropy in the angle gather generation as mapping of the energy is negatively altered otherwise. In the case of a titled axis of symmetry (TTI), the same VTI formulation is applicable but requires a rotation of the wavenumbers. © 2010 European Association of Geoscientists & Engineers.

Tissue feature-based intra-fractional motion tracking for stereoscopic x-ray image guided radiotherapy

Science.gov (United States)

Xie, Yaoqin; Xing, Lei; Gu, Jia; Liu, Wu

2013-06-01

Real-time knowledge of tumor position during radiation therapy is essential to overcome the adverse effect of intra-fractional organ motion. The goal of this work is to develop a tumor tracking strategy by effectively utilizing the inherent image features of stereoscopic x-ray images acquired during dose delivery. In stereoscopic x-ray image guided radiation delivery, two orthogonal x-ray images are acquired either simultaneously or sequentially. The essence of markerless tumor tracking is the reliable identification of inherent points with distinct tissue features on each projection image and their association between two images. The identification of the feature points on a planar x-ray image is realized by searching for points with high intensity gradient. The feature points are associated by using the scale invariance features transform descriptor. The performance of the proposed technique is evaluated by using images of a motion phantom and four archived clinical cases acquired using either a CyberKnife equipped with a stereoscopic x-ray imaging system, or a LINAC equipped with an onboard kV imager and an electronic portal imaging device. In the phantom study, the results obtained using the proposed method agree with the measurements to within 2 mm in all three directions. In the clinical study, the mean error is 0.48 ± 0.46 mm for four patient data with 144 sequential images. In this work, a tissue feature-based tracking method for stereoscopic x-ray image guided radiation therapy is developed. The technique avoids the invasive procedure of fiducial implantation and may greatly facilitate the clinical workflow.

Tissue feature-based intra-fractional motion tracking for stereoscopic x-ray image guided radiotherapy

International Nuclear Information System (INIS)

Xie Yaoqin; Gu Jia; Xing Lei; Liu Wu

2013-01-01

Real-time knowledge of tumor position during radiation therapy is essential to overcome the adverse effect of intra-fractional organ motion. The goal of this work is to develop a tumor tracking strategy by effectively utilizing the inherent image features of stereoscopic x-ray images acquired during dose delivery. In stereoscopic x-ray image guided radiation delivery, two orthogonal x-ray images are acquired either simultaneously or sequentially. The essence of markerless tumor tracking is the reliable identification of inherent points with distinct tissue features on each projection image and their association between two images. The identification of the feature points on a planar x-ray image is realized by searching for points with high intensity gradient. The feature points are associated by using the scale invariance features transform descriptor. The performance of the proposed technique is evaluated by using images of a motion phantom and four archived clinical cases acquired using either a CyberKnife equipped with a stereoscopic x-ray imaging system, or a LINAC equipped with an onboard kV imager and an electronic portal imaging device. In the phantom study, the results obtained using the proposed method agree with the measurements to within 2 mm in all three directions. In the clinical study, the mean error is 0.48 ± 0.46 mm for four patient data with 144 sequential images. In this work, a tissue feature-based tracking method for stereoscopic x-ray image guided radiation therapy is developed. The technique avoids the invasive procedure of fiducial implantation and may greatly facilitate the clinical workflow. (paper)

Azimuth and angle gathers from wave equation imaging in VTI media

KAUST Repository

Alkhalifah, Tariq Ali

2009-01-01

Angles in common-image angle domain gathers refer to the scattering angle at the reflector and provide a natural access to analyzing migration velocities and amplitudes. In the case of anisotropic media, the importance of angle gathers is enhanced by the need to properly estimate multiple anisotropic parameters for a proper representation of the medium. We extract angle gathers for each downward-continuation step from converting offset-space-frequency planes into angle-space planes simultaneously with applying the imaging condition in a transversely isotropic (VTI) medium. The analytic equations, though cumbersome, are exact within the framework of the acoustic approximation. They are also easily programmable and show that angle gather mapping in the case anisotropic media differs from its isotropic counterpart, difference depending mainly on the strength of anisotropy.

RESEARCH ON FOREST FLAME RECOGNITION ALGORITHM BASED ON IMAGE FEATURE

Directory of Open Access Journals (Sweden)

Z. Wang

2017-09-01

Full Text Available In recent years, fire recognition based on image features has become a hotspot in fire monitoring. However, due to the complexity of forest environment, the accuracy of forest fireworks recognition based on image features is low. Based on this, this paper proposes a feature extraction algorithm based on YCrCb color space and K-means clustering. Firstly, the paper prepares and analyzes the color characteristics of a large number of forest fire image samples. Using the K-means clustering algorithm, the forest flame model is obtained by comparing the two commonly used color spaces, and the suspected flame area is discriminated and extracted. The experimental results show that the extraction accuracy of flame area based on YCrCb color model is higher than that of HSI color model, which can be applied in different scene forest fire identification, and it is feasible in practice.

Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

Directory of Open Access Journals (Sweden)

Minh-Tan Pham

2017-10-01

Full Text Available A novel efficient method for content-based image retrieval (CBIR is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted from characteristic points (i.e., keypoints within the image. Then, dissimilarity measure between images is calculated based on the geometric distance between the topological feature spaces (i.e., manifolds formed by the sets of local descriptors generated from each image of the database. In this work, we propose to extract and use the local extrema pixels as our feature points. Then, the so-called local extrema-based descriptor (LED is generated for each keypoint by integrating all color, spatial as well as gradient information captured by its nearest local extrema. Hence, each image is encoded by an LED feature point cloud and Riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on several color texture databases including Vistex, STex, color Brodazt, USPtex and Outex TC-00013 using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods.

Application of contrast media in post-mortem imaging (CT and MRI).

Science.gov (United States)

Grabherr, Silke; Grimm, Jochen; Baumann, Pia; Mangin, Patrice

2015-09-01

The application of contrast media in post-mortem radiology differs from clinical approaches in living patients. Post-mortem changes in the vascular system and the absence of blood flow lead to specific problems that have to be considered for the performance of post-mortem angiography. In addition, interpreting the images is challenging due to technique-related and post-mortem artefacts that have to be known and that are specific for each applied technique. Although the idea of injecting contrast media is old, classic methods are not simply transferable to modern radiological techniques in forensic medicine, as they are mostly dedicated to single-organ studies or applicable only shortly after death. With the introduction of modern imaging techniques, such as post-mortem computed tomography (PMCT) and post-mortem magnetic resonance (PMMR), to forensic death investigations, intensive research started to explore their advantages and limitations compared to conventional autopsy. PMCT has already become a routine investigation in several centres, and different techniques have been developed to better visualise the vascular system and organ parenchyma in PMCT. In contrast, the use of PMMR is still limited due to practical issues, and research is now starting in the field of PMMR angiography. This article gives an overview of the problems in post-mortem contrast media application, the various classic and modern techniques, and the issues to consider by using different media.

Some features of South-Ukrainian NPP information centre's interaction with mass media in post Chernobyl period

International Nuclear Information System (INIS)

Kiyashko, S.; Kurilchik, N.

1995-01-01

Using the above methods of interaction with the off-site media helped the South Ukraine NPP information centre achieve meaningful results in restoring the image of nuclear power ad making it more trustworthy. This is supported by a review of media Publications since 1989. Quantitative and qualitative modifications of the information flow to the media have yielded substantial changes of the structure of news releases about the South Ukraine NPP and nuclear power. Background information has increased from 1. 5 to 50 percent. Negative information is no longer dominating, and the Chernobyl topic is counterbalanced with diverse NucNet materials about nuclear from throughout the world. (author)

Media Literacy as an Educational Method for Addressing College Women's Body Image Issues

Science.gov (United States)

Chambers, Karen L.; Alexander, Susan M.

2007-01-01

This study assesses the effectiveness of media literacy in the college classroom by comparing two modalities of learning, watching a video versus reading a text. The research questions guiding this project are: as teachers can we facilitate critical awareness among our students in order to alter the way women appropriate media images to evaluate…

Multi-example feature-constrained back-projection method for image super-resolution

Institute of Scientific and Technical Information of China (English)

Junlei Zhang; Dianguang Gai; Xin Zhang; Xuemei Li

2017-01-01

Example-based super-resolution algorithms,which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive k NN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image.Experimental results demonstrate that our method improves the visual quality of the high-resolution image.

Improved medical image modality classification using a combination of visual and textual features.

Science.gov (United States)

Dimitrovski, Ivica; Kocev, Dragi; Kitanovski, Ivan; Loskovska, Suzana; Džeroski, Sašo

2015-01-01

In this paper, we present the approach that we applied to the medical modality classification tasks at the ImageCLEF evaluation forum. More specifically, we used the modality classification databases from the ImageCLEF competitions in 2011, 2012 and 2013, described by four visual and one textual types of features, and combinations thereof. We used local binary patterns, color and edge directivity descriptors, fuzzy color and texture histogram and scale-invariant feature transform (and its variant opponentSIFT) as visual features and the standard bag-of-words textual representation coupled with TF-IDF weighting. The results from the extensive experimental evaluation identify the SIFT and opponentSIFT features as the best performing features for modality classification. Next, the low-level fusion of the visual features improves the predictive performance of the classifiers. This is because the different features are able to capture different aspects of an image, their combination offering a more complete representation of the visual content in an image. Moreover, adding textual features further increases the predictive performance. Finally, the results obtained with our approach are the best results reported on these databases so far. Copyright © 2014 Elsevier Ltd. All rights reserved.

Imaging features of multicentric Castleman's disease in HIV infection

International Nuclear Information System (INIS)

Hillier, J.C.; Shaw, P.; Miller, R.F.; Cartledge, J.D.; Nelson, M.; Bower, M.; Francis, N.; Padley, S.P.

2004-01-01

AIM: To describe the computed tomography (CT) features of human immunodeficiency virus (HIV)-associated Castleman's disease. MATERIALS AND METHODS: Nine HIV-positive patients with biopsy-proven Castleman's disease were studied. Clinical and demographic data, CD4 count, histological diagnosis and human herpes type 8 (HHV8) serology or immunostaining results were recorded. CT images were reviewed independently by two radiologists. RESULTS: CT findings included splenomegaly (n=7) and peripheral lymph node enlargement (axillary n=8, inguinal n=4). All nodes displayed mild to avid enhancement after intravenous administration of contrast material. Hepatomegaly was evident in seven patients. Other features included abdominal (n=6) and mediastinal (n=5) lymph node enlargement and pulmonary abnormalities (n=4). Patterns of parenchymal abnormality included bronchovascular nodularity (n=2), consolidation (n=1) and pleural effusion (n=2). On histological examination eight patients (spleen n=3, lymph node n=9, lung n=1 bone marrow n=1) had the plasma cell variant and one had mixed hyaline-vascular/plasma cell variant. The majority had either positive immunostaining for HHV8 or positive serology (n=8). CONCLUSION: Common imaging features of multicentric Castleman's disease in HIV infection are hepatosplenomegaly and peripheral lymph node enlargement. Although these imaging features may suggest the diagnosis in the appropriate clinical context, they lack specificity and so biopsy is needed for diagnosis. In distinction from multicentric Castleman's disease in other populations the plasma cell variant is most commonly encountered, splenomegaly is a universal feature and there is a strong association with Kaposi's sarcoma

Adaptive Colour Feature Identification in Image for Object Tracking

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Feng Su

2012-01-01

Full Text Available Identification and tracking of a moving object using computer vision techniques is important in robotic surveillance. In this paper, an adaptive colour filtering method is introduced for identifying and tracking a moving object appearing in image sequences. This filter is capable of automatically identifying the most salient colour feature of the moving object in the image and using this for a robot to track the object. The method enables the selected colour feature to adapt to surrounding condition when it is changed. A method of determining the region of interest of the moving target is also developed for the adaptive colour filter to extract colour information. Experimental results show that by using a camera mounted on a robot, the proposed methods can perform robustly in tracking a randomly moving object using adaptively selected colour features in a crowded environment.

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

Science.gov (United States)

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep

An adaptive clustering algorithm for image matching based on corner feature

Science.gov (United States)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-04-01

The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

Science.gov (United States)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

Curvature histogram features for retrieval of images of smooth 3D objects

International Nuclear Information System (INIS)

Zhdanov, I; Scherbakov, O; Potapov, A; Peterson, M

2014-01-01

We consider image features on the base of histograms of oriented gradients (HOG) with addition of contour curvature histogram (HOG-CH), and also compare it with results of known scale-invariant feature transform (SIFT) approach in application to retrieval of images of smooth 3D objects.

Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

Science.gov (United States)

Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

2013-04-01

In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

Bubble feature extracting based on image processing of coal flotation froth

Energy Technology Data Exchange (ETDEWEB)

Wang, F.; Wang, Y.; Lu, M.; Liu, W. [China University of Mining and Technology, Beijing (China). Dept of Chemical Engineering and Environment

2001-11-01

Using image processing the contrast ratio between the bubble on the surface of flotation froth and the image background was enhanced, and the edges of bubble were extracted. Thus a model about the relation between the statistic feature of the bubbles in the image and the cleaned coal can be established. It is feasible to extract the bubble by processing the froth image of coal flotation on the basis of analysing the shape of the bubble. By means of processing the 51 group images sampled from laboratory column, it is thought that the use of the histogram equalization of image gradation and the medium filtering can obviously improve the dynamic contrast range and the brightness of bubbles. Finally, the method of threshold value cut and the bubble edge detecting for extracting the bubble were also discussed to describe the bubble feature, such as size and shape, in the froth image and to distinguish the froth image of coal flotation. 6 refs., 3 figs.

Imaging features of maxillary osteoblastoma and its malignant transformation

International Nuclear Information System (INIS)

Ueno, Hiroshi; Ariji, Ei-ichiro; Tanaka, Takemasa; Kanda, Shigenobu; Mori, Shin-ichiro; Goto, Masaaki; Mizuno, Akio; Okabe, Haruo; Nakamura, Takashi

1994-01-01

We report two cases of osteoblastoma, one of them an unusual case in a 32-year-old woman in whom a maxillary tumor was confidently diagnosed as an osteoblastoma at the time of primary excision and subsequently transformed into an osteosarcoma 7 years after the onset of clinical symptoms. The other patient developed osteosarcoma arising in the maxilla, which was diagnosed 3 years after the primary excision and is very suggestive of malignant transformation in osteoblastoma. We present the radiological features, including computed tomographic and magnetic resonance imaging studies, of this unusual event of transformed tumor and compare imaging features of benign and dedifferentiated counterparts of this rare tumor complex. (orig.)

The content of social media's shared images about Ebola: a retrospective study.

Science.gov (United States)

Seltzer, E K; Jean, N S; Kramer-Golinkoff, E; Asch, D A; Merchant, R M

2015-09-01

Social media have strongly influenced awareness and perceptions of public health emergencies, but a considerable amount of social media content is now carried through images, rather than just text. This study's objective is to explore how image-sharing platforms are used for information dissemination in public health emergencies. Retrospective review of images posted on two popular image-sharing platforms to characterize public discourse about Ebola. Using the keyword '#ebola' we identified a 1% sample of images posted on Instagram and Flickr across two sequential weeks in November 2014. Images from both platforms were independently coded by two reviewers and characterized by themes. We reviewed 1217 images posted on Instagram and Flickr and identified themes. Nine distinct themes were identified. These included: images of health care workers and professionals [308 (25%)], West Africa [75 (6%)], the Ebola virus [59 (5%)], and artistic renderings of Ebola [64 (5%)]. Also identified were images with accompanying embedded text related to Ebola and associated: facts [68 (6%)], fears [40 (3%)], politics [46 (4%)], and jokes [284 (23%)]. Several [273 (22%)] images were unrelated to Ebola or its sequelae. Instagram images were primarily coded as jokes [255 (42%)] or unrelated [219 (36%)], while Flickr images primarily depicted health care workers and other professionals [281 (46%)] providing care or other services for prevention or treatment. Image sharing platforms are being used for information exchange about public health crises, like Ebola. Use differs by platform and discerning these differences can help inform future uses for health care professionals and researchers seeking to assess public fears and misinformation or provide targeted education/awareness interventions. Copyright © 2015 The Royal Institute of Public Health. All rights reserved.

An efficient fractal image coding algorithm using unified feature and DCT

International Nuclear Information System (INIS)

Zhou Yiming; Zhang Chao; Zhang Zengke

2009-01-01

Fractal image compression is a promising technique to improve the efficiency of image storage and image transmission with high compression ratio, however, the huge time consumption for the fractal image coding is a great obstacle to the practical applications. In order to improve the fractal image coding, efficient fractal image coding algorithms using a special unified feature and a DCT coder are proposed in this paper. Firstly, based on a necessary condition to the best matching search rule during fractal image coding, the fast algorithm using a special unified feature (UFC) is addressed, and it can reduce the search space obviously and exclude most inappropriate matching subblocks before the best matching search. Secondly, on the basis of UFC algorithm, in order to improve the quality of the reconstructed image, a DCT coder is combined to construct a hybrid fractal image algorithm (DUFC). Experimental results show that the proposed algorithms can obtain good quality of the reconstructed images and need much less time than the baseline fractal coding algorithm.

SAR Data Fusion Imaging Method Oriented to Target Feature Extraction

Directory of Open Access Journals (Sweden)

Yang Wei

2015-02-01

Full Text Available To deal with the difficulty for target outlines extracting precisely due to neglect of target scattering characteristic variation during the processing of high-resolution space-borne SAR data, a novel fusion imaging method is proposed oriented to target feature extraction. Firstly, several important aspects that affect target feature extraction and SAR image quality are analyzed, including curved orbit, stop-and-go approximation, atmospheric delay, and high-order residual phase error. Furthermore, the corresponding compensation methods are addressed as well. Based on the analysis, the mathematical model of SAR echo combined with target space-time spectrum is established for explaining the space-time-frequency change rule of target scattering characteristic. Moreover, a fusion imaging strategy and method under high-resolution and ultra-large observation angle range conditions are put forward to improve SAR quality by fusion processing in range-doppler and image domain. Finally, simulations based on typical military targets are used to verify the effectiveness of the fusion imaging method.

Migrant Image as an "Other Some" in the Russian Mass Media

Directory of Open Access Journals (Sweden)

Roman P. Bakanov

2017-11-01

Full Text Available This article reveals the main approaches and methods of forming a perception stereotype of the migrant image as "other some" in the Russian mass media. The authors of this article analyzed three federal print publications - "Rossiyskaya Gazeta", "Trud" and "Komsomolskaya Pravda", characterized as mass editions, which enabled to make the most complete coverage of several segments of the readership and to highlight the most widespread meaning of the "migrant" concept in the public consciousness. The study of the dynamics of using the "migrant" concept for the period of 2000-2013 in the Russian print media made it possible to conclude that there were shifts towards a positive assessment of "other some". If the "language of enmity" was used in the public consciousness to form the migrant image at the beginning of the period under investigation, which led to the consolidation of migrant phobia, then the situation changed by 2013 - the migrant image was more often associated with a victim of interethnic tension and corruption. The new migrant image is the image of a migrant who has not found a better life, but has adopted Russia as a second home and therefore trying to adopt new standards and living conditions and helping to solve many of the social and economic problems of Russian society.

DIFET: DISTRIBUTED FEATURE EXTRACTION TOOL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES

Directory of Open Access Journals (Sweden)

S. Eken

2017-11-01

Full Text Available In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

Research on Remote Sensing Image Classification Based on Feature Level Fusion

Science.gov (United States)

Yuan, L.; Zhu, G.

2018-04-01

Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

Research of image retrieval technology based on color feature

Science.gov (United States)

Fu, Yanjun; Jiang, Guangyu; Chen, Fengying

2009-10-01

Recently, with the development of the communication and the computer technology and the improvement of the storage technology and the capability of the digital image equipment, more and more image resources are given to us than ever. And thus the solution of how to locate the proper image quickly and accurately is wanted.The early method is to set up a key word for searching in the database, but now the method has become very difficult when we search much more picture that we need. In order to overcome the limitation of the traditional searching method, content based image retrieval technology was aroused. Now, it is a hot research subject.Color image retrieval is the important part of it. Color is the most important feature for color image retrieval. Three key questions on how to make use of the color characteristic are discussed in the paper: the expression of color, the abstraction of color characteristic and the measurement of likeness based on color. On the basis, the extraction technology of the color histogram characteristic is especially discussed. Considering the advantages and disadvantages of the overall histogram and the partition histogram, a new method based the partition-overall histogram is proposed. The basic thought of it is to divide the image space according to a certain strategy, and then calculate color histogram of each block as the color feature of this block. Users choose the blocks that contain important space information, confirming the right value. The system calculates the distance between the corresponding blocks that users choosed. Other blocks merge into part overall histograms again, and the distance should be calculated. Then accumulate all the distance as the real distance between two pictures. The partition-overall histogram comprehensive utilizes advantages of two methods above, by choosing blocks makes the feature contain more spatial information which can improve performance; the distances between partition-overall histogram

CT Imaging Features in the Characterization of Non-Growing Solid Pulmonary Nodules in Non-Smokers

International Nuclear Information System (INIS)

Perandini, Simone; Soardi, Gian Alberto; Motton, Massimiliano; Augelli, Raffaele; Zantedeschi, Lisa; Montemezzi, Stefania

2016-01-01

A disappearing or persistent solid pulmonary nodule is a neglected clinical entity that still poses serious interpretative issues to date. Traditional knowledge deriving from previous reports suggests particular features, such as smooth edges or regular shape, to be significantly associated with benignity. A large number of benign nodules are reported among smokers in lung cancer screening programmes. The aim of this single-center retrospective study was to correlate specific imaging features to verify if traditional knowledge as well as more recent acquisitions regarding benign SPNs can be considered reliable in a current case series of nodules collected in a non-smoker cohort of patients. Fifty-three solid SPNs proven as non-growing during follow-up imaging were analyzed with regard to their imaging features at thin-section CT, their predicted malignancy risk according to three major risk assessment models, minimum density analysis and contrast enhanced-CT in the relative subgroups of nodules which underwent such tests. Eleven nodules disappeared during follow-up, 29 showed volume loss and 16 had a VDT of 1121 days or higher. There were 48 nodules located peripherally (85.71%). Evaluation of the enhancement after contrast media (n=29) showed mean enhancement ±SD of 25.72±35.03 HU, median of 18 HU, ranging from 0 to 190 HU. Minimum density assessment (n=30) showed mean minimum HU ±SD of −28.27±47.86 HU, median of −25 HU, ranging from −144 to 68 HU. Mean malignancy risk ±SD was 15.05±26.69% for the BIMC model, 17.22±19.00% for the Mayo Clinic model and 19.07±33.16% for the Gurney’s model. Our analysis suggests caution in using traditional knowledge when dealing with current small solid peripheral indeterminate SPNs and highlights how quantitative growth at follow-up should be the cornerstone of characterization

Illumination invariant feature point matching for high-resolution planetary remote sensing images

Science.gov (United States)

Wu, Bo; Zeng, Hai; Hu, Han

2018-03-01

Despite its success with regular close-range and remote-sensing images, the scale-invariant feature transform (SIFT) algorithm is essentially not invariant to illumination differences due to the use of gradients for feature description. In planetary remote sensing imagery, which normally lacks sufficient textural information, salient regions are generally triggered by the shadow effects of keypoints, reducing the matching performance of classical SIFT. Based on the observation of dual peaks in a histogram of the dominant orientations of SIFT keypoints, this paper proposes an illumination-invariant SIFT matching method for high-resolution planetary remote sensing images. First, as the peaks in the orientation histogram are generally aligned closely with the sub-solar azimuth angle at the time of image collection, an adaptive suppression Gaussian function is tuned to level the histogram and thereby alleviate the differences in illumination caused by a changing solar angle. Next, the suppression function is incorporated into the original SIFT procedure for obtaining feature descriptors, which are used for initial image matching. Finally, as the distribution of feature descriptors changes after anisotropic suppression, and the ratio check used for matching and outlier removal in classical SIFT may produce inferior results, this paper proposes an improved matching procedure based on cross-checking and template image matching. The experimental results for several high-resolution remote sensing images from both the Moon and Mars, with illumination differences of 20°-180°, reveal that the proposed method retrieves about 40%-60% more matches than the classical SIFT method. The proposed method is of significance for matching or co-registration of planetary remote sensing images for their synergistic use in various applications. It also has the potential to be useful for flyby and rover images by integrating with the affine invariant feature detectors.

Diabetic mastopathy: Imaging features and the role of image-guided biopsy in its diagnosis

Energy Technology Data Exchange (ETDEWEB)

Kim, Jong Hyeon; Kim, Eun Kyung; Kim, Min Jung; Moon, Hee Jung; Yoon, Jung Hyun [Dept. of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul (Korea, Republic of)

2016-03-15

The goal of this study was to evaluate the imaging features of diabetic mastopathy (DMP) and the role of image-guided biopsy in its diagnosis. Two experienced radiologists retrospectively reviewed the mammographic and sonographic images of 19 pathologically confirmed DMP patients. The techniques and results of the biopsies performed in each patient were also reviewed. Mammograms showed negative findings in 78% of the patients. On ultrasonography (US), 13 lesions were seen as masses and six as non-mass lesions. The US features of the mass lesions were as follows: irregular shape (69%), oval shape (31%), indistinct margin (69%), angular margin (15%), microlobulated margin (8%), well-defined margin (8%), heterogeneous echogenicity (62%), hypoechoic echogenicity (38%), posterior shadowing (92%), parallel orientation (100%), the absence of calcifications (100%), and the absence of vascularity (100%). Based on the US findings, 17 lesions (89%) were classified as Breast Imaging Reporting and Data System category 4 and two (11%) as category 3. US-guided core biopsy was performed in 18 patients, and 10 (56%) were diagnosed with DMP on that basis. An additional vacuum-assisted biopsy was performed in seven patients and all were diagnosed with DMP. The US features of DMP were generally suspicious for malignancy, whereas the mammographic findings were often negative or showed only focal asymmetry. Core biopsy is an adequate method for initial pathological diagnosis. However, since it yields non-diagnostic results in a considerable number of cases, the evaluation of correlations between imaging and pathology plays an important role in the diagnostic process.

Diabetic mastopathy: Imaging features and the role of image-guided biopsy in its diagnosis

International Nuclear Information System (INIS)

Kim, Jong Hyeon; Kim, Eun Kyung; Kim, Min Jung; Moon, Hee Jung; Yoon, Jung Hyun

2016-01-01

The goal of this study was to evaluate the imaging features of diabetic mastopathy (DMP) and the role of image-guided biopsy in its diagnosis. Two experienced radiologists retrospectively reviewed the mammographic and sonographic images of 19 pathologically confirmed DMP patients. The techniques and results of the biopsies performed in each patient were also reviewed. Mammograms showed negative findings in 78% of the patients. On ultrasonography (US), 13 lesions were seen as masses and six as non-mass lesions. The US features of the mass lesions were as follows: irregular shape (69%), oval shape (31%), indistinct margin (69%), angular margin (15%), microlobulated margin (8%), well-defined margin (8%), heterogeneous echogenicity (62%), hypoechoic echogenicity (38%), posterior shadowing (92%), parallel orientation (100%), the absence of calcifications (100%), and the absence of vascularity (100%). Based on the US findings, 17 lesions (89%) were classified as Breast Imaging Reporting and Data System category 4 and two (11%) as category 3. US-guided core biopsy was performed in 18 patients, and 10 (56%) were diagnosed with DMP on that basis. An additional vacuum-assisted biopsy was performed in seven patients and all were diagnosed with DMP. The US features of DMP were generally suspicious for malignancy, whereas the mammographic findings were often negative or showed only focal asymmetry. Core biopsy is an adequate method for initial pathological diagnosis. However, since it yields non-diagnostic results in a considerable number of cases, the evaluation of correlations between imaging and pathology plays an important role in the diagnostic process

TU-F-CAMPUS-J-05: Effect of Uncorrelated Noise Texture On Computed Tomography Quantitative Image Features

International Nuclear Information System (INIS)

Oliver, J; Budzevich, M; Moros, E; Zhang, G; Hunt, D

2015-01-01

Purpose: To investigate the relationship between quantitative image features (i.e. radiomics) and statistical fluctuations (i.e. electronic noise) in clinical Computed Tomography (CT) using the standardized American College of Radiology (ACR) CT accreditation phantom and patient images. Methods: Three levels of uncorrelated Gaussian noise were added to CT images of phantom and patients (20) acquired in static mode and respiratory tracking mode. We calculated the noise-power spectrum (NPS) of the original CT images of the phantom, and of the phantom images with added Gaussian noise with means of 50, 80, and 120 HU. Concurrently, on patient images (original and noise-added images), image features were calculated: 14 shape, 19 intensity (1st order statistics from intensity volume histograms), 18 GLCM features (2nd order statistics from grey level co-occurrence matrices) and 11 RLM features (2nd order statistics from run-length matrices). These features provide the underlying structural information of the images. GLCM (size 128x128) was calculated with a step size of 1 voxel in 13 directions and averaged. RLM feature calculation was performed in 13 directions with grey levels binning into 128 levels. Results: Adding the electronic noise to the images modified the quality of the NPS, shifting the noise from mostly correlated to mostly uncorrelated voxels. The dramatic increase in noise texture did not affect image structure/contours significantly for patient images. However, it did affect the image features and textures significantly as demonstrated by GLCM differences. Conclusion: Image features are sensitive to acquisition factors (simulated by adding uncorrelated Gaussian noise). We speculate that image features will be more difficult to detect in the presence of electronic noise (an uncorrelated noise contributor) or, for that matter, any other highly correlated image noise. This work focuses on the effect of electronic, uncorrelated, noise and future work shall

Feature Recognition of Froth Images Based on Energy Distribution Characteristics

Directory of Open Access Journals (Sweden)

WU Yanpeng

2014-09-01

Full Text Available This paper proposes a determining algorithm for froth image features based on the amplitude spectrum energy statistics by applying Fast Fourier Transformation to analyze the energy distribution of various-sized froth. The proposed algorithm has been used to do a froth feature analysis of the froth images from the alumina flotation processing site, and the results show that the consistency rate reaches 98.1 % and the usability rate 94.2 %; with its good robustness and high efficiency, the algorithm is quite suitable for flotation processing state recognition.

Images of protest in social media: Struggle over visibility and visual narratives

DEFF Research Database (Denmark)

Neumayer, Christina; Rossi, Luca

2018-01-01

in the Blockupy protests against the opening of the European Central Bank headquarters in Frankfurt am Main on 18 March 2015. It does so through a novel combination of quantitative analysis, content analysis of images, and identification of narratives. The article concludes by arguing that the visual in political......While political protest is essentially a visual expression of dissent, both social movement research and media studies have thus far been hesitant to focus on visual social media data from protest events. This research explores the visual dimension (photos and videos) of Twitter communication...... protest in social media reproduces existing visualities and hierarchies rather than challenges them. This research enhances our conceptual understanding of how activists’ struggles play out in the visual and contributes to developing methods for empirical inquiry into visual social media content....

Edge enhancement and noise suppression for infrared image based on feature analysis

Science.gov (United States)

Jiang, Meng

2018-06-01

Infrared images are often suffering from background noise, blurred edges, few details and low signal-to-noise ratios. To improve infrared image quality, it is essential to suppress noise and enhance edges simultaneously. To realize it in this paper, we propose a novel algorithm based on feature analysis in shearlet domain. Firstly, as one of multi-scale geometric analysis (MGA), we introduce the theory and superiority of shearlet transform. Secondly, after analyzing the defects of traditional thresholding technique to suppress noise, we propose a novel feature extraction distinguishing image structures from noise well and use it to improve the traditional thresholding technique. Thirdly, with computing the correlations between neighboring shearlet coefficients, the feature attribute maps identifying the weak detail and strong edges are completed to improve the generalized unsharped masking (GUM). At last, experiment results with infrared images captured in different scenes demonstrate that the proposed algorithm suppresses noise efficiently and enhances image edges adaptively.

Feature selection from a facial image for distinction of sasang constitution.

Science.gov (United States)

Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun; Kim, Keun Ho

2009-09-01

Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here.

Image feature extraction based on the camouflage effectiveness evaluation

Science.gov (United States)

Yuan, Xin; Lv, Xuliang; Li, Ling; Wang, Xinzhu; Zhang, Zhi

2018-04-01

The key step of camouflage effectiveness evaluation is how to combine the human visual physiological features, psychological features to select effectively evaluation indexes. Based on the predecessors' camo comprehensive evaluation method, this paper chooses the suitable indexes combining with the image quality awareness, and optimizes those indexes combining with human subjective perception. Thus, it perfects the theory of index extraction.

AFM imaging of bacteria in liquid media immobilized on gelatin coated mica surfaces

Energy Technology Data Exchange (ETDEWEB)

Doktycz, M.J.; Sullivan, C.J.; Hoyt, P.R.; Pelletier, D.A.; Wu, S.; Allison, D.P

2003-10-15

Immobilization of particulates, especially biomolecules and cells, onto surfaces is critical for imaging with the atomic force microscope (AFM). In this paper, gelatin coated mica surfaces are shown to be suitable for immobilizing and imaging both gram positive, Staphylococcus aureus, and gram negative, Escherichia coli, bacteria in both air and liquid environments. Gelatin coated surfaces are shown to be superior to poly-L-lysine coated surfaces that are commonly used for the immobilization of cells. This cell immobilization technique is being developed primarily for live cell imaging of Rhodopseudomonas palustris. The genome of R. palustris has been sequenced and the organism is the target of intensive studies aimed at understanding genome function. Images of R. palustris grown both aerobically and anaerobically in liquid media are presented. Images in liquid media show the bacteria is rod shaped and smooth while images in air show marked irregularity and folding of the surface. Significant differences in the vertical dimension are also apparent with the height of the bacteria in liquid being substantially greater than images taken in air. In air immobilized bacterial flagella are clearly seen while in liquid this structure is not visible. Additionally, significant morphological differences are observed that depend on the method of bacterial growth.

Traveltime computation and imaging from rugged topography in 3D TTI media

Science.gov (United States)

Liu, Shaoyong; Wang, Huazhong; Yang, Qinyong; Fang, Wubao

2014-02-01

Foothill areas with rugged topography are of great potential for oil and gas seismic exploration, but subsurface imaging in these areas is very challenging. Seismic acquisition with larger offset and wider azimuth is necessary for seismic imaging in complex areas. However, the scale anisotropy in this case must be taken into account. To generalize the pre-stack depth migration (PSDM) to 3D transversely isotropic media with vertical symmetry axes (VTI) and tilted symmetry axes (TTI) from rugged topography, a new dynamic programming approach for the first-arrival traveltime computation method is proposed. The first-arrival time on every uniform mesh point is calculated based on Fermat's principle with simple calculus techniques and a systematic mapping scheme. In order to calculate the minimum traveltime, a set of nonlinear equations is solved on each mesh point, where the group velocity is determined by the group angle. Based on the new first-arrival time calculation method, the corresponding PSDM and migration velocity analysis workflow for 3D anisotropic media from rugged surface is developed. Numerical tests demonstrate that the proposed traveltime calculation method is effective in both VTI and TTI media. The migration results for 3D field data show that it is necessary to choose a smooth datum to remove the high wavenumber move-out components for PSDM with rugged topography and take anisotropy into account to achieve better images.

Thin plate spline feature point matching for organ surfaces in minimally invasive surgery imaging

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Lin, Bingxiong; Sun, Yu; Qian, Xiaoning

2013-03-01

Robust feature point matching for images with large view angle changes in Minimally Invasive Surgery (MIS) is a challenging task due to low texture and specular reflections in these images. This paper presents a new approach that can improve feature matching performance by exploiting the inherent geometric property of the organ surfaces. Recently, intensity based template image tracking using a Thin Plate Spline (TPS) model has been extended for 3D surface tracking with stereo cameras. The intensity based tracking is also used here for 3D reconstruction of internal organ surfaces. To overcome the small displacement requirement of intensity based tracking, feature point correspondences are used for proper initialization of the nonlinear optimization in the intensity based method. Second, we generate simulated images from the reconstructed 3D surfaces under all potential view positions and orientations, and then extract feature points from these simulated images. The obtained feature points are then filtered and re-projected to the common reference image. The descriptors of the feature points under different view angles are stored to ensure that the proposed method can tolerate a large range of view angles. We evaluate the proposed method with silicon phantoms and in vivo images. The experimental results show that our method is much more robust with respect to the view angle changes than other state-of-the-art methods.

Skull base chordoid meningioma: Imaging features and pathology

International Nuclear Information System (INIS)

Soo, Mark Y.S.; Gomes, Lavier; Ng, Thomas; Cruz, Malville Da; Dexter, Mark

2004-01-01

The clinical, imaging and pathological features of a skull base chordoid meningioma (CM) are described. The huge tumour resulted in obstructive hydrocephalus and partial erosion of the clivus such that a chordoma was suspected. The lesion's MRI findings were similar to those of a meningioma. Light microscopic, immunohistochemistry and ultrastructural features were diagnostic of CM. Chordoid meningioma is a rare subtype of meningioma and has a great tendency to recur should surgical resection be incomplete Copyright (2004) Blackwell Publishing Asia Pty Ltd

Estimating perception of scene layout properties from global image features.

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Ross, Michael G; Oliva, Aude

2010-01-08

The relationship between image features and scene structure is central to the study of human visual perception and computer vision, but many of the specifics of real-world layout perception remain unknown. We do not know which image features are relevant to perceiving layout properties, or whether those features provide the same information for every type of image. Furthermore, we do not know the spatial resolutions required for perceiving different properties. This paper describes an experiment and a computational model that provides new insights on these issues. Humans perceive the global spatial layout properties such as dominant depth, openness, and perspective, from a single image. This work describes an algorithm that reliably predicts human layout judgments. This model's predictions are general, not specific to the observers it trained on. Analysis reveals that the optimal spatial resolutions for determining layout vary with the content of the space and the property being estimated. Openness is best estimated at high resolution, depth is best estimated at medium resolution, and perspective is best estimated at low resolution. Given the reliability and simplicity of estimating the global layout of real-world environments, this model could help resolve perceptual ambiguities encountered by more detailed scene reconstruction schemas.

Focusing on media body ideal images triggers food intake among restrained eaters: a test of restraint theory and the elaboration likelihood model.

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Boyce, Jessica A; Kuijer, Roeline G

2014-04-01

Although research consistently shows that images of thin women in the media (media body ideals) affect women negatively (e.g., increased weight dissatisfaction and food intake), this effect is less clear among restrained eaters. The majority of experiments demonstrate that restrained eaters - identified with the Restraint Scale - consume more food than do other participants after viewing media body ideal images; whereas a minority of experiments suggest that such images trigger restrained eaters' dietary restraint. Weight satisfaction and mood results are just as variable. One reason for these inconsistent results might be that different methods of image exposure (e.g., slideshow vs. film) afford varying levels of attention. Therefore, we manipulated attention levels and measured participants' weight satisfaction and food intake. We based our hypotheses on the elaboration likelihood model and on restraint theory. We hypothesised that advertent (i.e., processing the images via central routes of persuasion) and inadvertent (i.e., processing the images via peripheral routes of persuasion) exposure would trigger differing degrees of weight dissatisfaction and dietary disinhibition among restrained eaters (cf. restraint theory). Participants (N = 174) were assigned to one of four conditions: advertent or inadvertent exposure to media or control images. The dependent variables were measured in a supposedly unrelated study. Although restrained eaters' weight satisfaction was not significantly affected by either media exposure condition, advertent (but not inadvertent) media exposure triggered restrained eaters' eating. These results suggest that teaching restrained eaters how to pay less attention to media body ideal images might be an effective strategy in media-literary interventions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Profiles of US and CT imaging features with a high probability of appendicitis

International Nuclear Information System (INIS)

Randen, A. van; Lameris, W.; Es, H.W. van; Hove, W. ten; Bouma, W.H.; Leeuwen, M.S. van; Keulen, E.M. van; Hulst, V.P.M. van der; Henneman, O.D.; Bossuyt, P.M.; Boermeester, M.A.; Stoker, J.

2010-01-01

To identify and evaluate profiles of US and CT features associated with acute appendicitis. Consecutive patients presenting with acute abdominal pain at the emergency department were invited to participate in this study. All patients underwent US and CT. Imaging features known to be associated with appendicitis, and an imaging diagnosis were prospectively recorded by two independent radiologists. A final diagnosis was assigned after 6 months. Associations between appendiceal imaging features and a final diagnosis of appendicitis were evaluated with logistic regression analysis. Appendicitis was assigned to 284 of 942 evaluated patients (30%). All evaluated features were associated with appendicitis. Imaging profiles were created after multivariable logistic regression analysis. Of 147 patients with a thickened appendix, local transducer tenderness and peri-appendiceal fat infiltration on US, 139 (95%) had appendicitis. On CT, 119 patients in whom the appendix was completely visualised, thickened, with peri-appendiceal fat infiltration and appendiceal enhancement, 114 had a final diagnosis of appendicitis (96%). When at least two of these essential features were present on US or CT, sensitivity was 92% (95% CI 89-96%) and 96% (95% CI 93-98%), respectively. Most patients with appendicitis can be categorised within a few imaging profiles on US and CT. When two of the essential features are present the diagnosis of appendicitis can be made accurately. (orig.)

Profiles of US and CT imaging features with a high probability of appendicitis

Energy Technology Data Exchange (ETDEWEB)

Randen, A. van; Lameris, W. [University of Amsterdam, Department of Radiology, Academic Medical Center, Amsterdam (Netherlands); University of Amsterdam, Department of Surgery, Academic Medical Center, Amsterdam (Netherlands); Es, H.W. van [St Antonius Hospital, Department of Radiology, Nieuwegein (Netherlands); Hove, W. ten; Bouma, W.H. [Gelre Hospitals, Department of Surgery, Apeldoorn (Netherlands); Leeuwen, M.S. van [University Medical Centre, Department of Radiology, Utrecht (Netherlands); Keulen, E.M. van [Tergooi Hospitals, Department of Radiology, Hilversum (Netherlands); Hulst, V.P.M. van der [Onze Lieve Vrouwe Gasthuis, Department of Radiology, Amsterdam (Netherlands); Henneman, O.D. [Bronovo Hospital, Department of Radiology, The Hague (Netherlands); Bossuyt, P.M. [University of Amsterdam, Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Academic Medical Center, Amsterdam (Netherlands); Boermeester, M.A. [University of Amsterdam, Department of Surgery, Academic Medical Center, Amsterdam (Netherlands); Stoker, J. [University of Amsterdam, Department of Radiology, Academic Medical Center, Amsterdam (Netherlands)

2010-07-15

To identify and evaluate profiles of US and CT features associated with acute appendicitis. Consecutive patients presenting with acute abdominal pain at the emergency department were invited to participate in this study. All patients underwent US and CT. Imaging features known to be associated with appendicitis, and an imaging diagnosis were prospectively recorded by two independent radiologists. A final diagnosis was assigned after 6 months. Associations between appendiceal imaging features and a final diagnosis of appendicitis were evaluated with logistic regression analysis. Appendicitis was assigned to 284 of 942 evaluated patients (30%). All evaluated features were associated with appendicitis. Imaging profiles were created after multivariable logistic regression analysis. Of 147 patients with a thickened appendix, local transducer tenderness and peri-appendiceal fat infiltration on US, 139 (95%) had appendicitis. On CT, 119 patients in whom the appendix was completely visualised, thickened, with peri-appendiceal fat infiltration and appendiceal enhancement, 114 had a final diagnosis of appendicitis (96%). When at least two of these essential features were present on US or CT, sensitivity was 92% (95% CI 89-96%) and 96% (95% CI 93-98%), respectively. Most patients with appendicitis can be categorised within a few imaging profiles on US and CT. When two of the essential features are present the diagnosis of appendicitis can be made accurately. (orig.)

“SELLING” SELF-IMAGE IN THE ERA OF NEW MEDIA

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Rulli Nasrullah

2014-02-01

Full Text Available The rise of citizen journalism gives the public an opportunity for producing news which has been previously controlled by the mass media institutions. In this case, the citizen acts not only a news consumer, but also as a news producer and consumer at the same time (prosumer. Based on these phenomena, this research tries to see how citizens carry out commodification. Analyses of computer-mediated communication with the media of Kompasiana reveal that space for citizen journalism has become a medium for self commodification in accordance with their target or values they want to achieve. The results of this research also show that the citizens used the media in order to gain personal motives such as to get material benefits or to build a self-image. In the level of media space, the facilities provided by Kompasiana like a description of profile and comment column can be used for prosumer activity. It is possible for the audience to employ this facility to promote the products or the users themselves as products. Based on research questions and findings in the field, some conclusions can be drawn. First, the presence of media citizen journalism no longer positions the audience as a passive audience entity, affected only by information produced by the media industry. Second, the audience is involved as a subject who gives information in citizen journalism for private interest such as for practical economic activity.

Multimodal Image Alignment via Linear Mapping between Feature Modalities.

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Jiang, Yanyun; Zheng, Yuanjie; Hou, Sujuan; Chang, Yuchou; Gee, James

2017-01-01

We propose a novel landmark matching based method for aligning multimodal images, which is accomplished uniquely by resolving a linear mapping between different feature modalities. This linear mapping results in a new measurement on similarity of images captured from different modalities. In addition, our method simultaneously solves this linear mapping and the landmark correspondences by minimizing a convex quadratic function. Our method can estimate complex image relationship between different modalities and nonlinear nonrigid spatial transformations even in the presence of heavy noise, as shown in our experiments carried out by using a variety of image modalities.

Hyperspectral Image Classification Based on the Combination of Spatial-spectral Feature and Sparse Representation

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YANG Zhaoxia

2015-07-01

Full Text Available In order to avoid the problem of being over-dependent on high-dimensional spectral feature in the traditional hyperspectral image classification, a novel approach based on the combination of spatial-spectral feature and sparse representation is proposed in this paper. Firstly, we extract the spatial-spectral feature by reorganizing the local image patch with the first d principal components(PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Secondly, we learn the dictionary through a supervised method, and use it to code the features from test samples afterwards. Finally, we embed the resulting sparse feature coding into the support vector machine(SVM for hyperspectral image classification. Experiments using three hyperspectral data show that the proposed method can effectively improve the classification accuracy comparing with traditional classification methods.

Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models

International Nuclear Information System (INIS)

Khalvati, Farzad; Wong, Alexander; Haider, Masoom A.

2015-01-01

Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data. In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models. The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy. Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a

Feature Tracking for High Speed AFM Imaging of Biopolymers.

Science.gov (United States)

Hartman, Brett; Andersson, Sean B

2018-03-31

The scanning speed of atomic force microscopes continues to advance with some current commercial microscopes achieving on the order of one frame per second and at least one reaching 10 frames per second. Despite the success of these instruments, even higher frame rates are needed with scan ranges larger than are currently achievable. Moreover, there is a significant installed base of slower instruments that would benefit from algorithmic approaches to increasing their frame rate without requiring significant hardware modifications. In this paper, we present an experimental demonstration of high speed scanning on an existing, non-high speed instrument, through the use of a feedback-based, feature-tracking algorithm that reduces imaging time by focusing on features of interest to reduce the total imaging area. Experiments on both circular and square gratings, as well as silicon steps and DNA strands show a reduction in imaging time by a factor of 3-12 over raster scanning, depending on the parameters chosen.

Features and limitations of mobile tablet devices for viewing radiological images.

Science.gov (United States)

Grunert, J H

2015-03-01

Mobile radiological image display systems are becoming increasingly common, necessitating a comparison of the features of these systems, specifically the operating system employed, connection to stationary PACS, data security and rang of image display and image analysis functions. In the fall of 2013, a total of 17 PACS suppliers were surveyed regarding the technical features of 18 mobile radiological image display systems using a standardized questionnaire. The study also examined to what extent the technical specifications of the mobile image display systems satisfy the provisions of the Germany Medical Devices Act as well as the provisions of the German X-ray ordinance (RöV). There are clear differences in terms of how the mobile systems connected to the stationary PACS. Web-based solutions allow the mobile image display systems to function independently of their operating systems. The examined systems differed very little in terms of image display and image analysis functions. Mobile image display systems complement stationary PACS and can be used to view images. The impacts of the new quality assurance guidelines (QS-RL) as well as the upcoming new standard DIN 6868 - 157 on the acceptance testing of mobile image display units for the purpose of image evaluation are discussed. © Georg Thieme Verlag KG Stuttgart · New York.

Image features dependant correlation-weighting function for efficient PRNU based source camera identification.

Science.gov (United States)

Tiwari, Mayank; Gupta, Bhupendra

2018-04-01

For source camera identification (SCI), photo response non-uniformity (PRNU) has been widely used as the fingerprint of the camera. The PRNU is extracted from the image by applying a de-noising filter then taking the difference between the original image and the de-noised image. However, it is observed that intensity-based features and high-frequency details (edges and texture) of the image, effect quality of the extracted PRNU. This effects correlation calculation and creates problems in SCI. For solving this problem, we propose a weighting function based on image features. We have experimentally identified image features (intensity and high-frequency contents) effect on the estimated PRNU, and then develop a weighting function which gives higher weights to image regions which give reliable PRNU and at the same point it gives comparatively less weights to the image regions which do not give reliable PRNU. Experimental results show that the proposed weighting function is able to improve the accuracy of SCI up to a great extent. Copyright © 2018 Elsevier B.V. All rights reserved.

Robust and efficient method for matching features in omnidirectional images

Science.gov (United States)

Zhu, Qinyi; Zhang, Zhijiang; Zeng, Dan

2018-04-01

Binary descriptors have been widely used in many real-time applications due to their efficiency. These descriptors are commonly designed for perspective images but perform poorly on omnidirectional images, which are severely distorted. To address this issue, this paper proposes tangent plane BRIEF (TPBRIEF) and adapted log polar grid-based motion statistics (ALPGMS). TPBRIEF projects keypoints to a unit sphere and applies the fixed test set in BRIEF descriptor on the tangent plane of the unit sphere. The fixed test set is then backprojected onto the original distorted images to construct the distortion invariant descriptor. TPBRIEF directly enables keypoint detecting and feature describing on original distorted images, whereas other approaches correct the distortion through image resampling, which introduces artifacts and adds time cost. With ALPGMS, omnidirectional images are divided into circular arches named adapted log polar grids. Whether a match is true or false is then determined by simply thresholding the match numbers in a grid pair where the two matched points located. Experiments show that TPBRIEF greatly improves the feature matching accuracy and ALPGMS robustly removes wrong matches. Our proposed method outperforms the state-of-the-art methods.

MR imaging features of hemispherical spondylosclerosis

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Vicentini, Joao R.T.; Martinez-Salazar, Edgar L.; Chang, Connie Y.; Bredella, Miriam A.; Rosenthal, Daniel I.; Torriani, Martin [Massachusetts General Hospital and Harvard Medical School, Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Boston, MA (United States)

2017-10-15

Hemispherical spondylosclerosis (HS) is a rare degenerative entity characterized by dome-shaped sclerosis of a single vertebral body that may pose a diagnostic dilemma. The goal of this study was to describe the MR imaging features of HS. We identified spine radiographs and CT examinations of subjects with HS who also had MR imaging for correlation. Two musculoskeletal radiologists independently assessed sclerosis characteristics, presence of endplate erosions, marrow signal intensity, and disk degeneration (Pfirrmann scale). We identified 11 subjects (six males, five females, mean 48 ± 10 years) with radiographic/CT findings of HS. The most commonly affected vertebral body was L4 (6/11; 55%). On MR imaging, variable signal intensity was noted, being most commonly low on T1 (8/11, 73%) and high on fat-suppressed T2-weighted (8/11, 73%) images. In two subjects, diffuse post-contrast enhancement was seen in the lesion. Moderate disk degeneration and endplate bone erosions adjacent to sclerosis were present in all subjects. Erosions of the opposite endplate were present in two subjects (2/11, 18%). CT data from nine subjects showed the mean attenuation value of HS was 472 ± 96 HU. HS appearance on MR imaging is variable and may not correlate with the degree of sclerosis seen on radiographs or CT. Disk degenerative changes and asymmetric endplate erosions are consistent markers of HS. (orig.)

Feature Detection of Curve Traffic Sign Image on The Bandung - Jakarta Highway

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Naseer, M.; Supriadi, I.; Supangkat, S. H.

2018-03-01

Unsealed roadside and problems with the road surface are common causes of road crashes, particularly when those are combined with curves. Curve traffic sign is an important component for giving early warning to driver on traffic, especially on high-speed traffic like on the highway. Traffic sign detection has became a very interesting research now, and in this paper will be discussed about the detection of curve traffic sign. There are two types of curve signs are discussed, namely the curve turn to the left and the curve turn to the right and the all data sample used are the curves taken / recorded from some signs on the Bandung - Jakarta Highway. Feature detection of the curve signs use Speed Up Robust Feature (SURF) method, where the detected scene image is 800x450. From 45 curve turn to the right images, the system can detect the feature well to 35 images, where the success rate is 77,78%, while from the 45 curve turn to the left images, the system can detect the feature well to 34 images and the success rate is 75,56%, so the average accuracy in the detection process is 76,67%. While the average time for the detection process is 0.411 seconds.

Pinterest or Thinterest?: Social Comparison and Body Image on Social Media

Directory of Open Access Journals (Sweden)

Jennifer Lewallen

2016-03-01

Full Text Available Social media have become increasingly popular mechanisms for communication. Past research suggests a link between using social media, upward social comparison, and negative affect. This online experiment of US women ( N  = 118 takes a media psychology approach to understanding how fitness images on the social networking website Pinterest contribute to social comparison as well as intentions to engage in extreme weight-loss behaviors. Findings suggest that individuals who follow more fitness boards on Pinterest are more likely to report intentions to engage in extreme weight-loss behaviors. Additionally, endorsement of an ideal female body type was positively related to both social comparison and intentions to engage in extreme weight-loss behaviors. Findings are discussed in light of social comparison theory, and suggestions are made are made for future experimental work.

The Feature Extraction Based on Texture Image Information for Emotion Sensing in Speech

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Kun-Ching Wang

2014-09-01

Full Text Available In this paper, we present a novel texture image feature for Emotion Sensing in Speech (ESS. This idea is based on the fact that the texture images carry emotion-related information. The feature extraction is derived from time-frequency representation of spectrogram images. First, we transform the spectrogram as a recognizable image. Next, we use a cubic curve to enhance the image contrast. Then, the texture image information (TII derived from the spectrogram image can be extracted by using Laws’ masks to characterize emotional state. In order to evaluate the effectiveness of the proposed emotion recognition in different languages, we use two open emotional databases including the Berlin Emotional Speech Database (EMO-DB and eNTERFACE corpus and one self-recorded database (KHUSC-EmoDB, to evaluate the performance cross-corpora. The results of the proposed ESS system are presented using support vector machine (SVM as a classifier. Experimental results show that the proposed TII-based feature extraction inspired by visual perception can provide significant classification for ESS systems. The two-dimensional (2-D TII feature can provide the discrimination between different emotions in visual expressions except for the conveyance pitch and formant tracks. In addition, the de-noising in 2-D images can be more easily completed than de-noising in 1-D speech.

Significance of the impact of motion compensation on the variability of PET image features

Science.gov (United States)

Carles, M.; Bach, T.; Torres-Espallardo, I.; Baltas, D.; Nestle, U.; Martí-Bonmatí, L.

2018-03-01

In lung cancer, quantification by positron emission tomography/computed tomography (PET/CT) imaging presents challenges due to respiratory movement. Our primary aim was to study the impact of motion compensation implied by retrospectively gated (4D)-PET/CT on the variability of PET quantitative parameters. Its significance was evaluated by comparison with the variability due to (i) the voxel size in image reconstruction and (ii) the voxel size in image post-resampling. The method employed for feature extraction was chosen based on the analysis of (i) the effect of discretization of the standardized uptake value (SUV) on complementarity between texture features (TF) and conventional indices, (ii) the impact of the segmentation method on the variability of image features, and (iii) the variability of image features across the time-frame of 4D-PET. Thirty-one PET-features were involved. Three SUV discretization methods were applied: a constant width (SUV resolution) of the resampling bin (method RW), a constant number of bins (method RN) and RN on the image obtained after histogram equalization (method EqRN). The segmentation approaches evaluated were 40% of SUVmax and the contrast oriented algorithm (COA). Parameters derived from 4D-PET images were compared with values derived from the PET image obtained for (i) the static protocol used in our clinical routine (3D) and (ii) the 3D image post-resampled to the voxel size of the 4D image and PET image derived after modifying the reconstruction of the 3D image to comprise the voxel size of the 4D image. Results showed that TF complementarity with conventional indices was sensitive to the SUV discretization method. In the comparison of COA and 40% contours, despite the values not being interchangeable, all image features showed strong linear correlations (r  >  0.91, p\\ll 0.001 ). Across the time-frames of 4D-PET, all image features followed a normal distribution in most patients. For our patient cohort, the

Comparison on imaging features of central serous chorioretinopathy fundus

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Ji-Jin Zhang

2014-10-01

Full Text Available AIM: To explore and analyze the image features, diagnosis and treatment of the central serous chorioretinopathy(CSCRfundus. METHODS: From May 2008 to May 2014, 97 cases of 121 eyes with central serous chorioretinopathy were treated in in our hospital. The imaging features were compared and analyzed through different methods. RESULTS: Sixty-one cases(61 eyeswere ≤45 years, including 13 case with disease in both eyes, single stove leak accounted for 48.6%, multifocal leakage(25.7%, atypical leakage accounted for 25.7%. Thirty-six cases(47 eyeswere >45 years, 11 cases with disease in both eyes, single focal leakage(8.5%, multifocal leakage(48.9%, atypical leakage accounted for 42.6%. FFA results showed acute hairstyle at the beginning of 89 eyes, chronic deferment type 32 eyes. OCT examination showed that the main features were neuroepithelial detachment, as well as the change of the retinal pigment epithelium(RPElayer, which was divided into RPE layer detachment 93 eyes, accounting for 76.9%, rough and RPE little ridges in 28 cases, accounting for 23.1%. The average thickness of macular center concave on the cortex of microns was 137.87±19.21μm, and there was no significant difference conpared with normal(137.32±4.98μmmicrons(t=0.30, P>0.05. The closer leakage area to macular fovea, the worse of eyesight.. CONCLUSION: Different imaging examination on central serous chorioretinopathy can show different features. For clinical diagnosis and treatment it had different and complementary roles, but were given significant help for diseases treatment.

Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality

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Fang Yang

2017-01-01

Full Text Available Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and K-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer.

Three-dimensional imaging through turbid media based on polarization-difference liquid-crystal microlens array

Science.gov (United States)

Xin, Zhaowei; Wei, Dong; Li, Dapeng; Xie, Xingwang; Chen, Mingce; Zhang, Xinyu; Wang, Haiwei; Xie, Changsheng

2018-02-01

In this paper, a polarization difference liquid-crystal microlens array (PD-LCMLA) for three dimensional imaging application through turbid media is fabricated and demonstrated. This device is composed of a twisted nematic liquidcrystal cell (TNLCC), a polarizer and a liquid-crystal microlens array. The polarizer is sandwiched between the TNLCC and LCMLA to help the polarization difference system achieving the orthogonal polarization raw images. The prototyped camera for polarization difference imaging has been constructed by integrating the PD-LCMLA with an image sensor. The orthogonally polarized light-field images are recorded by switching the working state of the TNLCC. Here, by using a special microstructure in conjunction with the polarization-difference algorithm, we demonstrate that the three-dimensional information in the scattering media can be retrieved from the polarization-difference imaging system with an electrically tunable PD-LCMLA. We further investigate the system's potential function based on the flexible microstructure. The microstructure provides a wide operation range in the manipulation of incident beams and also emerges multiple operation modes for imaging applications, such as conventional planar imaging, polarization imaging mode, and polarization-difference imaging mode. Since the PD-LCMLA demonstrates a very low power consumption, multiple imaging modes and simple manufacturing, this kind of device presents a potential to be used in many other optical and electro-optical systems.

Medical Image Fusion Algorithm Based on Nonlinear Approximation of Contourlet Transform and Regional Features

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Hui Huang

2017-01-01

Full Text Available According to the pros and cons of contourlet transform and multimodality medical imaging, here we propose a novel image fusion algorithm that combines nonlinear approximation of contourlet transform with image regional features. The most important coefficient bands of the contourlet sparse matrix are retained by nonlinear approximation. Low-frequency and high-frequency regional features are also elaborated to fuse medical images. The results strongly suggested that the proposed algorithm could improve the visual effects of medical image fusion and image quality, image denoising, and enhancement.

Feature Selection from a Facial Image for Distinction of Sasang Constitution

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Imhoi Koo

2009-01-01

Full Text Available Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here.

Feature Selection from a Facial Image for Distinction of Sasang Constitution

Science.gov (United States)

Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun

2009-01-01

Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here. PMID:19745013

Fast hybrid fractal image compression using an image feature and neural network

International Nuclear Information System (INIS)

Zhou Yiming; Zhang Chao; Zhang Zengke

2008-01-01

Since fractal image compression could maintain high-resolution reconstructed images at very high compression ratio, it has great potential to improve the efficiency of image storage and image transmission. On the other hand, fractal image encoding is time consuming for the best matching search between range blocks and domain blocks, which limits the algorithm to practical application greatly. In order to solve this problem, two strategies are adopted to improve the fractal image encoding algorithm in this paper. Firstly, based on the definition of an image feature, a necessary condition of the best matching search and FFC algorithm are proposed, and it could reduce the search space observably and exclude most inappropriate domain blocks according to each range block before the best matching search. Secondly, on the basis of FFC algorithm, in order to reduce the mapping error during the best matching search, a special neural network is constructed to modify the mapping scheme for the subblocks, in which the pixel values fluctuate greatly (FNFC algorithm). Experimental results show that the proposed algorithms could obtain good quality of the reconstructed images and need much less time than the baseline encoding algorithm

A roughly mapped terra incognita: Image of the child in adult-oriented media contents

Directory of Open Access Journals (Sweden)

Korać Nada M.

2003-01-01

Full Text Available The study analyzes the image of the child in the media contents intended for adult audiences in Serbia, considering the importance of the role media play in shaping public opinion on children, as well as the influence of such public opinion on adults' attitudes, decisions and actions concerning children. The study focuses on visibility and portrayal of children in the media, in order to determine to what extent children are present in them, and in what way. Relevant data were collected for three media - press, radio and television - mostly covering the entire territory of Serbia, over two consecutive months (April - May 2001. Content analysis revealed that children are not only underrepresented, but also misrepresented, in Serbian media.

Indications and radiological findings of acute otitis media and its complications.

Science.gov (United States)

Pont, Elena; Mazón, Miguel

Most cases of acute otitis media resolve with antibiotics and imaging is not required. When treatment fails or a complication is suspected, imaging plays a crucial role. Since the introduction of antibiotic treatment, the complication rate has decreased dramatically. Nevertheless, given the critical clinical relevance of complications, the importance of early diagnosis is vital. Our objective was to review the clinical and radiological features of acute otitis media and its complications. They were classified based on their location, as intratemporal or intracranial. Imaging makes it possible to diagnose the complications of acute otitis media and to institute appropriate treatment. Computed tomography is the initial technique of choice and, in most cases, the ultimate. Magnetic resonance is useful for evaluating the inner ear and when accurate evaluation of disease extent or better characterization of intracranial complications is required. Copyright © 2016 Elsevier España, S.L.U. and Sociedad Española de Otorrinolaringología y Cirugía de Cabeza y Cuello. All rights reserved.

Extended feature-fusion guidelines to improve image-based multi-modal biometrics

CSIR Research Space (South Africa)

Brown, Dane

2016-09-01

Full Text Available The feature-level, unlike the match score-level, lacks multi-modal fusion guidelines. This work demonstrates a practical approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint...

Primary Neuroendocrine Tumor of the Breast: Imaging Features

International Nuclear Information System (INIS)

Chang, Eun Deok; Kim, Min Kyun; Kim, Jeong Soo; Whang, In Yong

2013-01-01

Focal neuroendocrine differentiation can be found in diverse histological types of breast tumors. However, the term, neuroendocrine breast tumor, indicates the diffuse expression of neuroendocrine markers in more than 50% of the tumor cell population. The imaging features of neuroendocrine breast tumor have not been accurately described due to extreme rarity of this tumor type. We present a case of a pathologically confirmed, primary neuroendocrine breast tumor in a 42-year-old woman, with imaging findings difficult to be differentiated from that of invasive ductal carcinoma

a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

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Li, L.; Yang, H.; Chen, Q.; Liu, X.

2018-04-01

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

Optimization of wavelet decomposition for image compression and feature preservation.

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Lo, Shih-Chung B; Li, Huai; Freedman, Matthew T

2003-09-01

A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet whose low-pass filter coefficients are 0.32252136, 0.85258927, 1.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.

Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

International Nuclear Information System (INIS)

Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin; Hollingsworth, Alan B.; Qian, Wei

2015-01-01

Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy

Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

Energy Technology Data Exchange (ETDEWEB)

Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin, E-mail: Bin.Zheng-1@ou.edu [School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019 (United States); Hollingsworth, Alan B. [Mercy Women’s Center, Mercy Health Center, Oklahoma City, Oklahoma 73120 (United States); Qian, Wei [Department of Electrical and Computer Engineering, University of Texas, El Paso, Texas 79968 (United States)

2015-11-15

Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.

Enhancement and feature extraction of RS images from seismic area and seismic disaster recognition technologies

Science.gov (United States)

Zhang, Jingfa; Qin, Qiming

2003-09-01

Many types of feature extracting of RS image are analyzed, and the work procedure of pattern recognizing in RS images of seismic disaster is proposed. The aerial RS image of Tangshan Great Earthquake is processed, and the digital features of various typical seismic disaster on the RS image is calculated.

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

Science.gov (United States)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

Human gait recognition by pyramid of HOG feature on silhouette images

Science.gov (United States)

Yang, Guang; Yin, Yafeng; Park, Jeanrok; Man, Hong

2013-03-01

As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.

Traveltime computation and imaging from rugged topography in 3D TTI media

International Nuclear Information System (INIS)