Social Image Tag Ranking by Two-View Learning

NASA Astrophysics Data System (ADS)

Zhuang, Jinfeng; Hoi, Steven C. H.

Tags play a central role in text-based social image retrieval and browsing. However, the tags annotated by web users could be noisy, irrelevant, and often incomplete for describing the image contents, which may severely deteriorate the performance of text-based image retrieval models. In order to solve this problem, researchers have proposed techniques to rank the annotated tags of a social image according to their relevance to the visual content of the image. In this paper, we aim to overcome the challenge of social image tag ranking for a corpus of social images with rich user-generated tags by proposing a novel two-view learning approach. It can effectively exploit both textual and visual contents of social images to discover the complicated relationship between tags and images. Unlike the conventional learning approaches that usually assumes some parametric models, our method is completely data-driven and makes no assumption about the underlying models, making the proposed solution practically more effective. We formulate our method as an optimization task and present an efficient algorithm to solve it. To evaluate the efficacy of our method, we conducted an extensive set of experiments by applying our technique to both text-based social image retrieval and automatic image annotation tasks. Our empirical results showed that the proposed method can be more effective than the conventional approaches.

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

PubMed

Piccinini, Filippo; Balassa, Tamas; Szkalisity, Abel; Molnar, Csaba; Paavolainen, Lassi; Kujala, Kaisa; Buzas, Krisztina; Sarazova, Marie; Pietiainen, Vilja; Kutay, Ulrike; Smith, Kevin; Horvath, Peter

2017-06-28

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright © 2017 Elsevier Inc. All rights reserved.

MetaSEEk: a content-based metasearch engine for images

NASA Astrophysics Data System (ADS)

Beigi, Mandis; Benitez, Ana B.; Chang, Shih-Fu

1997-12-01

Search engines are the most powerful resources for finding information on the rapidly expanding World Wide Web (WWW). Finding the desired search engines and learning how to use them, however, can be very time consuming. The integration of such search tools enables the users to access information across the world in a transparent and efficient manner. These systems are called meta-search engines. The recent emergence of visual information retrieval (VIR) search engines on the web is leading to the same efficiency problem. This paper describes and evaluates MetaSEEk, a content-based meta-search engine used for finding images on the Web based on their visual information. MetaSEEk is designed to intelligently select and interface with multiple on-line image search engines by ranking their performance for different classes of user queries. User feedback is also integrated in the ranking refinement. We compare MetaSEEk with a base line version of meta-search engine, which does not use the past performance of the different search engines in recommending target search engines for future queries.

Using an image-extended relational database to support content-based image retrieval in a PACS.

PubMed

Traina, Caetano; Traina, Agma J M; Araújo, Myrian R B; Bueno, Josiane M; Chino, Fabio J T; Razente, Humberto; Azevedo-Marques, Paulo M

2005-12-01

This paper presents a new Picture Archiving and Communication System (PACS), called cbPACS, which has content-based image retrieval capabilities. The cbPACS answers range and k-nearest- neighbor similarity queries, employing a relational database manager extended to support images. The images are compared through their features, which are extracted by an image-processing module and stored in the extended relational database. The database extensions were developed aiming at efficiently answering similarity queries by taking advantage of specialized indexing methods. The main concept supporting the extensions is the definition, inside the relational manager, of distance functions based on features extracted from the images. An extension to the SQL language enables the construction of an interpreter that intercepts the extended commands and translates them to standard SQL, allowing any relational database server to be used. By now, the system implemented works on features based on color distribution of the images through normalized histograms as well as metric histograms. Metric histograms are invariant regarding scale, translation and rotation of images and also to brightness transformations. The cbPACS is prepared to integrate new image features, based on texture and shape of the main objects in the image.

A Double-function Digital Watermarking Algorithm Based on Chaotic System and LWT

NASA Astrophysics Data System (ADS)

Yuxia, Zhao; Jingbo, Fan

A double- function digital watermarking technology is studied and a double-function digital watermarking algorithm of colored image is presented based on chaotic system and the lifting wavelet transformation (LWT).The algorithm has realized the double aims of the copyright protection and the integrity authentication of image content. Making use of feature of human visual system (HVS), the watermark image is embedded into the color image's low frequency component and middle frequency components by different means. The algorithm has great security by using two kinds chaotic mappings and Arnold to scramble the watermark image at the same time. The algorithm has good efficiency by using LWT. The emulation experiment indicates the algorithm has great efficiency and security, and the effect of concealing is really good.

Deeply learnt hashing forests for content based image retrieval in prostate MR images

NASA Astrophysics Data System (ADS)

Shah, Amit; Conjeti, Sailesh; Navab, Nassir; Katouzian, Amin

2016-03-01

Deluge in the size and heterogeneity of medical image databases necessitates the need for content based retrieval systems for their efficient organization. In this paper, we propose such a system to retrieve prostate MR images which share similarities in appearance and content with a query image. We introduce deeply learnt hashing forests (DL-HF) for this image retrieval task. DL-HF effectively leverages the semantic descriptiveness of deep learnt Convolutional Neural Networks. This is used in conjunction with hashing forests which are unsupervised random forests. DL-HF hierarchically parses the deep-learnt feature space to encode subspaces with compact binary code words. We propose a similarity preserving feature descriptor called Parts Histogram which is derived from DL-HF. Correlation defined on this descriptor is used as a similarity metric for retrieval from the database. Validations on publicly available multi-center prostate MR image database established the validity of the proposed approach. The proposed method is fully-automated without any user-interaction and is not dependent on any external image standardization like image normalization and registration. This image retrieval method is generalizable and is well-suited for retrieval in heterogeneous databases other imaging modalities and anatomies.

Compressive hyperspectral time-resolved wide-field fluorescence lifetime imaging

NASA Astrophysics Data System (ADS)

Pian, Qi; Yao, Ruoyang; Sinsuebphon, Nattawut; Intes, Xavier

2017-07-01

Spectrally resolved fluorescence lifetime imaging and spatial multiplexing have offered information content and collection-efficiency boosts in microscopy, but efficient implementations for macroscopic applications are still lacking. An imaging platform based on time-resolved structured light and hyperspectral single-pixel detection has been developed to perform quantitative macroscopic fluorescence lifetime imaging (MFLI) over a large field of view (FOV) and multiple spectral bands simultaneously. The system makes use of three digital micromirror device (DMD)-based spatial light modulators (SLMs) to generate spatial optical bases and reconstruct N by N images over 16 spectral channels with a time-resolved capability (∼40 ps temporal resolution) using fewer than N2 optical measurements. We demonstrate the potential of this new imaging platform by quantitatively imaging near-infrared (NIR) Förster resonance energy transfer (FRET) both in vitro and in vivo. The technique is well suited for quantitative hyperspectral lifetime imaging with a high sensitivity and paves the way for many important biomedical applications.

Efficient content-based low-altitude images correlated network and strips reconstruction

NASA Astrophysics Data System (ADS)

He, Haiqing; You, Qi; Chen, Xiaoyong

2017-01-01

The manual intervention method is widely used to reconstruct strips for further aerial triangulation in low-altitude photogrammetry. Clearly the method for fully automatic photogrammetric data processing is not an expected way. In this paper, we explore a content-based approach without manual intervention or external information for strips reconstruction. Feature descriptors in the local spatial patterns are extracted by SIFT to construct vocabulary tree, in which these features are encoded in terms of TF-IDF numerical statistical algorithm to generate new representation for each low-altitude image. Then images correlated network is reconstructed by similarity measure, image matching and geometric graph theory. Finally, strips are reconstructed automatically by tracing straight lines and growing adjacent images gradually. Experimental results show that the proposed approach is highly effective in automatically rearranging strips of lowaltitude images and can provide rough relative orientation for further aerial triangulation.

Three-dimensional holoscopic image coding scheme using high-efficiency video coding with kernel-based minimum mean-square-error estimation

NASA Astrophysics Data System (ADS)

Liu, Deyang; An, Ping; Ma, Ran; Yang, Chao; Shen, Liquan; Li, Kai

2016-07-01

Three-dimensional (3-D) holoscopic imaging, also known as integral imaging, light field imaging, or plenoptic imaging, can provide natural and fatigue-free 3-D visualization. However, a large amount of data is required to represent the 3-D holoscopic content. Therefore, efficient coding schemes for this particular type of image are needed. A 3-D holoscopic image coding scheme with kernel-based minimum mean square error (MMSE) estimation is proposed. In the proposed scheme, the coding block is predicted by an MMSE estimator under statistical modeling. In order to obtain the signal statistical behavior, kernel density estimation (KDE) is utilized to estimate the probability density function of the statistical modeling. As bandwidth estimation (BE) is a key issue in the KDE problem, we also propose a BE method based on kernel trick. The experimental results demonstrate that the proposed scheme can achieve a better rate-distortion performance and a better visual rendering quality.

Tracking transcriptional activities with high-content epifluorescent imaging

NASA Astrophysics Data System (ADS)

Hua, Jianping; Sima, Chao; Cypert, Milana; Gooden, Gerald C.; Shack, Sonsoles; Alla, Lalitamba; Smith, Edward A.; Trent, Jeffrey M.; Dougherty, Edward R.; Bittner, Michael L.

2012-04-01

High-content cell imaging based on fluorescent protein reporters has recently been used to track the transcriptional activities of multiple genes under different external stimuli for extended periods. This technology enhances our ability to discover treatment-induced regulatory mechanisms, temporally order their onsets and recognize their relationships. To fully realize these possibilities and explore their potential in biological and pharmaceutical applications, we introduce a new data processing procedure to extract information about the dynamics of cell processes based on this technology. The proposed procedure contains two parts: (1) image processing, where the fluorescent images are processed to identify individual cells and allow their transcriptional activity levels to be quantified; and (2) data representation, where the extracted time course data are summarized and represented in a way that facilitates efficient evaluation. Experiments show that the proposed procedure achieves fast and robust image segmentation with sufficient accuracy. The extracted cellular dynamics are highly reproducible and sensitive enough to detect subtle activity differences and identify mechanisms responding to selected perturbations. This method should be able to help biologists identify the alterations of cellular mechanisms that allow drug candidates to change cell behavior and thereby improve the efficiency of drug discovery and treatment design.

A novel content-based active contour model for brain tumor segmentation.

PubMed

Sachdeva, Jainy; Kumar, Vinod; Gupta, Indra; Khandelwal, Niranjan; Ahuja, Chirag Kamal

2012-06-01

Brain tumor segmentation is a crucial step in surgical and treatment planning. Intensity-based active contour models such as gradient vector flow (GVF), magneto static active contour (MAC) and fluid vector flow (FVF) have been proposed to segment homogeneous objects/tumors in medical images. In this study, extensive experiments are done to analyze the performance of intensity-based techniques for homogeneous tumors on brain magnetic resonance (MR) images. The analysis shows that the state-of-art methods fail to segment homogeneous tumors against similar background or when these tumors show partial diversity toward the background. They also have preconvergence problem in case of false edges/saddle points. However, the presence of weak edges and diffused edges (due to edema around the tumor) leads to oversegmentation by intensity-based techniques. Therefore, the proposed method content-based active contour (CBAC) uses both intensity and texture information present within the active contour to overcome above-stated problems capturing large range in an image. It also proposes a novel use of Gray-Level Co-occurrence Matrix to define texture space for tumor segmentation. The effectiveness of this method is tested on two different real data sets (55 patients - more than 600 images) containing five different types of homogeneous, heterogeneous, diffused tumors and synthetic images (non-MR benchmark images). Remarkable results are obtained in segmenting homogeneous tumors of uniform intensity, complex content heterogeneous, diffused tumors on MR images (T1-weighted, postcontrast T1-weighted and T2-weighted) and synthetic images (non-MR benchmark images of varying intensity, texture, noise content and false edges). Further, tumor volume is efficiently extracted from 2-dimensional slices and is named as 2.5-dimensional segmentation. Copyright © 2012 Elsevier Inc. All rights reserved.

Fast content-based image retrieval using dynamic cluster tree

NASA Astrophysics Data System (ADS)

Chen, Jinyan; Sun, Jizhou; Wu, Rongteng; Zhang, Yaping

2008-03-01

A novel content-based image retrieval data structure is developed in present work. It can improve the searching efficiency significantly. All images are organized into a tree, in which every node is comprised of images with similar features. Images in a children node have more similarity (less variance) within themselves in relative to its parent. It means that every node is a cluster and each of its children nodes is a sub-cluster. Information contained in a node includes not only the number of images, but also the center and the variance of these images. Upon the addition of new images, the tree structure is capable of dynamically changing to ensure the minimization of total variance of the tree. Subsequently, a heuristic method has been designed to retrieve the information from this tree. Given a sample image, the probability of a tree node that contains the similar images is computed using the center of the node and its variance. If the probability is higher than a certain threshold, this node will be recursively checked to locate the similar images. So will its children nodes if their probability is also higher than that threshold. If no sufficient similar images were founded, a reduced threshold value would be adopted to initiate a new seeking from the root node. The search terminates when it found sufficient similar images or the threshold value is too low to give meaningful sense. Experiments have shown that the proposed dynamic cluster tree is able to improve the searching efficiency notably.

Medical Image Retrieval: A Multimodal Approach

PubMed Central

Cao, Yu; Steffey, Shawn; He, Jianbiao; Xiao, Degui; Tao, Cui; Chen, Ping; Müller, Henning

2014-01-01

Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system. PMID:26309389

Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases

PubMed Central

Lin, Chen; Mak, Wayne; Hong, Pengyu; Sepp, Katharine; Perrimon, Norbert

2010-01-01

Recently, High-content screening (HCS) has been combined with RNA interference (RNAi) to become an essential image-based high-throughput method for studying genes and biological networks through RNAi-induced cellular phenotype analyses. However, a genome-wide RNAi-HCS screen typically generates tens of thousands of images, most of which remain uncategorized due to the inadequacies of existing HCS image analysis tools. Until now, it still requires highly trained scientists to browse a prohibitively large RNAi-HCS image database and produce only a handful of qualitative results regarding cellular morphological phenotypes. For this reason we have developed intelligent interfaces to facilitate the application of the HCS technology in biomedical research. Our new interfaces empower biologists with computational power not only to effectively and efficiently explore large-scale RNAi-HCS image databases, but also to apply their knowledge and experience to interactive mining of cellular phenotypes using Content-Based Image Retrieval (CBIR) with Relevance Feedback (RF) techniques. PMID:21278820

Feature hashing for fast image retrieval

NASA Astrophysics Data System (ADS)

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.

A cloud-based multimodality case file for mobile devices.

PubMed

Balkman, Jason D; Loehfelm, Thomas W

2014-01-01

Recent improvements in Web and mobile technology, along with the widespread use of handheld devices in radiology education, provide unique opportunities for creating scalable, universally accessible, portable image-rich radiology case files. A cloud database and a Web-based application for radiologic images were developed to create a mobile case file with reasonable usability, download performance, and image quality for teaching purposes. A total of 75 radiology cases related to breast, thoracic, gastrointestinal, musculoskeletal, and neuroimaging subspecialties were included in the database. Breast imaging cases are the focus of this article, as they best demonstrate handheld display capabilities across a wide variety of modalities. This case subset also illustrates methods for adapting radiologic content to cloud platforms and mobile devices. Readers will gain practical knowledge about storage and retrieval of cloud-based imaging data, an awareness of techniques used to adapt scrollable and high-resolution imaging content for the Web, and an appreciation for optimizing images for handheld devices. The evaluation of this software demonstrates the feasibility of adapting images from most imaging modalities to mobile devices, even in cases of full-field digital mammograms, where high resolution is required to represent subtle pathologic features. The cloud platform allows cases to be added and modified in real time by using only a standard Web browser with no application-specific software. Challenges remain in developing efficient ways to generate, modify, and upload radiologic and supplementary teaching content to this cloud-based platform. Online supplemental material is available for this article. ©RSNA, 2014.

A JPEG backward-compatible HDR image compression

NASA Astrophysics Data System (ADS)

Korshunov, Pavel; Ebrahimi, Touradj

2012-10-01

High Dynamic Range (HDR) imaging is expected to become one of the technologies that could shape next generation of consumer digital photography. Manufacturers are rolling out cameras and displays capable of capturing and rendering HDR images. The popularity and full public adoption of HDR content is however hindered by the lack of standards in evaluation of quality, file formats, and compression, as well as large legacy base of Low Dynamic Range (LDR) displays that are unable to render HDR. To facilitate wide spread of HDR usage, the backward compatibility of HDR technology with commonly used legacy image storage, rendering, and compression is necessary. Although many tone-mapping algorithms were developed for generating viewable LDR images from HDR content, there is no consensus on which algorithm to use and under which conditions. This paper, via a series of subjective evaluations, demonstrates the dependency of perceived quality of the tone-mapped LDR images on environmental parameters and image content. Based on the results of subjective tests, it proposes to extend JPEG file format, as the most popular image format, in a backward compatible manner to also deal with HDR pictures. To this end, the paper provides an architecture to achieve such backward compatibility with JPEG and demonstrates efficiency of a simple implementation of this framework when compared to the state of the art HDR image compression.

Content-based image retrieval in medical applications for picture archiving and communication systems

NASA Astrophysics Data System (ADS)

Lehmann, Thomas M.; Guld, Mark O.; Thies, Christian; Fischer, Benedikt; Keysers, Daniel; Kohnen, Michael; Schubert, Henning; Wein, Berthold B.

2003-05-01

Picture archiving and communication systems (PACS) aim to efficiently provide the radiologists with all images in a suitable quality for diagnosis. Modern standards for digital imaging and communication in medicine (DICOM) comprise alphanumerical descriptions of study, patient, and technical parameters. Currently, this is the only information used to select relevant images within PACS. Since textual descriptions insufficiently describe the great variety of details in medical images, content-based image retrieval (CBIR) is expected to have a strong impact when integrated into PACS. However, existing CBIR approaches usually are limited to a distinct modality, organ, or diagnostic study. In this state-of-the-art report, we present first results implementing a general approach to content-based image retrieval in medical applications (IRMA) and discuss its integration into PACS environments. Usually, a PACS consists of a DICOM image server and several DICOM-compliant workstations, which are used by radiologists for reading the images and reporting the findings. Basic IRMA components are the relational database, the scheduler, and the web server, which all may be installed on the DICOM image server, and the IRMA daemons running on distributed machines, e.g., the radiologists" workstations. These workstations can also host the web-based front-ends of IRMA applications. Integrating CBIR and PACS, a special focus is put on (a) location and access transparency for data, methods, and experiments, (b) replication transparency for methods in development, (c) concurrency transparency for job processing and feature extraction, (d) system transparency at method implementation time, and (e) job distribution transparency when issuing a query. Transparent integration will have a certain impact on diagnostic quality supporting both evidence-based medicine and case-based reasoning.

Breast Histopathological Image Retrieval Based on Latent Dirichlet Allocation.

PubMed

Ma, Yibing; Jiang, Zhiguo; Zhang, Haopeng; Xie, Fengying; Zheng, Yushan; Shi, Huaqiang; Zhao, Yu

2017-07-01

In the field of pathology, whole slide image (WSI) has become the major carrier of visual and diagnostic information. Content-based image retrieval among WSIs can aid the diagnosis of an unknown pathological image by finding its similar regions in WSIs with diagnostic information. However, the huge size and complex content of WSI pose several challenges for retrieval. In this paper, we propose an unsupervised, accurate, and fast retrieval method for a breast histopathological image. Specifically, the method presents a local statistical feature of nuclei for morphology and distribution of nuclei, and employs the Gabor feature to describe the texture information. The latent Dirichlet allocation model is utilized for high-level semantic mining. Locality-sensitive hashing is used to speed up the search. Experiments on a WSI database with more than 8000 images from 15 types of breast histopathology demonstrate that our method achieves about 0.9 retrieval precision as well as promising efficiency. Based on the proposed framework, we are developing a search engine for an online digital slide browsing and retrieval platform, which can be applied in computer-aided diagnosis, pathology education, and WSI archiving and management.

A new method of content based medical image retrieval and its applications to CT imaging sign retrieval.

PubMed

Ma, Ling; Liu, Xiabi; Gao, Yan; Zhao, Yanfeng; Zhao, Xinming; Zhou, Chunwu

2017-02-01

This paper proposes a new method of content based medical image retrieval through considering fused, context-sensitive similarity. Firstly, we fuse the semantic and visual similarities between the query image and each image in the database as their pairwise similarities. Then, we construct a weighted graph whose nodes represent the images and edges measure their pairwise similarities. By using the shortest path algorithm over the weighted graph, we obtain a new similarity measure, context-sensitive similarity measure, between the query image and each database image to complete the retrieval process. Actually, we use the fused pairwise similarity to narrow down the semantic gap for obtaining a more accurate pairwise similarity measure, and spread it on the intrinsic data manifold to achieve the context-sensitive similarity for a better retrieval performance. The proposed method has been evaluated on the retrieval of the Common CT Imaging Signs of Lung Diseases (CISLs) and achieved not only better retrieval results but also the satisfactory computation efficiency. Copyright © 2017 Elsevier Inc. All rights reserved.

Efficient Access Control in Multimedia Social Networks

NASA Astrophysics Data System (ADS)

Sachan, Amit; Emmanuel, Sabu

Multimedia social networks (MMSNs) have provided a convenient way to share multimedia contents such as images, videos, blogs, etc. Contents shared by a person can be easily accessed by anybody else over the Internet. However, due to various privacy, security, and legal concerns people often want to selectively share the contents only with their friends, family, colleagues, etc. Access control mechanisms play an important role in this situation. With access control mechanisms one can decide the persons who can access a shared content and who cannot. But continuously growing content uploads and accesses, fine grained access control requirements (e.g. different access control parameters for different parts in a picture), and specific access control requirements for multimedia contents can make the time complexity of access control to be very large. So, it is important to study an efficient access control mechanism suitable for MMSNs. In this chapter we present an efficient bit-vector transform based access control mechanism for MMSNs. The proposed approach is also compatible with other requirements of MMSNs, such as access rights modification, content deletion, etc. Mathematical analysis and experimental results show the effectiveness and efficiency of our proposed approach.

Classification-Based Spatial Error Concealment for Visual Communications

NASA Astrophysics Data System (ADS)

Chen, Meng; Zheng, Yefeng; Wu, Min

2006-12-01

In an error-prone transmission environment, error concealment is an effective technique to reconstruct the damaged visual content. Due to large variations of image characteristics, different concealment approaches are necessary to accommodate the different nature of the lost image content. In this paper, we address this issue and propose using classification to integrate the state-of-the-art error concealment techniques. The proposed approach takes advantage of multiple concealment algorithms and adaptively selects the suitable algorithm for each damaged image area. With growing awareness that the design of sender and receiver systems should be jointly considered for efficient and reliable multimedia communications, we proposed a set of classification-based block concealment schemes, including receiver-side classification, sender-side attachment, and sender-side embedding. Our experimental results provide extensive performance comparisons and demonstrate that the proposed classification-based error concealment approaches outperform the conventional approaches.

A novel methodology for querying web images

NASA Astrophysics Data System (ADS)

Prabhakara, Rashmi; Lee, Ching Cheng

2005-01-01

Ever since the advent of Internet, there has been an immense growth in the amount of image data that is available on the World Wide Web. With such a magnitude of image availability, an efficient and effective image retrieval system is required to make use of this information. This research presents an effective image matching and indexing technique that improvises on existing integrated image retrieval methods. The proposed technique follows a two-phase approach, integrating query by topic and query by example specification methods. The first phase consists of topic-based image retrieval using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. It consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. The second phase uses the query by example specification to perform a low-level content-based image match for the retrieval of smaller and relatively closer results of the example image. Information related to the image feature is automatically extracted from the query image by the image processing system. A technique that is not computationally intensive based on color feature is used to perform content-based matching of images. The main goal is to develop a functional image search and indexing system and to demonstrate that better retrieval results can be achieved with this proposed hybrid search technique.

A novel methodology for querying web images

NASA Astrophysics Data System (ADS)

Prabhakara, Rashmi; Lee, Ching Cheng

2004-12-01

Ever since the advent of Internet, there has been an immense growth in the amount of image data that is available on the World Wide Web. With such a magnitude of image availability, an efficient and effective image retrieval system is required to make use of this information. This research presents an effective image matching and indexing technique that improvises on existing integrated image retrieval methods. The proposed technique follows a two-phase approach, integrating query by topic and query by example specification methods. The first phase consists of topic-based image retrieval using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. It consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. The second phase uses the query by example specification to perform a low-level content-based image match for the retrieval of smaller and relatively closer results of the example image. Information related to the image feature is automatically extracted from the query image by the image processing system. A technique that is not computationally intensive based on color feature is used to perform content-based matching of images. The main goal is to develop a functional image search and indexing system and to demonstrate that better retrieval results can be achieved with this proposed hybrid search technique.

Intelligent distributed medical image management

NASA Astrophysics Data System (ADS)

Garcia, Hong-Mei C.; Yun, David Y.

1995-05-01

The rapid advancements in high performance global communication have accelerated cooperative image-based medical services to a new frontier. Traditional image-based medical services such as radiology and diagnostic consultation can now fully utilize multimedia technologies in order to provide novel services, including remote cooperative medical triage, distributed virtual simulation of operations, as well as cross-country collaborative medical research and training. Fast (efficient) and easy (flexible) retrieval of relevant images remains a critical requirement for the provision of remote medical services. This paper describes the database system requirements, identifies technological building blocks for meeting the requirements, and presents a system architecture for our target image database system, MISSION-DBS, which has been designed to fulfill the goals of Project MISSION (medical imaging support via satellite integrated optical network) -- an experimental high performance gigabit satellite communication network with access to remote supercomputing power, medical image databases, and 3D visualization capabilities in addition to medical expertise anywhere and anytime around the country. The MISSION-DBS design employs a synergistic fusion of techniques in distributed databases (DDB) and artificial intelligence (AI) for storing, migrating, accessing, and exploring images. The efficient storage and retrieval of voluminous image information is achieved by integrating DDB modeling and AI techniques for image processing while the flexible retrieval mechanisms are accomplished by combining attribute- based and content-based retrievals.

A spatiotemporal decomposition strategy for personal home video management

NASA Astrophysics Data System (ADS)

Yi, Haoran; Kozintsev, Igor; Polito, Marzia; Wu, Yi; Bouguet, Jean-Yves; Nefian, Ara; Dulong, Carole

2007-01-01

With the advent and proliferation of low cost and high performance digital video recorder devices, an increasing number of personal home video clips are recorded and stored by the consumers. Compared to image data, video data is lager in size and richer in multimedia content. Efficient access to video content is expected to be more challenging than image mining. Previously, we have developed a content-based image retrieval system and the benchmarking framework for personal images. In this paper, we extend our personal image retrieval system to include personal home video clips. A possible initial solution to video mining is to represent video clips by a set of key frames extracted from them thus converting the problem into an image search one. Here we report that a careful selection of key frames may improve the retrieval accuracy. However, because video also has temporal dimension, its key frame representation is inherently limited. The use of temporal information can give us better representation for video content at semantic object and concept levels than image-only based representation. In this paper we propose a bottom-up framework to combine interest point tracking, image segmentation and motion-shape factorization to decompose the video into spatiotemporal regions. We show an example application of activity concept detection using the trajectories extracted from the spatio-temporal regions. The proposed approach shows good potential for concise representation and indexing of objects and their motion in real-life consumer video.

Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research.

PubMed

Ababneh, Sufyan Y; Prescott, Jeff W; Gurcan, Metin N

2011-08-01

In this paper, a new, fully automated, content-based system is proposed for knee bone segmentation from magnetic resonance images (MRI). The purpose of the bone segmentation is to support the discovery and characterization of imaging biomarkers for the incidence and progression of osteoarthritis, a debilitating joint disease, which affects a large portion of the aging population. The segmentation algorithm includes a novel content-based, two-pass disjoint block discovery mechanism, which is designed to support automation, segmentation initialization, and post-processing. The block discovery is achieved by classifying the image content to bone and background blocks according to their similarity to the categories in the training data collected from typical bone structures. The classified blocks are then used to design an efficient graph-cut based segmentation algorithm. This algorithm requires constructing a graph using image pixel data followed by applying a maximum-flow algorithm which generates a minimum graph-cut that corresponds to an initial image segmentation. Content-based refinements and morphological operations are then applied to obtain the final segmentation. The proposed segmentation technique does not require any user interaction and can distinguish between bone and highly similar adjacent structures, such as fat tissues with high accuracy. The performance of the proposed system is evaluated by testing it on 376 MR images from the Osteoarthritis Initiative (OAI) database. This database included a selection of single images containing the femur and tibia from 200 subjects with varying levels of osteoarthritis severity. Additionally, a full three-dimensional segmentation of the bones from ten subjects with 14 slices each, and synthetic images with background having intensity and spatial characteristics similar to those of bone are used to assess the robustness and consistency of the developed algorithm. The results show an automatic bone detection rate of 0.99 and an average segmentation accuracy of 0.95 using the Dice similarity index. Copyright © 2011 Elsevier B.V. All rights reserved.

Ground Field-Based Hyperspectral Imaging: A Preliminary Study to Assess the Potential of Established Vegetation Indices to Infer Variation in Water-Use Efficiency.

NASA Astrophysics Data System (ADS)

Pelech, E. A.; McGrath, J.; Pederson, T.; Bernacchi, C.

2017-12-01

Increases in the global average temperature will consequently induce a higher occurrence of severe environmental conditions such as drought on arable land. To mitigate these threats, crops for fuel and food must be bred for higher water-use efficiencies (WUE). Defining genomic variation through high-throughput phenotypic analysis in field conditions has the potential to relieve the major bottleneck in linking desirable genetic traits to the associated phenotypic response. This can subsequently enable breeders to create new agricultural germplasm that supports the need for higher water-use efficient crops. From satellites to field-based aerial and ground sensors, the reflectance properties of vegetation measured by hyperspectral imaging is becoming a rapid high-throughput phenotyping technique. A variety of physiological traits can be inferred by regression analysis with leaf reflectance which is controlled by the properties and abundance of water, carbon, nitrogen and pigments. Although, given that the current established vegetation indices are designed to accentuate these properties from spectral reflectance, it becomes a challenge to infer relative measurements of WUE at a crop canopy scale without ground-truth data collection. This study aims to correlate established biomass and canopy-water-content indices with ground-truth data. Five bioenergy sorghum genotypes (Sorghum bicolor L. Moench) that have differences in WUE and wild-type Tobacco (Nicotiana tabacum var. Samsun) under irrigated and rainfed field conditions were examined. A linear regression analysis was conducted to determine if variation in canopy water content and biomass, driven by natural genotypic and artificial treatment influences, can be inferred using established vegetation indices. The results from this study will elucidate the ability of ground field-based hyperspectral imaging to assess variation in water content, biomass and water-use efficiency. This can lead to improved opportunities to select ideal genotypes for an increasing water-limited environment and to help parameterize and validate terrestrial vegetation models that require a better representation of genetic variation within crop species.

[Estimation and Visualization of Nitrogen Content in Citrus Canopy Based on Two Band Vegetation Index (TBVI)].

PubMed

Wang, Qiao-nan; Ye, Xu-jun; Li, Jin-meng; Xiao, Yu-zhao; He, Yong

2015-03-01

Nitrogen is a necessary and important element for the growth and development of fruit orchards. Timely, accurate and nondestructive monitoring of nitrogen status in fruit orchards would help maintain the fruit quality and efficient production of the orchard, and mitigate the pollution of water resources caused by excessive nitrogen fertilization. This study investigated the capability of hyperspectral imagery for estimating and visualizing the nitrogen content in citrus canopy. Hyperspectral images were obtained for leaf samples in laboratory as well as for the whole canopy in the field with ImSpector V10E (Spectral Imaging Ltd., Oulu, Finland). The spectral datas for each leaf sample were represented by the average spectral data extracted from the selected region of interest (ROI) in the hyperspectral images with the aid of ENVI software. The nitrogen content in each leaf sample was measured by the Dumas combustion method with the rapid N cube (Elementar Analytical, Germany). Simple correlation analysis and the two band vegetation index (TBVI) were then used to develop the spectra data-based nitrogen content prediction models. Results obtained through the formula calculation indicated that the model with the two band vegetation index (TBVI) based on the wavelengths 811 and 856 nm achieved the optimal estimation of nitrogen content in citrus leaves (R2 = 0.607 1). Furthermore, the canopy image for the identified TBVI was calculated, and the nitrogen content of the canopy was visualized by incorporating the model into the TBVI image. The tender leaves, middle-aged leaves and elder leaves showed distinct nitrogen status from highto low-levels in the canopy image. The results suggested the potential of hyperspectral imagery for the nondestructive detection and diagnosis of nitrogen status in citrus canopy in real time. Different from previous studies focused on nitrogen content prediction at leaf level, this study succeeded in predicting and visualizing the nutrient content of fruit trees at canopy level. This would provide valuable information for the implementation of individual tree-based fertilization schemes in precision orchard management practices.

A novel method for efficient archiving and retrieval of biomedical images using MPEG-7

NASA Astrophysics Data System (ADS)

Meyer, Joerg; Pahwa, Ash

2004-10-01

Digital archiving and efficient retrieval of radiological scans have become critical steps in contemporary medical diagnostics. Since more and more images and image sequences (single scans or video) from various modalities (CT/MRI/PET/digital X-ray) are now available in digital formats (e.g., DICOM-3), hospitals and radiology clinics need to implement efficient protocols capable of managing the enormous amounts of data generated daily in a typical clinical routine. We present a method that appears to be a viable way to eliminate the tedious step of manually annotating image and video material for database indexing. MPEG-7 is a new framework that standardizes the way images are characterized in terms of color, shape, and other abstract, content-related criteria. A set of standardized descriptors that are automatically generated from an image is used to compare an image to other images in a database, and to compute the distance between two images for a given application domain. Text-based database queries can be replaced with image-based queries using MPEG-7. Consequently, image queries can be conducted without any prior knowledge of the keys that were used as indices in the database. Since the decoding and matching steps are not part of the MPEG-7 standard, this method also enables searches that were not planned by the time the keys were generated.

High brightness MEMS mirror based head-up display (HUD) modules with wireless data streaming capability

NASA Astrophysics Data System (ADS)

Milanovic, Veljko; Kasturi, Abhishek; Hachtel, Volker

2015-02-01

A high brightness Head-Up Display (HUD) module was demonstrated with a fast, dual-axis MEMS mirror that displays vector images and text, utilizing its ~8kHz bandwidth on both axes. Two methodologies were evaluated: in one, the mirror steers a laser at wide angles of <48° on transparent multi-color fluorescent emissive film and displays content directly on the windshield, and in the other the mirror displays content on reflective multi-color emissive phosphor plates reflected off the windshield to create a virtual image for the driver. The display module is compact, consisting of a single laser diode, off-the-shelf lenses and a MEMS mirror in combination with a MEMS controller to enable precise movement of the mirror's X- and Y-axis. The MEMS controller offers both USB and wireless streaming capability and we utilize a library of functions on a host computer for creating content and controlling the mirror. Integration with smart phone applications is demonstrated, utilizing the mobile device both for content generation based on various messages or data, and for content streaming to the MEMS controller via Bluetooth interface. The display unit is highly resistant to vibrations and shock, and requires only ~1.5W to operate, even with content readable in sunlit outdoor conditions. The low power requirement is in part due to a vector graphics approach, allowing the efficient use of laser power, and also due to the use of a single, relatively high efficiency laser and simple optics.

Profile based image analysis for identification of chopped biomass stem nodes and internodes

USDA-ARS?s Scientific Manuscript database

Because of their significant variation in chemical composition, segregation of chopped biomass into nodes and internodes helps in efficient utilization of these feedstocks. Stem internodes having low ash content are a better feedstock for bioenergy and biofuel applications than nodes. However, separ...

Relationship between leaf optical properties, chlorophyll fluorescence and pigment changes in senescing Acer saccharum leaves.

PubMed

Junker, Laura Verena; Ensminger, Ingo

2016-06-01

The ability of plants to sequester carbon is highly variable over the course of the year and reflects seasonal variation in photosynthetic efficiency. This seasonal variation is most prominent during autumn, when leaves of deciduous tree species such as sugar maple (Acer saccharum Marsh.) undergo senescence, which is associated with downregulation of photosynthesis and a change of leaf color. The remote sensing of leaf color by spectral reflectance measurements and digital repeat images is increasingly used to improve models of growing season length and seasonal variation in carbon sequestration. Vegetation indices derived from spectral reflectance measurements and digital repeat images might not adequately reflect photosynthetic efficiency of red-senescing tree species during autumn due to the changes in foliar pigment content associated with autumn phenology. In this study, we aimed to assess how effectively several widely used vegetation indices capture autumn phenology and reflect the changes in physiology and photosynthetic pigments during autumn. Chlorophyll fluorescence and pigment content of green, yellow, orange and red leaves were measured to represent leaf senescence during autumn and used as a reference to validate and compare vegetation indices derived from leaf-level spectral reflectance measurements and color analysis of digital images. Vegetation indices varied in their suitability to track the decrease of photosynthetic efficiency and chlorophyll content despite increasing anthocyanin content. Commonly used spectral reflectance indices such as the normalized difference vegetation index and photochemical reflectance index showed major constraints arising from a limited representation of gradual decreases in chlorophyll content and an influence of high foliar anthocyanin levels. The excess green index and green-red vegetation index were more suitable to assess the process of senescence. Similarly, digital image analysis revealed that vegetation indices such as Hue and normalized difference index are superior compared with the often-used green chromatic coordinate. We conclude that indices based on red and green color information generally represent autumn phenology most efficiently. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Design of Open Content Social Learning That Increases Learning Efficiency and Engagement Based on Open Pedagogy

ERIC Educational Resources Information Center

John, Benneaser; Thavavel, V.; Jayaraj, Jayakumar; Muthukumar, A.; Jeevanandam, Poornaselvan Kittu

2016-01-01

Academic writing skills are crucial when students, e.g., in teacher education programs, write their undergraduate theses. A multi-modal web-based and self-regulated learning resource on academic writing was developed, using texts, hypertext, moving images, podcasts and templates. A study, using surveys and a focus group, showed that students used…

Global Contrast Based Salient Region Detection.

PubMed

Cheng, Ming-Ming; Mitra, Niloy J; Huang, Xiaolei; Torr, Philip H S; Hu, Shi-Min

2015-03-01

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

Context-dependent JPEG backward-compatible high-dynamic range image compression

NASA Astrophysics Data System (ADS)

Korshunov, Pavel; Ebrahimi, Touradj

2013-10-01

High-dynamic range (HDR) imaging is expected, together with ultrahigh definition and high-frame rate video, to become a technology that may change photo, TV, and film industries. Many cameras and displays capable of capturing and rendering both HDR images and video are already available in the market. The popularity and full-public adoption of HDR content is, however, hindered by the lack of standards in evaluation of quality, file formats, and compression, as well as large legacy base of low-dynamic range (LDR) displays that are unable to render HDR. To facilitate the wide spread of HDR usage, the backward compatibility of HDR with commonly used legacy technologies for storage, rendering, and compression of video and images are necessary. Although many tone-mapping algorithms are developed for generating viewable LDR content from HDR, there is no consensus of which algorithm to use and under which conditions. We, via a series of subjective evaluations, demonstrate the dependency of the perceptual quality of the tone-mapped LDR images on the context: environmental factors, display parameters, and image content itself. Based on the results of subjective tests, it proposes to extend JPEG file format, the most popular image format, in a backward compatible manner to deal with HDR images also. An architecture to achieve such backward compatibility with JPEG is proposed. A simple implementation of lossy compression demonstrates the efficiency of the proposed architecture compared with the state-of-the-art HDR image compression.

A Subdivision-Based Representation for Vector Image Editing.

PubMed

Liao, Zicheng; Hoppe, Hugues; Forsyth, David; Yu, Yizhou

2012-11-01

Vector graphics has been employed in a wide variety of applications due to its scalability and editability. Editability is a high priority for artists and designers who wish to produce vector-based graphical content with user interaction. In this paper, we introduce a new vector image representation based on piecewise smooth subdivision surfaces, which is a simple, unified and flexible framework that supports a variety of operations, including shape editing, color editing, image stylization, and vector image processing. These operations effectively create novel vector graphics by reusing and altering existing image vectorization results. Because image vectorization yields an abstraction of the original raster image, controlling the level of detail of this abstraction is highly desirable. To this end, we design a feature-oriented vector image pyramid that offers multiple levels of abstraction simultaneously. Our new vector image representation can be rasterized efficiently using GPU-accelerated subdivision. Experiments indicate that our vector image representation achieves high visual quality and better supports editing operations than existing representations.

Marginal Fisher analysis and its variants for human gait recognition and content- based image retrieval.

PubMed

Xu, Dong; Yan, Shuicheng; Tao, Dacheng; Lin, Stephen; Zhang, Hong-Jiang

2007-11-01

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for human gait recognition and content-based image retrieval (CBIR). In this paper, we present extensions of our recently proposed marginal Fisher analysis (MFA) to address these problems. For human gait recognition, we first present a direct application of MFA, then inspired by recent advances in matrix and tensor-based dimensionality reduction algorithms, we present matrix-based MFA for directly handling 2-D input in the form of gray-level averaged images. For CBIR, we deal with the relevance feedback problem by extending MFA to marginal biased analysis, in which within-class compactness is characterized only by the distances between each positive sample and its neighboring positive samples. In addition, we present a new technique to acquire a direct optimal solution for MFA without resorting to objective function modification as done in many previous algorithms. We conduct comprehensive experiments on the USF HumanID gait database and the Corel image retrieval database. Experimental results demonstrate that MFA and its extensions outperform related algorithms in both applications.

A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback.

PubMed

Rahman, Md Mahmudur; Bhattacharya, Prabir; Desai, Bipin C

2007-01-01

A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework.

Study on Hybrid Image Search Technology Based on Texts and Contents

NASA Astrophysics Data System (ADS)

Wang, H. T.; Ma, F. L.; Yan, C.; Pan, H.

2018-05-01

Image search was studied first here based on texts and contents, respectively. The text-based image feature extraction was put forward by integrating the statistical and topic features in view of the limitation of extraction of keywords only by means of statistical features of words. On the other hand, a search-by-image method was put forward based on multi-feature fusion in view of the imprecision of the content-based image search by means of a single feature. The layered-searching method depended on primarily the text-based image search method and additionally the content-based image search was then put forward in view of differences between the text-based and content-based methods and their difficult direct fusion. The feasibility and effectiveness of the hybrid search algorithm were experimentally verified.

Blurry-frame detection and shot segmentation in colonoscopy videos

NASA Astrophysics Data System (ADS)

Oh, JungHwan; Hwang, Sae; Tavanapong, Wallapak; de Groen, Piet C.; Wong, Johnny

2003-12-01

Colonoscopy is an important screening procedure for colorectal cancer. During this procedure, the endoscopist visually inspects the colon. Human inspection, however, is not without error. We hypothesize that colonoscopy videos may contain additional valuable information missed by the endoscopist. Video segmentation is the first necessary step for the content-based video analysis and retrieval to provide efficient access to the important images and video segments from a large colonoscopy video database. Based on the unique characteristics of colonoscopy videos, we introduce a new scheme to detect and remove blurry frames, and segment the videos into shots based on the contents. Our experimental results show that the average precision and recall of the proposed scheme are over 90% for the detection of non-blurry images. The proposed method of blurry frame detection and shot segmentation is extensible to the videos captured from other endoscopic procedures such as upper gastrointestinal endoscopy, enteroscopy, cystoscopy, and laparoscopy.

Managing biomedical image metadata for search and retrieval of similar images.

PubMed

Korenblum, Daniel; Rubin, Daniel; Napel, Sandy; Rodriguez, Cesar; Beaulieu, Chris

2011-08-01

Radiology images are generally disconnected from the metadata describing their contents, such as imaging observations ("semantic" metadata), which are usually described in text reports that are not directly linked to the images. We developed a system, the Biomedical Image Metadata Manager (BIMM) to (1) address the problem of managing biomedical image metadata and (2) facilitate the retrieval of similar images using semantic feature metadata. Our approach allows radiologists, researchers, and students to take advantage of the vast and growing repositories of medical image data by explicitly linking images to their associated metadata in a relational database that is globally accessible through a Web application. BIMM receives input in the form of standard-based metadata files using Web service and parses and stores the metadata in a relational database allowing efficient data query and maintenance capabilities. Upon querying BIMM for images, 2D regions of interest (ROIs) stored as metadata are automatically rendered onto preview images included in search results. The system's "match observations" function retrieves images with similar ROIs based on specific semantic features describing imaging observation characteristics (IOCs). We demonstrate that the system, using IOCs alone, can accurately retrieve images with diagnoses matching the query images, and we evaluate its performance on a set of annotated liver lesion images. BIMM has several potential applications, e.g., computer-aided detection and diagnosis, content-based image retrieval, automating medical analysis protocols, and gathering population statistics like disease prevalences. The system provides a framework for decision support systems, potentially improving their diagnostic accuracy and selection of appropriate therapies.

Plant leaf chlorophyll content retrieval based on a field imaging spectroscopy system.

PubMed

Liu, Bo; Yue, Yue-Min; Li, Ru; Shen, Wen-Jing; Wang, Ke-Lin

2014-10-23

A field imaging spectrometer system (FISS; 380-870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%-35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector.

Plant Leaf Chlorophyll Content Retrieval Based on a Field Imaging Spectroscopy System

PubMed Central

Liu, Bo; Yue, Yue-Min; Li, Ru; Shen, Wen-Jing; Wang, Ke-Lin

2014-01-01

A field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%–35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector. PMID:25341439

Spatial compression algorithm for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R [Albuquerque, NM

2008-07-15

A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.

FLIM FRET Technology for Drug Discovery: Automated Multiwell-Plate High-Content Analysis, Multiplexed Readouts and Application in Situ**

PubMed Central

Kumar, Sunil; Alibhai, Dominic; Margineanu, Anca; Laine, Romain; Kennedy, Gordon; McGinty, James; Warren, Sean; Kelly, Douglas; Alexandrov, Yuriy; Munro, Ian; Talbot, Clifford; Stuckey, Daniel W; Kimberly, Christopher; Viellerobe, Bertrand; Lacombe, Francois; Lam, Eric W-F; Taylor, Harriet; Dallman, Margaret J; Stamp, Gordon; Murray, Edward J; Stuhmeier, Frank; Sardini, Alessandro; Katan, Matilda; Elson, Daniel S; Neil, Mark A A; Dunsby, Chris; French, Paul M W

2011-01-01

A fluorescence lifetime imaging (FLIM) technology platform intended to read out changes in Förster resonance energy transfer (FRET) efficiency is presented for the study of protein interactions across the drug-discovery pipeline. FLIM provides a robust, inherently ratiometric imaging modality for drug discovery that could allow the same sensor constructs to be translated from automated cell-based assays through small transparent organisms such as zebrafish to mammals. To this end, an automated FLIM multiwell-plate reader is described for high content analysis of fixed and live cells, tomographic FLIM in zebrafish and FLIM FRET of live cells via confocal endomicroscopy. For cell-based assays, an exemplar application reading out protein aggregation using FLIM FRET is presented, and the potential for multiple simultaneous FLIM (FRET) readouts in microscopy is illustrated. PMID:21337485

Triazole-based Zn²⁺-specific molecular marker for fluorescence bioimaging.

PubMed

Sinha, Sougata; Mukherjee, Trinetra; Mathew, Jomon; Mukhopadhyay, Subhra K; Ghosh, Subrata

2014-04-25

Fluorescence bioimaging potential, both in vitro and in vivo, of a yellow emissive triazole-based molecular marker has been investigated and demonstrated. Three different kinds of cells, viz Bacillus thuringiensis, Candida albicans, and Techoma stans pollen grains were used to investigate the intracellular zinc imaging potential of 1 (in vitro studies). Fluorescence imaging of translocation of zinc through the stem of small herb, Peperomia pellucida, having transparent stem proved in vivo bioimaging capability of 1. This approach will enable in screening cell permeability and biostability of a newly developed probe. Similarly, the current method for detection and localization of zinc in Gram seed sprouts could be an easy and potential alternative of the existing analytical methods to investigate the efficiency of various strategies applied for increasing zinc-content in cereal crops. The probe-zinc ensemble has efficiently been applied for detecting phosphate-based biomolecules. Copyright © 2014 Elsevier B.V. All rights reserved.

Video and image retrieval beyond the cognitive level: the needs and possibilities

NASA Astrophysics Data System (ADS)

Hanjalic, Alan

2000-12-01

The worldwide research efforts in the are of image and video retrieval have concentrated so far on increasing the efficiency and reliability of extracting the elements of image and video semantics and so on improving the search and retrieval performance at the cognitive level of content abstraction. At this abstraction level, the user is searching for 'factual' or 'objective' content such as image showing a panorama of San Francisco, an outdoor or an indoor image, a broadcast news report on a defined topic, a movie dialog between the actors A and B or the parts of a basketball game showing fast breaks, steals and scores. These efforts, however, do not address the retrieval applications at the so-called affective level of content abstraction where the 'ground truth' is not strictly defined. Such applications are, for instance, those where subjectivity of the user plays the major role, e.g. the task of retrieving all images that the user 'likes most', and those that are based on 'recognizing emotions' in audiovisual data. Typical examples are searching for all images that 'radiate happiness', identifying all 'sad' movie fragments and looking for the 'romantic landscapes', 'sentimental' movie segments, 'movie highlights' or 'most exciting' moments of a sport event. This paper discusses the needs and possibilities for widening the current scope of research in the area of image and video search and retrieval in order to enable applications at the affective level of content abstraction.

Video and image retrieval beyond the cognitive level: the needs and possibilities

NASA Astrophysics Data System (ADS)

Hanjalic, Alan

2001-01-01

The worldwide research efforts in the are of image and video retrieval have concentrated so far on increasing the efficiency and reliability of extracting the elements of image and video semantics and so on improving the search and retrieval performance at the cognitive level of content abstraction. At this abstraction level, the user is searching for 'factual' or 'objective' content such as image showing a panorama of San Francisco, an outdoor or an indoor image, a broadcast news report on a defined topic, a movie dialog between the actors A and B or the parts of a basketball game showing fast breaks, steals and scores. These efforts, however, do not address the retrieval applications at the so-called affective level of content abstraction where the 'ground truth' is not strictly defined. Such applications are, for instance, those where subjectivity of the user plays the major role, e.g. the task of retrieving all images that the user 'likes most', and those that are based on 'recognizing emotions' in audiovisual data. Typical examples are searching for all images that 'radiate happiness', identifying all 'sad' movie fragments and looking for the 'romantic landscapes', 'sentimental' movie segments, 'movie highlights' or 'most exciting' moments of a sport event. This paper discusses the needs and possibilities for widening the current scope of research in the area of image and video search and retrieval in order to enable applications at the affective level of content abstraction.

A memory learning framework for effective image retrieval.

PubMed

Han, Junwei; Ngan, King N; Li, Mingjing; Zhang, Hong-Jiang

2005-04-01

Most current content-based image retrieval systems are still incapable of providing users with their desired results. The major difficulty lies in the gap between low-level image features and high-level image semantics. To address the problem, this study reports a framework for effective image retrieval by employing a novel idea of memory learning. It forms a knowledge memory model to store the semantic information by simply accumulating user-provided interactions. A learning strategy is then applied to predict the semantic relationships among images according to the memorized knowledge. Image queries are finally performed based on a seamless combination of low-level features and learned semantics. One important advantage of our framework is its ability to efficiently annotate images and also propagate the keyword annotation from the labeled images to unlabeled images. The presented algorithm has been integrated into a practical image retrieval system. Experiments on a collection of 10,000 general-purpose images demonstrate the effectiveness of the proposed framework.

Automatic Sea Bird Detection from High Resolution Aerial Imagery

NASA Astrophysics Data System (ADS)

Mader, S.; Grenzdörffer, G. J.

2016-06-01

Great efforts are presently taken in the scientific community to develop computerized and (fully) automated image processing methods allowing for an efficient and automatic monitoring of sea birds and marine mammals in ever-growing amounts of aerial imagery. Currently the major part of the processing, however, is still conducted by especially trained professionals, visually examining the images and detecting and classifying the requested subjects. This is a very tedious task, particularly when the rate of void images regularly exceeds the mark of 90%. In the content of this contribution we will present our work aiming to support the processing of aerial images by modern methods from the field of image processing. We will especially focus on the combination of local, region-based feature detection and piecewise global image segmentation for automatic detection of different sea bird species. Large image dimensions resulting from the use of medium and large-format digital cameras in aerial surveys inhibit the applicability of image processing methods based on global operations. In order to efficiently handle those image sizes and to nevertheless take advantage of globally operating segmentation algorithms, we will describe the combined usage of a simple performant feature detector based on local operations on the original image with a complex global segmentation algorithm operating on extracted sub-images. The resulting exact segmentation of possible candidates then serves as a basis for the determination of feature vectors for subsequent elimination of false candidates and for classification tasks.

TBIdoc: 3D content-based CT image retrieval system for traumatic brain injury

NASA Astrophysics Data System (ADS)

Li, Shimiao; Gong, Tianxia; Wang, Jie; Liu, Ruizhe; Tan, Chew Lim; Leong, Tze Yun; Pang, Boon Chuan; Lim, C. C. Tchoyoson; Lee, Cheng Kiang; Tian, Qi; Zhang, Zhuo

2010-03-01

Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction. However, current hospital database system does not provide an efficient and intuitive tool for doctors to search out cases relevant to the current study case. In this paper, we present the TBIdoc system: a content-based image retrieval (CBIR) system which works on the TBI CT images. In this web-based system, user can query by uploading CT image slices from one study, retrieval result is a list of TBI cases ranked according to their 3D visual similarity to the query case. Specifically, cases of TBI CT images often present diffuse or focal lesions. In TBIdoc system, these pathological image features are represented as bin-based binary feature vectors. We use the Jaccard-Needham measure as the similarity measurement. Based on these, we propose a 3D similarity measure for computing the similarity score between two series of CT slices. nDCG is used to evaluate the system performance, which shows the system produces satisfactory retrieval results. The system is expected to improve the current hospital data management in TBI and to give better support for the clinical decision-making process. It may also contribute to the computer-aided education in TBI.

LandEx - Fast, FOSS-Based Application for Query and Retrieval of Land Cover Patterns

NASA Astrophysics Data System (ADS)

Netzel, P.; Stepinski, T.

2012-12-01

The amount of satellite-based spatial data is continuously increasing making a development of efficient data search tools a priority. The bulk of existing research on searching satellite-gathered data concentrates on images and is based on the concept of Content-Based Image Retrieval (CBIR); however, available solutions are not efficient and robust enough to be put to use as deployable web-based search tools. Here we report on development of a practical, deployable tool that searches classified, rather than raw image. LandEx (Landscape Explorer) is a GeoWeb-based tool for Content-Based Pattern Retrieval (CBPR) contained within the National Land Cover Dataset 2006 (NLCD2006). The USGS-developed NLCD2006 is derived from Landsat multispectral images; it covers the entire conterminous U.S. with the resolution of 30 meters/pixel and it depicts 16 land cover classes. The size of NLCD2006 is about 10 Gpixels (161,000 x 100,000 pixels). LandEx is a multi-tier GeoWeb application based on Open Source Software. Main components are: GeoExt/OpenLayers (user interface), GeoServer (OGC WMS, WCS and WPS server), and GRASS (calculation engine). LandEx performs search using query-by-example approach: user selects a reference scene (exhibiting a chosen pattern of land cover classes) and the tool produces, in real time, a map indicating a degree of similarity between the reference pattern and all local patterns across the U.S. Scene pattern is encapsulated by a 2D histogram of classes and sizes of single-class clumps. Pattern similarity is based on the notion of mutual information. The resultant similarity map can be viewed and navigated in a web browser, or it can download as a GeoTiff file for more in-depth analysis. The LandEx is available at http://sil.uc.edu

The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cooke, diane J.; Ketkar, Nikhil; Aksoy, Selim

2008-01-01

Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

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

PubMed

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.

High-order distance-based multiview stochastic learning in image classification.

PubMed

Yu, Jun; Rui, Yong; Tang, Yuan Yan; Tao, Dacheng

2014-12-01

How do we find all images in a larger set of images which have a specific content? Or estimate the position of a specific object relative to the camera? Image classification methods, like support vector machine (supervised) and transductive support vector machine (semi-supervised), are invaluable tools for the applications of content-based image retrieval, pose estimation, and optical character recognition. However, these methods only can handle the images represented by single feature. In many cases, different features (or multiview data) can be obtained, and how to efficiently utilize them is a challenge. It is inappropriate for the traditionally concatenating schema to link features of different views into a long vector. The reason is each view has its specific statistical property and physical interpretation. In this paper, we propose a high-order distance-based multiview stochastic learning (HD-MSL) method for image classification. HD-MSL effectively combines varied features into a unified representation and integrates the labeling information based on a probabilistic framework. In comparison with the existing strategies, our approach adopts the high-order distance obtained from the hypergraph to replace pairwise distance in estimating the probability matrix of data distribution. In addition, the proposed approach can automatically learn a combination coefficient for each view, which plays an important role in utilizing the complementary information of multiview data. An alternative optimization is designed to solve the objective functions of HD-MSL and obtain different views on coefficients and classification scores simultaneously. Experiments on two real world datasets demonstrate the effectiveness of HD-MSL in image classification.

A mathematical model of neuro-fuzzy approximation in image classification

NASA Astrophysics Data System (ADS)

Gopalan, Sasi; Pinto, Linu; Sheela, C.; Arun Kumar M., N.

2016-06-01

Image digitization and explosion of World Wide Web has made traditional search for image, an inefficient method for retrieval of required grassland image data from large database. For a given input query image Content-Based Image Retrieval (CBIR) system retrieves the similar images from a large database. Advances in technology has increased the use of grassland image data in diverse areas such has agriculture, art galleries, education, industry etc. In all the above mentioned diverse areas it is necessary to retrieve grassland image data efficiently from a large database to perform an assigned task and to make a suitable decision. A CBIR system based on grassland image properties and it uses the aid of a feed-forward back propagation neural network for an effective image retrieval is proposed in this paper. Fuzzy Memberships plays an important role in the input space of the proposed system which leads to a combined neural fuzzy approximation in image classification. The CBIR system with mathematical model in the proposed work gives more clarity about fuzzy-neuro approximation and the convergence of the image features in a grassland image.

Global-Context Based Salient Region Detection in Nature Images

NASA Astrophysics Data System (ADS)

Bao, Hong; Xu, De; Tang, Yingjun

Visually saliency detection provides an alternative methodology to image description in many applications such as adaptive content delivery and image retrieval. One of the main aims of visual attention in computer vision is to detect and segment the salient regions in an image. In this paper, we employ matrix decomposition to detect salient object in nature images. To efficiently eliminate high contrast noise regions in the background, we integrate global context information into saliency detection. Therefore, the most salient region can be easily selected as the one which is globally most isolated. The proposed approach intrinsically provides an alternative methodology to model attention with low implementation complexity. Experiments show that our approach achieves much better performance than that from the existing state-of-art methods.

Augmented reality on poster presentations, in the field and in the classroom

NASA Astrophysics Data System (ADS)

Hawemann, Friedrich; Kolawole, Folarin

2017-04-01

Augmented reality (AR) is the direct addition of virtual information through an interface to a real-world environment. In practice, through a mobile device such as a tablet or smartphone, information can be projected onto a target- for example, an image on a poster. Mobile devices are widely distributed today such that augmented reality is easily accessible to almost everyone. Numerous studies have shown that multi-dimensional visualization is essential for efficient perception of the spatial, temporal and geometrical configuration of geological structures and processes. Print media, such as posters and handouts lack the ability to display content in the third and fourth dimensions, which might be in space-domain as seen in three-dimensional (3-D) objects, or time-domain (four-dimensional, 4-D) expressible in the form of videos. Here, we show that augmented reality content can be complimentary to geoscience poster presentations, hands-on material and in the field. In the latter example, location based data is loaded and for example, a virtual geological profile can be draped over a real-world landscape. In object based AR, the application is trained to recognize an image or object through the camera of the user's mobile device, such that specific content is automatically downloaded and displayed on the screen of the device, and positioned relative to the trained image or object. We used ZapWorks, a commercially-available software application to create and present examples of content that is poster-based, in which important supplementary information is presented as interactive virtual images, videos and 3-D models. We suggest that the flexibility and real-time interactivity offered by AR makes it an invaluable tool for effective geoscience poster presentation, class-room and field geoscience learning.

A hierarchical SVG image abstraction layer for medical imaging

NASA Astrophysics Data System (ADS)

Kim, Edward; Huang, Xiaolei; Tan, Gang; Long, L. Rodney; Antani, Sameer

2010-03-01

As medical imaging rapidly expands, there is an increasing need to structure and organize image data for efficient analysis, storage and retrieval. In response, a large fraction of research in the areas of content-based image retrieval (CBIR) and picture archiving and communication systems (PACS) has focused on structuring information to bridge the "semantic gap", a disparity between machine and human image understanding. An additional consideration in medical images is the organization and integration of clinical diagnostic information. As a step towards bridging the semantic gap, we design and implement a hierarchical image abstraction layer using an XML based language, Scalable Vector Graphics (SVG). Our method encodes features from the raw image and clinical information into an extensible "layer" that can be stored in a SVG document and efficiently searched. Any feature extracted from the raw image including, color, texture, orientation, size, neighbor information, etc., can be combined in our abstraction with high level descriptions or classifications. And our representation can natively characterize an image in a hierarchical tree structure to support multiple levels of segmentation. Furthermore, being a world wide web consortium (W3C) standard, SVG is able to be displayed by most web browsers, interacted with by ECMAScript (standardized scripting language, e.g. JavaScript, JScript), and indexed and retrieved by XML databases and XQuery. Using these open source technologies enables straightforward integration into existing systems. From our results, we show that the flexibility and extensibility of our abstraction facilitates effective storage and retrieval of medical images.

What Can Pictures Tell Us About Web Pages? Improving Document Search Using Images.

PubMed

Rodriguez-Vaamonde, Sergio; Torresani, Lorenzo; Fitzgibbon, Andrew W

2015-06-01

Traditional Web search engines do not use the images in the HTML pages to find relevant documents for a given query. Instead, they typically operate by computing a measure of agreement between the keywords provided by the user and only the text portion of each page. In this paper we study whether the content of the pictures appearing in a Web page can be used to enrich the semantic description of an HTML document and consequently boost the performance of a keyword-based search engine. We present a Web-scalable system that exploits a pure text-based search engine to find an initial set of candidate documents for a given query. Then, the candidate set is reranked using visual information extracted from the images contained in the pages. The resulting system retains the computational efficiency of traditional text-based search engines with only a small additional storage cost needed to encode the visual information. We test our approach on one of the TREC Million Query Track benchmarks where we show that the exploitation of visual content yields improvement in accuracies for two distinct text-based search engines, including the system with the best reported performance on this benchmark. We further validate our approach by collecting document relevance judgements on our search results using Amazon Mechanical Turk. The results of this experiment confirm the improvement in accuracy produced by our image-based reranker over a pure text-based system.

Interactive radiographic image retrieval system.

PubMed

Kundu, Malay Kumar; Chowdhury, Manish; Das, Sudeb

2017-02-01

Content based medical image retrieval (CBMIR) systems enable fast diagnosis through quantitative assessment of the visual information and is an active research topic over the past few decades. Most of the state-of-the-art CBMIR systems suffer from various problems: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering schemes. Inability to properly handle the "semantic gap" and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database). This yields an exigent demand for developing highly effective and computationally efficient retrieval system. We propose a novel interactive two-stage CBMIR system for diverse collection of medical radiographic images. Initially, Pulse Coupled Neural Network based shape features are used to find out the most probable (similar) image classes using a novel "similarity positional score" mechanism. This is followed by retrieval using Non-subsampled Contourlet Transform based texture features considering only the images of the pre-identified classes. Maximal information compression index is used for unsupervised feature selection to achieve better results. To reduce the semantic gap problem, the proposed system uses a novel fuzzy index based relevance feedback mechanism by incorporating subjectivity of human perception in an analytic manner. Extensive experiments were carried out to evaluate the effectiveness of the proposed CBMIR system on a subset of Image Retrieval in Medical Applications (IRMA)-2009 database consisting of 10,902 labeled radiographic images of 57 different modalities. We obtained overall average precision of around 98% after only 2-3 iterations of relevance feedback mechanism. We assessed the results by comparisons with some of the state-of-the-art CBMIR systems for radiographic images. Unlike most of the existing CBMIR systems, in the proposed two-stage hierarchical framework, main importance is given on constructing efficient and compact feature vector representation, search-space reduction and handling the "semantic gap" problem effectively, without compromising the retrieval performance. Experimental results and comparisons show that the proposed system performs efficiently in the radiographic medical image retrieval field. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

MR efficiency using automated MRI-desktop eProtocol

NASA Astrophysics Data System (ADS)

Gao, Fei; Xu, Yanzhe; Panda, Anshuman; Zhang, Min; Hanson, James; Su, Congzhe; Wu, Teresa; Pavlicek, William; James, Judy R.

2017-03-01

MRI protocols are instruction sheets that radiology technologists use in routine clinical practice for guidance (e.g., slice position, acquisition parameters etc.). In Mayo Clinic Arizona (MCA), there are over 900 MR protocols (ranging across neuro, body, cardiac, breast etc.) which makes maintaining and updating the protocol instructions a labor intensive effort. The task is even more challenging given different vendors (Siemens, GE etc.). This is a universal problem faced by all the hospitals and/or medical research institutions. To increase the efficiency of the MR practice, we designed and implemented a web-based platform (eProtocol) to automate the management of MRI protocols. It is built upon a database that automatically extracts protocol information from DICOM compliant images and provides a user-friendly interface to the technologists to create, edit and update the protocols. Advanced operations such as protocol migrations from scanner to scanner and capability to upload Multimedia content were also implemented. To the best of our knowledge, eProtocol is the first MR protocol automated management tool used clinically. It is expected that this platform will significantly improve the radiology operations efficiency including better image quality and exam consistency, fewer repeat examinations and less acquisition errors. These protocols instructions will be readily available to the technologists during scans. In addition, this web-based platform can be extended to other imaging modalities such as CT, Mammography, and Interventional Radiology and different vendors for imaging protocol management.

A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

NASA Astrophysics Data System (ADS)

Oszust, Mariusz

2016-12-01

The advances in the development of imaging devices resulted in the need of an automatic quality evaluation of displayed visual content in a way that is consistent with human visual perception. In this paper, an approach to full-reference image quality assessment (IQA) is proposed, in which several IQA measures, representing different approaches to modelling human visual perception, are efficiently combined in order to produce objective quality evaluation of examined images, which is highly correlated with evaluation provided by human subjects. In the paper, an optimisation problem of selection of several IQA measures for creating a regression-based IQA hybrid measure, or a multimeasure, is defined and solved using a genetic algorithm. Experimental evaluation on four largest IQA benchmarks reveals that the multimeasures obtained using the proposed approach outperform state-of-the-art full-reference IQA techniques, including other recently developed fusion approaches.

Omega-3 chicken egg detection system using a mobile-based image processing segmentation method

NASA Astrophysics Data System (ADS)

Nurhayati, Oky Dwi; Kurniawan Teguh, M.; Cintya Amalia, P.

2017-02-01

An Omega-3 chicken egg is a chicken egg produced through food engineering technology. It is produced by hen fed with high omega-3 fatty acids. So, it has fifteen times nutrient content of omega-3 higher than Leghorn's. Visually, its shell has the same shape and colour as Leghorn's. Each egg can be distinguished by breaking the egg's shell and testing the egg yolk's nutrient content in a laboratory. But, those methods were proven not effective and efficient. Observing this problem, the purpose of this research is to make an application to detect the type of omega-3 chicken egg by using a mobile-based computer vision. This application was built in OpenCV computer vision library to support Android Operating System. This experiment required some chicken egg images taken using an egg candling box. We used 60 omega-3 chicken and Leghorn eggs as samples. Then, using an Android smartphone, image acquisition of the egg was obtained. After that, we applied several steps using image processing methods such as Grab Cut, convert RGB image to eight bit grayscale, median filter, P-Tile segmentation, and morphology technique in this research. The next steps were feature extraction which was used to extract feature values via mean, variance, skewness, and kurtosis from each image. Finally, using digital image measurement, some chicken egg images were classified. The result showed that omega-3 chicken egg and Leghorn egg had different values. This system is able to provide accurate reading around of 91%.

Wavelet-based image analysis system for soil texture analysis

NASA Astrophysics Data System (ADS)

Sun, Yun; Long, Zhiling; Jang, Ping-Rey; Plodinec, M. John

2003-05-01

Soil texture is defined as the relative proportion of clay, silt and sand found in a given soil sample. It is an important physical property of soil that affects such phenomena as plant growth and agricultural fertility. Traditional methods used to determine soil texture are either time consuming (hydrometer), or subjective and experience-demanding (field tactile evaluation). Considering that textural patterns observed at soil surfaces are uniquely associated with soil textures, we propose an innovative approach to soil texture analysis, in which wavelet frames-based features representing texture contents of soil images are extracted and categorized by applying a maximum likelihood criterion. The soil texture analysis system has been tested successfully with an accuracy of 91% in classifying soil samples into one of three general categories of soil textures. In comparison with the common methods, this wavelet-based image analysis approach is convenient, efficient, fast, and objective.

Quiet echo planar imaging for functional and diffusion MRI

PubMed Central

Price, Anthony N.; Cordero‐Grande, Lucilio; Malik, Shaihan; Ferrazzi, Giulio; Gaspar, Andreia; Hughes, Emer J.; Christiaens, Daan; McCabe, Laura; Schneider, Torben; Rutherford, Mary A.; Hajnal, Joseph V.

2017-01-01

Purpose To develop a purpose‐built quiet echo planar imaging capability for fetal functional and diffusion scans, for which acoustic considerations often compromise efficiency and resolution as well as angular/temporal coverage. Methods The gradient waveforms in multiband‐accelerated single‐shot echo planar imaging sequences have been redesigned to minimize spectral content. This includes a sinusoidal read‐out with a single fundamental frequency, a constant phase encoding gradient, overlapping smoothed CAIPIRINHA blips, and a novel strategy to merge the crushers in diffusion MRI. These changes are then tuned in conjunction with the gradient system frequency response function. Results Maintained image quality, SNR, and quantitative diffusion values while reducing acoustic noise up to 12 dB (A) is illustrated in two adult experiments. Fetal experiments in 10 subjects covering a range of parameters depict the adaptability and increased efficiency of quiet echo planar imaging. Conclusion Purpose‐built for highly efficient multiband fetal echo planar imaging studies, the presented framework reduces acoustic noise for all echo planar imaging‐based sequences. Full optimization by tuning to the gradient frequency response functions allows for a maximally time‐efficient scan within safe limits. This allows ambitious in‐utero studies such as functional brain imaging with high spatial/temporal resolution and diffusion scans with high angular/spatial resolution to be run in a highly efficient manner at acceptable sound levels. Magn Reson Med 79:1447–1459, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. PMID:28653363

A similarity learning approach to content-based image retrieval: application to digital mammography.

PubMed

El-Naqa, Issam; Yang, Yongyi; Galatsanos, Nikolas P; Nishikawa, Robert M; Wernick, Miles N

2004-10-01

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.

Content-based retrieval of historical Ottoman documents stored as textual images.

PubMed

Saykol, Ediz; Sinop, Ali Kemal; Güdükbay, Ugur; Ulusoy, Ozgür; Cetin, A Enis

2004-03-01

There is an accelerating demand to access the visual content of documents stored in historical and cultural archives. Availability of electronic imaging tools and effective image processing techniques makes it feasible to process the multimedia data in large databases. In this paper, a framework for content-based retrieval of historical documents in the Ottoman Empire archives is presented. The documents are stored as textual images, which are compressed by constructing a library of symbols occurring in a document, and the symbols in the original image are then replaced with pointers into the codebook to obtain a compressed representation of the image. The features in wavelet and spatial domain based on angular and distance span of shapes are used to extract the symbols. In order to make content-based retrieval in historical archives, a query is specified as a rectangular region in an input image and the same symbol-extraction process is applied to the query region. The queries are processed on the codebook of documents and the query images are identified in the resulting documents using the pointers in textual images. The querying process does not require decompression of images. The new content-based retrieval framework is also applicable to many other document archives using different scripts.

Complex Event Processing for Content-Based Text, Image, and Video Retrieval

DTIC Science & Technology

2016-06-01

NY): Wiley- Interscience; 2000. Feldman R, Sanger J. The text mining handbook: advanced approaches in analyzing unstructured data. New York (NY...ARL-TR-7705 ● JUNE 2016 US Army Research Laboratory Complex Event Processing for Content-Based Text , Image, and Video Retrieval...ARL-TR-7705 ● JUNE 2016 US Army Research Laboratory Complex Event Processing for Content-Based Text , Image, and Video Retrieval

Solutions-based climate change education for K-Gray: Renewable energy and energy efficiency

NASA Astrophysics Data System (ADS)

Petrone, C.

2017-12-01

Through the National Science Foundation-funded MADE CLEAR (www.madeclear.org) climate change education project's Informal Climate Change Education (ICCE) Community, funds were received to collaboratively train teachers, informal educators, students, and university docents in climate change basics and solutions, specifically renewable energy and energy efficiency. In all, 10 docents, 50 classroom teachers, over 600 K-16 students, and several hundred science-interested citizens participated in programs and workshops lasting between one and seven hours. Using commercially available kits and other DIY projects, program participants used science content and engineering to develop models of wind turbines, wind mills, solar cells, solar cookers, solar stills, and wind-powered cars. Using thermal imaging cameras, Kill-a-Watt meters, "Carbon Food Print" kit, "Energy Matters" kit, and other tools, program participants learned about energy efficiency as not only a global climate change mitigation strategy, but also a way to save money. ICCE Community members and external partners, such as local electric cooperative personnel, university researchers, and state-sponsored energy efficiency program personnel, provided content presentations, discussions, and hands-on activities to program participants.

A scale-based connected coherence tree algorithm for image segmentation.

PubMed

Ding, Jundi; Ma, Runing; Chen, Songcan

2008-02-01

This paper presents a connected coherence tree algorithm (CCTA) for image segmentation with no prior knowledge. It aims to find regions of semantic coherence based on the proposed epsilon-neighbor coherence segmentation criterion. More specifically, with an adaptive spatial scale and an appropriate intensity-difference scale, CCTA often achieves several sets of coherent neighboring pixels which maximize the probability of being a single image content (including kinds of complex backgrounds). In practice, each set of coherent neighboring pixels corresponds to a coherence class (CC). The fact that each CC just contains a single equivalence class (EC) ensures the separability of an arbitrary image theoretically. In addition, the resultant CCs are represented by tree-based data structures, named connected coherence tree (CCT)s. In this sense, CCTA is a graph-based image analysis algorithm, which expresses three advantages: 1) its fundamental idea, epsilon-neighbor coherence segmentation criterion, is easy to interpret and comprehend; 2) it is efficient due to a linear computational complexity in the number of image pixels; 3) both subjective comparisons and objective evaluation have shown that it is effective for the tasks of semantic object segmentation and figure-ground separation in a wide variety of images. Those images either contain tiny, long and thin objects or are severely degraded by noise, uneven lighting, occlusion, poor illumination, and shadow.

Practical life log video indexing based on content and context

NASA Astrophysics Data System (ADS)

Tancharoen, Datchakorn; Yamasaki, Toshihiko; Aizawa, Kiyoharu

2006-01-01

Today, multimedia information has gained an important role in daily life and people can use imaging devices to capture their visual experiences. In this paper, we present our personal Life Log system to record personal experiences in form of wearable video and environmental data; in addition, an efficient retrieval system is demonstrated to recall the desirable media. We summarize the practical video indexing techniques based on Life Log content and context to detect talking scenes by using audio/visual cues and semantic key frames from GPS data. Voice annotation is also demonstrated as a practical indexing method. Moreover, we apply body media sensors to record continuous life style and use body media data to index the semantic key frames. In the experiments, we demonstrated various video indexing results which provided their semantic contents and showed Life Log visualizations to examine personal life effectively.

Web Applications for Patient Communication.

PubMed

Lewis, Kristopher; Reicher, Murray A

2016-12-01

Communication between imaging professionals and patients can help achieve many goals, including improved patient understanding of imaging-related diagnostic and treatment options, better compliance with appropriate imaging screening procedures, and improved efficiency of service. The explosive growth of out-of-pocket consumer spending on health care has heightened health care shopping, thus making patient communication an important goal of any imaging practice or health care organization. Furthermore, the Merit-Based Incentive Payment System introduced by CMS will publicly disclose physicians' quality ratings, which are in part dependent on patient engagement. The authors summarize the rationale for web communication with patients, the range of content that should be considered, and the technology options. The aim is to help imaging providers develop organized patient communication strategic and implementation plans. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Semantics-Based Intelligent Indexing and Retrieval of Digital Images - A Case Study

NASA Astrophysics Data System (ADS)

Osman, Taha; Thakker, Dhavalkumar; Schaefer, Gerald

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they typically rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this chapter we present a semantically enabled image annotation and retrieval engine that is designed to satisfy the requirements of commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as presenting our initial thoughts on exploiting lexical databases for explicit semantic-based query expansion.

Depth-aware image seam carving.

PubMed

Shen, Jianbing; Wang, Dapeng; Li, Xuelong

2013-10-01

Image seam carving algorithm should preserve important and salient objects as much as possible when changing the image size, while not removing the secondary objects in the scene. However, it is still difficult to determine the important and salient objects that avoid the distortion of these objects after resizing the input image. In this paper, we develop a novel depth-aware single image seam carving approach by taking advantage of the modern depth cameras such as the Kinect sensor, which captures the RGB color image and its corresponding depth map simultaneously. By considering both the depth information and the just noticeable difference (JND) model, we develop an efficient JND-based significant computation approach using the multiscale graph cut based energy optimization. Our method achieves the better seam carving performance by cutting the near objects less seams while removing distant objects more seams. To the best of our knowledge, our algorithm is the first work to use the true depth map captured by Kinect depth camera for single image seam carving. The experimental results demonstrate that the proposed approach produces better seam carving results than previous content-aware seam carving methods.

Sub-Selective Quantization for Learning Binary Codes in Large-Scale Image Search.

PubMed

Li, Yeqing; Liu, Wei; Huang, Junzhou

2018-06-01

Recently with the explosive growth of visual content on the Internet, large-scale image search has attracted intensive attention. It has been shown that mapping high-dimensional image descriptors to compact binary codes can lead to considerable efficiency gains in both storage and performing similarity computation of images. However, most existing methods still suffer from expensive training devoted to large-scale binary code learning. To address this issue, we propose a sub-selection based matrix manipulation algorithm, which can significantly reduce the computational cost of code learning. As case studies, we apply the sub-selection algorithm to several popular quantization techniques including cases using linear and nonlinear mappings. Crucially, we can justify the resulting sub-selective quantization by proving its theoretic properties. Extensive experiments are carried out on three image benchmarks with up to one million samples, corroborating the efficacy of the sub-selective quantization method in terms of image retrieval.

System for pathology categorization and retrieval in chest radiographs

NASA Astrophysics Data System (ADS)

Avni, Uri; Greenspan, Hayit; Konen, Eli; Sharon, Michal; Goldberger, Jacob

2011-03-01

In this paper we present an overview of a system we have been developing for the past several years for efficient image categorization and retrieval in large radiograph archives. The methodology is based on local patch representation of the image content, using a bag of visual words approach and similarity-based categorization with a kernel based SVM classifier. We show an application to pathology-level categorization of chest x-ray data, the most popular examination in radiology. Our study deals with pathology detection and identification of individual pathologies including right and left pleural effusion, enlarged heart and cases of enlarged mediastinum. The input from a radiologist provided a global label for the entire image (healthy/pathology), and the categorization was conducted on the entire image, with no need for segmentation algorithms or any geometrical rules. An automatic diagnostic-level categorization, even on such an elementary level as healthy vs pathological, provides a useful tool for radiologists on this popular and important examination. This is a first step towards similarity-based categorization, which has a major clinical implications for computer-assisted diagnostics.

Backward Registration Based Aspect Ratio Similarity (ARS) for Image Retargeting Quality Assessment.

PubMed

Zhang, Yabin; Fang, Yuming; Lin, Weisi; Zhang, Xinfeng; Li, Leida

2016-06-28

During the past few years, there have been various kinds of content-aware image retargeting operators proposed for image resizing. However, the lack of effective objective retargeting quality assessment metrics limits the further development of image retargeting techniques. Different from traditional Image Quality Assessment (IQA) metrics, the quality degradation during image retargeting is caused by artificial retargeting modifications, and the difficulty for Image Retargeting Quality Assessment (IRQA) lies in the alternation of the image resolution and content, which makes it impossible to directly evaluate the quality degradation like traditional IQA. In this paper, we interpret the image retargeting in a unified framework of resampling grid generation and forward resampling. We show that the geometric change estimation is an efficient way to clarify the relationship between the images. We formulate the geometric change estimation as a Backward Registration problem with Markov Random Field (MRF) and provide an effective solution. The geometric change aims to provide the evidence about how the original image is resized into the target image. Under the guidance of the geometric change, we develop a novel Aspect Ratio Similarity metric (ARS) to evaluate the visual quality of retargeted images by exploiting the local block changes with a visual importance pooling strategy. Experimental results on the publicly available MIT RetargetMe and CUHK datasets demonstrate that the proposed ARS can predict more accurate visual quality of retargeted images compared with state-of-the-art IRQA metrics.

Method for the reduction of image content redundancy in large image databases

DOEpatents

Tobin, Kenneth William; Karnowski, Thomas P.

2010-03-02

A method of increasing information content for content-based image retrieval (CBIR) systems includes the steps of providing a CBIR database, the database having an index for a plurality of stored digital images using a plurality of feature vectors, the feature vectors corresponding to distinct descriptive characteristics of the images. A visual similarity parameter value is calculated based on a degree of visual similarity between features vectors of an incoming image being considered for entry into the database and feature vectors associated with a most similar of the stored images. Based on said visual similarity parameter value it is determined whether to store or how long to store the feature vectors associated with the incoming image in the database.

Content-based quality evaluation of color images: overview and proposals

NASA Astrophysics Data System (ADS)

Tremeau, Alain; Richard, Noel; Colantoni, Philippe; Fernandez-Maloigne, Christine

2003-12-01

The automatic prediction of perceived quality from image data in general, and the assessment of particular image characteristics or attributes that may need improvement in particular, becomes an increasingly important part of intelligent imaging systems. The purpose of this paper is to propose to the color imaging community in general to develop a software package available on internet to help the user to select among all these approaches which is better appropriated to a given application. The ultimate goal of this project is to propose, next to implement, an open and unified color imaging system to set up a favourable context for the evaluation and analysis of color imaging processes. Many different methods for measuring the performance of a process have been proposed by different researchers. In this paper, we will discuss the advantages and shortcomings of most of main analysis criteria and performance measures currently used. The aim is not to establish a harsh competition between algorithms or processes, but rather to test and compare the efficiency of methodologies firstly to highlight strengths and weaknesses of a given algorithm or methodology on a given image type and secondly to have these results publicly available. This paper is focused on two important unsolved problems. Why it is so difficult to select a color space which gives better results than another one? Why it is so difficult to select an image quality metric which gives better results than another one, with respect to the judgment of the Human Visual System? Several methods used either in color imaging or in image quality will be thus discussed. Proposals for content-based image measures and means of developing a standard test suite for will be then presented. The above reference advocates for an evaluation protocol based on an automated procedure. This is the ultimate goal of our proposal.

Content dependent selection of image enhancement parameters for mobile displays

NASA Astrophysics Data System (ADS)

Lee, Yoon-Gyoo; Kang, Yoo-Jin; Kim, Han-Eol; Kim, Ka-Hee; Kim, Choon-Woo

2011-01-01

Mobile devices such as cellular phones and portable multimedia player with capability of playing terrestrial digital multimedia broadcasting (T-DMB) contents have been introduced into consumer market. In this paper, content dependent image quality enhancement method for sharpness and colorfulness and noise reduction is presented to improve perceived image quality on mobile displays. Human visual experiments are performed to analyze viewers' preference. Relationship between the objective measures and the optimal values of image control parameters are modeled by simple lookup tables based on the results of human visual experiments. Content dependent values of image control parameters are determined based on the calculated measures and predetermined lookup tables. Experimental results indicate that dynamic selection of image control parameters yields better image quality.

Efficient stereoscopic contents file format on the basis of ISO base media file format

NASA Astrophysics Data System (ADS)

Kim, Kyuheon; Lee, Jangwon; Suh, Doug Young; Park, Gwang Hoon

2009-02-01

A lot of 3D contents haven been widely used for multimedia services, however, real 3D video contents have been adopted for a limited applications such as a specially designed 3D cinema. This is because of the difficulty of capturing real 3D video contents and the limitation of display devices available in a market. However, diverse types of display devices for stereoscopic video contents for real 3D video contents have been recently released in a market. Especially, a mobile phone with a stereoscopic camera has been released in a market, which provides a user as a consumer to have more realistic experiences without glasses, and also, as a content creator to take stereoscopic images or record the stereoscopic video contents. However, a user can only store and display these acquired stereoscopic contents with his/her own devices due to the non-existence of a common file format for these contents. This limitation causes a user not share his/her contents with any other users, which makes it difficult the relevant market to stereoscopic contents is getting expanded. Therefore, this paper proposes the common file format on the basis of ISO base media file format for stereoscopic contents, which enables users to store and exchange pure stereoscopic contents. This technology is also currently under development for an international standard of MPEG as being called as a stereoscopic video application format.

Landmark Image Retrieval by Jointing Feature Refinement and Multimodal Classifier Learning.

PubMed

Zhang, Xiaoming; Wang, Senzhang; Li, Zhoujun; Ma, Shuai; Xiaoming Zhang; Senzhang Wang; Zhoujun Li; Shuai Ma; Ma, Shuai; Zhang, Xiaoming; Wang, Senzhang; Li, Zhoujun

2018-06-01

Landmark retrieval is to return a set of images with their landmarks similar to those of the query images. Existing studies on landmark retrieval focus on exploiting the geometries of landmarks for visual similarity matches. However, the visual content of social images is of large diversity in many landmarks, and also some images share common patterns over different landmarks. On the other side, it has been observed that social images usually contain multimodal contents, i.e., visual content and text tags, and each landmark has the unique characteristic of both visual content and text content. Therefore, the approaches based on similarity matching may not be effective in this environment. In this paper, we investigate whether the geographical correlation among the visual content and the text content could be exploited for landmark retrieval. In particular, we propose an effective multimodal landmark classification paradigm to leverage the multimodal contents of social image for landmark retrieval, which integrates feature refinement and landmark classifier with multimodal contents by a joint model. The geo-tagged images are automatically labeled for classifier learning. Visual features are refined based on low rank matrix recovery, and multimodal classification combined with group sparse is learned from the automatically labeled images. Finally, candidate images are ranked by combining classification result and semantic consistence measuring between the visual content and text content. Experiments on real-world datasets demonstrate the superiority of the proposed approach as compared to existing methods.

Learning to rank using user clicks and visual features for image retrieval.

PubMed

Yu, Jun; Tao, Dacheng; Wang, Meng; Rui, Yong

2015-04-01

The inconsistency between textual features and visual contents can cause poor image search results. To solve this problem, click features, which are more reliable than textual information in justifying the relevance between a query and clicked images, are adopted in image ranking model. However, the existing ranking model cannot integrate visual features, which are efficient in refining the click-based search results. In this paper, we propose a novel ranking model based on the learning to rank framework. Visual features and click features are simultaneously utilized to obtain the ranking model. Specifically, the proposed approach is based on large margin structured output learning and the visual consistency is integrated with the click features through a hypergraph regularizer term. In accordance with the fast alternating linearization method, we design a novel algorithm to optimize the objective function. This algorithm alternately minimizes two different approximations of the original objective function by keeping one function unchanged and linearizing the other. We conduct experiments on a large-scale dataset collected from the Microsoft Bing image search engine, and the results demonstrate that the proposed learning to rank models based on visual features and user clicks outperforms state-of-the-art algorithms.

Method and simulation to study 3D crosstalk perception

NASA Astrophysics Data System (ADS)

Khaustova, Dar'ya; Blondé, Laurent; Huynh-Thu, Quan; Vienne, Cyril; Doyen, Didier

2012-03-01

To various degrees, all modern 3DTV displays suffer from crosstalk, which can lead to a decrease of both visual quality and visual comfort, and also affect perception of depth. In the absence of a perfect 3D display technology, crosstalk has to be taken into account when studying perception of 3D stereoscopic content. In order to improve 3D presentation systems and understand how to efficiently eliminate crosstalk, it is necessary to understand its impact on human perception. In this paper, we present a practical method to study the perception of crosstalk. The approach consists of four steps: (1) physical measurements of a 3DTV, (2) building of a crosstalk surface based on those measurements and representing specifically the behavior of that 3TV, (3) manipulation of the crosstalk function and application on reference images to produce test images degraded by crosstalk in various ways, and (4) psychophysical tests. Our approach allows both a realistic representation of the behavior of a 3DTV and the easy manipulation of its resulting crosstalk in order to conduct psycho-visual experiments. Our approach can be used in all studies requiring the understanding of how crosstalk affects perception of stereoscopic content and how it can be corrected efficiently.

Effectiveness of image features and similarity measures in cluster-based approaches for content-based image retrieval

NASA Astrophysics Data System (ADS)

Du, Hongbo; Al-Jubouri, Hanan; Sellahewa, Harin

2014-05-01

Content-based image retrieval is an automatic process of retrieving images according to image visual contents instead of textual annotations. It has many areas of application from automatic image annotation and archive, image classification and categorization to homeland security and law enforcement. The key issues affecting the performance of such retrieval systems include sensible image features that can effectively capture the right amount of visual contents and suitable similarity measures to find similar and relevant images ranked in a meaningful order. Many different approaches, methods and techniques have been developed as a result of very intensive research in the past two decades. Among many existing approaches, is a cluster-based approach where clustering methods are used to group local feature descriptors into homogeneous regions, and search is conducted by comparing the regions of the query image against those of the stored images. This paper serves as a review of works in this area. The paper will first summarize the existing work reported in the literature and then present the authors' own investigations in this field. The paper intends to highlight not only achievements made by recent research but also challenges and difficulties still remaining in this area.

A comparative study of multi-sensor data fusion methods for highly accurate assessment of manufactured parts

NASA Astrophysics Data System (ADS)

Hannachi, Ammar; Kohler, Sophie; Lallement, Alex; Hirsch, Ernest

2015-04-01

3D modeling of scene contents takes an increasing importance for many computer vision based applications. In particular, industrial applications of computer vision require efficient tools for the computation of this 3D information. Routinely, stereo-vision is a powerful technique to obtain the 3D outline of imaged objects from the corresponding 2D images. As a consequence, this approach provides only a poor and partial description of the scene contents. On another hand, for structured light based reconstruction techniques, 3D surfaces of imaged objects can often be computed with high accuracy. However, the resulting active range data in this case lacks to provide data enabling to characterize the object edges. Thus, in order to benefit from the positive points of various acquisition techniques, we introduce in this paper promising approaches, enabling to compute complete 3D reconstruction based on the cooperation of two complementary acquisition and processing techniques, in our case stereoscopic and structured light based methods, providing two 3D data sets describing respectively the outlines and surfaces of the imaged objects. We present, accordingly, the principles of three fusion techniques and their comparison based on evaluation criterions related to the nature of the workpiece and also the type of the tackled application. The proposed fusion methods are relying on geometric characteristics of the workpiece, which favour the quality of the registration. Further, the results obtained demonstrate that the developed approaches are well adapted for 3D modeling of manufactured parts including free-form surfaces and, consequently quality control applications using these 3D reconstructions.

Visible Light Image-Based Method for Sugar Content Classification of Citrus

PubMed Central

Wang, Xuefeng; Wu, Chunyan; Hirafuji, Masayuki

2016-01-01

Visible light imaging of citrus fruit from Mie Prefecture of Japan was performed to determine whether an algorithm could be developed to predict the sugar content. This nondestructive classification showed that the accurate segmentation of different images can be realized by a correlation analysis based on the threshold value of the coefficient of determination. There is an obvious correlation between the sugar content of citrus fruit and certain parameters of the color images. The selected image parameters were connected by addition algorithm. The sugar content of citrus fruit can be predicted by the dummy variable method. The results showed that the small but orange citrus fruits often have a high sugar content. The study shows that it is possible to predict the sugar content of citrus fruit and to perform a classification of the sugar content using light in the visible spectrum and without the need for an additional light source. PMID:26811935

Multifunctional gadolinium-based dendritic macromolecules as liver targeting imaging probes.

PubMed

Luo, Kui; Liu, Gang; He, Bin; Wu, Yao; Gong, Qingyong; Song, Bin; Ai, Hua; Gu, Zhongwei

2011-04-01

The quest for highly efficient and safe contrast agents has become the key factor for successful application of magnetic resonance imaging (MRI). The gadolinium (Gd) based dendritic macromolecules, with precise and tunable nanoscopic sizes, are excellent candidates as multivalent MRI probes. In this paper, a novel series of Gd-based multifunctional peptide dendritic probes (generation 2, 3, and 4) possessing highly controlled structures and single molecular weight were designed and prepared as liver MRI probes. These macromolecular Gd-ligand agents exhibited up to 3-fold increase in T(1) relaxivity comparing to Gd-DTPA complexes. No obvious in vitro cytotoxicity was observed from the measured concentrations. These dendritic probes were further functionalized with multiple galactosyl moieties and led to much higher cell uptake in vitro as demonstrated in T(1)-weighted scans. During in vivo animal studies, the probes provided better signal intensity (SI) enhancement in mouse liver, especially at 60 min post-injection, with the most efficient enhancement from the galactosyl moiety decorated third generation dendrimer. The imaging results were verified with analysis of Gd content in liver tissues. The design strategy of multifunctional Gd-ligand peptide dendritic macromolecules in this study may be used for developing other sensitive MRI probes with targeting capability. Copyright © 2011 Elsevier Ltd. All rights reserved.

Resolution enhancement of low-quality videos using a high-resolution frame

NASA Astrophysics Data System (ADS)

Pham, Tuan Q.; van Vliet, Lucas J.; Schutte, Klamer

2006-01-01

This paper proposes an example-based Super-Resolution (SR) algorithm of compressed videos in the Discrete Cosine Transform (DCT) domain. Input to the system is a Low-Resolution (LR) compressed video together with a High-Resolution (HR) still image of similar content. Using a training set of corresponding LR-HR pairs of image patches from the HR still image, high-frequency details are transferred from the HR source to the LR video. The DCT-domain algorithm is much faster than example-based SR in spatial domain 6 because of a reduction in search dimensionality, which is a direct result of the compact and uncorrelated DCT representation. Fast searching techniques like tree-structure vector quantization 16 and coherence search1 are also key to the improved efficiency. Preliminary results on MJPEG sequence show promising result of the DCT-domain SR synthesis approach.

Segmentation and classification of cell cycle phases in fluorescence imaging.

PubMed

Ersoy, Ilker; Bunyak, Filiz; Chagin, Vadim; Cardoso, M Christina; Palaniappan, Kannappan

2009-01-01

Current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. Stable cell lines expressing fluorescently tagged protein GFP-PCNA produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the S-phase. Variable fluorescence patterns, drastic changes in SNR, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. We extend the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. We utilize surface shape properties of GFP-PCNA intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.

Research of image retrieval technology based on color feature

NASA Astrophysics Data System (ADS)

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 make rotating and translation does not change. The HSV color space is used to show color characteristic of image, which is suitable to the visual characteristic of human. Taking advance of human's feeling to color, it quantifies color sector with unequal interval, and get characteristic vector. Finally, it matches the similarity of image with the algorithm of the histogram intersection and the partition-overall histogram. Users can choose a demonstration image to show inquired vision require, and also can adjust several right value through the relevance-feedback method to obtain the best result of search.An image retrieval system based on these approaches is presented. The result of the experiments shows that the image retrieval based on partition-overall histogram can keep the space distribution information while abstracting color feature efficiently, and it is superior to the normal color histograms in precision rate while researching. The query precision rate is more than 95%. In addition, the efficient block expression will lower the complicate degree of the images to be searched, and thus the searching efficiency will be increased. The image retrieval algorithms based on the partition-overall histogram proposed in the paper is efficient and effective.

Concurrent access to a virtual microscope using a web service oriented architecture

NASA Astrophysics Data System (ADS)

Corredor, Germán.; Iregui, Marcela; Arias, Viviana; Romero, Eduardo

2013-11-01

Virtual microscopy (VM) facilitates visualization and deployment of histopathological virtual slides (VS), a useful tool for education, research and diagnosis. In recent years, it has become popular, yet its use is still limited basically because of the very large sizes of VS, typically of the order of gigabytes. Such volume of data requires efficacious and efficient strategies to access the VS content. In an educative or research scenario, several users may require to access and interact with VS at the same time, so, due to large data size, a very expensive and powerful infrastructure is usually required. This article introduces a novel JPEG2000-based service oriented architecture for streaming and visualizing very large images under scalable strategies, which in addition need not require very specialized infrastructure. Results suggest that the proposed architecture enables transmission and simultaneous visualization of large images, while it is efficient using resources and offering users proper response times.

Backwards compatible high dynamic range video compression

NASA Astrophysics Data System (ADS)

Dolzhenko, Vladimir; Chesnokov, Vyacheslav; Edirisinghe, Eran A.

2014-02-01

This paper presents a two layer CODEC architecture for high dynamic range video compression. The base layer contains the tone mapped video stream encoded with 8 bits per component which can be decoded using conventional equipment. The base layer content is optimized for rendering on low dynamic range displays. The enhancement layer contains the image difference, in perceptually uniform color space, between the result of inverse tone mapped base layer content and the original video stream. Prediction of the high dynamic range content reduces the redundancy in the transmitted data while still preserves highlights and out-of-gamut colors. Perceptually uniform colorspace enables using standard ratedistortion optimization algorithms. We present techniques for efficient implementation and encoding of non-uniform tone mapping operators with low overhead in terms of bitstream size and number of operations. The transform representation is based on human vision system model and suitable for global and local tone mapping operators. The compression techniques include predicting the transform parameters from previously decoded frames and from already decoded data for current frame. Different video compression techniques are compared: backwards compatible and non-backwards compatible using AVC and HEVC codecs.

Two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images.

PubMed

He, Lifeng; Chao, Yuyan; Suzuki, Kenji

2011-08-01

Whenever one wants to distinguish, recognize, and/or measure objects (connected components) in binary images, labeling is required. This paper presents two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images. One is voxel based and the other is run based. For the voxel-based one, we present an efficient method of deciding the order for checking voxels in the mask. For the run-based one, instead of assigning each foreground voxel, we assign each run a provisional label. Moreover, we use run data to label foreground voxels without scanning any background voxel in the second scan. Experimental results have demonstrated that our voxel-based algorithm is efficient for 3-D binary images with complicated connected components, that our run-based one is efficient for those with simple connected components, and that both are much more efficient than conventional 3-D labeling algorithms.

Toward privacy-preserving JPEG image retrieval

NASA Astrophysics Data System (ADS)

Cheng, Hang; Wang, Jingyue; Wang, Meiqing; Zhong, Shangping

2017-07-01

This paper proposes a privacy-preserving retrieval scheme for JPEG images based on local variance. Three parties are involved in the scheme: the content owner, the server, and the authorized user. The content owner encrypts JPEG images for privacy protection by jointly using permutation cipher and stream cipher, and then, the encrypted versions are uploaded to the server. With an encrypted query image provided by an authorized user, the server may extract blockwise local variances in different directions without knowing the plaintext content. After that, it can calculate the similarity between the encrypted query image and each encrypted database image by a local variance-based feature comparison mechanism. The authorized user with the encryption key can decrypt the returned encrypted images with plaintext content similar to the query image. The experimental results show that the proposed scheme not only provides effective privacy-preserving retrieval service but also ensures both format compliance and file size preservation for encrypted JPEG images.

Score-Level Fusion of Phase-Based and Feature-Based Fingerprint Matching Algorithms

NASA Astrophysics Data System (ADS)

Ito, Koichi; Morita, Ayumi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. In our previous work, we have already proposed an efficient fingerprint recognition algorithm using Phase-Only Correlation (POC), and developed commercial fingerprint verification units for access control applications. The use of Fourier phase information of fingerprint images makes it possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. This paper presents an idea of improving the performance of POC-based fingerprint matching by combining it with feature-based matching, where feature-based matching is introduced in order to improve recognition efficiency for images with nonlinear distortion. Experimental evaluation using two different types of fingerprint image databases demonstrates efficient recognition performance of the combination of the POC-based algorithm and the feature-based algorithm.

Information content exploitation of imaging spectrometer's images for lossless compression

NASA Astrophysics Data System (ADS)

Wang, Jianyu; Zhu, Zhenyu; Lin, Kan

1996-11-01

Imaging spectrometer, such as MAIS produces a tremendous volume of image data with up to 5.12 Mbps raw data rate, which needs urgently a real-time, efficient and reversible compression implementation. Between the lossy scheme with high compression ratio and the lossless scheme with high fidelity, we must make our choice based on the particular information content analysis of each imaging spectrometer's image data. In this paper, we present a careful analysis of information-preserving compression of imaging spectrometer MAIS with an entropy and autocorrelation study on the hyperspectral images. First, the statistical information in an actual MAIS image, captured in Marble Bar Australia, is measured with its entropy, conditional entropy, mutual information and autocorrelation coefficients on both spatial dimensions and spectral dimension. With these careful analyses, it is shown that there is high redundancy existing in the spatial dimensions, but the correlation in spectral dimension of the raw images is smaller than expected. The main reason of the nonstationarity on spectral dimension is attributed to the instruments's discrepancy on detector's response and channel's amplification in different spectral bands. To restore its natural correlation, we preprocess the signal in advance. There are two methods to accomplish this requirement: onboard radiation calibration and normalization. A better result can be achieved by the former one. After preprocessing, the spectral correlation increases so high that it contributes much redundancy in addition to spatial correlation. At last, an on-board hardware implementation for the lossless compression is presented with an ideal result.

An integrated content and metadata based retrieval system for art.

PubMed

Lewis, Paul H; Martinez, Kirk; Abas, Fazly Salleh; Fauzi, Mohammad Faizal Ahmad; Chan, Stephen C Y; Addis, Matthew J; Boniface, Mike J; Grimwood, Paul; Stevenson, Alison; Lahanier, Christian; Stevenson, James

2004-03-01

A new approach to image retrieval is presented in the domain of museum and gallery image collections. Specialist algorithms, developed to address specific retrieval tasks, are combined with more conventional content and metadata retrieval approaches, and implemented within a distributed architecture to provide cross-collection searching and navigation in a seamless way. External systems can access the different collections using interoperability protocols and open standards, which were extended to accommodate content based as well as text based retrieval paradigms. After a brief overview of the complete system, we describe the novel design and evaluation of some of the specialist image analysis algorithms including a method for image retrieval based on sub-image queries, retrievals based on very low quality images and retrieval using canvas crack patterns. We show how effective retrieval results can be achieved by real end-users consisting of major museums and galleries, accessing the distributed but integrated digital collections.

A novel biomedical image indexing and retrieval system via deep preference learning.

PubMed

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images. We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications. Copyright © 2018 Elsevier B.V. All rights reserved.

Using mobile electronic devices to deliver educational resources in developing countries.

PubMed

Mazal, Jonathan Robert; Ludwig, Rebecca

2015-01-01

Developing countries have far fewer trained radiography professionals than developed countries, which exacerbates the limited access to imaging services. The lack of trained radiographers reflects, in part, limited availability of radiographer-specific educational resources. Historically, organizations that provided such resources in the developing world faced challenges related to the limited stock of current materials as well as expenses associated with shipping and delivery. Four mobile electronic devices (MEDs) were loaded with educational content (e-books, PDFs, and digital applications) spanning major radiography topics. The MEDs were distributed to 4 imaging departments in Ghana, India, Nepal, and Nigeria based on evidence of need for radiography-specific resources, as revealed by survey responses. A cost comparison of postal delivery vs digital delivery of educational content was performed. The effectiveness of delivering additional content via Wi-Fi transmission also was evaluated. Feedback was solicited on users' experience with the MEDs as a delivery tool for educational content. An initial average per e-book expense of $30.05, which included the cost of the device, was calculated for the MED delivery method compared with $15.56 for postal delivery of printed materials. The cost of the MED delivery method was reduced to an average of $10.05 for subsequent e-book deliveries. Additional content was successfully delivered via Wi-Fi transmission to all recipients during the 3-month follow-up period. Overall user feedback on the experience was positive, and ideas for enhancing the MED-based method were identified. Using MEDs to deliver radiography-specific educational content appears to be more cost effective than postal delivery of printed materials on a long-term basis. MEDs are more efficient for providing updates to educational materials. Customization of content to department needs, and using projector devices could enhance the usefulness of MEDs for radiographer training.

Application of content-based image compression to telepathology

NASA Astrophysics Data System (ADS)

Varga, Margaret J.; Ducksbury, Paul G.; Callagy, Grace

2002-05-01

Telepathology is a means of practicing pathology at a distance, viewing images on a computer display rather than directly through a microscope. Without compression, images take too long to transmit to a remote location and are very expensive to store for future examination. However, to date the use of compressed images in pathology remains controversial. This is because commercial image compression algorithms such as JPEG achieve data compression without knowledge of the diagnostic content. Often images are lossily compressed at the expense of corrupting informative content. None of the currently available lossy compression techniques are concerned with what information has been preserved and what data has been discarded. Their sole objective is to compress and transmit the images as fast as possible. By contrast, this paper presents a novel image compression technique, which exploits knowledge of the slide diagnostic content. This 'content based' approach combines visually lossless and lossy compression techniques, judiciously applying each in the appropriate context across an image so as to maintain 'diagnostic' information while still maximising the possible compression. Standard compression algorithms, e.g. wavelets, can still be used, but their use in a context sensitive manner can offer high compression ratios and preservation of diagnostically important information. When compared with lossless compression the novel content-based approach can potentially provide the same degree of information with a smaller amount of data. When compared with lossy compression it can provide more information for a given amount of compression. The precise gain in the compression performance depends on the application (e.g. database archive or second opinion consultation) and the diagnostic content of the images.

Attention to local and global levels of hierarchical Navon figures affects rapid scene categorization.

PubMed

Brand, John; Johnson, Aaron P

2014-01-01

In four experiments, we investigated how attention to local and global levels of hierarchical Navon figures affected the selection of diagnostic spatial scale information used in scene categorization. We explored this issue by asking observers to classify hybrid images (i.e., images that contain low spatial frequency (LSF) content of one image, and high spatial frequency (HSF) content from a second image) immediately following global and local Navon tasks. Hybrid images can be classified according to either their LSF, or HSF content; thus, making them ideal for investigating diagnostic spatial scale preference. Although observers were sensitive to both spatial scales (Experiment 1), they overwhelmingly preferred to classify hybrids based on LSF content (Experiment 2). In Experiment 3, we demonstrated that LSF based hybrid categorization was faster following global Navon tasks, suggesting that LSF processing associated with global Navon tasks primed the selection of LSFs in hybrid images. In Experiment 4, replicating Experiment 3 but suppressing the LSF information in Navon letters by contrast balancing the stimuli examined this hypothesis. Similar to Experiment 3, observers preferred to classify hybrids based on LSF content; however and in contrast, LSF based hybrid categorization was slower following global than local Navon tasks.

Attention to local and global levels of hierarchical Navon figures affects rapid scene categorization

PubMed Central

Brand, John; Johnson, Aaron P.

2014-01-01

In four experiments, we investigated how attention to local and global levels of hierarchical Navon figures affected the selection of diagnostic spatial scale information used in scene categorization. We explored this issue by asking observers to classify hybrid images (i.e., images that contain low spatial frequency (LSF) content of one image, and high spatial frequency (HSF) content from a second image) immediately following global and local Navon tasks. Hybrid images can be classified according to either their LSF, or HSF content; thus, making them ideal for investigating diagnostic spatial scale preference. Although observers were sensitive to both spatial scales (Experiment 1), they overwhelmingly preferred to classify hybrids based on LSF content (Experiment 2). In Experiment 3, we demonstrated that LSF based hybrid categorization was faster following global Navon tasks, suggesting that LSF processing associated with global Navon tasks primed the selection of LSFs in hybrid images. In Experiment 4, replicating Experiment 3 but suppressing the LSF information in Navon letters by contrast balancing the stimuli examined this hypothesis. Similar to Experiment 3, observers preferred to classify hybrids based on LSF content; however and in contrast, LSF based hybrid categorization was slower following global than local Navon tasks. PMID:25520675

Visual Based Retrieval Systems and Web Mining--Introduction.

ERIC Educational Resources Information Center

Iyengar, S. S.

2001-01-01

Briefly discusses Web mining and image retrieval techniques, and then presents a summary of articles in this special issue. Articles focus on Web content mining, artificial neural networks as tools for image retrieval, content-based image retrieval systems, and personalizing the Web browsing experience using media agents. (AEF)

Skin image retrieval using Gabor wavelet texture feature.

PubMed

Ou, X; Pan, W; Zhang, X; Xiao, P

2016-12-01

Skin imaging plays a key role in many clinical studies. We have used many skin imaging techniques, including the recently developed capacitive contact skin imaging based on fingerprint sensors. The aim of this study was to develop an effective skin image retrieval technique using Gabor wavelet transform, which can be used on different types of skin images, but with a special focus on skin capacitive contact images. Content-based image retrieval (CBIR) is a useful technology to retrieve stored images from database by supplying query images. In a typical CBIR, images are retrieved based on colour, shape, texture, etc. In this study, texture feature is used for retrieving skin images, and Gabor wavelet transform is used for texture feature description and extraction. The results show that the Gabor wavelet texture features can work efficiently on different types of skin images. Although Gabor wavelet transform is slower compared with other image retrieval techniques, such as principal component analysis (PCA) and grey-level co-occurrence matrix (GLCM), Gabor wavelet transform is the best for retrieving skin capacitive contact images and facial images with different orientations. Gabor wavelet transform can also work well on facial images with different expressions and skin cancer/disease images. We have developed an effective skin image retrieval method based on Gabor wavelet transform, that it is useful for retrieving different types of images, namely digital colour face images, digital colour skin cancer and skin disease images, and particularly greyscale skin capacitive contact images. Gabor wavelet transform can also be potentially useful for face recognition (with different orientation and expressions) and skin cancer/disease diagnosis. © 2016 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

A Neural Network Architecture For Rapid Model Indexing In Computer Vision Systems

NASA Astrophysics Data System (ADS)

Pawlicki, Ted

1988-03-01

Models of objects stored in memory have been shown to be useful for guiding the processing of computer vision systems. A major consideration in such systems, however, is how stored models are initially accessed and indexed by the system. As the number of stored models increases, the time required to search memory for the correct model becomes high. Parallel distributed, connectionist, neural networks' have been shown to have appealing content addressable memory properties. This paper discusses an architecture for efficient storage and reference of model memories stored as stable patterns of activity in a parallel, distributed, connectionist, neural network. The emergent properties of content addressability and resistance to noise are exploited to perform indexing of the appropriate object centered model from image centered primitives. The system consists of three network modules each of which represent information relative to a different frame of reference. The model memory network is a large state space vector where fields in the vector correspond to ordered component objects and relative, object based spatial relationships between the component objects. The component assertion network represents evidence about the existence of object primitives in the input image. It establishes local frames of reference for object primitives relative to the image based frame of reference. The spatial relationship constraint network is an intermediate representation which enables the association between the object based and the image based frames of reference. This intermediate level represents information about possible object orderings and establishes relative spatial relationships from the image based information in the component assertion network below. It is also constrained by the lawful object orderings in the model memory network above. The system design is consistent with current psychological theories of recognition by component. It also seems to support Marr's notions of hierarchical indexing. (i.e. the specificity, adjunct, and parent indices) It supports the notion that multiple canonical views of an object may have to be stored in memory to enable its efficient identification. The use of variable fields in the state space vectors appears to keep the number of required nodes in the network down to a tractable number while imposing a semantic value on different areas of the state space. This semantic imposition supports an interface between the analogical aspects of neural networks and the propositional paradigms of symbolic processing.

Evaluation of camouflage effectiveness using hyperspectral images

NASA Astrophysics Data System (ADS)

Zavvartorbati, Ahmad; Dehghani, Hamid; Rashidi, Ali Jabar

2017-10-01

Recent advances in camouflage engineering have made it more difficult to detect targets. Assessing the effectiveness of camouflage against different target detection methods leads to identifying the strengths and weaknesses of camouflage designs. One of the target detection methods is to analyze the content of the scene using remote sensing hyperspectral images. In the process of evaluating camouflage designs, there must be comprehensive and efficient evaluation criteria. Three parameters were considered as the main factors affecting the target detection and based on these factors, camouflage effectiveness assessment criteria were proposed. To combine the criteria in the form of a single equation, the equation used in target visual search models was employed and for determining the criteria, a model was presented based on the structure of the computational visual attention systems. Also, in software implementations on the HyMap hyperspectral image, a variety of camouflage levels were created for the real targets in the image. Assessing the camouflage levels using the proposed criteria, comparing and analyzing the results can show that the provided criteria and model are effective for the evaluation of camouflage designs using hyperspectral images.

Use of neural image analysis methods in the process to determine the dry matter content in the compost

NASA Astrophysics Data System (ADS)

Wojcieszak, D.; Przybył, J.; Lewicki, A.; Ludwiczak, A.; Przybylak, A.; Boniecki, P.; Koszela, K.; Zaborowicz, M.; Przybył, K.; Witaszek, K.

2015-07-01

The aim of this research was investigate the possibility of using methods of computer image analysis and artificial neural networks for to assess the amount of dry matter in the tested compost samples. The research lead to the conclusion that the neural image analysis may be a useful tool in determining the quantity of dry matter in the compost. Generated neural model may be the beginning of research into the use of neural image analysis assess the content of dry matter and other constituents of compost. The presented model RBF 19:19-2-1:1 characterized by test error 0.092189 may be more efficient.

Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing

PubMed Central

Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing

2017-01-01

Remote sensing technologies have been widely applied in urban environments’ monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the “salt and pepper” phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive. PMID:28604641

Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing.

PubMed

Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing

2017-06-12

Remote sensing technologies have been widely applied in urban environments' monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the "salt and pepper" phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.

Content-based image retrieval by matching hierarchical attributed region adjacency graphs

NASA Astrophysics Data System (ADS)

Fischer, Benedikt; Thies, Christian J.; Guld, Mark O.; Lehmann, Thomas M.

2004-05-01

Content-based image retrieval requires a formal description of visual information. In medical applications, all relevant biological objects have to be represented by this description. Although color as the primary feature has proven successful in publicly available retrieval systems of general purpose, this description is not applicable to most medical images. Additionally, it has been shown that global features characterizing the whole image do not lead to acceptable results in the medical context or that they are only suitable for specific applications. For a general purpose content-based comparison of medical images, local, i.e. regional features that are collected on multiple scales must be used. A hierarchical attributed region adjacency graph (HARAG) provides such a representation and transfers image comparison to graph matching. However, building a HARAG from an image requires a restriction in size to be computationally feasible while at the same time all visually plausible information must be preserved. For this purpose, mechanisms for the reduction of the graph size are presented. Even with a reduced graph, the problem of graph matching remains NP-complete. In this paper, the Similarity Flooding approach and Hopfield-style neural networks are adapted from the graph matching community to the needs of HARAG comparison. Based on synthetic image material build from simple geometric objects, all visually similar regions were matched accordingly showing the framework's general applicability to content-based image retrieval of medical images.

Tumor targeting efficiency of bare nanoparticles does not mean the efficacy of loaded anticancer drugs: importance of radionuclide imaging for optimization of highly selective tumor targeting polymeric nanoparticles with or without drug.

PubMed

Lee, Beom Suk; Park, Kyeongsoon; Park, Sangjin; Kim, Gui Chul; Kim, Hyo Jung; Lee, Sangjoo; Kil, Heeseup; Oh, Seung Jun; Chi, Daeyoon; Kim, Kwangmeyung; Choi, Kuiwon; Kwon, Ick Chan; Kim, Sang Yoon

2010-10-15

The better understanding of polymeric nanoparticles as a drug delivery carrier is a decisive factor to get more efficient therapeutic response in vivo. Here, we report the non-invasive imaging of bare polymeric nanoparticles and drug-loaded polymeric nanoparticles to evaluate biodistribution in tumor bearing mice. To make nano-sized drug delivery carrier, glycol chitosan was modified with different degrees of hydrophobic N-acetyl histidine (NAcHis-GC-1, -2, and -3). The biodistribution of polymeric nanoparticles and drug was confirmed by using gamma camera with (131)I-labeled NAcHis-GC and (131)I-labeled doxorubicin (DOX) and by using in vivo live animal imaging with near-infrared fluorescence Cy5.5-labeled NAcHis-GC. Among bare nanoparticles, NAcHis-GC3 (7.8% NAcHis content) showed much higher tumor targeting efficiency than NAcHis-GC1 (3.3% NAcHis content) and NAcHis-GC2 (6.8% NAcHis content). In contrast, for drug-loaded nanoparticles, DOX-NAcHis-GC1 displayed two-fold higher tumor targeting property than DOX-NAcHis-GC3. These data imply that the biodistribution and tumor targeting efficiency between bare and drug-loaded nanoparticles may be greatly different. Therapeutic responses for NAcHis-GC nanoparticles after drug loading were also evaluated. In xenograft animal model, we could find out that DOX-NAcHis-GC1 with higher tumor targeting of DOX has more excellent therapeutic effect than DOX-NAcHis-GC3 and free DOX. These results mean that the hydrophobic core stability might be a critical factor for tumor targeting efficiency of nanoparticles. The present study indicates that by using molecular imaging, we can select more appropriate nanoparticles with the highest tumor targeting properties, leading to exerting more excellent therapeutic results in cancer therapy. Copyright © 2010 Elsevier B.V. All rights reserved.

Dictionary Pruning with Visual Word Significance for Medical Image Retrieval

PubMed Central

Zhang, Fan; Song, Yang; Cai, Weidong; Hauptmann, Alexander G.; Liu, Sidong; Pujol, Sonia; Kikinis, Ron; Fulham, Michael J; Feng, David Dagan; Chen, Mei

2016-01-01

Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency. PMID:27688597

Dictionary Pruning with Visual Word Significance for Medical Image Retrieval.

PubMed

Zhang, Fan; Song, Yang; Cai, Weidong; Hauptmann, Alexander G; Liu, Sidong; Pujol, Sonia; Kikinis, Ron; Fulham, Michael J; Feng, David Dagan; Chen, Mei

2016-02-12

Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.

Supervised learning of tools for content-based search of image databases

NASA Astrophysics Data System (ADS)

Delanoy, Richard L.

1996-03-01

A computer environment, called the Toolkit for Image Mining (TIM), is being developed with the goal of enabling users with diverse interests and varied computer skills to create search tools for content-based image retrieval and other pattern matching tasks. Search tools are generated using a simple paradigm of supervised learning that is based on the user pointing at mistakes of classification made by the current search tool. As mistakes are identified, a learning algorithm uses the identified mistakes to build up a model of the user's intentions, construct a new search tool, apply the search tool to a test image, display the match results as feedback to the user, and accept new inputs from the user. Search tools are constructed in the form of functional templates, which are generalized matched filters capable of knowledge- based image processing. The ability of this system to learn the user's intentions from experience contrasts with other existing approaches to content-based image retrieval that base searches on the characteristics of a single input example or on a predefined and semantically- constrained textual query. Currently, TIM is capable of learning spectral and textural patterns, but should be adaptable to the learning of shapes, as well. Possible applications of TIM include not only content-based image retrieval, but also quantitative image analysis, the generation of metadata for annotating images, data prioritization or data reduction in bandwidth-limited situations, and the construction of components for larger, more complex computer vision algorithms.

Summarizing Audiovisual Contents of a Video Program

NASA Astrophysics Data System (ADS)

Gong, Yihong

2003-12-01

In this paper, we focus on video programs that are intended to disseminate information and knowledge such as news, documentaries, seminars, etc, and present an audiovisual summarization system that summarizes the audio and visual contents of the given video separately, and then integrating the two summaries with a partial alignment. The audio summary is created by selecting spoken sentences that best present the main content of the audio speech while the visual summary is created by eliminating duplicates/redundancies and preserving visually rich contents in the image stream. The alignment operation aims to synchronize each spoken sentence in the audio summary with its corresponding speaker's face and to preserve the rich content in the visual summary. A Bipartite Graph-based audiovisual alignment algorithm is developed to efficiently find the best alignment solution that satisfies these alignment requirements. With the proposed system, we strive to produce a video summary that: (1) provides a natural visual and audio content overview, and (2) maximizes the coverage for both audio and visual contents of the original video without having to sacrifice either of them.

Hierarchical content-based image retrieval by dynamic indexing and guided search

NASA Astrophysics Data System (ADS)

You, Jane; Cheung, King H.; Liu, James; Guo, Linong

2003-12-01

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.

Content based image retrieval using local binary pattern operator and data mining techniques.

PubMed

Vatamanu, Oana Astrid; Frandeş, Mirela; Lungeanu, Diana; Mihalaş, Gheorghe-Ioan

2015-01-01

Content based image retrieval (CBIR) concerns the retrieval of similar images from image databases, using feature vectors extracted from images. These feature vectors globally define the visual content present in an image, defined by e.g., texture, colour, shape, and spatial relations between vectors. Herein, we propose the definition of feature vectors using the Local Binary Pattern (LBP) operator. A study was performed in order to determine the optimum LBP variant for the general definition of image feature vectors. The chosen LBP variant is then subsequently used to build an ultrasound image database, and a database with images obtained from Wireless Capsule Endoscopy. The image indexing process is optimized using data clustering techniques for images belonging to the same class. Finally, the proposed indexing method is compared to the classical indexing technique, which is nowadays widely used.

NDSI products system based on Hadoop platform

NASA Astrophysics Data System (ADS)

Zhou, Yan; Jiang, He; Yang, Xiaoxia; Geng, Erhui

2015-12-01

Snow is solid state of water resources on earth, and plays an important role in human life. Satellite remote sensing is significant in snow extraction with the advantages of cyclical, macro, comprehensiveness, objectivity, timeliness. With the continuous development of remote sensing technology, remote sensing data access to the trend of multiple platforms, multiple sensors and multiple perspectives. At the same time, in view of the remote sensing data of compute-intensive applications demand increase gradually. However, current the producing system of remote sensing products is in a serial mode, and this kind of production system is used for professional remote sensing researchers mostly, and production systems achieving automatic or semi-automatic production are relatively less. Facing massive remote sensing data, the traditional serial mode producing system with its low efficiency has been difficult to meet the requirements of mass data timely and efficient processing. In order to effectively improve the production efficiency of NDSI products, meet the demand of large-scale remote sensing data processed timely and efficiently, this paper build NDSI products production system based on Hadoop platform, and the system mainly includes the remote sensing image management module, NDSI production module, and system service module. Main research contents and results including: (1)The remote sensing image management module: includes image import and image metadata management two parts. Import mass basis IRS images and NDSI product images (the system performing the production task output) into HDFS file system; At the same time, read the corresponding orbit ranks number, maximum/minimum longitude and latitude, product date, HDFS storage path, Hadoop task ID (NDSI products), and other metadata information, and then create thumbnails, and unique ID number for each record distribution, import it into base/product image metadata database. (2)NDSI production module: includes the index calculation, production tasks submission and monitoring two parts. Read HDF images related to production task in the form of a byte stream, and use Beam library to parse image byte stream to the form of Product; Use MapReduce distributed framework to perform production tasks, at the same time monitoring task status; When the production task complete, calls remote sensing image management module to store NDSI products. (3)System service module: includes both image search and DNSI products download. To image metadata attributes described in JSON format, return to the image sequence ID existing in the HDFS file system; For the given MapReduce task ID, package several task output NDSI products into ZIP format file, and return to the download link (4)System evaluation: download massive remote sensing data and use the system to process it to get the NDSI products testing the performance, and the result shows that the system has high extendibility, strong fault tolerance, fast production speed, and the image processing results with high accuracy.

DANoC: An Efficient Algorithm and Hardware Codesign of Deep Neural Networks on Chip.

PubMed

Zhou, Xichuan; Li, Shengli; Tang, Fang; Hu, Shengdong; Lin, Zhi; Zhang, Lei

2017-07-18

Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an algorithm and hardware codesign for efficient deep neural computation. A hardware-oriented deep learning algorithm, named the deep adaptive network, is proposed to explore the sparsity of neural connections. By adaptively removing the majority of neural connections and robustly representing the reserved connections using binary integers, the proposed algorithm could save up to 99.9% memory utility and computational resources without undermining classification accuracy. An efficient sparse-mapping-memory-based hardware architecture is proposed to fully take advantage of the algorithmic optimization. Different from traditional Von Neumann architecture, the deep-adaptive network on chip (DANoC) brings communication and computation in close proximity to avoid power-hungry parameter transfers between on-board memory and on-chip computational units. Experiments over different image classification benchmarks show that the DANoC system achieves competitively high accuracy and efficiency comparing with the state-of-the-art approaches.

Enhanced photocatalytic activity of electrospun nanofibrous TiO2/g-C3N4 heterojunction photocatalyst under simulated solar light

NASA Astrophysics Data System (ADS)

Wang, Chunlei; Hu, Liming; Chai, Bo; Yan, Juntao; Li, Jianfen

2018-02-01

Electrospun nanofibrous TiO2/g-C3N4 heterojunction photocatalysts with different TiO2 content have been synthesized via a facile electrospinning and subsequent in situ evaporation and calcination process for the first time, which are examined in terms of morphology, component content, optical properties, PL spectra, photocurrent response, EIS measurement, photocatalytic activity and mechanism. SEM images exhibit TiO2/g-C3N4-4 heterojunction photocatalyst possesses the excellent 1D structure. HRTEM and element mapping images confirm the formation of heterojunction structure. DRS tests identify that TiO2/g-C3N4-4 heterojunction exhibits the intensitive absorption in both UV and visible light region. The photoelectrochemical tests prove that the recombination between electrons and holes are effectively inhibited. Based on TG analysis and photodegradation experiments, TiO2/g-C3N4-4 heterojunction photocatalyst with TiO2 content of 29.30 wt% possesses the best photocatalytic degradation efficiency for the RhB among the g-C3N4, TiO2 and their mixture under simulated sunlight irradiation. Moreover, 1D morphology of TiO2/g-C3N4-4 heterojunction photocatalyst is in favor of separating from solution for reuse and transferring the electrons, and maintains a very high photocatalytic degradation efficiency of 96% even after four recycles experiments, which is beneficial for practical application.

The effect of multispectral image fusion enhancement on human efficiency.

PubMed

Bittner, Jennifer L; Schill, M Trent; Mohd-Zaid, Fairul; Blaha, Leslie M

2017-01-01

The visual system can be highly influenced by changes to visual presentation. Thus, numerous techniques have been developed to augment imagery in an attempt to improve human perception. The current paper examines the potential impact of one such enhancement, multispectral image fusion, where imagery captured in varying spectral bands (e.g., visible, thermal, night vision) is algorithmically combined to produce an output to strengthen visual perception. We employ ideal observer analysis over a series of experimental conditions to (1) establish a framework for testing the impact of image fusion over the varying aspects surrounding its implementation (e.g., stimulus content, task) and (2) examine the effectiveness of fusion on human information processing efficiency in a basic application. We used a set of rotated Landolt C images captured with a number of individual sensor cameras and combined across seven traditional fusion algorithms (e.g., Laplacian pyramid, principal component analysis, averaging) in a 1-of-8 orientation task. We found that, contrary to the idea of fused imagery always producing a greater impact on perception, single-band imagery can be just as influential. Additionally, efficiency data were shown to fluctuate based on sensor combination instead of fusion algorithm, suggesting the need for examining multiple factors to determine the success of image fusion. Our use of ideal observer analysis, a popular technique from the vision sciences, provides not only a standard for testing fusion in direct relation to the visual system but also allows for comparable examination of fusion across its associated problem space of application.

Kingfisher: a system for remote sensing image database management

NASA Astrophysics Data System (ADS)

Bruzzo, Michele; Giordano, Ferdinando; Dellepiane, Silvana G.

2003-04-01

At present retrieval methods in remote sensing image database are mainly based on spatial-temporal information. The increasing amount of images to be collected by the ground station of earth observing systems emphasizes the need for database management with intelligent data retrieval capabilities. The purpose of the proposed method is to realize a new content based retrieval system for remote sensing images database with an innovative search tool based on image similarity. This methodology is quite innovative for this application, at present many systems exist for photographic images, as for example QBIC and IKONA, but they are not able to extract and describe properly remote image content. The target database is set by an archive of images originated from an X-SAR sensor (spaceborne mission, 1994). The best content descriptors, mainly texture parameters, guarantees high retrieval performances and can be extracted without losses independently of image resolution. The latter property allows DBMS (Database Management System) to process low amount of information, as in the case of quick-look images, improving time performance and memory access without reducing retrieval accuracy. The matching technique has been designed to enable image management (database population and retrieval) independently of dimensions (width and height). Local and global content descriptors are compared, during retrieval phase, with the query image and results seem to be very encouraging.

Color Based Bags-of-Emotions

NASA Astrophysics Data System (ADS)

Solli, Martin; Lenz, Reiner

In this paper we describe how to include high level semantic information, such as aesthetics and emotions, into Content Based Image Retrieval. We present a color-based emotion-related image descriptor that can be used for describing the emotional content of images. The color emotion metric used is derived from psychophysical experiments and based on three variables: activity, weight and heat. It was originally designed for single-colors, but recent research has shown that the same emotion estimates can be applied in the retrieval of multi-colored images. Here we describe a new approach, based on the assumption that perceived color emotions in images are mainly affected by homogenous regions, defined by the emotion metric, and transitions between regions. RGB coordinates are converted to emotion coordinates, and for each emotion channel, statistical measurements of gradient magnitudes within a stack of low-pass filtered images are used for finding interest points corresponding to homogeneous regions and transitions between regions. Emotion characteristics are derived for patches surrounding each interest point, and saved in a bag-of-emotions, that, for instance, can be used for retrieving images based on emotional content.

CAMEL: concept annotated image libraries

NASA Astrophysics Data System (ADS)

Natsev, Apostol; Chadha, Atul; Soetarman, Basuki; Vitter, Jeffrey S.

2001-01-01

The problem of content-based image searching has received considerable attention in the last few years. Thousands of images are now available on the Internet, and many important applications require searching of images in domains such as E-commerce, medical imaging, weather prediction, satellite imagery, and so on. Yet, content-based image querying is still largely unestablished as a mainstream field, nor is it widely used by search engines. We believe that two of the major hurdles for this poor acceptance are poor retrieval quality and usability.

CAMEL: concept annotated image libraries

NASA Astrophysics Data System (ADS)

Natsev, Apostol; Chadha, Atul; Soetarman, Basuki; Vitter, Jeffrey S.

2000-12-01

The problem of content-based image searching has received considerable attention in the last few years. Thousands of images are now available on the Internet, and many important applications require searching of images in domains such as E-commerce, medical imaging, weather prediction, satellite imagery, and so on. Yet, content-based image querying is still largely unestablished as a mainstream field, nor is it widely used by search engines. We believe that two of the major hurdles for this poor acceptance are poor retrieval quality and usability.

A content-based image retrieval method for optical colonoscopy images based on image recognition techniques

NASA Astrophysics Data System (ADS)

Nosato, Hirokazu; Sakanashi, Hidenori; Takahashi, Eiichi; Murakawa, Masahiro

2015-03-01

This paper proposes a content-based image retrieval method for optical colonoscopy images that can find images similar to ones being diagnosed. Optical colonoscopy is a method of direct observation for colons and rectums to diagnose bowel diseases. It is the most common procedure for screening, surveillance and treatment. However, diagnostic accuracy for intractable inflammatory bowel diseases, such as ulcerative colitis (UC), is highly dependent on the experience and knowledge of the medical doctor, because there is considerable variety in the appearances of colonic mucosa within inflammations with UC. In order to solve this issue, this paper proposes a content-based image retrieval method based on image recognition techniques. The proposed retrieval method can find similar images from a database of images diagnosed as UC, and can potentially furnish the medical records associated with the retrieved images to assist the UC diagnosis. Within the proposed method, color histogram features and higher order local auto-correlation (HLAC) features are adopted to represent the color information and geometrical information of optical colonoscopy images, respectively. Moreover, considering various characteristics of UC colonoscopy images, such as vascular patterns and the roughness of the colonic mucosa, we also propose an image enhancement method to highlight the appearances of colonic mucosa in UC. In an experiment using 161 UC images from 32 patients, we demonstrate that our method improves the accuracy of retrieving similar UC images.

Hyperspectral reflectance imaging technique for visualization of moisture distribution in cooked chicken breast

USDA-ARS?s Scientific Manuscript database

Spectroscopy has proven to be an efficient tool for measuring the properties of meat. In this article, the hyperspectral imaging (HSI) technique is investigated for the determination of moisture content in cooked chicken breast over the VIS/NIR (400–1000 nm) spectral ranges. Moisture measurements we...

Real-time image annotation by manifold-based biased Fisher discriminant analysis

NASA Astrophysics Data System (ADS)

Ji, Rongrong; Yao, Hongxun; Wang, Jicheng; Sun, Xiaoshuai; Liu, Xianming

2008-01-01

Automatic Linguistic Annotation is a promising solution to bridge the semantic gap in content-based image retrieval. However, two crucial issues are not well addressed in state-of-art annotation algorithms: 1. The Small Sample Size (3S) problem in keyword classifier/model learning; 2. Most of annotation algorithms can not extend to real-time online usage due to their low computational efficiencies. This paper presents a novel Manifold-based Biased Fisher Discriminant Analysis (MBFDA) algorithm to address these two issues by transductive semantic learning and keyword filtering. To address the 3S problem, Co-Training based Manifold learning is adopted for keyword model construction. To achieve real-time annotation, a Bias Fisher Discriminant Analysis (BFDA) based semantic feature reduction algorithm is presented for keyword confidence discrimination and semantic feature reduction. Different from all existing annotation methods, MBFDA views image annotation from a novel Eigen semantic feature (which corresponds to keywords) selection aspect. As demonstrated in experiments, our manifold-based biased Fisher discriminant analysis annotation algorithm outperforms classical and state-of-art annotation methods (1.K-NN Expansion; 2.One-to-All SVM; 3.PWC-SVM) in both computational time and annotation accuracy with a large margin.

Joint Chroma Subsampling and Distortion-Minimization-Based Luma Modification for RGB Color Images With Application.

PubMed

Chung, Kuo-Liang; Hsu, Tsu-Chun; Huang, Chi-Chao

2017-10-01

In this paper, we propose a novel and effective hybrid method, which joins the conventional chroma subsampling and the distortion-minimization-based luma modification together, to improve the quality of the reconstructed RGB full-color image. Assume the input RGB full-color image has been transformed to a YUV image, prior to compression. For each 2×2 UV block, one 4:2:0 subsampling is applied to determine the one subsampled U and V components, U s and V s . Based on U s , V s , and the corresponding 2×2 original RGB block, a main theorem is provided to determine the ideally modified 2×2 luma block in constant time such that the color peak signal-to-noise ratio (CPSNR) quality distortion between the original 2×2 RGB block and the reconstructed 2×2 RGB block can be minimized in a globally optimal sense. Furthermore, the proposed hybrid method and the delivered theorem are adjusted to tackle the digital time delay integration images and the Bayer mosaic images whose Bayer CFA structure has been widely used in modern commercial digital cameras. Based on the IMAX, Kodak, and screen content test image sets, the experimental results demonstrate that in high efficiency video coding, the proposed hybrid method has substantial quality improvement, in terms of the CPSNR quality, visual effect, CPSNR-bitrate trade-off, and Bjøntegaard delta PSNR performance, of the reconstructed RGB images when compared with existing chroma subsampling schemes.

Ultrafast Microfluidic Cellular Imaging by Optical Time-Stretch.

PubMed

Lau, Andy K S; Wong, Terence T W; Shum, Ho Cheung; Wong, Kenneth K Y; Tsia, Kevin K

2016-01-01

There is an unmet need in biomedicine for measuring a multitude of parameters of individual cells (i.e., high content) in a large population efficiently (i.e., high throughput). This is particularly driven by the emerging interest in bringing Big-Data analysis into this arena, encompassing pathology, drug discovery, rare cancer cell detection, emulsion microdroplet assays, to name a few. This momentum is particularly evident in recent advancements in flow cytometry. They include scaling of the number of measurable colors from the labeled cells and incorporation of imaging capability to access the morphological information of the cells. However, an unspoken predicament appears in the current technologies: higher content comes at the expense of lower throughput, and vice versa. For example, accessing additional spatial information of individual cells, imaging flow cytometers only achieve an imaging throughput ~1000 cells/s, orders of magnitude slower than the non-imaging flow cytometers. In this chapter, we introduce an entirely new imaging platform, namely optical time-stretch microscopy, for ultrahigh speed and high contrast label-free single-cell (in a ultrafast microfluidic flow up to 10 m/s) imaging and analysis with an ultra-fast imaging line-scan rate as high as tens of MHz. Based on this technique, not only morphological information of the individual cells can be obtained in an ultrafast manner, quantitative evaluation of cellular information (e.g., cell volume, mass, refractive index, stiffness, membrane tension) at nanometer scale based on the optical phase is also possible. The technology can also be integrated with conventional fluorescence measurements widely adopted in the non-imaging flow cytometers. Therefore, these two combinatorial and complementary measurement capabilities in long run is an attractive platform for addressing the pressing need for expanding the "parameter space" in high-throughput single-cell analysis. This chapter provides the general guidelines of constructing the optical system for time stretch imaging, fabrication and design of the microfluidic chip for ultrafast fluidic flow, as well as the image acquisition and processing.

An Open Source Agenda for Research Linking Text and Image Content Features.

ERIC Educational Resources Information Center

Goodrum, Abby A.; Rorvig, Mark E.; Jeong, Ki-Tai; Suresh, Chitturi

2001-01-01

Proposes methods to utilize image primitives to support term assignment for image classification. Proposes to release code for image analysis in a common tool set for other researchers to use. Of particular focus is the expansion of work by researchers in image indexing to include image content-based feature extraction capabilities in their work.…

Systems and methods for optically measuring properties of hydrocarbon fuel gases

DOEpatents

Adler-Golden, S.; Bernstein, L.S.; Bien, F.; Gersh, M.E.; Goldstein, N.

1998-10-13

A system and method for optical interrogation and measurement of a hydrocarbon fuel gas includes a light source generating light at near-visible wavelengths. A cell containing the gas is optically coupled to the light source which is in turn partially transmitted by the sample. A spectrometer disperses the transmitted light and captures an image thereof. The image is captured by a low-cost silicon-based two-dimensional CCD array. The captured spectral image is processed by electronics for determining energy or BTU content and composition of the gas. The innovative optical approach provides a relatively inexpensive, durable, maintenance-free sensor and method which is reliable in the field and relatively simple to calibrate. In view of the above, accurate monitoring is possible at a plurality of locations along the distribution chain leading to more efficient distribution. 14 figs.

Systems and methods for optically measuring properties of hydrocarbon fuel gases

DOEpatents

Adler-Golden, Steven; Bernstein, Lawrence S.; Bien, Fritz; Gersh, Michael E.; Goldstein, Neil

1998-10-13

A system and method for optical interrogation and measurement of a hydrocarbon fuel gas includes a light source generating light at near-visible wavelengths. A cell containing the gas is optically coupled to the light source which is in turn partially transmitted by the sample. A spectrometer disperses the transmitted light and captures an image thereof. The image is captured by a low-cost silicon-based two-dimensional CCD array. The captured spectral image is processed by electronics for determining energy or BTU content and composition of the gas. The innovative optical approach provides a relatively inexpensive, durable, maintenance-free sensor and method which is reliable in the field and relatively simple to calibrate. In view of the above, accurate monitoring is possible at a plurality of locations along the distribution chain leading to more efficient distribution.

A spectral water index based on visual bands

NASA Astrophysics Data System (ADS)

Basaeed, Essa; Bhaskar, Harish; Al-Mualla, Mohammed

2013-10-01

Land-water segmentation is an important preprocessing step in a number of remote sensing applications such as target detection, environmental monitoring, and map updating. A Normalized Optical Water Index (NOWI) is proposed to accurately discriminate between land and water regions in multi-spectral satellite imagery data from DubaiSat-1. NOWI exploits the spectral characteristics of water content (using visible bands) and uses a non-linear normalization procedure that renders strong emphasize on small changes in lower brightness values whilst guaranteeing that the segmentation process remains image-independent. The NOWI representation is validated through systematic experiments, evaluated using robust metrics, and compared against various supervised classification algorithms. Analysis has indicated that NOWI has the advantages that it: a) is a pixel-based method that requires no global knowledge of the scene under investigation, b) can be easily implemented in parallel processing, c) is image-independent and requires no training, d) works in different environmental conditions, e) provides high accuracy and efficiency, and f) works directly on the input image without any form of pre-processing.

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm.

PubMed

Yang, Mengzhao; Song, Wei; Mei, Haibin

2017-07-23

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient.

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm

PubMed Central

Song, Wei; Mei, Haibin

2017-01-01

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient. PMID:28737699

Content-based management service for medical videos.

PubMed

Mendi, Engin; Bayrak, Coskun; Cecen, Songul; Ermisoglu, Emre

2013-01-01

Development of health information technology has had a dramatic impact to improve the efficiency and quality of medical care. Developing interoperable health information systems for healthcare providers has the potential to improve the quality and equitability of patient-centered healthcare. In this article, we describe an automated content-based medical video analysis and management service that provides convenience and ease in accessing the relevant medical video content without sequential scanning. The system facilitates effective temporal video segmentation and content-based visual information retrieval that enable a more reliable understanding of medical video content. The system is implemented as a Web- and mobile-based service and has the potential to offer a knowledge-sharing platform for the purpose of efficient medical video content access.

Audiovisual focus of attention and its application to Ultra High Definition video compression

NASA Astrophysics Data System (ADS)

Rerabek, Martin; Nemoto, Hiromi; Lee, Jong-Seok; Ebrahimi, Touradj

2014-02-01

Using Focus of Attention (FoA) as a perceptual process in image and video compression belongs to well-known approaches to increase coding efficiency. It has been shown that foveated coding, when compression quality varies across the image according to region of interest, is more efficient than the alternative coding, when all region are compressed in a similar way. However, widespread use of such foveated compression has been prevented due to two main conflicting causes, namely, the complexity and the efficiency of algorithms for FoA detection. One way around these is to use as much information as possible from the scene. Since most video sequences have an associated audio, and moreover, in many cases there is a correlation between the audio and the visual content, audiovisual FoA can improve efficiency of the detection algorithm while remaining of low complexity. This paper discusses a simple yet efficient audiovisual FoA algorithm based on correlation of dynamics between audio and video signal components. Results of audiovisual FoA detection algorithm are subsequently taken into account for foveated coding and compression. This approach is implemented into H.265/HEVC encoder producing a bitstream which is fully compliant to any H.265/HEVC decoder. The influence of audiovisual FoA in the perceived quality of high and ultra-high definition audiovisual sequences is explored and the amount of gain in compression efficiency is analyzed.

Automatic Depth Extraction from 2D Images Using a Cluster-Based Learning Framework.

PubMed

Herrera, Jose L; Del-Blanco, Carlos R; Garcia, Narciso

2018-07-01

There has been a significant increase in the availability of 3D players and displays in the last years. Nonetheless, the amount of 3D content has not experimented an increment of such magnitude. To alleviate this problem, many algorithms for converting images and videos from 2D to 3D have been proposed. Here, we present an automatic learning-based 2D-3D image conversion approach, based on the key hypothesis that color images with similar structure likely present a similar depth structure. The presented algorithm estimates the depth of a color query image using the prior knowledge provided by a repository of color + depth images. The algorithm clusters this database attending to their structural similarity, and then creates a representative of each color-depth image cluster that will be used as prior depth map. The selection of the appropriate prior depth map corresponding to one given color query image is accomplished by comparing the structural similarity in the color domain between the query image and the database. The comparison is based on a K-Nearest Neighbor framework that uses a learning procedure to build an adaptive combination of image feature descriptors. The best correspondences determine the cluster, and in turn the associated prior depth map. Finally, this prior estimation is enhanced through a segmentation-guided filtering that obtains the final depth map estimation. This approach has been tested using two publicly available databases, and compared with several state-of-the-art algorithms in order to prove its efficiency.

On the efficiency of the image encryption and decryption by using logistic-sine chaotic system and logistic-tent chaotic system

NASA Astrophysics Data System (ADS)

Chiun, Lee Chia; Mandangan, Arif; Daud, Muhamad Azlan; Hussin, Che Haziqah Che

2017-04-01

We may secure the content of text, audio, image and video during their transmission from one party to another party via an open channel such as the internet by using cryptograph. Logistic-Sine System (LSS) is a combination on two 1D chaotic maps which are Logistic Map and Sine Map. By applying the LSS into cryptography, the image encryption and decryption can be performed. This study is focusing on the performance test of the image encryption and decryption processes by using the LSS. For comparison purpose, we compare the performance of the encryption and decryption by using two different chaotic systems, which are the LSS and Logistic-Tent System (LTS). The result shows that system with LSS is less efficient than LTS in term of encryption time but both systems have similar efficiency in term of decryption time.

A method for predicting DCT-based denoising efficiency for grayscale images corrupted by AWGN and additive spatially correlated noise

NASA Astrophysics Data System (ADS)

Rubel, Aleksey S.; Lukin, Vladimir V.; Egiazarian, Karen O.

2015-03-01

Results of denoising based on discrete cosine transform for a wide class of images corrupted by additive noise are obtained. Three types of noise are analyzed: additive white Gaussian noise and additive spatially correlated Gaussian noise with middle and high correlation levels. TID2013 image database and some additional images are taken as test images. Conventional DCT filter and BM3D are used as denoising techniques. Denoising efficiency is described by PSNR and PSNR-HVS-M metrics. Within hard-thresholding denoising mechanism, DCT-spectrum coefficient statistics are used to characterize images and, subsequently, denoising efficiency for them. Results of denoising efficiency are fitted for such statistics and efficient approximations are obtained. It is shown that the obtained approximations provide high accuracy of prediction of denoising efficiency.

New generation of the multimedia search engines

NASA Astrophysics Data System (ADS)

Mijes Cruz, Mario Humberto; Soto Aldaco, Andrea; Maldonado Cano, Luis Alejandro; López Rodríguez, Mario; Rodríguez Vázqueza, Manuel Antonio; Amaya Reyes, Laura Mariel; Cano Martínez, Elizabeth; Pérez Rosas, Osvaldo Gerardo; Rodríguez Espejo, Luis; Flores Secundino, Jesús Abimelek; Rivera Martínez, José Luis; García Vázquez, Mireya Saraí; Zamudio Fuentes, Luis Miguel; Sánchez Valenzuela, Juan Carlos; Montoya Obeso, Abraham; Ramírez Acosta, Alejandro Álvaro

2016-09-01

Current search engines are based upon search methods that involve the combination of words (text-based search); which has been efficient until now. However, the Internet's growing demand indicates that there's more diversity on it with each passing day. Text-based searches are becoming limited, as most of the information on the Internet can be found in different types of content denominated multimedia content (images, audio files, video files). Indeed, what needs to be improved in current search engines is: search content, and precision; as well as an accurate display of expected search results by the user. Any search can be more precise if it uses more text parameters, but it doesn't help improve the content or speed of the search itself. One solution is to improve them through the characterization of the content for the search in multimedia files. In this article, an analysis of the new generation multimedia search engines is presented, focusing the needs according to new technologies. Multimedia content has become a central part of the flow of information in our daily life. This reflects the necessity of having multimedia search engines, as well as knowing the real tasks that it must comply. Through this analysis, it is shown that there are not many search engines that can perform content searches. The area of research of multimedia search engines of new generation is a multidisciplinary area that's in constant growth, generating tools that satisfy the different needs of new generation systems.

A Sensitive Measurement for Estimating Impressions of Image-Contents

NASA Astrophysics Data System (ADS)

Sato, Mie; Matouge, Shingo; Mori, Toshifumi; Suzuki, Noboru; Kasuga, Masao

We have investigated Kansei Content that appeals maker's intention to viewer's kansei. An SD method is a very good way to evaluate subjective impression of image-contents. However, because the SD method is performed after subjects view the image-contents, it is difficult to examine impression of detailed scenes of the image-contents in real time. To measure viewer's impression of the image-contents in real time, we have developed a Taikan sensor. With the Taikan sensor, we investigate relations among the image-contents, the grip strength and the body temperature. We also explore the interface of the Taikan sensor to use it easily. In our experiment, a horror movie is used that largely affects emotion of the subjects. Our results show that there is a possibility that the grip strength increases when the subjects view a strained scene and that it is easy to use the Taikan sensor without its circle base that is originally installed.

Self-adaptive relevance feedback based on multilevel image content analysis

NASA Astrophysics Data System (ADS)

Gao, Yongying; Zhang, Yujin; Fu, Yu

2001-01-01

In current content-based image retrieval systems, it is generally accepted that obtaining high-level image features is a key to improve the querying. Among the related techniques, relevance feedback has become a hot research aspect because it combines the information from the user to refine the querying results. In practice, many methods have been proposed to achieve the goal of relevance feedback. In this paper, a new scheme for relevance feedback is proposed. Unlike previous methods for relevance feedback, our scheme provides a self-adaptive operation. First, based on multi- level image content analysis, the relevant images from the user could be automatically analyzed in different levels and the querying could be modified in terms of different analysis results. Secondly, to make it more convenient to the user, the procedure of relevance feedback could be led with memory or without memory. To test the performance of the proposed method, a practical semantic-based image retrieval system has been established, and the querying results gained by our self-adaptive relevance feedback are given.

Self-adaptive relevance feedback based on multilevel image content analysis

NASA Astrophysics Data System (ADS)

Gao, Yongying; Zhang, Yujin; Fu, Yu

2000-12-01

In current content-based image retrieval systems, it is generally accepted that obtaining high-level image features is a key to improve the querying. Among the related techniques, relevance feedback has become a hot research aspect because it combines the information from the user to refine the querying results. In practice, many methods have been proposed to achieve the goal of relevance feedback. In this paper, a new scheme for relevance feedback is proposed. Unlike previous methods for relevance feedback, our scheme provides a self-adaptive operation. First, based on multi- level image content analysis, the relevant images from the user could be automatically analyzed in different levels and the querying could be modified in terms of different analysis results. Secondly, to make it more convenient to the user, the procedure of relevance feedback could be led with memory or without memory. To test the performance of the proposed method, a practical semantic-based image retrieval system has been established, and the querying results gained by our self-adaptive relevance feedback are given.

Endocytotic potential governs magnetic particle loading in dividing neural cells: studying modes of particle inheritance

PubMed Central

Tickle, Jacqueline A; Jenkins, Stuart I; Polyak, Boris; Pickard, Mark R; Chari, Divya M

2016-01-01

Aim: To achieve high and sustained magnetic particle loading in a proliferative and endocytotically active neural transplant population (astrocytes) through tailored magnetite content in polymeric iron oxide particles. Materials & methods: MPs of varying magnetite content were applied to primary-derived rat cortical astrocytes ± static/oscillating magnetic fields to assess labeling efficiency and safety. Results: Higher magnetite content particles display high but safe accumulation in astrocytes, with longer-term label retention versus lower/no magnetite content particles. Magnetic fields enhanced loading extent. Dynamic live cell imaging of dividing labeled astrocytes demonstrated that particle distribution into daughter cells is predominantly ‘asymmetric’. Conclusion: These findings could inform protocols to achieve efficient MP loading into neural transplant cells, with significant implications for post-transplantation tracking/localization. PMID:26785794

High content image based analysis identifies cell cycle inhibitors as regulators of Ebola virus infection.

PubMed

Kota, Krishna P; Benko, Jacqueline G; Mudhasani, Rajini; Retterer, Cary; Tran, Julie P; Bavari, Sina; Panchal, Rekha G

2012-09-25

Viruses modulate a number of host biological responses including the cell cycle to favor their replication. In this study, we developed a high-content imaging (HCI) assay to measure DNA content and identify different phases of the cell cycle. We then investigated the potential effects of cell cycle arrest on Ebola virus (EBOV) infection. Cells arrested in G1 phase by serum starvation or G1/S phase using aphidicolin or G2/M phase using nocodazole showed much reduced EBOV infection compared to the untreated control. Release of cells from serum starvation or aphidicolin block resulted in a time-dependent increase in the percentage of EBOV infected cells. The effect of EBOV infection on cell cycle progression was found to be cell-type dependent. Infection of asynchronous MCF-10A cells with EBOV resulted in a reduced number of cells in G2/M phase with concomitant increase of cells in G1 phase. However, these effects were not observed in HeLa or A549 cells. Together, our studies suggest that EBOV requires actively proliferating cells for efficient replication. Furthermore, multiplexing of HCI based assays to detect viral infection, cell cycle status and other phenotypic changes in a single cell population will provide useful information during screening campaigns using siRNA and small molecule therapeutics.

High-Content, High-Throughput Screening for the Identification of Cytotoxic Compounds Based on Cell Morphology and Cell Proliferation Markers

PubMed Central

Martin, Heather L.; Adams, Matthew; Higgins, Julie; Bond, Jacquelyn; Morrison, Ewan E.; Bell, Sandra M.; Warriner, Stuart; Nelson, Adam; Tomlinson, Darren C.

2014-01-01

Toxicity is a major cause of failure in drug discovery and development, and whilst robust toxicological testing occurs, efficiency could be improved if compounds with cytotoxic characteristics were identified during primary compound screening. The use of high-content imaging in primary screening is becoming more widespread, and by utilising phenotypic approaches it should be possible to incorporate cytotoxicity counter-screens into primary screens. Here we present a novel phenotypic assay that can be used as a counter-screen to identify compounds with adverse cellular effects. This assay has been developed using U2OS cells, the PerkinElmer Operetta high-content/high-throughput imaging system and Columbus image analysis software. In Columbus, algorithms were devised to identify changes in nuclear morphology, cell shape and proliferation using DAPI, TOTO-3 and phosphohistone H3 staining, respectively. The algorithms were developed and tested on cells treated with doxorubicin, taxol and nocodazole. The assay was then used to screen a novel, chemical library, rich in natural product-like molecules of over 300 compounds, 13.6% of which were identified as having adverse cellular effects. This assay provides a relatively cheap and rapid approach for identifying compounds with adverse cellular effects during screening assays, potentially reducing compound rejection due to toxicity in subsequent in vitro and in vivo assays. PMID:24505478

Evaluation of contents-based image retrieval methods for a database of logos on drug tablets

NASA Astrophysics Data System (ADS)

Geradts, Zeno J.; Hardy, Huub; Poortman, Anneke; Bijhold, Jurrien

2001-02-01

In this research an evaluation has been made of the different ways of contents based image retrieval of logos of drug tablets. On a database of 432 illicitly produced tablets (mostly containing MDMA), we have compared different retrieval methods. Two of these methods were available from commercial packages, QBIC and Imatch, where the implementation of the contents based image retrieval methods are not exactly known. We compared the results for this database with the MPEG-7 shape comparison methods, which are the contour-shape, bounding box and region-based shape methods. In addition, we have tested the log polar method that is available from our own research.

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

PubMed

Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

2012-04-01

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

Method for Separation of Blood Vessels on the Three-Color Images of Biological Tissues

NASA Astrophysics Data System (ADS)

Lisenko, S. A.

2017-07-01

A new technology was developed to improve the visibility of blood vessels on images of tissues of hollow human organs(the alimentary tract and respiratory system) based on the relation between the color components of the image, the scattering properties of the tissue, and its hemoglobin content. A statistical operator was presented to convert the three-color image of the tissue into a parametric map objectively characterizing the concentration of hemoglobin in the tissue regardless of the illumination and shooting conditions. An algorithm for obtaining conversion parameters for image systems with known spectral characteristics was presented. An image of a multilayer multiple-scattering medium modeling bronchial tissue was synthesized and was used to evaluate the efficiency of the proposed conversion system. It was shown that the conversion made it possible to increase the contrast of the blood vessels by almost two orders of magnitude, to significantly improve the clarity of the display of their borders, and to eliminate almost completely the influence of background and nonuniform illumination of the medium in comparison with the original image.

A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features

NASA Astrophysics Data System (ADS)

Srinivasan, Yeshwanth; Hernes, Dana; Tulpule, Bhakti; Yang, Shuyu; Guo, Jiangling; Mitra, Sunanda; Yagneswaran, Sriraja; Nutter, Brian; Jeronimo, Jose; Phillips, Benny; Long, Rodney; Ferris, Daron

2005-04-01

Automated segmentation and classification of diagnostic markers in medical imagery are challenging tasks. Numerous algorithms for segmentation and classification based on statistical approaches of varying complexity are found in the literature. However, the design of an efficient and automated algorithm for precise classification of desired diagnostic markers is extremely image-specific. The National Library of Medicine (NLM), in collaboration with the National Cancer Institute (NCI), is creating an archive of 60,000 digitized color images of the uterine cervix. NLM is developing tools for the analysis and dissemination of these images over the Web for the study of visual features correlated with precancerous neoplasia and cancer. To enable indexing of images of the cervix, it is essential to develop algorithms for the segmentation of regions of interest, such as acetowhitened regions, and automatic identification and classification of regions exhibiting mosaicism and punctation. Success of such algorithms depends, primarily, on the selection of relevant features representing the region of interest. We present color and geometric features based statistical classification and segmentation algorithms yielding excellent identification of the regions of interest. The distinct classification of the mosaic regions from the non-mosaic ones has been obtained by clustering multiple geometric and color features of the segmented sections using various morphological and statistical approaches. Such automated classification methodologies will facilitate content-based image retrieval from the digital archive of uterine cervix and have the potential of developing an image based screening tool for cervical cancer.

Region of interest extraction based on multiscale visual saliency analysis for remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Yinggang; Zhang, Libao; Yu, Xianchuan

2015-01-01

Region of interest (ROI) extraction is an important component of remote sensing image processing. However, traditional ROI extraction methods are usually prior knowledge-based and depend on classification, segmentation, and a global searching solution, which are time-consuming and computationally complex. We propose a more efficient ROI extraction model for remote sensing images based on multiscale visual saliency analysis (MVS), implemented in the CIE L*a*b* color space, which is similar to visual perception of the human eye. We first extract the intensity, orientation, and color feature of the image using different methods: the visual attention mechanism is used to eliminate the intensity feature using a difference of Gaussian template; the integer wavelet transform is used to extract the orientation feature; and color information content analysis is used to obtain the color feature. Then, a new feature-competition method is proposed that addresses the different contributions of each feature map to calculate the weight of each feature image for combining them into the final saliency map. Qualitative and quantitative experimental results of the MVS model as compared with those of other models show that it is more effective and provides more accurate ROI extraction results with fewer holes inside the ROI.

Requirements for benchmarking personal image retrieval systems

NASA Astrophysics Data System (ADS)

Bouguet, Jean-Yves; Dulong, Carole; Kozintsev, Igor; Wu, Yi

2006-01-01

It is now common to have accumulated tens of thousands of personal ictures. Efficient access to that many pictures can only be done with a robust image retrieval system. This application is of high interest to Intel processor architects. It is highly compute intensive, and could motivate end users to upgrade their personal computers to the next generations of processors. A key question is how to assess the robustness of a personal image retrieval system. Personal image databases are very different from digital libraries that have been used by many Content Based Image Retrieval Systems.1 For example a personal image database has a lot of pictures of people, but a small set of different people typically family, relatives, and friends. Pictures are taken in a limited set of places like home, work, school, and vacation destination. The most frequent queries are searched for people, and for places. These attributes, and many others affect how a personal image retrieval system should be benchmarked, and benchmarks need to be different from existing ones based on art images, or medical images for examples. The attributes of the data set do not change the list of components needed for the benchmarking of such systems as specified in2: - data sets - query tasks - ground truth - evaluation measures - benchmarking events. This paper proposed a way to build these components to be representative of personal image databases, and of the corresponding usage models.

Exemplar-Based Image and Video Stylization Using Fully Convolutional Semantic Features.

PubMed

Zhu, Feida; Yan, Zhicheng; Bu, Jiajun; Yu, Yizhou

2017-05-10

Color and tone stylization in images and videos strives to enhance unique themes with artistic color and tone adjustments. It has a broad range of applications from professional image postprocessing to photo sharing over social networks. Mainstream photo enhancement softwares, such as Adobe Lightroom and Instagram, provide users with predefined styles, which are often hand-crafted through a trial-and-error process. Such photo adjustment tools lack a semantic understanding of image contents and the resulting global color transform limits the range of artistic styles it can represent. On the other hand, stylistic enhancement needs to apply distinct adjustments to various semantic regions. Such an ability enables a broader range of visual styles. In this paper, we first propose a novel deep learning architecture for exemplar-based image stylization, which learns local enhancement styles from image pairs. Our deep learning architecture consists of fully convolutional networks (FCNs) for automatic semantics-aware feature extraction and fully connected neural layers for adjustment prediction. Image stylization can be efficiently accomplished with a single forward pass through our deep network. To extend our deep network from image stylization to video stylization, we exploit temporal superpixels (TSPs) to facilitate the transfer of artistic styles from image exemplars to videos. Experiments on a number of datasets for image stylization as well as a diverse set of video clips demonstrate the effectiveness of our deep learning architecture.

Automatic content-based analysis of georeferenced image data: Detection of Beggiatoa mats in seafloor video mosaics from the HÅkon Mosby Mud Volcano

NASA Astrophysics Data System (ADS)

Jerosch, K.; Lüdtke, A.; Schlüter, M.; Ioannidis, G. T.

2007-02-01

The combination of new underwater technology as remotely operating vehicles (ROVs), high-resolution video imagery, and software to compute georeferenced mosaics of the seafloor provides new opportunities for marine geological or biological studies and applications in offshore industry. Even during single surveys by ROVs or towed systems large amounts of images are compiled. While these underwater techniques are now well-engineered, there is still a lack of methods for the automatic analysis of the acquired image data. During ROV dives more than 4200 georeferenced video mosaics were compiled for the HÅkon Mosby Mud Volcano (HMMV). Mud volcanoes as HMMV are considered as significant source locations for methane characterised by unique chemoautotrophic communities as Beggiatoa mats. For the detection and quantification of the spatial distribution of Beggiatoa mats an automated image analysis technique was developed, which applies watershed transformation and relaxation-based labelling of pre-segmented regions. Comparison of the data derived by visual inspection of 2840 video images with the automated image analysis revealed similarities with a precision better than 90%. We consider this as a step towards a time-efficient and accurate analysis of seafloor images for computation of geochemical budgets and identification of habitats at the seafloor.

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images

PubMed Central

Kather, Jakob Nikolas; Weis, Cleo-Aron; Marx, Alexander; Schuster, Alexander K.; Schad, Lothar R.; Zöllner, Frank Gerrit

2015-01-01

Background Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions. Methods and Results In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin—3,3’-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images. Validation To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images. Context Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics. PMID:26717571

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images.

PubMed

Kather, Jakob Nikolas; Weis, Cleo-Aron; Marx, Alexander; Schuster, Alexander K; Schad, Lothar R; Zöllner, Frank Gerrit

2015-01-01

Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions. In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin-3,3'-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images. To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images. Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.

Content-based multiple bitstream image transmission over noisy channels.

PubMed

Cao, Lei; Chen, Chang Wen

2002-01-01

In this paper, we propose a novel combined source and channel coding scheme for image transmission over noisy channels. The main feature of the proposed scheme is a systematic decomposition of image sources so that unequal error protection can be applied according to not only bit error sensitivity but also visual content importance. The wavelet transform is adopted to hierarchically decompose the image. The association between the wavelet coefficients and what they represent spatially in the original image is fully exploited so that wavelet blocks are classified based on their corresponding image content. The classification produces wavelet blocks in each class with similar content and statistics, therefore enables high performance source compression using the set partitioning in hierarchical trees (SPIHT) algorithm. To combat the channel noise, an unequal error protection strategy with rate-compatible punctured convolutional/cyclic redundancy check (RCPC/CRC) codes is implemented based on the bit contribution to both peak signal-to-noise ratio (PSNR) and visual quality. At the receiving end, a postprocessing method making use of the SPIHT decoding structure and the classification map is developed to restore the degradation due to the residual error after channel decoding. Experimental results show that the proposed scheme is indeed able to provide protection both for the bits that are more sensitive to errors and for the more important visual content under a noisy transmission environment. In particular, the reconstructed images illustrate consistently better visual quality than using the single-bitstream-based schemes.

Method for indexing and retrieving manufacturing-specific digital imagery based on image content

DOEpatents

Ferrell, Regina K.; Karnowski, Thomas P.; Tobin, Jr., Kenneth W.

2004-06-15

A method for indexing and retrieving manufacturing-specific digital images based on image content comprises three steps. First, at least one feature vector can be extracted from a manufacturing-specific digital image stored in an image database. In particular, each extracted feature vector corresponds to a particular characteristic of the manufacturing-specific digital image, for instance, a digital image modality and overall characteristic, a substrate/background characteristic, and an anomaly/defect characteristic. Notably, the extracting step includes generating a defect mask using a detection process. Second, using an unsupervised clustering method, each extracted feature vector can be indexed in a hierarchical search tree. Third, a manufacturing-specific digital image associated with a feature vector stored in the hierarchicial search tree can be retrieved, wherein the manufacturing-specific digital image has image content comparably related to the image content of the query image. More particularly, can include two data reductions, the first performed based upon a query vector extracted from a query image. Subsequently, a user can select relevant images resulting from the first data reduction. From the selection, a prototype vector can be calculated, from which a second-level data reduction can be performed. The second-level data reduction can result in a subset of feature vectors comparable to the prototype vector, and further comparable to the query vector. An additional fourth step can include managing the hierarchical search tree by substituting a vector average for several redundant feature vectors encapsulated by nodes in the hierarchical search tree.

Image/text automatic indexing and retrieval system using context vector approach

NASA Astrophysics Data System (ADS)

Qing, Kent P.; Caid, William R.; Ren, Clara Z.; McCabe, Patrick

1995-11-01

Thousands of documents and images are generated daily both on and off line on the information superhighway and other media. Storage technology has improved rapidly to handle these data but indexing this information is becoming very costly. HNC Software Inc. has developed a technology for automatic indexing and retrieval of free text and images. This technique is demonstrated and is based on the concept of `context vectors' which encode a succinct representation of the associated text and features of sub-image. In this paper, we will describe the Automated Librarian System which was designed for free text indexing and the Image Content Addressable Retrieval System (ICARS) which extends the technique from the text domain into the image domain. Both systems have the ability to automatically assign indices for a new document and/or image based on the content similarities in the database. ICARS also has the capability to retrieve images based on similarity of content using index terms, text description, and user-generated images as a query without performing segmentation or object recognition.

Development of a personalized training system using the Lung Image Database Consortium and Image Database resource Initiative Database.

PubMed

Lin, Hongli; Wang, Weisheng; Luo, Jiawei; Yang, Xuedong

2014-12-01

The aim of this study was to develop a personalized training system using the Lung Image Database Consortium (LIDC) and Image Database resource Initiative (IDRI) Database, because collecting, annotating, and marking a large number of appropriate computed tomography (CT) scans, and providing the capability of dynamically selecting suitable training cases based on the performance levels of trainees and the characteristics of cases are critical for developing a efficient training system. A novel approach is proposed to develop a personalized radiology training system for the interpretation of lung nodules in CT scans using the Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which provides a Content-Boosted Collaborative Filtering (CBCF) algorithm for predicting the difficulty level of each case of each trainee when selecting suitable cases to meet individual needs, and a diagnostic simulation tool to enable trainees to analyze and diagnose lung nodules with the help of an image processing tool and a nodule retrieval tool. Preliminary evaluation of the system shows that developing a personalized training system for interpretation of lung nodules is needed and useful to enhance the professional skills of trainees. The approach of developing personalized training systems using the LIDC/IDRL database is a feasible solution to the challenges of constructing specific training program in terms of cost and training efficiency. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Content-aware photo collage using circle packing.

PubMed

Yu, Zongqiao; Lu, Lin; Guo, Yanwen; Fan, Rongfei; Liu, Mingming; Wang, Wenping

2014-02-01

In this paper, we present a novel approach for automatically creating the photo collage that assembles the interest regions of a given group of images naturally. Previous methods on photo collage are generally built upon a well-defined optimization framework, which computes all the geometric parameters and layer indices for input photos on the given canvas by optimizing a unified objective function. The complex nonlinear form of optimization function limits their scalability and efficiency. From the geometric point of view, we recast the generation of collage as a region partition problem such that each image is displayed in its corresponding region partitioned from the canvas. The core of this is an efficient power-diagram-based circle packing algorithm that arranges a series of circles assigned to input photos compactly in the given canvas. To favor important photos, the circles are associated with image importances determined by an image ranking process. A heuristic search process is developed to ensure that salient information of each photo is displayed in the polygonal area resulting from circle packing. With our new formulation, each factor influencing the state of a photo is optimized in an independent stage, and computation of the optimal states for neighboring photos are completely decoupled. This improves the scalability of collage results and ensures their diversity. We also devise a saliency-based image fusion scheme to generate seamless compositive collage. Our approach can generate the collages on nonrectangular canvases and supports interactive collage that allows the user to refine collage results according to his/her personal preferences. We conduct extensive experiments and show the superiority of our algorithm by comparing against previous methods.

Real-time determination of sarcomere length of a single cardiomyocyte during contraction

PubMed Central

Kalda, Mari; Vendelin, Marko

2013-01-01

Sarcomere length of a cardiomyocyte is an important control parameter for physiology studies on a single cell level; for instance, its accurate determination in real time is essential for performing single cardiomyocyte contraction experiments. The aim of this work is to develop an efficient and accurate method for estimating a mean sarcomere length of a contracting cardiomyocyte using microscopy images as an input. The novelty in developed method lies in 1) using unbiased measure of similarities to eliminate systematic errors from conventional autocorrelation function (ACF)-based methods when applied to region of interest of an image, 2) using a semianalytical, seminumerical approach for evaluating the similarity measure to take into account spatial dependence of neighboring image pixels, and 3) using a detrend algorithm to extract the sarcomere striation pattern content from the microscopy images. The developed sarcomere length estimation procedure has superior computational efficiency and estimation accuracy compared with the conventional ACF and spectral analysis-based methods using fast Fourier transform. As shown by analyzing synthetic images with the known periodicity, the estimates obtained by the developed method are more accurate at the subpixel level than ones obtained using ACF analysis. When applied in practice on rat cardiomyocytes, our method was found to be robust to the choice of the region of interest that may 1) include projections of carbon fibers and nucleus, 2) have uneven background, and 3) be slightly disoriented with respect to average direction of sarcomere striation pattern. The developed method is implemented in open-source software. PMID:23255581

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics.

PubMed

Sharma, Harshita; Alekseychuk, Alexander; Leskovsky, Peter; Hellwich, Olaf; Anand, R S; Zerbe, Norman; Hufnagl, Peter

2012-10-04

Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923.

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics

PubMed Central

2012-01-01

Background Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. Methods The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. Results The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. Conclusion The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. Virtual Slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923. PMID:23035717

Automatic textual annotation of video news based on semantic visual object extraction

NASA Astrophysics Data System (ADS)

Boujemaa, Nozha; Fleuret, Francois; Gouet, Valerie; Sahbi, Hichem

2003-12-01

In this paper, we present our work for automatic generation of textual metadata based on visual content analysis of video news. We present two methods for semantic object detection and recognition from a cross modal image-text thesaurus. These thesaurus represent a supervised association between models and semantic labels. This paper is concerned with two semantic objects: faces and Tv logos. In the first part, we present our work for efficient face detection and recogniton with automatic name generation. This method allows us also to suggest the textual annotation of shots close-up estimation. On the other hand, we were interested to automatically detect and recognize different Tv logos present on incoming different news from different Tv Channels. This work was done jointly with the French Tv Channel TF1 within the "MediaWorks" project that consists on an hybrid text-image indexing and retrieval plateform for video news.

Non-invasive sex assessment in bovine semen by Raman spectroscopy

NASA Astrophysics Data System (ADS)

De Luca, A. C.; Managó, S.; Ferrara, M. A.; Rendina, I.; Sirleto, L.; Puglisi, R.; Balduzzi, D.; Galli, A.; Ferraro, P.; Coppola, G.

2014-05-01

X- and Y-chromosome-bearing sperm cell sorting is of great interest, especially for animal production management systems and genetic improvement programs. Here, we demonstrate an optical method based on Raman spectroscopy to separate X- and Y-chromosome-bearing sperm cells, overcoming many of the limitations associated with current sex-sorting protocols. A priori Raman imaging of bull spermatozoa was utilized to select the sampling points (head-neck region), which were then used to discriminate cells based on a spectral classification model. Main variations of Raman peaks associated with the DNA content were observed together with a variation due to the sex membrane proteins. Next, we used principal component analysis to determine the efficiency of our device as a cell sorting method. The results (>90% accuracy) demonstrated that Raman spectroscopy is a powerful candidate for the development of a highly efficient, non-invasive, and non-destructive tool for sperm sexing.

Automatic image enhancement based on multi-scale image decomposition

NASA Astrophysics Data System (ADS)

Feng, Lu; Wu, Zhuangzhi; Pei, Luo; Long, Xiong

2014-01-01

In image processing and computational photography, automatic image enhancement is one of the long-range objectives. Recently the automatic image enhancement methods not only take account of the globe semantics, like correct color hue and brightness imbalances, but also the local content of the image, such as human face and sky of landscape. In this paper we describe a new scheme for automatic image enhancement that considers both global semantics and local content of image. Our automatic image enhancement method employs the multi-scale edge-aware image decomposition approach to detect the underexposure regions and enhance the detail of the salient content. The experiment results demonstrate the effectiveness of our approach compared to existing automatic enhancement methods.

Duplicate document detection in DocBrowse

NASA Astrophysics Data System (ADS)

Chalana, Vikram; Bruce, Andrew G.; Nguyen, Thien

1998-04-01

Duplicate documents are frequently found in large databases of digital documents, such as those found in digital libraries or in the government declassification effort. Efficient duplicate document detection is important not only to allow querying for similar documents, but also to filter out redundant information in large document databases. We have designed three different algorithm to identify duplicate documents. The first algorithm is based on features extracted from the textual content of a document, the second algorithm is based on wavelet features extracted from the document image itself, and the third algorithm is a combination of the first two. These algorithms are integrated within the DocBrowse system for information retrieval from document images which is currently under development at MathSoft. DocBrowse supports duplicate document detection by allowing (1) automatic filtering to hide duplicate documents, and (2) ad hoc querying for similar or duplicate documents. We have tested the duplicate document detection algorithms on 171 documents and found that text-based method has an average 11-point precision of 97.7 percent while the image-based method has an average 11- point precision of 98.9 percent. However, in general, the text-based method performs better when the document contains enough high-quality machine printed text while the image- based method performs better when the document contains little or no quality machine readable text.

A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.

PubMed

Spanier, A B; Caplan, N; Sosna, J; Acar, B; Joskowicz, L

2018-01-01

The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases. We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan. Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively. Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.

A content analysis of thinspiration images and text posts on Tumblr.

PubMed

Wick, Madeline R; Harriger, Jennifer A

2018-03-01

Thinspiration is content advocating extreme weight loss by means of images and/or text posts. While past content analyses have examined thinspiration content on social media and other websites, no research to date has examined thinspiration content on Tumblr. Over the course of a week, 222 images and text posts were collected after entering the keyword 'thinspiration' into the Tumblr search bar. These images were then rated on a variety of characteristics. The majority of thinspiration images included a thin woman adhering to culturally based beauty, often posing in a manner that accentuated her thinness or sexuality. The most common themes for thinspiration text posts included dieting/restraint, weight loss, food guilt, and body guilt. The thinspiration content on Tumblr appears to be consistent with that on other mediums. Future research should utilize experimental methods to examine the potential effects of consuming thinspiration content on Tumblr. Copyright © 2017 Elsevier Ltd. All rights reserved.

Real-time free-viewpoint DIBR for large-size 3DLED

NASA Astrophysics Data System (ADS)

Wang, NengWen; Sang, Xinzhu; Guo, Nan; Wang, Kuiru

2017-10-01

Three-dimensional (3D) display technologies make great progress in recent years, and lenticular array based 3D display is a relatively mature technology, which most likely to commercial. In naked-eye-3D display, the screen size is one of the most important factors that affect the viewing experience. In order to construct a large-size naked-eye-3D display system, the LED display is used. However, the pixel misalignment is an inherent defect of the LED screen, which will influences the rendering quality. To address this issue, an efficient image synthesis algorithm is proposed. The Texture-Plus-Depth(T+D) format is chosen for the display content, and the modified Depth Image Based Rendering (DIBR) method is proposed to synthesize new views. In order to achieve realtime, the whole algorithm is implemented on GPU. With the state-of-the-art hardware and the efficient algorithm, a naked-eye-3D display system with a LED screen size of 6m × 1.8m is achieved. Experiment shows that the algorithm can process the 43-view 3D video with 4K × 2K resolution in real time on GPU, and vivid 3D experience is perceived.

Compressibility-aware media retargeting with structure preserving.

PubMed

Wang, Shu-Fan; Lai, Shang-Hong

2011-03-01

A number of algorithms have been proposed for intelligent image/video retargeting with image content retained as much as possible. However, they usually suffer from some artifacts in the results, such as ridge or structure twist. In this paper, we present a structure-preserving media retargeting technique that preserves the content and image structure as best as possible. Different from the previous pixel or grid based methods, we estimate the image content saliency from the structure of the content. A block structure energy is introduced with a top-down strategy to constrain the image structure inside to deform uniformly in either x or y direction. However, the flexibilities for retargeting are quite different for different images. To cope with this problem, we propose a compressibility assessment scheme for media retargeting by combining the entropies of image gradient magnitude and orientation distributions. Thus, the resized media is produced to preserve the image content and structure as best as possible. Our experiments demonstrate that the proposed method provides resized images/videos with better preservation of content and structure than those by the previous methods.

An Invisible Text Watermarking Algorithm using Image Watermark

NASA Astrophysics Data System (ADS)

Jalil, Zunera; Mirza, Anwar M.

Copyright protection of digital contents is very necessary in today's digital world with efficient communication mediums as internet. Text is the dominant part of the internet contents and there are very limited techniques available for text protection. This paper presents a novel algorithm for protection of plain text, which embeds the logo image of the copyright owner in the text and this logo can be extracted from the text later to prove ownership. The algorithm is robust against content-preserving modifications and at the same time, is capable of detecting malicious tampering. Experimental results demonstrate the effectiveness of the algorithm against tampering attacks by calculating normalized hamming distances. The results are also compared with a recent work in this domain

Tag-Based Social Image Search: Toward Relevant and Diverse Results

NASA Astrophysics Data System (ADS)

Yang, Kuiyuan; Wang, Meng; Hua, Xian-Sheng; Zhang, Hong-Jiang

Recent years have witnessed a great success of social media websites. Tag-based image search is an important approach to access the image content of interest on these websites. However, the existing ranking methods for tag-based image search frequently return results that are irrelevant or lack of diversity. This chapter presents a diverse relevance ranking scheme which simultaneously takes relevance and diversity into account by exploring the content of images and their associated tags. First, it estimates the relevance scores of images with respect to the query term based on both visual information of images and semantic information of associated tags. Then semantic similarities of social images are estimated based on their tags. Based on the relevance scores and the similarities, the ranking list is generated by a greedy ordering algorithm which optimizes Average Diverse Precision (ADP), a novel measure that is extended from the conventional Average Precision (AP). Comprehensive experiments and user studies demonstrate the effectiveness of the approach.

Energy Efficient Image/Video Data Transmission on Commercial Multi-Core Processors

PubMed Central

Lee, Sungju; Kim, Heegon; Chung, Yongwha; Park, Daihee

2012-01-01

In transmitting image/video data over Video Sensor Networks (VSNs), energy consumption must be minimized while maintaining high image/video quality. Although image/video compression is well known for its efficiency and usefulness in VSNs, the excessive costs associated with encoding computation and complexity still hinder its adoption for practical use. However, it is anticipated that high-performance handheld multi-core devices will be used as VSN processing nodes in the near future. In this paper, we propose a way to improve the energy efficiency of image and video compression with multi-core processors while maintaining the image/video quality. We improve the compression efficiency at the algorithmic level or derive the optimal parameters for the combination of a machine and compression based on the tradeoff between the energy consumption and the image/video quality. Based on experimental results, we confirm that the proposed approach can improve the energy efficiency of the straightforward approach by a factor of 2∼5 without compromising image/video quality. PMID:23202181

Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

NASA Astrophysics Data System (ADS)

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.

Novel Algorithm for Classification of Medical Images

NASA Astrophysics Data System (ADS)

Bhushan, Bharat; Juneja, Monika

2010-11-01

Content-based image retrieval (CBIR) methods in medical image databases have been designed to support specific tasks, such as retrieval of medical images. These methods cannot be transferred to other medical applications since different imaging modalities require different types of processing. To enable content-based queries in diverse collections of medical images, the retrieval system must be familiar with the current Image class prior to the query processing. Further, almost all of them deal with the DICOM imaging format. In this paper a novel algorithm based on energy information obtained from wavelet transform for the classification of medical images according to their modalities is described. For this two types of wavelets have been used and have been shown that energy obtained in either case is quite distinct for each of the body part. This technique can be successfully applied to different image formats. The results are shown for JPEG imaging format.

Document image retrieval through word shape coding.

PubMed

Lu, Shijian; Li, Linlin; Tan, Chew Lim

2008-11-01

This paper presents a document retrieval technique that is capable of searching document images without OCR (optical character recognition). The proposed technique retrieves document images by a new word shape coding scheme, which captures the document content through annotating each word image by a word shape code. In particular, we annotate word images by using a set of topological shape features including character ascenders/descenders, character holes, and character water reservoirs. With the annotated word shape codes, document images can be retrieved by either query keywords or a query document image. Experimental results show that the proposed document image retrieval technique is fast, efficient, and tolerant to various types of document degradation.

Forensic hash for multimedia information

NASA Astrophysics Data System (ADS)

Lu, Wenjun; Varna, Avinash L.; Wu, Min

2010-01-01

Digital multimedia such as images and videos are prevalent on today's internet and cause significant social impact, which can be evidenced by the proliferation of social networking sites with user generated contents. Due to the ease of generating and modifying images and videos, it is critical to establish trustworthiness for online multimedia information. In this paper, we propose novel approaches to perform multimedia forensics using compact side information to reconstruct the processing history of a document. We refer to this as FASHION, standing for Forensic hASH for informatION assurance. Based on the Radon transform and scale space theory, the proposed forensic hash is compact and can effectively estimate the parameters of geometric transforms and detect local tampering that an image may have undergone. Forensic hash is designed to answer a broader range of questions regarding the processing history of multimedia data than the simple binary decision from traditional robust image hashing, and also offers more efficient and accurate forensic analysis than multimedia forensic techniques that do not use any side information.

Speckle tracking and speckle content based composite strain imaging for solid and fluid filled lesions.

PubMed

Rabbi, Md Shifat-E; Hasan, Md Kamrul

2017-02-01

Strain imaging though for solid lesions provides an effective way for determining their pathologic condition by displaying the tissue stiffness contrast, for fluid filled lesions such an imaging is yet an open problem. In this paper, we propose a novel speckle content based strain imaging technique for visualization and classification of fluid filled lesions in elastography after automatic identification of the presence of fluid filled lesions. Speckle content based strain, defined as a function of speckle density based on the relationship between strain and speckle density, gives an indirect strain value for fluid filled lesions. To measure the speckle density of the fluid filled lesions, two new criteria based on oscillation count of the windowed radio frequency signal and local variance of the normalized B-mode image are used. An improved speckle tracking technique is also proposed for strain imaging of the solid lesions and background. A wavelet-based integration technique is then proposed for combining the strain images from these two techniques for visualizing both the solid and fluid filled lesions from a common framework. The final output of our algorithm is a high quality composite strain image which can effectively visualize both solid and fluid filled breast lesions in addition to the speckle content of the fluid filled lesions for their discrimination. The performance of our algorithm is evaluated using the in vivo patient data and compared with recently reported techniques. The results show that both the solid and fluid filled lesions can be better visualized using our technique and the fluid filled lesions can be classified with good accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.

A novel false color mapping model-based fusion method of visual and infrared images

NASA Astrophysics Data System (ADS)

Qi, Bin; Kun, Gao; Tian, Yue-xin; Zhu, Zhen-yu

2013-12-01

A fast and efficient image fusion method is presented to generate near-natural colors from panchromatic visual and thermal imaging sensors. Firstly, a set of daytime color reference images are analyzed and the false color mapping principle is proposed according to human's visual and emotional habits. That is, object colors should remain invariant after color mapping operations, differences between infrared and visual images should be enhanced and the background color should be consistent with the main scene content. Then a novel nonlinear color mapping model is given by introducing the geometric average value of the input visual and infrared image gray and the weighted average algorithm. To determine the control parameters in the mapping model, the boundary conditions are listed according to the mapping principle above. Fusion experiments show that the new fusion method can achieve the near-natural appearance of the fused image, and has the features of enhancing color contrasts and highlighting the infrared brilliant objects when comparing with the traditional TNO algorithm. Moreover, it owns the low complexity and is easy to realize real-time processing. So it is quite suitable for the nighttime imaging apparatus.

A comparison of liver fat content as determined by magnetic resonance imaging-proton density fat fraction and MRS versus liver histology in non-alcoholic fatty liver disease.

PubMed

Idilman, Ilkay S; Keskin, Onur; Celik, Azim; Savas, Berna; Elhan, Atilla Halil; Idilman, Ramazan; Karcaaltincaba, Musturay

2016-03-01

Many imaging methods have been defined for quantification of hepatic steatosis in non-alcoholic fatty liver disease (NAFLD). However, studies comparing the efficiency of magnetic resonance imaging-proton density fat fraction (MRI-PDFF), magnetic resonance spectroscopy (MRS), and liver histology for quantification of liver fat content are limited. To compare the efficiency of MRI-PDFF and MRS in the quantification of liver fat content in individuals with NAFLD. A total of 19 NAFLD patients underwent MRI-PDFF, MRS, and liver biopsy for quantification of liver fat content. The MR examinations were performed on a 1.5 HDx MRI system. The MRI protocol included T1-independent volumetric multi-echo gradient-echo imaging with T2* correction and spectral fat modeling and MRS with STEAM technique. A close correlation was observed between liver MRI-PDFF- and histology- determined steatosis (r = 0.743, P < 0.001) and between liver MRS- and histology-determined steatosis (r = 0.712, P < 0.001), with no superiority between them (ƶ = 0.19, P = 0.849). For quantification of hepatic steatosis, a high correlation was observed between the two MRI methods (r = 0.986, P < 0.001). MRI-PDFF and MRS accurately differentiated moderate/severe steatosis from mild/no hepatic steatosis (P = 0.007 and 0.013, respectively), with no superiority between them (AUCMRI-PDFF = 0.881 ± 0.0856 versus AUCMRS = 0.857 ± 0.0924, P = 0.461). Both MRI-PDFF and MRS can be used for accurate quantification of hepatic steatosis. © The Foundation Acta Radiologica 2015.

Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach

PubMed Central

Alexakis, Dimitrios D.; Mexis, Filippos-Dimitrios K.; Vozinaki, Anthi-Eirini K.; Daliakopoulos, Ioannis N.; Tsanis, Ioannis K.

2017-01-01

A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence angle parameters are assessed for their potential to infer ground measurements of SMC, collected at the top 5 cm. A non-linear approach using Artificial Neural Networks (ANNs) is tested. The methodology is applied in Western Crete, Greece, where a SMC gauge network was deployed during 2015. The performance of the proposed algorithm is evaluated using leave-one-out cross validation and sensitivity analysis. ANNs prove to be the most efficient in SMC estimation yielding R2 values between 0.7 and 0.9. The proposed methodology is used to support a hydrological simulation with the HEC-HMS model, applied at the Keramianos basin which is ungauged for SMC. Results and model sensitivity highlight the contribution of combining Sentinel-1 SAR and Landsat 8 images for improving SMC estimates and supporting hydrological studies. PMID:28635625

Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach.

PubMed

Alexakis, Dimitrios D; Mexis, Filippos-Dimitrios K; Vozinaki, Anthi-Eirini K; Daliakopoulos, Ioannis N; Tsanis, Ioannis K

2017-06-21

A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence angle parameters are assessed for their potential to infer ground measurements of SMC, collected at the top 5 cm. A non-linear approach using Artificial Neural Networks (ANNs) is tested. The methodology is applied in Western Crete, Greece, where a SMC gauge network was deployed during 2015. The performance of the proposed algorithm is evaluated using leave-one-out cross validation and sensitivity analysis. ANNs prove to be the most efficient in SMC estimation yielding R² values between 0.7 and 0.9. The proposed methodology is used to support a hydrological simulation with the HEC-HMS model, applied at the Keramianos basin which is ungauged for SMC. Results and model sensitivity highlight the contribution of combining Sentinel-1 SAR and Landsat 8 images for improving SMC estimates and supporting hydrological studies.

Quality Scalability Aware Watermarking for Visual Content.

PubMed

Bhowmik, Deepayan; Abhayaratne, Charith

2016-11-01

Scalable coding-based content adaptation poses serious challenges to traditional watermarking algorithms, which do not consider the scalable coding structure and hence cannot guarantee correct watermark extraction in media consumption chain. In this paper, we propose a novel concept of scalable blind watermarking that ensures more robust watermark extraction at various compression ratios while not effecting the visual quality of host media. The proposed algorithm generates scalable and robust watermarked image code-stream that allows the user to constrain embedding distortion for target content adaptations. The watermarked image code-stream consists of hierarchically nested joint distortion-robustness coding atoms. The code-stream is generated by proposing a new wavelet domain blind watermarking algorithm guided by a quantization based binary tree. The code-stream can be truncated at any distortion-robustness atom to generate the watermarked image with the desired distortion-robustness requirements. A blind extractor is capable of extracting watermark data from the watermarked images. The algorithm is further extended to incorporate a bit-plane discarding-based quantization model used in scalable coding-based content adaptation, e.g., JPEG2000. This improves the robustness against quality scalability of JPEG2000 compression. The simulation results verify the feasibility of the proposed concept, its applications, and its improved robustness against quality scalable content adaptation. Our proposed algorithm also outperforms existing methods showing 35% improvement. In terms of robustness to quality scalable video content adaptation using Motion JPEG2000 and wavelet-based scalable video coding, the proposed method shows major improvement for video watermarking.

Geographical Topics Learning of Geo-Tagged Social Images.

PubMed

Zhang, Xiaoming; Ji, Shufan; Wang, Senzhang; Li, Zhoujun; Lv, Xueqiang

2016-03-01

With the availability of cheap location sensors, geotagging of images in online social media is very popular. With a large amount of geo-tagged social images, it is interesting to study how these images are shared across geographical regions and how the geographical language characteristics and vision patterns are distributed across different regions. Unlike textual document, geo-tagged social image contains multiple types of content, i.e., textual description, visual content, and geographical information. Existing approaches usually mine geographical characteristics using a subset of multiple types of image contents or combining those contents linearly, which ignore correlations between different types of contents, and their geographical distributions. Therefore, in this paper, we propose a novel method to discover geographical characteristics of geo-tagged social images using a geographical topic model called geographical topic model of social images (GTMSIs). GTMSI integrates multiple types of social image contents as well as the geographical distributions, in which image topics are modeled based on both vocabulary and visual features. In GTMSI, each region of the image would have its own topic distribution, and hence have its own language model and vision pattern. Experimental results show that our GTMSI could identify interesting topics and vision patterns, as well as provide location prediction and image tagging.

Analysis of breast thermograms using Gabor wavelet anisotropy index.

PubMed

Suganthi, S S; Ramakrishnan, S

2014-09-01

In this study, an attempt is made to distinguish the normal and abnormal tissues in breast thermal images using Gabor wavelet transform. Thermograms having normal, benign and malignant tissues are considered in this study and are obtained from public online database. Segmentation of breast tissues is performed by multiplying raw image and ground truth mask. Left and right breast regions are separated after removing the non-breast regions from the segmented image. Based on the pathological conditions, the separated breast regions are grouped as normal and abnormal tissues. Gabor features such as energy and amplitude in different scales and orientations are extracted. Anisotropy and orientation measures are calculated from the extracted features and analyzed. A distinctive variation is observed among different orientations of the extracted features. It is found that the anisotropy measure is capable of differentiating the structural changes due to varied metabolic conditions. Further, the Gabor features also showed relative variations among different pathological conditions. It appears that these features can be used efficiently to identify normal and abnormal tissues and hence, improve the relevance of breast thermography in early detection of breast cancer and content based image retrieval.

Content-based image retrieval with ontological ranking

NASA Astrophysics Data System (ADS)

Tsai, Shen-Fu; Tsai, Min-Hsuan; Huang, Thomas S.

2010-02-01

Images are a much more powerful medium of expression than text, as the adage says: "One picture is worth a thousand words." It is because compared with text consisting of an array of words, an image has more degrees of freedom and therefore a more complicated structure. However, the less limited structure of images presents researchers in the computer vision community a tough task of teaching machines to understand and organize images, especially when a limit number of learning examples and background knowledge are given. The advance of internet and web technology in the past decade has changed the way human gain knowledge. People, hence, can exchange knowledge with others by discussing and contributing information on the web. As a result, the web pages in the internet have become a living and growing source of information. One is therefore tempted to wonder whether machines can learn from the web knowledge base as well. Indeed, it is possible to make computer learn from the internet and provide human with more meaningful knowledge. In this work, we explore this novel possibility on image understanding applied to semantic image search. We exploit web resources to obtain links from images to keywords and a semantic ontology constituting human's general knowledge. The former maps visual content to related text in contrast to the traditional way of associating images with surrounding text; the latter provides relations between concepts for machines to understand to what extent and in what sense an image is close to the image search query. With the aid of these two tools, the resulting image search system is thus content-based and moreover, organized. The returned images are ranked and organized such that semantically similar images are grouped together and given a rank based on the semantic closeness to the input query. The novelty of the system is twofold: first, images are retrieved not only based on text cues but their actual contents as well; second, the grouping is different from pure visual similarity clustering. More specifically, the inferred concepts of each image in the group are examined in the context of a huge concept ontology to determine their true relations with what people have in mind when doing image search.

Towards building high performance medical image management system for clinical trials

NASA Astrophysics Data System (ADS)

Wang, Fusheng; Lee, Rubao; Zhang, Xiaodong; Saltz, Joel

2011-03-01

Medical image based biomarkers are being established for therapeutic cancer clinical trials, where image assessment is among the essential tasks. Large scale image assessment is often performed by a large group of experts by retrieving images from a centralized image repository to workstations to markup and annotate images. In such environment, it is critical to provide a high performance image management system that supports efficient concurrent image retrievals in a distributed environment. There are several major challenges: high throughput of large scale image data over the Internet from the server for multiple concurrent client users, efficient communication protocols for transporting data, and effective management of versioning of data for audit trails. We study the major bottlenecks for such a system, propose and evaluate a solution by using a hybrid image storage with solid state drives and hard disk drives, RESTfulWeb Services based protocols for exchanging image data, and a database based versioning scheme for efficient archive of image revision history. Our experiments show promising results of our methods, and our work provides a guideline for building enterprise level high performance medical image management systems.

Perylene-diimide-based nanoparticles as highly efficient photoacoustic agents for deep brain tumor imaging in living mice

DOE PAGES

Fan, Quli; Cheng, Kai; Yang, Zhen; ...

2014-11-06

In order to promote preclinical and clinical applications of photoacoustic imaging, novel photoacoustic contrast agents are highly desired for molecular imaging of diseases, especially for deep tumor imaging. In this paper, perylene-3,4,9,10-tetracarboxylic diiimide-based near-infrared-absorptive organic nanoparticles are reported as an efficient agent for photoacoustic imaging of deep brain tumors in living mice with enhanced permeability and retention effect

Teaching Concepts of Natural Sciences to Foreigners through Content-Based Instruction: The Adjunct Model

ERIC Educational Resources Information Center

Satilmis, Yilmaz; Yakup, Doganay; Selim, Guvercin; Aybarsha, Islam

2015-01-01

This study investigates three models of content-based instruction in teaching concepts and terms of natural sciences in order to increase the efficiency of teaching these kinds of concepts in realization and to prove that the content-based instruction is a teaching strategy that helps students understand concepts of natural sciences. Content-based…

PIRIA: a general tool for indexing, search, and retrieval of multimedia content

NASA Astrophysics Data System (ADS)

Joint, Magali; Moellic, Pierre-Alain; Hede, P.; Adam, P.

2004-05-01

The Internet is a continuously expanding source of multimedia content and information. There are many products in development to search, retrieve, and understand multimedia content. But most of the current image search/retrieval engines, rely on a image database manually pre-indexed with keywords. Computers are still powerless to understand the semantic meaning of still or animated image content. Piria (Program for the Indexing and Research of Images by Affinity), the search engine we have developed brings this possibility closer to reality. Piria is a novel search engine that uses the query by example method. A user query is submitted to the system, which then returns a list of images ranked by similarity, obtained by a metric distance that operates on every indexed image signature. These indexed images are compared according to several different classifiers, not only Keywords, but also Form, Color and Texture, taking into account geometric transformations and variance like rotation, symmetry, mirroring, etc. Form - Edges extracted by an efficient segmentation algorithm. Color - Histogram, semantic color segmentation and spatial color relationship. Texture - Texture wavelets and local edge patterns. If required, Piria is also able to fuse results from multiple classifiers with a new classification of index categories: Single Indexer Single Call (SISC), Single Indexer Multiple Call (SIMC), Multiple Indexers Single Call (MISC) or Multiple Indexers Multiple Call (MIMC). Commercial and industrial applications will be explored and discussed as well as current and future development.

Superparamagnetic iron oxide nanoparticle-labeled cells as an effective vehicle for tracking the GFP gene marker using magnetic resonance imaging

PubMed Central

Zhang, Z; Mascheri, N; Dharmakumar, R; Fan, Z; Paunesku, T; Woloschak, G; Li, D

2010-01-01

Background Detection of a gene using magnetic resonance imaging (MRI) is hindered by the magnetic resonance (MR) targeting gene technique. Therefore it may be advantageous to image gene-expressing cells labeled with superparamagnetic iron oxide (SPIO) nanoparticles by MRI. Methods The GFP-R3230Ac (GFP) cell line was incubated for 24 h using SPIO nanoparticles at a concentration of 20 μg Fe/mL. Cell samples were prepared for iron content analysis and cell function evaluation. The labeled cells were imaged using fluorescent microscopy and MRI. Results SPIO was used to label GFP cells effectively, with no effects on cell function and GFP expression. Iron-loaded GFP cells were successfully imaged with both fluorescent microscopy and T2*-weighted MRI. Prussian blue staining showed intracellular iron accumulation in the cells. All cells were labeled (100% labeling efficiency). The average iron content per cell was 4.75±0.11 pg Fe/cell (P<0.05 versus control). Discussion This study demonstrates that the GFP expression of cells is not altered by the SPIO labeling process. SPIO-labeled GFP cells can be visualized by MRI; therefore, GFP, a gene marker, was tracked indirectly with the SPIO-loaded cells using MRI. The technique holds promise for monitoring the temporal and spatial migration of cells with a gene marker and enhancing the understanding of cell- and gene-based therapeutic strategies. PMID:18956269

Image Location Estimation by Salient Region Matching.

PubMed

Qian, Xueming; Zhao, Yisi; Han, Junwei

2015-11-01

Nowadays, locations of images have been widely used in many application scenarios for large geo-tagged image corpora. As to images which are not geographically tagged, we estimate their locations with the help of the large geo-tagged image set by content-based image retrieval. In this paper, we exploit spatial information of useful visual words to improve image location estimation (or content-based image retrieval performances). We proposed to generate visual word groups by mean-shift clustering. To improve the retrieval performance, spatial constraint is utilized to code the relative position of visual words. We proposed to generate a position descriptor for each visual word and build fast indexing structure for visual word groups. Experiments show the effectiveness of our proposed approach.

Adaptive removal of background and white space from document images using seam categorization

NASA Astrophysics Data System (ADS)

Fillion, Claude; Fan, Zhigang; Monga, Vishal

2011-03-01

Document images are obtained regularly by rasterization of document content and as scans of printed documents. Resizing via background and white space removal is often desired for better consumption of these images, whether on displays or in print. While white space and background are easy to identify in images, existing methods such as naïve removal and content aware resizing (seam carving) each have limitations that can lead to undesirable artifacts, such as uneven spacing between lines of text or poor arrangement of content. An adaptive method based on image content is hence needed. In this paper we propose an adaptive method to intelligently remove white space and background content from document images. Document images are different from pictorial images in structure. They typically contain objects (text letters, pictures and graphics) separated by uniform background, which include both white paper space and other uniform color background. Pixels in uniform background regions are excellent candidates for deletion if resizing is required, as they introduce less change in document content and style, compared with deletion of object pixels. We propose a background deletion method that exploits both local and global context. The method aims to retain the document structural information and image quality.

Visual content highlighting via automatic extraction of embedded captions on MPEG compressed video

NASA Astrophysics Data System (ADS)

Yeo, Boon-Lock; Liu, Bede

1996-03-01

Embedded captions in TV programs such as news broadcasts, documentaries and coverage of sports events provide important information on the underlying events. In digital video libraries, such captions represent a highly condensed form of key information on the contents of the video. In this paper we propose a scheme to automatically detect the presence of captions embedded in video frames. The proposed method operates on reduced image sequences which are efficiently reconstructed from compressed MPEG video and thus does not require full frame decompression. The detection, extraction and analysis of embedded captions help to capture the highlights of visual contents in video documents for better organization of video, to present succinctly the important messages embedded in the images, and to facilitate browsing, searching and retrieval of relevant clips.

Efficient storage and management of radiographic images using a novel wavelet-based multiscale vector quantizer

NASA Astrophysics Data System (ADS)

Yang, Shuyu; Mitra, Sunanda

2002-05-01

Due to the huge volumes of radiographic images to be managed in hospitals, efficient compression techniques yielding no perceptual loss in the reconstructed images are becoming a requirement in the storage and management of such datasets. A wavelet-based multi-scale vector quantization scheme that generates a global codebook for efficient storage and transmission of medical images is presented in this paper. The results obtained show that even at low bit rates one is able to obtain reconstructed images with perceptual quality higher than that of the state-of-the-art scalar quantization method, the set partitioning in hierarchical trees.

Experimental study on water content detection of traditional masonry based on infrared thermal image

NASA Astrophysics Data System (ADS)

Zhang, Baoqing; Lei, Zukang

2017-10-01

Based on infrared thermal imaging technology for seepage test of two kinds of brick masonry, find out the relationship between the distribution of one-dimensional two brick surface temperature distribution and one-dimensional surface moisture content were determined after seepage brick masonry minimum temperature zone and water content determination method of the highest point of the regression equation, the relationship between temperature and moisture content of the brick masonry reflected the quantitative and establish the initial wet masonry building disease analysis method, then the infrared technology is applied to the protection of historic buildings in.

a Clustering-Based Approach for Evaluation of EO Image Indexing

NASA Astrophysics Data System (ADS)

Bahmanyar, R.; Rigoll, G.; Datcu, M.

2013-09-01

The volume of Earth Observation data is increasing immensely in order of several Terabytes a day. Therefore, to explore and investigate the content of this huge amount of data, developing more sophisticated Content-Based Information Retrieval (CBIR) systems are highly demanded. These systems should be able to not only discover unknown structures behind the data, but also provide relevant results to the users' queries. Since in any retrieval system the images are processed based on a discrete set of their features (i.e., feature descriptors), study and assessment of the structure of feature space, build by different feature descriptors, is of high importance. In this paper, we introduce a clustering-based approach to study the content of image collections. In our approach, we claim that using both internal and external evaluation of clusters for different feature descriptors, helps to understand the structure of feature space. Moreover, the semantic understanding of users about the images also can be assessed. To validate the performance of our approach, we used an annotated Synthetic Aperture Radar (SAR) image collection. Quantitative results besides the visualization of feature space demonstrate the applicability of our approach.

Coding visual features extracted from video sequences.

PubMed

Baroffio, Luca; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

2014-05-01

Visual features are successfully exploited in several applications (e.g., visual search, object recognition and tracking, etc.) due to their ability to efficiently represent image content. Several visual analysis tasks require features to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required bit budget, while attaining a target level of efficiency. In this paper, we propose, for the first time, a coding architecture designed for local features (e.g., SIFT, SURF) extracted from video sequences. To achieve high coding efficiency, we exploit both spatial and temporal redundancy by means of intraframe and interframe coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. The proposed coding scheme can be conveniently adopted to implement the analyze-then-compress (ATC) paradigm in the context of visual sensor networks. That is, sets of visual features are extracted from video frames, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast to the traditional compress-then-analyze (CTA) paradigm, in which video sequences acquired at a node are compressed and then sent to a central unit for further processing. In this paper, we compare these coding paradigms using metrics that are routinely adopted to evaluate the suitability of visual features in the context of content-based retrieval, object recognition, and tracking. Experimental results demonstrate that, thanks to the significant coding gains achieved by the proposed coding scheme, ATC outperforms CTA with respect to all evaluation metrics.

Content-Based Medical Image Retrieval

NASA Astrophysics Data System (ADS)

Müller, Henning; Deserno, Thomas M.

This chapter details the necessity for alternative access concepts to the currently mainly text-based methods in medical information retrieval. This need is partly due to the large amount of visual data produced, the increasing variety of medical imaging data and changing user patterns. The stored visual data contain large amounts of unused information that, if well exploited, can help diagnosis, teaching and research. The chapter briefly reviews the history of image retrieval and its general methods before technologies that have been developed in the medical domain are focussed. We also discuss evaluation of medical content-based image retrieval (CBIR) systems and conclude with pointing out their strengths, gaps, and further developments. As examples, the MedGIFT project and the Image Retrieval in Medical Applications (IRMA) framework are presented.

A new user-assisted segmentation and tracking technique for an object-based video editing system

NASA Astrophysics Data System (ADS)

Yu, Hong Y.; Hong, Sung-Hoon; Lee, Mike M.; Choi, Jae-Gark

2004-03-01

This paper presents a semi-automatic segmentation method which can be used to generate video object plane (VOP) for object based coding scheme and multimedia authoring environment. Semi-automatic segmentation can be considered as a user-assisted segmentation technique. A user can initially mark objects of interest around the object boundaries and then the user-guided and selected objects are continuously separated from the unselected areas through time evolution in the image sequences. The proposed segmentation method consists of two processing steps: partially manual intra-frame segmentation and fully automatic inter-frame segmentation. The intra-frame segmentation incorporates user-assistance to define the meaningful complete visual object of interest to be segmentation and decides precise object boundary. The inter-frame segmentation involves boundary and region tracking to obtain temporal coherence of moving object based on the object boundary information of previous frame. The proposed method shows stable efficient results that could be suitable for many digital video applications such as multimedia contents authoring, content based coding and indexing. Based on these results, we have developed objects based video editing system with several convenient editing functions.

Cloud-based application for rice moisture content measurement using image processing technique and perceptron neural network

NASA Astrophysics Data System (ADS)

Cruz, Febus Reidj G.; Padilla, Dionis A.; Hortinela, Carlos C.; Bucog, Krissel C.; Sarto, Mildred C.; Sia, Nirlu Sebastian A.; Chung, Wen-Yaw

2017-02-01

This study is about the determination of moisture content of milled rice using image processing technique and perceptron neural network algorithm. The algorithm involves several inputs that produces an output which is the moisture content of the milled rice. Several types of milled rice are used in this study, namely: Jasmine, Kokuyu, 5-Star, Ifugao, Malagkit, and NFA rice. The captured images are processed using MATLAB R2013a software. There is a USB dongle connected to the router which provided internet connection for online web access. The GizDuino IOT-644 is used for handling the temperature and humidity sensor, and for sending and receiving of data from computer to the cloud storage. The result is compared to the actual moisture content range using a moisture tester for milled rice. Based on results, this study provided accurate data in determining the moisture content of the milled rice.

Image aesthetic quality evaluation using convolution neural network embedded learning

NASA Astrophysics Data System (ADS)

Li, Yu-xin; Pu, Yuan-yuan; Xu, Dan; Qian, Wen-hua; Wang, Li-peng

2017-11-01

A way of embedded learning convolution neural network (ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.

Recommending images of user interests from the biomedical literature

NASA Astrophysics Data System (ADS)

Clukey, Steven; Xu, Songhua

2013-03-01

Every year hundreds of thousands of biomedical images are published in journals and conferences. Consequently, finding images relevant to one's interests becomes an ever daunting task. This vast amount of literature creates a need for intelligent and easy-to-use tools that can help researchers effectively navigate through the content corpus and conveniently locate materials of their interests. Traditionally, literature search tools allow users to query content using topic keywords. However, manual query composition is often time and energy consuming. A better system would be one that can automatically deliver relevant content to a researcher without having the end user manually manifest one's search intent and interests via search queries. Such a computer-aided assistance for information access can be provided by a system that first determines a researcher's interests automatically and then recommends images relevant to the person's interests accordingly. The technology can greatly improve a researcher's ability to stay up to date in their fields of study by allowing them to efficiently browse images and documents matching their needs and interests among the vast amount of the biomedical literature. A prototype system implementation of the technology can be accessed via http://www.smartdataware.com.

Informatics in radiology (infoRAD): multimedia extension of medical imaging resource center teaching files.

PubMed

Yang, Guo Liang; Aziz, Aamer; Narayanaswami, Banukumar; Anand, Ananthasubramaniam; Lim, C C Tchoyoson; Nowinski, Wieslaw Lucjan

2005-01-01

A new method has been developed for multimedia enhancement of electronic teaching files created by using the standard protocols and formats offered by the Medical Imaging Resource Center (MIRC) project of the Radiological Society of North America. The typical MIRC electronic teaching file consists of static pages only; with the new method, audio and visual content may be added to the MIRC electronic teaching file so that the entire image interpretation process can be recorded for teaching purposes. With an efficient system for encoding the audiovisual record of on-screen manipulation of radiologic images, the multimedia teaching files generated are small enough to be transmitted via the Internet with acceptable resolution. Students may respond with the addition of new audio and visual content and thereby participate in a discussion about a particular case. MIRC electronic teaching files with multimedia enhancement have the potential to augment the effectiveness of diagnostic radiology teaching. RSNA, 2005.

Content-based image retrieval for interstitial lung diseases using classification confidence

NASA Astrophysics Data System (ADS)

Dash, Jatindra Kumar; Mukhopadhyay, Sudipta; Prabhakar, Nidhi; Garg, Mandeep; Khandelwal, Niranjan

2013-02-01

Content Based Image Retrieval (CBIR) system could exploit the wealth of High-Resolution Computed Tomography (HRCT) data stored in the archive by finding similar images to assist radiologists for self learning and differential diagnosis of Interstitial Lung Diseases (ILDs). HRCT findings of ILDs are classified into several categories (e.g. consolidation, emphysema, ground glass, nodular etc.) based on their texture like appearances. Therefore, analysis of ILDs is considered as a texture analysis problem. Many approaches have been proposed for CBIR of lung images using texture as primitive visual content. This paper presents a new approach to CBIR for ILDs. The proposed approach makes use of a trained neural network (NN) to find the output class label of query image. The degree of confidence of the NN classifier is analyzed using Naive Bayes classifier that dynamically takes a decision on the size of the search space to be used for retrieval. The proposed approach is compared with three simple distance based and one classifier based texture retrieval approaches. Experimental results show that the proposed technique achieved highest average percentage precision of 92.60% with lowest standard deviation of 20.82%.

What do you think of my picture? Investigating factors of influence in profile images context perception

NASA Astrophysics Data System (ADS)

Mazza, F.; Da Silva, M. P.; Le Callet, P.; Heynderickx, I. E. J.

2015-03-01

Multimedia quality assessment has been an important research topic during the last decades. The original focus on artifact visibility has been extended during the years to aspects as image aesthetics, interestingness and memorability. More recently, Fedorovskaya proposed the concept of 'image psychology': this concept focuses on additional quality dimensions related to human content processing. While these additional dimensions are very valuable in understanding preferences, it is very hard to define, isolate and measure their effect on quality. In this paper we continue our research on face pictures investigating which image factors influence context perception. We collected perceived fit of a set of images to various content categories. These categories were selected based on current typologies in social networks. Logistic regression was adopted to model category fit based on images features. In this model we used both low level and high level features, the latter focusing on complex features related to image content. In order to extract these high level features, we relied on crowdsourcing, since computer vision algorithms are not yet sufficiently accurate for the features we needed. Our results underline the importance of some high level content features, e.g. the dress of the portrayed person and scene setting, in categorizing image.

Holographic imaging based on time-domain data of natural-fiber-containing materials

DOEpatents

Bunch, Kyle J.; McMakin, Douglas L.

2012-09-04

Methods and apparatuses for imaging material properties in natural-fiber-containing materials can utilize time-domain data. In particular, images can be constructed that provide quantified measures of localized moisture content. For example, one or more antennas and at least one transceiver can be configured to collect time-domain data from radiation interacting with the natural-fiber-containing materials. The antennas and the transceivers are configured to transmit and receive electromagnetic radiation at one or more frequencies, which are between 50 MHz and 1 THz, according to a time-domain impulse function. A computing device is configured to transform the time-domain data to frequency-domain data, to apply a synthetic imaging algorithm for constructing a three-dimensional image of the natural-fiber-containing materials, and to provide a quantified measure of localized moisture content based on a pre-determined correlation of moisture content to frequency-domain data.

Hierarchical layered and semantic-based image segmentation using ergodicity map

NASA Astrophysics Data System (ADS)

Yadegar, Jacob; Liu, Xiaoqing

2010-04-01

Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic, robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano- Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been conducted within the maritime image environment where the segmented layered semantic objects include the basic level objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects/regions with contextual topological relationships.

Latent Semantic Analysis as a Method of Content-Based Image Retrieval in Medical Applications

ERIC Educational Resources Information Center

Makovoz, Gennadiy

2010-01-01

The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of M computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic…

Learning deep similarity in fundus photography

NASA Astrophysics Data System (ADS)

Chudzik, Piotr; Al-Diri, Bashir; Caliva, Francesco; Ometto, Giovanni; Hunter, Andrew

2017-02-01

Similarity learning is one of the most fundamental tasks in image analysis. The ability to extract similar images in the medical domain as part of content-based image retrieval (CBIR) systems has been researched for many years. The vast majority of methods used in CBIR systems are based on hand-crafted feature descriptors. The approximation of a similarity mapping for medical images is difficult due to the big variety of pixel-level structures of interest. In fundus photography (FP) analysis, a subtle difference in e.g. lesions and vessels shape and size can result in a different diagnosis. In this work, we demonstrated how to learn a similarity function for image patches derived directly from FP image data without the need of manually designed feature descriptors. We used a convolutional neural network (CNN) with a novel architecture adapted for similarity learning to accomplish this task. Furthermore, we explored and studied multiple CNN architectures. We show that our method can approximate the similarity between FP patches more efficiently and accurately than the state-of- the-art feature descriptors, including SIFT and SURF using a publicly available dataset. Finally, we observe that our approach, which is purely data-driven, learns that features such as vessels calibre and orientation are important discriminative factors, which resembles the way how humans reason about similarity. To the best of authors knowledge, this is the first attempt to approximate a visual similarity mapping in FP.

Context indexing of digital cardiac ultrasound records in PACS

NASA Astrophysics Data System (ADS)

Lobodzinski, S. Suave; Meszaros, Georg N.

1998-07-01

Recent wide adoption of the DICOM 3.0 standard by ultrasound equipment vendors created a need for practical clinical implementations of cardiac imaging study visualization, management and archiving, DICOM 3.0 defines only a logical and physical format for exchanging image data (still images, video, patient and study demographics). All DICOM compliant imaging studies must presently be archived on a 650 Mb recordable compact disk. This is a severe limitation for ultrasound applications where studies of 3 to 10 minutes long are a common practice. In addition, DICOM digital echocardiography objects require physiological signal indexing, content segmentation and characterization. Since DICOM 3.0 is an interchange standard only, it does not define how to database composite video objects. The goal of this research was therefore to address the issues of efficient storage, retrieval and management of DICOM compliant cardiac video studies in a distributed PACS environment. Our Web based implementation has the advantage of accommodating both DICOM defined entity-relation modules (equipment data, patient data, video format, etc.) in standard relational database tables and digital indexed video with its attributes in an object relational database. Object relational data model facilitates content indexing of full motion cardiac imaging studies through bi-directional hyperlink generation that tie searchable video attributes and related objects to individual video frames in the temporal domain. Benefits realized from use of bi-directionally hyperlinked data models in an object relational database include: (1) real time video indexing during image acquisition, (2) random access and frame accurate instant playback of previously recorded full motion imaging data, and (3) time savings from faster and more accurate access to data through multiple navigation mechanisms such as multidimensional queries on an index, queries on a hyperlink attribute, free search and browsing.

Mosaicing of single plane illumination microscopy images using groupwise registration and fast content-based image fusion

NASA Astrophysics Data System (ADS)

Preibisch, Stephan; Rohlfing, Torsten; Hasak, Michael P.; Tomancak, Pavel

2008-03-01

Single Plane Illumination Microscopy (SPIM; Huisken et al., Nature 305(5686):1007-1009, 2004) is an emerging microscopic technique that enables live imaging of large biological specimens in their entirety. By imaging the living biological sample from multiple angles SPIM has the potential to achieve isotropic resolution throughout even relatively large biological specimens. For every angle, however, only a relatively shallow section of the specimen is imaged with high resolution, whereas deeper regions appear increasingly blurred. In order to produce a single, uniformly high resolution image, we propose here an image mosaicing algorithm that combines state of the art groupwise image registration for alignment with content-based image fusion to prevent degrading of the fused image due to regional blurring of the input images. For the registration stage, we introduce an application-specific groupwise transformation model that incorporates per-image as well as groupwise transformation parameters. We also propose a new fusion algorithm based on Gaussian filters, which is substantially faster than fusion based on local image entropy. We demonstrate the performance of our mosaicing method on data acquired from living embryos of the fruit fly, Drosophila, using four and eight angle acquisitions.

Marker Registration Technique for Handwritten Text Marker in Augmented Reality Applications

NASA Astrophysics Data System (ADS)

Thanaborvornwiwat, N.; Patanukhom, K.

2018-04-01

Marker registration is a fundamental process to estimate camera poses in marker-based Augmented Reality (AR) systems. We developed AR system that creates correspondence virtual objects on handwritten text markers. This paper presents a new method for registration that is robust for low-content text markers, variation of camera poses, and variation of handwritten styles. The proposed method uses Maximally Stable Extremal Regions (MSER) and polygon simplification for a feature point extraction. The experiment shows that we need to extract only five feature points per image which can provide the best registration results. An exhaustive search is used to find the best matching pattern of the feature points in two images. We also compared performance of the proposed method to some existing registration methods and found that the proposed method can provide better accuracy and time efficiency.

Integrated femtosecond stimulated Raman scattering and two-photon fluorescence imaging of subcellular lipid and vesicular structures

NASA Astrophysics Data System (ADS)

Li, Xuesong; Lam, Wen Jiun; Cao, Zhe; Hao, Yan; Sun, Qiqi; He, Sicong; Mak, Ho Yi; Qu, Jianan Y.

2015-11-01

The primary goal of this study is to demonstrate that stimulated Raman scattering (SRS) as a new imaging modality can be integrated into a femtosecond (fs) nonlinear optical (NLO) microscope system. The fs sources of high pulse peak power are routinely used in multimodal nonlinear microscopy to enable efficient excitation of multiple NLO signals. However, with fs excitations, the SRS imaging of subcellular lipid and vesicular structures encounters significant interference from proteins due to poor spectral resolution and a lack of chemical specificity, respectively. We developed a unique NLO microscope of fs excitation that enables rapid acquisition of SRS and multiple two-photon excited fluorescence (TPEF) signals. In the in vivo imaging of transgenic C. elegans animals, we discovered that by cross-filtering false positive lipid signals based on the TPEF signals from tryptophan-bearing endogenous proteins and lysosome-related organelles, the imaging system produced highly accurate assignment of SRS signals to lipid. Furthermore, we demonstrated that the multimodal NLO microscope system could sequentially image lipid structure/content and organelles, such as mitochondria, lysosomes, and the endoplasmic reticulum, which are intricately linked to lipid metabolism.

Automated image-based phenotypic analysis in zebrafish embryos

PubMed Central

Vogt, Andreas; Cholewinski, Andrzej; Shen, Xiaoqiang; Nelson, Scott; Lazo, John S.; Tsang, Michael; Hukriede, Neil A.

2009-01-01

Presently, the zebrafish is the only vertebrate model compatible with contemporary paradigms of drug discovery. Zebrafish embryos are amenable to automation necessary for high-throughput chemical screens, and optical transparency makes them potentially suited for image-based screening. However, the lack of tools for automated analysis of complex images presents an obstacle to utilizing the zebrafish as a high-throughput screening model. We have developed an automated system for imaging and analyzing zebrafish embryos in multi-well plates regardless of embryo orientation and without user intervention. Images of fluorescent embryos were acquired on a high-content reader and analyzed using an artificial intelligence-based image analysis method termed Cognition Network Technology (CNT). CNT reliably detected transgenic fluorescent embryos (Tg(fli1:EGFP)y1) arrayed in 96-well plates and quantified intersegmental blood vessel development in embryos treated with small molecule inhibitors of anigiogenesis. The results demonstrate it is feasible to adapt image-based high-content screening methodology to measure complex whole organism phenotypes. PMID:19235725

High-content analysis of single cells directly assembled on CMOS sensor based on color imaging.

PubMed

Tanaka, Tsuyoshi; Saeki, Tatsuya; Sunaga, Yoshihiko; Matsunaga, Tadashi

2010-12-15

A complementary metal oxide semiconductor (CMOS) image sensor was applied to high-content analysis of single cells which were assembled closely or directly onto the CMOS sensor surface. The direct assembling of cell groups on CMOS sensor surface allows large-field (6.66 mm×5.32 mm in entire active area of CMOS sensor) imaging within a second. Trypan blue-stained and non-stained cells in the same field area on the CMOS sensor were successfully distinguished as white- and blue-colored images under white LED light irradiation. Furthermore, the chemiluminescent signals of each cell were successfully visualized as blue-colored images on CMOS sensor only when HeLa cells were placed directly on the micro-lens array of the CMOS sensor. Our proposed approach will be a promising technique for real-time and high-content analysis of single cells in a large-field area based on color imaging. Copyright © 2010 Elsevier B.V. All rights reserved.

Disability in Physical Education Textbooks: An Analysis of Image Content

ERIC Educational Resources Information Center

Taboas-Pais, Maria Ines; Rey-Cao, Ana

2012-01-01

The aim of this paper is to show how images of disability are portrayed in physical education textbooks for secondary schools in Spain. The sample was composed of 3,316 images published in 36 textbooks by 10 publishing houses. A content analysis was carried out using a coding scheme based on categories employed in other similar studies and adapted…

Localization-based super-resolution imaging meets high-content screening.

PubMed

Beghin, Anne; Kechkar, Adel; Butler, Corey; Levet, Florian; Cabillic, Marine; Rossier, Olivier; Giannone, Gregory; Galland, Rémi; Choquet, Daniel; Sibarita, Jean-Baptiste

2017-12-01

Single-molecule localization microscopy techniques have proven to be essential tools for quantitatively monitoring biological processes at unprecedented spatial resolution. However, these techniques are very low throughput and are not yet compatible with fully automated, multiparametric cellular assays. This shortcoming is primarily due to the huge amount of data generated during imaging and the lack of software for automation and dedicated data mining. We describe an automated quantitative single-molecule-based super-resolution methodology that operates in standard multiwell plates and uses analysis based on high-content screening and data-mining software. The workflow is compatible with fixed- and live-cell imaging and allows extraction of quantitative data like fluorophore photophysics, protein clustering or dynamic behavior of biomolecules. We demonstrate that the method is compatible with high-content screening using 3D dSTORM and DNA-PAINT based super-resolution microscopy as well as single-particle tracking.

XML-based data model and architecture for a knowledge-based grid-enabled problem-solving environment for high-throughput biological imaging.

PubMed

Ahmed, Wamiq M; Lenz, Dominik; Liu, Jia; Paul Robinson, J; Ghafoor, Arif

2008-03-01

High-throughput biological imaging uses automated imaging devices to collect a large number of microscopic images for analysis of biological systems and validation of scientific hypotheses. Efficient manipulation of these datasets for knowledge discovery requires high-performance computational resources, efficient storage, and automated tools for extracting and sharing such knowledge among different research sites. Newly emerging grid technologies provide powerful means for exploiting the full potential of these imaging techniques. Efficient utilization of grid resources requires the development of knowledge-based tools and services that combine domain knowledge with analysis algorithms. In this paper, we first investigate how grid infrastructure can facilitate high-throughput biological imaging research, and present an architecture for providing knowledge-based grid services for this field. We identify two levels of knowledge-based services. The first level provides tools for extracting spatiotemporal knowledge from image sets and the second level provides high-level knowledge management and reasoning services. We then present cellular imaging markup language, an extensible markup language-based language for modeling of biological images and representation of spatiotemporal knowledge. This scheme can be used for spatiotemporal event composition, matching, and automated knowledge extraction and representation for large biological imaging datasets. We demonstrate the expressive power of this formalism by means of different examples and extensive experimental results.

MediaNet: a multimedia information network for knowledge representation

NASA Astrophysics Data System (ADS)

Benitez, Ana B.; Smith, John R.; Chang, Shih-Fu

2000-10-01

In this paper, we present MediaNet, which is a knowledge representation framework that uses multimedia content for representing semantic and perceptual information. The main components of MediaNet include conceptual entities, which correspond to real world objects, and relationships among concepts. MediaNet allows the concepts and relationships to be defined or exemplified by multimedia content such as images, video, audio, graphics, and text. MediaNet models the traditional relationship types such as generalization and aggregation but adds additional functionality by modeling perceptual relationships based on feature similarity. For example, MediaNet allows a concept such as car to be defined as a type of a transportation vehicle, but which is further defined and illustrated through example images, videos and sounds of cars. In constructing the MediaNet framework, we have built on the basic principles of semiotics and semantic networks in addition to utilizing the audio-visual content description framework being developed as part of the MPEG-7 multimedia content description standard. By integrating both conceptual and perceptual representations of knowledge, MediaNet has potential to impact a broad range of applications that deal with multimedia content at the semantic and perceptual levels. In particular, we have found that MediaNet can improve the performance of multimedia retrieval applications by using query expansion, refinement and translation across multiple content modalities. In this paper, we report on experiments that use MediaNet in searching for images. We construct the MediaNet knowledge base using both WordNet and an image network built from multiple example images and extracted color and texture descriptors. Initial experimental results demonstrate improved retrieval effectiveness using MediaNet in a content-based retrieval system.

Improving Science Communication with Responsive Web Design

NASA Astrophysics Data System (ADS)

Hilverda, M.

2013-12-01

Effective science communication requires clarity in both content and presentation. Content is increasingly being viewed via the Web across a broad range of devices, which can vary in screen size, resolution, and pixel density. Readers access the same content from desktop computers, tablets, smartphones, and wearable computing devices. Creating separate presentation formats optimized for each device is inefficient and unrealistic as new devices continually enter the marketplace. Responsive web design is an approach that puts content first within a presentation design that responds automatically to its environment. This allows for one platform to be maintained that can be used effectively for every screen. The layout adapts to screens of all sizes ensuring easy viewing of content for readers regardless of their device. Responsive design is accomplished primarily by the use of media queries within style sheets, which allows for changes to layout properties to be defined based on media types (i.e. screen, print) and resolution. Images and other types of multimedia can also be defined to scale automatically to fit different screen dimensions, although some media types require additional effort for proper implementation. Hardware changes, such as high pixel density screens, also present new challenges for effective presentation of content. High pixel density screens contain a greater number of pixels within a screen area increasing the pixels per inch (PPI) compared to standard screens. The result is increased clarity for text and vector media types, but often decreased clarity for standard resolution raster images. Media queries and other custom solutions can assist by specifying higher resolution images for high pixel density screens. Unfortunately, increasing image resolution results in significantly more data being transferred to the device. Web traffic on mobile devices such as smartphones and tablets is on a steady growth trajectory and many mobile devices around the world use low-bandwidth connections. Communicating science effectively includes efficient delivery of the information to the reader. To meet this criteria, responsive designs should also incorporate "mobile first" elements such as serving ideal image sizes (a low resolution cell phone does not need to receive a large desktop image) and a focus on fast, readable content delivery. The technical implementation of responsive web design is constantly changing as new web standards and approaches become available. However, fundamental design principles such as grid layouts, clear typography, and proper use of white space should be an important part of content delivery within any responsive design. This presentation will discuss current responsive design approaches for improving scientific communication across multiple devices, operating systems, and bandwidth capacities. The presentation will also include example responsive designs for scientific papers and websites. Implementing a responsive design approach with a focus on content and fundamental design principles is an important step to ensuring scientific information remains clear and accessible as screens and devices continue to evolve.

A picture tells a thousand words: A content analysis of concussion-related images online.

PubMed

Ahmed, Osman H; Lee, Hopin; Struik, Laura L

2016-09-01

Recently image-sharing social media platforms have become a popular medium for sharing health-related images and associated information. However within the field of sports medicine, and more specifically sports related concussion, the content of images and meta-data shared through these popular platforms have not been investigated. The aim of this study was to analyse the content of concussion-related images and its accompanying meta-data on image-sharing social media platforms. We retrieved 300 images from Pinterest, Instagram and Flickr by using a standardised search strategy. All images were screened and duplicate images were removed. We excluded images if they were: non-static images; illustrations; animations; or screenshots. The content and characteristics of each image was evaluated using a customised coding scheme to determine major content themes, and images were referenced to the current international concussion management guidelines. From 300 potentially relevant images, 176 images were included for analysis; 70 from Pinterest, 63 from Flickr, and 43 from Instagram. Most images were of another person or a scene (64%), with the primary content depicting injured individuals (39%). The primary purposes of the images were to share a concussion-related incident (33%) and to dispense education (19%). For those images where it could be evaluated, the majority (91%) were found to reflect the Sports Concussion Assessment Tool 3 (SCAT3) guidelines. The ability to rapidly disseminate rich information though photos, images, and infographics to a wide-reaching audience suggests that image-sharing social media platforms could be used as an effective communication tool for sports concussion. Public health strategies could direct educative content to targeted populations via the use of image-sharing platforms. Further research is required to understand how image-sharing platforms can be used to effectively relay evidence-based information to patients and sports medicine clinicians. Copyright © 2016 Elsevier Ltd. All rights reserved.

Imaging System Model Crammed Into A 32K Microcomputer

NASA Astrophysics Data System (ADS)

Tyson, Robert K.

1986-12-01

An imaging system model, based upon linear systems theory, has been developed for a microcomputer with less than 32K of free random access memory (RAM). The model includes diffraction effects of the optics, aberrations in the optics, and atmospheric propagation transfer functions. Variables include pupil geometry, magnitude and character of the aberrations, and strength of atmospheric turbulence ("seeing"). Both coherent and incoherent image formation can be evaluated. The techniques employed for crowding the model into a very small computer will be discussed in detail. Simplifying assumptions for the diffraction and aberration phenomena will be shown along with practical considerations in modeling the optical system. Particular emphasis is placed on avoiding inaccuracies in modeling the pupil and the associated optical transfer function knowing limits on spatial frequency content and resolution. Memory and runtime constraints are analyzed stressing the efficient use of assembly language Fourier transform routines, disk input/output, and graphic displays. The compromises between computer time, limited RAM, and scientific accuracy will be given with techniques for balancing these parameters for individual needs.

New approach for cognitive analysis and understanding of medical patterns and visualizations

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2003-11-01

This paper presents new opportunities for applying linguistic description of the picture merit content and AI methods to undertake tasks of the automatic understanding of images semantics in intelligent medical information systems. A successful obtaining of the crucial semantic content of the medical image may contribute considerably to the creation of new intelligent multimedia cognitive medical systems. Thanks to the new idea of cognitive resonance between stream of the data extracted from the image using linguistic methods and expectations taken from the representaion of the medical knowledge, it is possible to understand the merit content of the image even if teh form of the image is very different from any known pattern. This article proves that structural techniques of artificial intelligence may be applied in the case of tasks related to automatic classification and machine perception based on semantic pattern content in order to determine the semantic meaning of the patterns. In the paper are described some examples presenting ways of applying such techniques in the creation of cognitive vision systems for selected classes of medical images. On the base of scientific research described in the paper we try to build some new systems for collecting, storing, retrieving and intelligent interpreting selected medical images especially obtained in radiological and MRI examinations.

Discriminative and robust zero-watermarking scheme based on completed local binary pattern for authentication and copyright identification of medical images

NASA Astrophysics Data System (ADS)

Liu, Xiyao; Lou, Jieting; Wang, Yifan; Du, Jingyu; Zou, Beiji; Chen, Yan

2018-03-01

Authentication and copyright identification are two critical security issues for medical images. Although zerowatermarking schemes can provide durable, reliable and distortion-free protection for medical images, the existing zerowatermarking schemes for medical images still face two problems. On one hand, they rarely considered the distinguishability for medical images, which is critical because different medical images are sometimes similar to each other. On the other hand, their robustness against geometric attacks, such as cropping, rotation and flipping, is insufficient. In this study, a novel discriminative and robust zero-watermarking (DRZW) is proposed to address these two problems. In DRZW, content-based features of medical images are first extracted based on completed local binary pattern (CLBP) operator to ensure the distinguishability and robustness, especially against geometric attacks. Then, master shares and ownership shares are generated from the content-based features and watermark according to (2,2) visual cryptography. Finally, the ownership shares are stored for authentication and copyright identification. For queried medical images, their content-based features are extracted and master shares are generated. Their watermarks for authentication and copyright identification are recovered by stacking the generated master shares and stored ownership shares. 200 different medical images of 5 types are collected as the testing data and our experimental results demonstrate that DRZW ensures both the accuracy and reliability of authentication and copyright identification. When fixing the false positive rate to 1.00%, the average value of false negative rates by using DRZW is only 1.75% under 20 common attacks with different parameters.

Developing a comprehensive system for content-based retrieval of image and text data from a national survey

NASA Astrophysics Data System (ADS)

Antani, Sameer K.; Natarajan, Mukil; Long, Jonathan L.; Long, L. Rodney; Thoma, George R.

2005-04-01

The article describes the status of our ongoing R&D at the U.S. National Library of Medicine (NLM) towards the development of an advanced multimedia database biomedical information system that supports content-based image retrieval (CBIR). NLM maintains a collection of 17,000 digitized spinal X-rays along with text survey data from the Second National Health and Nutritional Examination Survey (NHANES II). These data serve as a rich data source for epidemiologists and researchers of osteoarthritis and musculoskeletal diseases. It is currently possible to access these through text keyword queries using our Web-based Medical Information Retrieval System (WebMIRS). CBIR methods developed specifically for biomedical images could offer direct visual searching of these images by means of example image or user sketch. We are building a system which supports hybrid queries that have text and image-content components. R&D goals include developing algorithms for robust image segmentation for localizing and identifying relevant anatomy, labeling the segmented anatomy based on its pathology, developing suitable indexing and similarity matching methods for images and image features, and associating the survey text information for query and retrieval along with the image data. Some highlights of the system developed in MATLAB and Java are: use of a networked or local centralized database for text and image data; flexibility to incorporate new research work; provides a means to control access to system components under development; and use of XML for structured reporting. The article details the design, features, and algorithms in this third revision of this prototype system, CBIR3.

Exploring access to scientific literature using content-based image retrieval

NASA Astrophysics Data System (ADS)

Deserno, Thomas M.; Antani, Sameer; Long, Rodney

2007-03-01

The number of articles published in the scientific medical literature is continuously increasing, and Web access to the journals is becoming common. Databases such as SPIE Digital Library, IEEE Xplore, indices such as PubMed, and search engines such as Google provide the user with sophisticated full-text search capabilities. However, information in images and graphs within these articles is entirely disregarded. In this paper, we quantify the potential impact of using content-based image retrieval (CBIR) to access this non-text data. Based on the Journal Citations Report (JCR), the journal Radiology was selected for this study. In 2005, 734 articles were published electronically in this journal. This included 2,587 figures, which yields a rate of 3.52 figures per article. Furthermore, 56.4% of these figures are composed of several individual panels, i.e. the figure combines different images and/or graphs. According to the Image Cross-Language Evaluation Forum (ImageCLEF), the error rate of automatic identification of medical images is about 15%. Therefore, it is expected that, by applying ImageCLEF-like techniques, already 95.5% of articles could be retrieved by means of CBIR. The challenge for CBIR in scientific literature, however, is the use of local texture properties to analyze individual image panels in composite illustrations. Using local features for content-based image representation, 8.81 images per article are available, and the predicted correctness rate may increase to 98.3%. From this study, we conclude that CBIR may have a high impact in medical literature research and suggest that additional research in this area is warranted.

Image BOSS: a biomedical object storage system

NASA Astrophysics Data System (ADS)

Stacy, Mahlon C.; Augustine, Kurt E.; Robb, Richard A.

1997-05-01

Researchers using biomedical images have data management needs which are oriented perpendicular to clinical PACS. The image BOSS system is designed to permit researchers to organize and select images based on research topic, image metadata, and a thumbnail of the image. Image information is captured from existing images in a Unix based filesystem, stored in an object oriented database, and presented to the user in a familiar laboratory notebook metaphor. In addition, the ImageBOSS is designed to provide an extensible infrastructure for future content-based queries directly on the images.

Active learning methods for interactive image retrieval.

PubMed

Gosselin, Philippe Henri; Cord, Matthieu

2008-07-01

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

Investigating the Link Between Radiologists Gaze, Diagnostic Decision, and Image Content

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

Tourassi, Georgia; Voisin, Sophie; Paquit, Vincent C

2013-01-01

Objective: To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. Methods: Gaze data and diagnostic decisions were collected from six radiologists who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Texture analysis was performed in mammographic regions that attracted radiologists attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored. Results: By poolingmore » the data from all radiologists machine learning produced highly accurate predictive models linking image content, gaze, cognition, and error. Merging radiologists gaze metrics and cognitive opinions with computer-extracted image features identified 59% of the radiologists diagnostic errors while confirming 96.2% of their correct diagnoses. The radiologists individual errors could be adequately predicted by modeling the behavior of their peers. However, personalized tuning appears to be beneficial in many cases to capture more accurately individual behavior. Conclusions: Machine learning algorithms combining image features with radiologists gaze data and diagnostic decisions can be effectively developed to recognize cognitive and perceptual errors associated with the diagnostic interpretation of mammograms.« less

FT-IR imaging for quantitative determination of liver fat content in non-alcoholic fatty liver.

PubMed

Kochan, K; Maslak, E; Chlopicki, S; Baranska, M

2015-08-07

In this work we apply FT-IR imaging of large areas of liver tissue cross-section samples (∼5 cm × 5 cm) for quantitative assessment of steatosis in murine model of Non-Alcoholic Fatty Liver (NAFLD). We quantified the area of liver tissue occupied by lipid droplets (LDs) by FT-IR imaging and Oil Red O (ORO) staining for comparison. Two alternative FT-IR based approaches are presented. The first, straightforward method, was based on average spectra from tissues and provided values of the fat content by using a PLS regression model and the reference method. The second one – the chemometric-based method – enabled us to determine the values of the fat content, independently of the reference method by means of k-means cluster (KMC) analysis. In summary, FT-IR images of large size liver sections may prove to be useful for quantifying liver steatosis without the need of tissue staining.

An Improved InSAR Image Co-Registration Method for Pairs with Relatively Big Distortions or Large Incoherent Areas

PubMed Central

Chen, Zhenwei; Zhang, Lei; Zhang, Guo

2016-01-01

Co-registration is one of the most important steps in interferometric synthetic aperture radar (InSAR) data processing. The standard offset-measurement method based on cross-correlating uniformly distributed patches takes no account of specific geometric transformation between images or characteristics of ground scatterers. Hence, it is inefficient and difficult to obtain satisfying co-registration results for image pairs with relatively big distortion or large incoherent areas. Given this, an improved co-registration strategy is proposed in this paper which takes both the geometric features and image content into consideration. Firstly, some geometric transformations including scale, flip, rotation, and shear between images were eliminated based on the geometrical information, and the initial co-registration polynomial was obtained. Then the registration points were automatically detected by integrating the signal-to-clutter-ratio (SCR) thresholds and the amplitude information, and a further co-registration process was performed to refine the polynomial. Several comparison experiments were carried out using 2 TerraSAR-X data from the Hong Kong airport and 21 PALSAR data from the Donghai Bridge. Experiment results demonstrate that the proposed method brings accuracy and efficiency improvements for co-registration and processing abilities in the cases of big distortion between images or large incoherent areas in the images. For most co-registrations, the proposed method can enhance the reliability and applicability of co-registration and thus promote the automation to a higher level. PMID:27649207

An Improved InSAR Image Co-Registration Method for Pairs with Relatively Big Distortions or Large Incoherent Areas.

PubMed

Chen, Zhenwei; Zhang, Lei; Zhang, Guo

2016-09-17

Co-registration is one of the most important steps in interferometric synthetic aperture radar (InSAR) data processing. The standard offset-measurement method based on cross-correlating uniformly distributed patches takes no account of specific geometric transformation between images or characteristics of ground scatterers. Hence, it is inefficient and difficult to obtain satisfying co-registration results for image pairs with relatively big distortion or large incoherent areas. Given this, an improved co-registration strategy is proposed in this paper which takes both the geometric features and image content into consideration. Firstly, some geometric transformations including scale, flip, rotation, and shear between images were eliminated based on the geometrical information, and the initial co-registration polynomial was obtained. Then the registration points were automatically detected by integrating the signal-to-clutter-ratio (SCR) thresholds and the amplitude information, and a further co-registration process was performed to refine the polynomial. Several comparison experiments were carried out using 2 TerraSAR-X data from the Hong Kong airport and 21 PALSAR data from the Donghai Bridge. Experiment results demonstrate that the proposed method brings accuracy and efficiency improvements for co-registration and processing abilities in the cases of big distortion between images or large incoherent areas in the images. For most co-registrations, the proposed method can enhance the reliability and applicability of co-registration and thus promote the automation to a higher level.

An efficient intensity-based ready-to-use X-ray image stitcher.

PubMed

Wang, Junchen; Zhang, Xiaohui; Sun, Zhen; Yuan, Fuzhen

2018-06-14

The limited field of view of the X-ray image intensifier makes it difficult to cover a large target area with a single X-ray image. X-ray image stitching techniques have been proposed to produce a panoramic X-ray image. This paper presents an efficient intensity-based X-ray image stitcher, which does not rely on accurate C-arm motion control or auxiliary devices and hence is ready to use in clinic. The stitcher consumes sequentially captured X-ray images with overlap areas and automatically produces a panoramic image. The gradient information for optimization of image alignment is obtained using a back-propagation scheme so that it is convenient to adopt various image warping models. The proposed stitcher has the following advantages over existing methods: (1) no additional hardware modification or auxiliary markers are needed; (2) it is robust against feature-based approaches; (3) arbitrary warping models and shapes of the region of interest are supported; (4) seamless stitching is achieved using multi-band blending. Experiments have been performed to confirm the effectiveness of the proposed method. The proposed X-ray image stitcher is efficient, accurate and ready to use in clinic. Copyright © 2018 John Wiley & Sons, Ltd.

Content-based image exploitation for situational awareness

NASA Astrophysics Data System (ADS)

Gains, David

2008-04-01

Image exploitation is of increasing importance to the enterprise of building situational awareness from multi-source data. It involves image acquisition, identification of objects of interest in imagery, storage, search and retrieval of imagery, and the distribution of imagery over possibly bandwidth limited networks. This paper describes an image exploitation application that uses image content alone to detect objects of interest, and that automatically establishes and preserves spatial and temporal relationships between images, cameras and objects. The application features an intuitive user interface that exposes all images and information generated by the system to an operator thus facilitating the formation of situational awareness.

Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval.

PubMed

Ferreira, José Raniery; de Azevedo-Marques, Paulo Mazzoncini; Oliveira, Marcelo Costa

2017-03-01

Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules. A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule's potential malignancy on the 10 most similar cases and by the parameters of precision and recall. Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval. Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.

Content Based Image Retrieval and Information Theory: A General Approach.

ERIC Educational Resources Information Center

Zachary, John; Iyengar, S. S.; Barhen, Jacob

2001-01-01

Proposes an alternative real valued representation of color based on the information theoretic concept of entropy. A theoretical presentation of image entropy is accompanied by a practical description of the merits and limitations of image entropy compared to color histograms. Results suggest that image entropy is a promising approach to image…

Diagnostic and procedural imaging curricula in physical therapist professional degree programs.

PubMed

Boissonnault, William G; White, Douglas M; Carney, Sara; Malin, Brittany; Smith, Wayne

2014-08-01

Descriptive survey. To describe the status of diagnostic and procedural imaging curricula within United States physical therapist professional degree programs. As patient direct access to physical therapy services increases, the ability to refer patients directly for diagnostic imaging could promote more efficient delivery of care. Appropriate patient referral is contingent on physical therapists having the requisite knowledge base and skills. While evidence describing imaging competence of physical therapists with advanced training in military institutions exists, evidence is lacking for other physical therapists, including new graduates of physical therapist professional degree programs. Faculty members teaching imaging at 206 United States physical therapist professional degree programs recognized by the Commission on Accreditation in Physical Therapy Education were recruited via e-mail correspondence. An e-mail attachment included the survey on which faculty reported imaging curricula and faculty qualifications, attitudes, and experiences. Faculty from 155 (75.2%) programs responded to the survey, with imaging being included in the curriculum of 152 programs. Content was integrated by required standalone courses or clinical science track courses, and/or through elective courses. The average reported estimate of imaging contact hours was 24.4 hours (range, 2-75 hours). Emphasis was on the musculoskeletal system, including 76.3% of the required standalone course content. Student competence was assessed in 147 (96.7%) programs, primarily by written (66.7%) and practical (19.7%) examinations. Faculty rated student competence on a scale of 1 (not competent) to 5 (competent), with ratings ranging from a high of 4.0 (identifying normal anatomy on plain-film radiography) to a low of 1.9 (identifying common tissue pathological processes/injuries on ultrasound). While a majority of programs reported including imaging curricula, variability was noted in all curricular aspects. These results may serve as a benchmark for faculty to assess existing curricula, allow for further development of imaging curricula, and provide a benchmark for the profession regarding current level of training for recent graduates of entry-level physical therapist professional degree programs.

Limitations and requirements of content-based multimedia authentication systems

NASA Astrophysics Data System (ADS)

Wu, Chai W.

2001-08-01

Recently, a number of authentication schemes have been proposed for multimedia data such as images and sound data. They include both label based systems and semifragile watermarks. The main requirement for such authentication systems is that minor modifications such as lossy compression which do not alter the content of the data preserve the authenticity of the data, whereas modifications which do modify the content render the data not authentic. These schemes can be classified into two main classes depending on the model of image authentication they are based on. One of the purposes of this paper is to look at some of the advantages and disadvantages of these image authentication schemes and their relationship with fundamental limitations of the underlying model of image authentication. In particular, we study feature-based algorithms which generate an authentication tag based on some inherent features in the image such as the location of edges. The main disadvantage of most proposed feature-based algorithms is that similar images generate similar features, and therefore it is possible for a forger to generate dissimilar images that have the same features. On the other hand, the class of hash-based algorithms utilizes a cryptographic hash function or a digital signature scheme to reduce the data and generate an authentication tag. It inherits the security of digital signatures to thwart forgery attacks. The main disadvantage of hash-based algorithms is that the image needs to be modified in order to be made authenticatable. The amount of modification is on the order of the noise the image can tolerate before it is rendered inauthentic. The other purpose of this paper is to propose a multimedia authentication scheme which combines some of the best features of both classes of algorithms. The proposed scheme utilizes cryptographic hash functions and digital signature schemes and the data does not need to be modified in order to be made authenticatable. Several applications including the authentication of images on CD-ROM and handwritten documents will be discussed.

Identifying spatially similar gene expression patterns in early stage fruit fly embryo images: binary feature versus invariant moment digital representations

PubMed Central

Gurunathan, Rajalakshmi; Van Emden, Bernard; Panchanathan, Sethuraman; Kumar, Sudhir

2004-01-01

Background Modern developmental biology relies heavily on the analysis of embryonic gene expression patterns. Investigators manually inspect hundreds or thousands of expression patterns to identify those that are spatially similar and to ultimately infer potential gene interactions. However, the rapid accumulation of gene expression pattern data over the last two decades, facilitated by high-throughput techniques, has produced a need for the development of efficient approaches for direct comparison of images, rather than their textual descriptions, to identify spatially similar expression patterns. Results The effectiveness of the Binary Feature Vector (BFV) and Invariant Moment Vector (IMV) based digital representations of the gene expression patterns in finding biologically meaningful patterns was compared for a small (226 images) and a large (1819 images) dataset. For each dataset, an ordered list of images, with respect to a query image, was generated to identify overlapping and similar gene expression patterns, in a manner comparable to what a developmental biologist might do. The results showed that the BFV representation consistently outperforms the IMV representation in finding biologically meaningful matches when spatial overlap of the gene expression pattern and the genes involved are considered. Furthermore, we explored the value of conducting image-content based searches in a dataset where individual expression components (or domains) of multi-domain expression patterns were also included separately. We found that this technique improves performance of both IMV and BFV based searches. Conclusions We conclude that the BFV representation consistently produces a more extensive and better list of biologically useful patterns than the IMV representation. The high quality of results obtained scales well as the search database becomes larger, which encourages efforts to build automated image query and retrieval systems for spatial gene expression patterns. PMID:15603586

Time-efficient high-resolution whole-brain three-dimensional macromolecular proton fraction mapping

PubMed Central

Yarnykh, Vasily L.

2015-01-01

Purpose Macromolecular proton fraction (MPF) mapping is a quantitative MRI method that reconstructs parametric maps of a relative amount of macromolecular protons causing the magnetization transfer (MT) effect and provides a biomarker of myelination in neural tissues. This study aimed to develop a high-resolution whole-brain MPF mapping technique utilizing a minimal possible number of source images for scan time reduction. Methods The described technique is based on replacement of an actually acquired reference image without MT saturation by a synthetic one reconstructed from R1 and proton density maps, thus requiring only three source images. This approach enabled whole-brain three-dimensional MPF mapping with isotropic 1.25×1.25×1.25 mm3 voxel size and scan time of 20 minutes. The synthetic reference method was validated against standard MPF mapping with acquired reference images based on data from 8 healthy subjects. Results Mean MPF values in segmented white and gray matter appeared in close agreement with no significant bias and small within-subject coefficients of variation (<2%). High-resolution MPF maps demonstrated sharp white-gray matter contrast and clear visualization of anatomical details including gray matter structures with high iron content. Conclusions Synthetic reference method improves resolution of MPF mapping and combines accurate MPF measurements with unique neuroanatomical contrast features. PMID:26102097

Efficient demodulation scheme for rolling-shutter-patterning of CMOS image sensor based visible light communications.

PubMed

Chen, Chia-Wei; Chow, Chi-Wai; Liu, Yang; Yeh, Chien-Hung

2017-10-02

Recently even the low-end mobile-phones are equipped with a high-resolution complementary-metal-oxide-semiconductor (CMOS) image sensor. This motivates using a CMOS image sensor for visible light communication (VLC). Here we propose and demonstrate an efficient demodulation scheme to synchronize and demodulate the rolling shutter pattern in image sensor based VLC. The implementation algorithm is discussed. The bit-error-rate (BER) performance and processing latency are evaluated and compared with other thresholding schemes.

Recruiting Young Gay and Bisexual Men for a Human Papillomavirus Vaccination Intervention Through Social Media: The Effects of Advertisement Content

PubMed Central

Katz, Mira L; Bauermeister, Jose A; Shoben, Abigail B; Paskett, Electra D; McRee, Annie-Laurie

2017-01-01

Background Web-based approaches, specifically social media sites, represent a promising approach for recruiting young gay and bisexual men for research studies. Little is known, however, about how the performance of social media advertisements (ads) used to recruit this population is affected by ad content (ie, image and text). Objective The aim of this study was to evaluate the effects of different images and text included in social media ads used to recruit young gay and bisexual men for the pilot test of a Web-based human papillomavirus (HPV) vaccination intervention. Methods In July and September 2016, we used paid Facebook advertisements to recruit men who were aged 18-25 years, self-identified as gay or bisexual, US resident, and had not received HPV vaccine. A 4x2x2 factorial experiment varied ad image (a single young adult male, a young adult male couple, a group of young adult men, or a young adult male talking to a doctor), content focus (text mentioning HPV or HPV vaccine), and disease framing (text mentioning cancer or a sexually transmitted disease [STD]). Poisson regression determined whether these experimental factors affected ad performance. Results The recruitment campaign reached a total of 35,646 users who viewed ads for 36,395 times. This resulted in an overall unique click-through rate of 2.01% (717/35,646) and an overall conversion rate of 0.66% (241/36,395). Reach was higher for ads that included an image of a couple (incidence rate ratio, IRR=4.91, 95% CI 2.68-8.97, P<.001) or a group (IRR=2.65, 95% CI 1.08-6.50, P=.03) compared with those that included an image of a single person. Ads that included an image of a couple also had a higher conversion rate (IRR=2.56, 95% CI 1.13-5.77, P=.02) than ads that included an image of a single person. Ads with text mentioning an STD had a higher unique click-through rate compared with ads with text mentioning cancer (IRR=1.34, 95% CI 1.06-1.69, P=.01). The campaign cost a total of US $413.72 and resulted in 150 eligible and enrolled individuals (US $2.76 per enrolled participant). Conclusions Facebook ads are a convenient and cost-efficient strategy for reaching and recruiting young gay and bisexual men for a Web-based HPV vaccination intervention. To help optimize ad performance among this population, researchers should consider the importance of the text and image included in the social media recruitment ads. PMID:28576758

Audio-based queries for video retrieval over Java enabled mobile devices

NASA Astrophysics Data System (ADS)

Ahmad, Iftikhar; Cheikh, Faouzi Alaya; Kiranyaz, Serkan; Gabbouj, Moncef

2006-02-01

In this paper we propose a generic framework for efficient retrieval of audiovisual media based on its audio content. This framework is implemented in a client-server architecture where the client application is developed in Java to be platform independent whereas the server application is implemented for the PC platform. The client application adapts to the characteristics of the mobile device where it runs such as screen size and commands. The entire framework is designed to take advantage of the high-level segmentation and classification of audio content to improve speed and accuracy of audio-based media retrieval. Therefore, the primary objective of this framework is to provide an adaptive basis for performing efficient video retrieval operations based on the audio content and types (i.e. speech, music, fuzzy and silence). Experimental results approve that such an audio based video retrieval scheme can be used from mobile devices to search and retrieve video clips efficiently over wireless networks.

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

PubMed Central

Mikut, Ralf

2017-01-01

Many automatically analyzable scientific questions are well-posed and a variety of information about expected outcomes is available a priori. Although often neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and by direct information about the ambiguity inherent in the extracted data. We present a new concept that increases the result quality awareness of image analysis operators by estimating and distributing the degree of uncertainty involved in their output based on prior knowledge. This allows the use of simple processing operators that are suitable for analyzing large-scale spatiotemporal (3D+t) microscopy images without compromising result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it to enhance the result quality of various processing operators. These concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. The functionality of the proposed approach is further validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. The generality of the concept makes it applicable to practically any field with processing strategies that are arranged as linear pipelines. The automated analysis of terabyte-scale microscopy data will especially benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout. PMID:29095927

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

NASA Astrophysics Data System (ADS)

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 detection rate by using facial features and skin color model. To harness all the features in the scene, we further developed another system using multiple types of local descriptors along with Bag-of-Visual Word framework. In addition, an investigation of new contour feature in detecting obscene content is presented.

Design and Verification of Remote Sensing Image Data Center Storage Architecture Based on Hadoop

NASA Astrophysics Data System (ADS)

Tang, D.; Zhou, X.; Jing, Y.; Cong, W.; Li, C.

2018-04-01

The data center is a new concept of data processing and application proposed in recent years. It is a new method of processing technologies based on data, parallel computing, and compatibility with different hardware clusters. While optimizing the data storage management structure, it fully utilizes cluster resource computing nodes and improves the efficiency of data parallel application. This paper used mature Hadoop technology to build a large-scale distributed image management architecture for remote sensing imagery. Using MapReduce parallel processing technology, it called many computing nodes to process image storage blocks and pyramids in the background to improve the efficiency of image reading and application and sovled the need for concurrent multi-user high-speed access to remotely sensed data. It verified the rationality, reliability and superiority of the system design by testing the storage efficiency of different image data and multi-users and analyzing the distributed storage architecture to improve the application efficiency of remote sensing images through building an actual Hadoop service system.

Design and Development of a Virtual Facility Tour Using iPIX(TM) Technology

NASA Technical Reports Server (NTRS)

Farley, Douglas L.

2002-01-01

The capabilities of the iPIX virtual tour software, in conjunction with a web-based interface create a unique and valuable system that provides users with an efficient virtual capability to tour facilities while being able to acquire the necessary technical content is demonstrated. A users guide to the Mechanics and Durability Branch's virtual tour is presented. The guide provides the user with instruction on operating both scripted and unscripted tours as well as a discussion of the tours for Buildings 1148, 1205 and 1256 and NASA Langley Research Center. Furthermore, an indepth discussion has been presented on how to develop a virtual tour using the iPIX software interface with conventional html and JavaScript. The main aspects for discussion are on network and computing issues associated with using this capability. A discussion of how to take the iPIX pictures, manipulate them and bond them together to form hemispherical images is also presented. Linking of images with additional multimedia content is discussed. Finally, a method to integrate the iPIX software with conventional HTML and JavaScript to facilitate linking with multi-media is presented.

Evaluation of color mapping algorithms in different color spaces

NASA Astrophysics Data System (ADS)

Bronner, Timothée.-Florian; Boitard, Ronan; Pourazad, Mahsa T.; Nasiopoulos, Panos; Ebrahimi, Touradj

2016-09-01

The color gamut supported by current commercial displays is only a subset of the full spectrum of colors visible by the human eye. In High-Definition (HD) television technology, the scope of the supported colors covers 35.9% of the full visible gamut. For comparison, Ultra High-Definition (UHD) television, which is currently being deployed on the market, extends this range to 75.8%. However, when reproducing content with a wider color gamut than that of a television, typically UHD content on HD television, some original color information may lie outside the reproduction capabilities of the television. Efficient gamut mapping techniques are required in order to fit the colors of any source content into the gamut of a given display. The goal of gamut mapping is to minimize the distortion, in terms of perceptual quality, when converting video from one color gamut to another. It is assumed that the efficiency of gamut mapping depends on the color space in which it is computed. In this article, we evaluate 14 gamut mapping techniques, 12 combinations of two projection methods across six color spaces as well as R'G'B' Clipping and wrong gamut interpretation. Objective results, using the CIEDE2000 metric, show that the R'G'B' Clipping is slightly outperformed by only one combination of color space and projection method. However, analysis of images shows that R'G'B' Clipping can result in loss of contrast in highly saturated images, greatly impairing the quality of the mapped image.

Integrating planning perception and action for informed object search.

PubMed

Manso, Luis J; Gutierrez, Marco A; Bustos, Pablo; Bachiller, Pilar

2018-05-01

This paper presents a method to reduce the time spent by a robot with cognitive abilities when looking for objects in unknown locations. It describes how machine learning techniques can be used to decide which places should be inspected first, based on images that the robot acquires passively. The proposal is composed of two concurrent processes. The first one uses the aforementioned images to generate a description of the types of objects found in each object container seen by the robot. This is done passively, regardless of the task being performed. The containers can be tables, boxes, shelves or any other kind of container of known shape whose contents can be seen from a distance. The second process uses the previously computed estimation of the contents of the containers to decide which is the most likely container having the object to be found. This second process is deliberative and takes place only when the robot needs to find an object, whether because it is explicitly asked to locate one or because it is needed as a step to fulfil the mission of the robot. Upon failure to guess the right container, the robot can continue making guesses until the object is found. Guesses are made based on the semantic distance between the object to find and the description of the types of the objects found in each object container. The paper provides quantitative results comparing the efficiency of the proposed method and two base approaches.

Dynamic autofocus for continuous-scanning time-delay-and-integration image acquisition in automated microscopy.

PubMed

Bravo-Zanoguera, Miguel E; Laris, Casey A; Nguyen, Lam K; Oliva, Mike; Price, Jeffrey H

2007-01-01

Efficient image cytometry of a conventional microscope slide means rapid acquisition and analysis of 20 gigapixels of image data (at 0.3-microm sampling). The voluminous data motivate increased acquisition speed to enable many biomedical applications. Continuous-motion time-delay-and-integrate (TDI) scanning has the potential to speed image acquisition while retaining sensitivity, but the challenge of implementing high-resolution autofocus operating simultaneously with acquisition has limited its adoption. We develop a dynamic autofocus system for this need using: 1. a "volume camera," consisting of nine fiber optic imaging conduits to charge-coupled device (CCD) sensors, that acquires images in parallel from different focal planes, 2. an array of mixed analog-digital processing circuits that measure the high spatial frequencies of the multiple image streams to create focus indices, and 3. a software system that reads and analyzes the focus data streams and calculates best focus for closed feedback loop control. Our system updates autofocus at 56 Hz (or once every 21 microm of stage travel) to collect sharply focused images sampled at 0.3x0.3 microm(2)/pixel at a stage speed of 2.3 mms. The system, tested by focusing in phase contrast and imaging long fluorescence strips, achieves high-performance closed-loop image-content-based autofocus in continuous scanning for the first time.

Extraction and labeling high-resolution images from PDF documents

NASA Astrophysics Data System (ADS)

Chachra, Suchet K.; Xue, Zhiyun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-12-01

Accuracy of content-based image retrieval is affected by image resolution among other factors. Higher resolution images enable extraction of image features that more accurately represent the image content. In order to improve the relevance of search results for our biomedical image search engine, Open-I, we have developed techniques to extract and label high-resolution versions of figures from biomedical articles supplied in the PDF format. Open-I uses the open-access subset of biomedical articles from the PubMed Central repository hosted by the National Library of Medicine. Articles are available in XML and in publisher supplied PDF formats. As these PDF documents contain little or no meta-data to identify the embedded images, the task includes labeling images according to their figure number in the article after they have been successfully extracted. For this purpose we use the labeled small size images provided with the XML web version of the article. This paper describes the image extraction process and two alternative approaches to perform image labeling that measure the similarity between two images based upon the image intensity projection on the coordinate axes and similarity based upon the normalized cross-correlation between the intensities of two images. Using image identification based on image intensity projection, we were able to achieve a precision of 92.84% and a recall of 82.18% in labeling of the extracted images.

Interfacing Lab-on-a-Chip Embryo Technology with High-Definition Imaging Cytometry.

PubMed

Zhu, Feng; Hall, Christopher J; Crosier, Philip S; Wlodkowic, Donald

2015-08-01

To spearhead deployment of zebrafish embryo biotests in large-scale drug discovery studies, automated platforms are needed to integrate embryo in-test positioning and immobilization (suitable for high-content imaging) with fluidic modules for continuous drug and medium delivery under microperfusion to developing embryos. In this work, we present an innovative design of a high-throughput three-dimensional (3D) microfluidic chip-based device for automated immobilization and culture and time-lapse imaging of developing zebrafish embryos under continuous microperfusion. The 3D Lab-on-a-Chip array was fabricated in poly(methyl methacrylate) (PMMA) transparent thermoplastic using infrared laser micromachining, while the off-chip interfaces were fabricated using additive manufacturing processes (fused deposition modelling and stereolithography). The system's design facilitated rapid loading and immobilization of a large number of embryos in predefined clusters of traps during continuous microperfusion of drugs/toxins. It was conceptually designed to seamlessly interface with both upright and inverted fluorescent imaging systems and also to directly interface with conventional microtiter plate readers that accept 96-well plates. Compared with the conventional Petri dish assays, the chip-based bioassay was much more convenient and efficient as only small amounts of drug solutions were required for the whole perfusion system running continuously over 72 h. Embryos were spatially separated in the traps that assisted tracing single embryos, preventing interembryo contamination and improving imaging accessibility.

Content based image retrieval for matching images of improvised explosive devices in which snake initialization is viewed as an inverse problem

NASA Astrophysics Data System (ADS)

Acton, Scott T.; Gilliam, Andrew D.; Li, Bing; Rossi, Adam

2008-02-01

Improvised explosive devices (IEDs) are common and lethal instruments of terrorism, and linking a terrorist entity to a specific device remains a difficult task. In the effort to identify persons associated with a given IED, we have implemented a specialized content based image retrieval system to search and classify IED imagery. The system makes two contributions to the art. First, we introduce a shape-based matching technique exploiting shape, color, and texture (wavelet) information, based on novel vector field convolution active contours and a novel active contour initialization method which treats coarse segmentation as an inverse problem. Second, we introduce a unique graph theoretic approach to match annotated printed circuit board images for which no schematic or connectivity information is available. The shape-based image retrieval method, in conjunction with the graph theoretic tool, provides an efficacious system for matching IED images. For circuit imagery, the basic retrieval mechanism has a precision of 82.1% and the graph based method has a precision of 98.1%. As of the fall of 2007, the working system has processed over 400,000 case images.

Color Imaging management in film processing

NASA Astrophysics Data System (ADS)

Tremeau, Alain; Konik, Hubert; Colantoni, Philippe

2003-12-01

The latest research projects in the laboratory LIGIV concerns capture, processing, archiving and display of color images considering the trichromatic nature of the Human Vision System (HSV). Among these projects one addresses digital cinematographic film sequences of high resolution and dynamic range. This project aims to optimize the use of content for the post-production operators and for the end user. The studies presented in this paper address the use of metadata to optimise the consumption of video content on a device of user's choice independent of the nature of the equipment that captured the content. Optimising consumption includes enhancing the quality of image reconstruction on a display. Another part of this project addresses the content-based adaptation of image display. Main focus is on Regions of Interest (ROI) operations, based on the ROI concepts of MPEG-7. The aim of this second part is to characterize and ensure the conditions of display even if display device or display media changes. This requires firstly the definition of a reference color space and the definition of bi-directional color transformations for each peripheral device (camera, display, film recorder, etc.). The complicating factor is that different devices have different color gamuts, depending on the chromaticity of their primaries and the ambient illumination under which they are viewed. To match the displayed image to the aimed appearance, all kind of production metadata (camera specification, camera colour primaries, lighting conditions) should be associated to the film material. Metadata and content build together rich content. The author is assumed to specify conditions as known from digital graphics arts. To control image pre-processing and image post-processing, these specifications should be contained in the film's metadata. The specifications are related to the ICC profiles but need additionally consider mesopic viewing conditions.

GPU-based simulation of optical propagation through turbulence for active and passive imaging

NASA Astrophysics Data System (ADS)

Monnier, Goulven; Duval, François-Régis; Amram, Solène

2014-10-01

IMOTEP is a GPU-based (Graphical Processing Units) software relying on a fast parallel implementation of Fresnel diffraction through successive phase screens. Its applications include active imaging, laser telemetry and passive imaging through turbulence with anisoplanatic spatial and temporal fluctuations. Thanks to parallel implementation on GPU, speedups ranging from 40X to 70X are achieved. The present paper gives a brief overview of IMOTEP models, algorithms, implementation and user interface. It then focuses on major improvements recently brought to the anisoplanatic imaging simulation method. Previously, we took advantage of the computational power offered by the GPU to develop a simulation method based on large series of deterministic realisations of the PSF distorted by turbulence. The phase screen propagation algorithm, by reproducing higher moments of the incident wavefront distortion, provides realistic PSFs. However, we first used a coarse gaussian model to fit the numerical PSFs and characterise there spatial statistics through only 3 parameters (two-dimensional displacements of centroid and width). Meanwhile, this approach was unable to reproduce the effects related to the details of the PSF structure, especially the "speckles" leading to prominent high-frequency content in short-exposure images. To overcome this limitation, we recently implemented a new empirical model of the PSF, based on Principal Components Analysis (PCA), ought to catch most of the PSF complexity. The GPU implementation allows estimating and handling efficiently the numerous (up to several hundreds) principal components typically required under the strong turbulence regime. A first demanding computational step involves PCA, phase screen propagation and covariance estimates. In a second step, realistic instantaneous images, fully accounting for anisoplanatic effects, are quickly generated. Preliminary results are presented.

DWT-based stereoscopic image watermarking

NASA Astrophysics Data System (ADS)

Chammem, A.; Mitrea, M.; Pr"teux, F.

2011-03-01

Watermarking already imposed itself as an effective and reliable solution for conventional multimedia content protection (image/video/audio/3D). By persistently (robustly) and imperceptibly (transparently) inserting some extra data into the original content, the illegitimate use of data can be detected without imposing any annoying constraint to a legal user. The present paper deals with stereoscopic image protection by means of watermarking techniques. That is, we first investigate the peculiarities of the visual stereoscopic content from the transparency and robustness point of view. Then, we advance a new watermarking scheme designed so as to reach the trade-off between transparency and robustness while ensuring a prescribed quantity of inserted information. Finally, this method is evaluated on two stereoscopic image corpora (natural image and medical data).

Assessment of 16 chemicals on proliferation and apoptosis in human neuroprogenitor cells using high-content image analysis (HCA).

EPA Science Inventory

The need for efficient methods of screening chemicals for the potential to cause developmental neurotoxicity is paramount. We previously described optimization of an HCA assay for proliferation and apoptosis in ReNcell CX cells (ReN), identifying appropriate controls. Utility of ...

ASSESSMENT OF CHEMICAL EFFECTS ON NEURITE OUTGROWTH, NEURONAL POLARIZATION AND SYNAPTOGENESIS IN RAT CORTICAL NEURONS USING HIGH CONTENT IMAGE ANALYSIS

EPA Science Inventory

There is a need for efficient, cost-effective methods for screening and prioritization of potential developmental neurotoxicants. One approach uses in vitro cell culture models that can recapitulate the critical processes of nervous system development. In vitro, primary cultures ...

IN VITRO SCREENING OF DEVELOPMENTAL NEUROTOXICANTS IN RAT PRIMARY CORTICAL NEURONS USING HIGH CONTENT IMAGE

EPA Science Inventory

There is a need for more efficient and cost-effective methods for identifying, characterizing and prioritizing chemicals which may result in developmental neurotoxicity. One approach is to utilize in vitro test systems which recapitulate the critical processes of nervous system d...

Mobile object retrieval in server-based image databases

NASA Astrophysics Data System (ADS)

Manger, D.; Pagel, F.; Widak, H.

2013-05-01

The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.

Efficient workflows for 3D building full-color model reconstruction using LIDAR long-range laser and image-based modeling techniques

NASA Astrophysics Data System (ADS)

Shih, Chihhsiong

2005-01-01

Two efficient workflow are developed for the reconstruction of a 3D full color building model. One uses a point wise sensing device to sample an unknown object densely and attach color textures from a digital camera separately. The other uses an image based approach to reconstruct the model with color texture automatically attached. The point wise sensing device reconstructs the CAD model using a modified best view algorithm that collects the maximum number of construction faces in one view. The partial views of the point clouds data are then glued together using a common face between two consecutive views. Typical overlapping mesh removal and coarsening procedures are adapted to generate a unified 3D mesh shell structure. A post processing step is then taken to combine the digital image content from a separate camera with the 3D mesh shell surfaces. An indirect uv mapping procedure first divide the model faces into groups within which every face share the same normal direction. The corresponding images of these faces in a group is then adjusted using the uv map as a guidance. The final assembled image is then glued back to the 3D mesh to present a full colored building model. The result is a virtual building that can reflect the true dimension and surface material conditions of a real world campus building. The image based modeling procedure uses a commercial photogrammetry package to reconstruct the 3D model. A novel view planning algorithm is developed to guide the photos taking procedure. This algorithm successfully generate a minimum set of view angles. The set of pictures taken at these view angles can guarantee that each model face shows up at least in two of the pictures set and no more than three. The 3D model can then be reconstructed with minimum amount of labor spent in correlating picture pairs. The finished model is compared with the original object in both the topological and dimensional aspects. All the test cases show exact same topology and reasonably low dimension error ratio. Again proving the applicability of the algorithm.

Retrieving the unretrievable in electronic imaging systems: emotions, themes, and stories

NASA Astrophysics Data System (ADS)

Joergensen, Corinne

1999-05-01

New paradigms such as 'affective computing' and user-based research are extending the realm of facets traditionally addressed in IR systems. This paper builds on previous research reported to the electronic imaging community concerning the need to provide access to more abstract attributes of images than those currently amenable to a variety of content-based and text-based indexing techniques. Empirical research suggest that, for visual materials, in addition to standard bibliographic data and broad subject, and in addition to such visually perceptual attributes such as color, texture, shape, and position or focal point, additional access points such as themes, abstract concepts, emotions, stories, and 'people-related' information such as social status would be useful in image retrieval. More recent research demonstrates that similar results are also obtained with 'fine arts' images, which generally have no access provided for these types of attributes. Current efforts to match image attributes as revealed in empirical research with those addressed both in current textural and content-based indexing systems are discussed, as well as the need for new representations for image attributes and for collaboration among diverse communities of researchers.

ART-ML - a novel XML format for the biological procedures modeling and the representation of blood flow simulation.

PubMed

Karvounis, E C; Tsakanikas, V D; Fotiou, E; Fotiadis, D I

2010-01-01

The paper proposes a novel Extensible Markup Language (XML) based format called ART-ML that aims at supporting the interoperability and the reuse of models of blood flow, mass transport and plaque formation, exported by ARTool. ARTool is a platform for the automatic processing of various image modalities of coronary and carotid arteries. The images and their content are fused to develop morphological models of the arteries in easy to handle 3D representations. The platform incorporates efficient algorithms which are able to perform blood flow simulation. In addition atherosclerotic plaque development is estimated taking into account morphological, flow and genetic factors. ART-ML provides a XML format that enables the representation and management of embedded models within the ARTool platform and the storage and interchange of well-defined information. This approach influences in the model creation, model exchange, model reuse and result evaluation.

Prioritization of brain MRI volumes using medical image perception model and tumor region segmentation.

PubMed

Mehmood, Irfan; Ejaz, Naveed; Sajjad, Muhammad; Baik, Sung Wook

2013-10-01

The objective of the present study is to explore prioritization methods in diagnostic imaging modalities to automatically determine the contents of medical images. In this paper, we propose an efficient prioritization of brain MRI. First, the visual perception of the radiologists is adapted to identify salient regions. Then this saliency information is used as an automatic label for accurate segmentation of brain lesion to determine the scientific value of that image. The qualitative and quantitative results prove that the rankings generated by the proposed method are closer to the rankings created by radiologists. Copyright © 2013 Elsevier Ltd. All rights reserved.

No-reference multiscale blur detection tool for content based image retrieval

NASA Astrophysics Data System (ADS)

Ezekiel, Soundararajan; Stocker, Russell; Harrity, Kyle; Alford, Mark; Ferris, David; Blasch, Erik; Gorniak, Mark

2014-06-01

In recent years, digital cameras have been widely used for image capturing. These devices are equipped in cell phones, laptops, tablets, webcams, etc. Image quality is an important component of digital image analysis. To assess image quality for these mobile products, a standard image is required as a reference image. In this case, Root Mean Square Error and Peak Signal to Noise Ratio can be used to measure the quality of the images. However, these methods are not possible if there is no reference image. In our approach, a discrete-wavelet transformation is applied to the blurred image, which decomposes into the approximate image and three detail sub-images, namely horizontal, vertical, and diagonal images. We then focus on noise-measuring the detail images and blur-measuring the approximate image to assess the image quality. We then compute noise mean and noise ratio from the detail images, and blur mean and blur ratio from the approximate image. The Multi-scale Blur Detection (MBD) metric provides both an assessment of the noise and blur content. These values are weighted based on a linear regression against full-reference y values. From these statistics, we can compare to normal useful image statistics for image quality without needing a reference image. We then test the validity of our obtained weights by R2 analysis as well as using them to estimate image quality of an image with a known quality measure. The result shows that our method provides acceptable results for images containing low to mid noise levels and blur content.

Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction.

PubMed

Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

2016-12-01

To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.

Stokes vector based interpolation method to improve the efficiency of bio-inspired polarization-difference imaging in turbid media

NASA Astrophysics Data System (ADS)

Guan, Jinge; Ren, Wei; Cheng, Yaoyu

2018-04-01

We demonstrate an efficient polarization-difference imaging system in turbid conditions by using the Stokes vector of light. The interaction of scattered light with the polarizer is analyzed by the Stokes-Mueller formalism. An interpolation method is proposed to replace the mechanical rotation of the polarization axis of the analyzer theoretically, and its performance is verified by the experiment at different turbidity levels. We show that compared with direct imaging, the Stokes vector based imaging method can effectively reduce the effect of light scattering and enhance the image contrast.

Text String Detection from Natural Scenes by Structure-based Partition and Grouping

PubMed Central

Yi, Chucai; Tian, YingLi

2012-01-01

Text information in natural scene images serves as important clues for many image-based applications such as scene understanding, content-based image retrieval, assistive navigation, and automatic geocoding. However, locating text from complex background with multiple colors is a challenging task. In this paper, we explore a new framework to detect text strings with arbitrary orientations in complex natural scene images. Our proposed framework of text string detection consists of two steps: 1) Image partition to find text character candidates based on local gradient features and color uniformity of character components. 2) Character candidate grouping to detect text strings based on joint structural features of text characters in each text string such as character size differences, distances between neighboring characters, and character alignment. By assuming that a text string has at least three characters, we propose two algorithms of text string detection: 1) adjacent character grouping method, and 2) text line grouping method. The adjacent character grouping method calculates the sibling groups of each character candidate as string segments and then merges the intersecting sibling groups into text string. The text line grouping method performs Hough transform to fit text line among the centroids of text candidates. Each fitted text line describes the orientation of a potential text string. The detected text string is presented by a rectangle region covering all characters whose centroids are cascaded in its text line. To improve efficiency and accuracy, our algorithms are carried out in multi-scales. The proposed methods outperform the state-of-the-art results on the public Robust Reading Dataset which contains text only in horizontal orientation. Furthermore, the effectiveness of our methods to detect text strings with arbitrary orientations is evaluated on the Oriented Scene Text Dataset collected by ourselves containing text strings in non-horizontal orientations. PMID:21411405

Text string detection from natural scenes by structure-based partition and grouping.

PubMed

Yi, Chucai; Tian, YingLi

2011-09-01

Text information in natural scene images serves as important clues for many image-based applications such as scene understanding, content-based image retrieval, assistive navigation, and automatic geocoding. However, locating text from a complex background with multiple colors is a challenging task. In this paper, we explore a new framework to detect text strings with arbitrary orientations in complex natural scene images. Our proposed framework of text string detection consists of two steps: 1) image partition to find text character candidates based on local gradient features and color uniformity of character components and 2) character candidate grouping to detect text strings based on joint structural features of text characters in each text string such as character size differences, distances between neighboring characters, and character alignment. By assuming that a text string has at least three characters, we propose two algorithms of text string detection: 1) adjacent character grouping method and 2) text line grouping method. The adjacent character grouping method calculates the sibling groups of each character candidate as string segments and then merges the intersecting sibling groups into text string. The text line grouping method performs Hough transform to fit text line among the centroids of text candidates. Each fitted text line describes the orientation of a potential text string. The detected text string is presented by a rectangle region covering all characters whose centroids are cascaded in its text line. To improve efficiency and accuracy, our algorithms are carried out in multi-scales. The proposed methods outperform the state-of-the-art results on the public Robust Reading Dataset, which contains text only in horizontal orientation. Furthermore, the effectiveness of our methods to detect text strings with arbitrary orientations is evaluated on the Oriented Scene Text Dataset collected by ourselves containing text strings in nonhorizontal orientations.

Hyperspectral Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken Breast

PubMed Central

Kandpal, Lalit Mohan; Lee, Hoonsoo; Kim, Moon S.; Mo, Changyeun; Cho, Byoung-Kwan

2013-01-01

Spectroscopy has proven to be an efficient tool for measuring the properties of meat. In this article, hyperspectral imaging (HSI) techniques are used to determine the moisture content in cooked chicken breast over the VIS/NIR (400–1,000 nm) spectral range. Moisture measurements were performed using an oven drying method. A partial least squares regression (PLSR) model was developed to extract a relationship between the HSI spectra and the moisture content. In the full wavelength range, the PLSR model possessed a maximum R2p of 0.90 and an SEP of 0.74%. For the NIR range, the PLSR model yielded an R2p of 0.94 and an SEP of 0.71%. The majority of the absorption peaks occurred around 760 and 970 nm, representing the water content in the samples. Finally, PLSR images were constructed to visualize the dehydration and water distribution within different sample regions. The high correlation coefficient and low prediction error from the PLSR analysis validates that HSI is an effective tool for visualizing the chemical properties of meat. PMID:24084119

Classification of Active Microwave and Passive Optical Data Based on Bayesian Theory and Mrf

NASA Astrophysics Data System (ADS)

Yu, F.; Li, H. T.; Han, Y. S.; Gu, H. Y.

2012-08-01

A classifier based on Bayesian theory and Markov random field (MRF) is presented to classify the active microwave and passive optical remote sensing data, which have demonstrated their respective advantages in inversion of surface soil moisture content. In the method, the VV, VH polarization of ASAR and all the 7 TM bands are taken as the input of the classifier to get the class labels of each pixel of the images. And the model is validated for the necessities of integration of TM and ASAR, it shows that, the total precision of classification in this paper is 89.4%. Comparing with the classification with single TM, the accuracy increase 11.5%, illustrating that synthesis of active and passive optical remote sensing data is efficient and potential in classification.

An efficient method for the fusion of light field refocused images

NASA Astrophysics Data System (ADS)

Wang, Yingqian; Yang, Jungang; Xiao, Chao; An, Wei

2018-04-01

Light field cameras have drawn much attention due to the advantage of post-capture adjustments such as refocusing after exposure. The depth of field in refocused images is always shallow because of the large equivalent aperture. As a result, a large number of multi-focus images are obtained and an all-in-focus image is demanded. Consider that most multi-focus image fusion algorithms do not particularly aim at large numbers of source images and traditional DWT-based fusion approach has serious problems in dealing with lots of multi-focus images, causing color distortion and ringing effect. To solve this problem, this paper proposes an efficient multi-focus image fusion method based on stationary wavelet transform (SWT), which can deal with a large quantity of multi-focus images with shallow depth of fields. We compare SWT-based approach with DWT-based approach on various occasions. And the results demonstrate that the proposed method performs much better both visually and quantitatively.

High efficiency transport of quantum dots into plant roots with the aid of silwet L-77.

PubMed

Hu, Yong; Li, Jun; Ma, Lu; Peng, Qionglin; Feng, Wei; Zhang, Lu; He, Shibin; Yang, Fei; Huang, Jing; Li, Lijia

2010-08-01

Quantum dots (QDs) are a novel type of small, photostable and bright fluorophores that have been successfully applied to mammalian and human live cell imaging. In this study, highly dispersive water-soluble mercaptoacetic acid (MAA)-coated CdSe/ZnS QDs were synthesized, which were suitable for investigation as fluorescent probe labels. The treatment of maize seedling roots with QDs showed that the surfactant silwet L-77 aided the efficient transport of QDs into maize roots. Under a concentration ranging from 0.128 to 1.28 microM, QDs caused very low cytotoxicity on maize seed germination and root growth. The addition of mercuric chloride to the Hoagland solution resulted in a decrease of QD content in root tissues, and this decrease was reversed upon the addition of beta-mercaptoethanol, which suggests that mercury-sensitive processes play a significant role in regulating QD flow in the maize root system. We speculate that the apoplastic pathway can contribute substantially to the total quantity of QDs reaching the stele. Therefore, based on this transport approach, MAA-coated QDs can be utilized for live imaging in plant systems to verify known physiological processes. Copyright 2010 Elsevier Masson SAS. All rights reserved.

Integrated approach to multimodal media content analysis

NASA Astrophysics Data System (ADS)

Zhang, Tong; Kuo, C.-C. Jay

1999-12-01

In this work, we present a system for the automatic segmentation, indexing and retrieval of audiovisual data based on the combination of audio, visual and textural content analysis. The video stream is demultiplexed into audio, image and caption components. Then, a semantic segmentation of the audio signal based on audio content analysis is conducted, and each segment is indexed as one of the basic audio types. The image sequence is segmented into shots based on visual information analysis, and keyframes are extracted from each shot. Meanwhile, keywords are detected from the closed caption. Index tables are designed for both linear and non-linear access to the video. It is shown by experiments that the proposed methods for multimodal media content analysis are effective. And that the integrated framework achieves satisfactory results for video information filtering and retrieval.

Validating a Geographical Image Retrieval System.

ERIC Educational Resources Information Center

Zhu, Bin; Chen, Hsinchun

2000-01-01

Summarizes a prototype geographical image retrieval system that demonstrates how to integrate image processing and information analysis techniques to support large-scale content-based image retrieval. Describes an experiment to validate the performance of this image retrieval system against that of human subjects by examining similarity analysis…

Image detection and compression for memory efficient system analysis

NASA Astrophysics Data System (ADS)

Bayraktar, Mustafa

2015-02-01

The advances in digital signal processing have been progressing towards efficient use of memory and processing. Both of these factors can be utilized efficiently by using feasible techniques of image storage by computing the minimum information of image which will enhance computation in later processes. Scale Invariant Feature Transform (SIFT) can be utilized to estimate and retrieve of an image. In computer vision, SIFT can be implemented to recognize the image by comparing its key features from SIFT saved key point descriptors. The main advantage of SIFT is that it doesn't only remove the redundant information from an image but also reduces the key points by matching their orientation and adding them together in different windows of image [1]. Another key property of this approach is that it works on highly contrasted images more efficiently because it`s design is based on collecting key points from the contrast shades of image.

A novel content-based medical image retrieval method based on query topic dependent image features (QTDIF)

NASA Astrophysics Data System (ADS)

Xiong, Wei; Qiu, Bo; Tian, Qi; Mueller, Henning; Xu, Changsheng

2005-04-01

Medical image retrieval is still mainly a research domain with a large variety of applications and techniques. With the ImageCLEF 2004 benchmark, an evaluation framework has been created that includes a database, query topics and ground truth data. Eleven systems (with a total of more than 50 runs) compared their performance in various configurations. The results show that there is not any one feature that performs well on all query tasks. Key to successful retrieval is rather the selection of features and feature weights based on a specific set of input features, thus on the query task. In this paper we propose a novel method based on query topic dependent image features (QTDIF) for content-based medical image retrieval. These feature sets are designed to capture both inter-category and intra-category statistical variations to achieve good retrieval performance in terms of recall and precision. We have used Gaussian Mixture Models (GMM) and blob representation to model medical images and construct the proposed novel QTDIF for CBIR. Finally, trained multi-class support vector machines (SVM) are used for image similarity ranking. The proposed methods have been tested over the Casimage database with around 9000 images, for the given 26 image topics, used for imageCLEF 2004. The retrieval performance has been compared with the medGIFT system, which is based on the GNU Image Finding Tool (GIFT). The experimental results show that the proposed QTDIF-based CBIR can provide significantly better performance than systems based general features only.

General Staining and Segmentation Procedures for High Content Imaging and Analysis.

PubMed

Chambers, Kevin M; Mandavilli, Bhaskar S; Dolman, Nick J; Janes, Michael S

2018-01-01

Automated quantitative fluorescence microscopy, also known as high content imaging (HCI), is a rapidly growing analytical approach in cell biology. Because automated image analysis relies heavily on robust demarcation of cells and subcellular regions, reliable methods for labeling cells is a critical component of the HCI workflow. Labeling of cells for image segmentation is typically performed with fluorescent probes that bind DNA for nuclear-based cell demarcation or with those which react with proteins for image analysis based on whole cell staining. These reagents, along with instrument and software settings, play an important role in the successful segmentation of cells in a population for automated and quantitative image analysis. In this chapter, we describe standard procedures for labeling and image segmentation in both live and fixed cell samples. The chapter will also provide troubleshooting guidelines for some of the common problems associated with these aspects of HCI.

QBIC project: querying images by content, using color, texture, and shape

NASA Astrophysics Data System (ADS)

Niblack, Carlton W.; Barber, Ron; Equitz, Will; Flickner, Myron D.; Glasman, Eduardo H.; Petkovic, Dragutin; Yanker, Peter; Faloutsos, Christos; Taubin, Gabriel

1993-04-01

In the query by image content (QBIC) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical (`Give me other images that contain a tumor with a texture like this one'), photo-journalism (`Give me images that have blue at the top and red at the bottom'), and many others in art, fashion, cataloging, retailing, and industry. Key issues include derivation and computation of attributes of images and objects that provide useful query functionality, retrieval methods based on similarity as opposed to exact match, query by image example or user drawn image, the user interfaces, query refinement and navigation, high dimensional database indexing, and automatic and semi-automatic database population. We currently have a prototype system written in X/Motif and C running on an RS/6000 that allows a variety of queries, and a test database of over 1000 images and 1000 objects populated from commercially available photo clip art images. In this paper we present the main algorithms for color texture, shape and sketch query that we use, show example query results, and discuss future directions.

Content-based cell pathology image retrieval by combining different features

NASA Astrophysics Data System (ADS)

Zhou, Guangquan; Jiang, Lu; Luo, Limin; Bao, Xudong; Shu, Huazhong

2004-04-01

Content Based Color Cell Pathology Image Retrieval is one of the newest computer image processing applications in medicine. Recently, some algorithms have been developed to achieve this goal. Because of the particularity of cell pathology images, the result of the image retrieval based on single characteristic is not satisfactory. A new method for pathology image retrieval by combining color, texture and morphologic features to search cell images is proposed. Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation and mathematics morphology. The features that include color, texture and morphologic features are extracted from single leukocyte to represent main attribute in the search query. The features are then normalized because the numerical value range and physical meaning of extracted features are different. Finally, the relevance feedback system is introduced. So that the system can automatically adjust the weights of different features and improve the results of retrieval system according to the feedback information. Retrieval results using the proposed method fit closely with human perception and are better than those obtained with the methods based on single feature.

High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles.

PubMed

Li, Fuhai; Zhou, Xiaobo; Zhu, Jinmin; Ma, Jinwen; Huang, Xudong; Wong, Stephen T C

2007-10-09

High content screening (HCS)-based image analysis is becoming an important and widely used research tool. Capitalizing this technology, ample cellular information can be extracted from the high content cellular images. In this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human H4 neuroglioma cells exposed to metal oxide nanoparticles. This system has been proved to be an essential tool in our study. The cellular images of H4 neuroglioma cells exposed to different concentrations of CuO nanoparticles were sampled using IN Cell Analyzer 1000. A fully automated cellular image analysis system has been developed to perform the image analysis for cell viability. A multiple adaptive thresholding method was used to classify the pixels of the nuclei image into three classes: bright nuclei, dark nuclei, and background. During the development of our image analysis methodology, we have achieved the followings: (1) The Gaussian filtering with proper scale has been applied to the cellular images for generation of a local intensity maximum inside each nucleus; (2) a novel local intensity maxima detection method based on the gradient vector field has been established; and (3) a statistical model based splitting method was proposed to overcome the under segmentation problem. Computational results indicate that 95.9% nuclei can be detected and segmented correctly by the proposed image analysis system. The proposed automated image analysis system can effectively segment the images of human H4 neuroglioma cells exposed to CuO nanoparticles. The computational results confirmed our biological finding that human H4 neuroglioma cells had a dose-dependent toxic response to the insult of CuO nanoparticles.

Advances in arterial spin labelling MRI methods for measuring perfusion and collateral flow.

PubMed

van Osch, Matthias Jp; Teeuwisse, Wouter M; Chen, Zhensen; Suzuki, Yuriko; Helle, Michael; Schmid, Sophie

2017-01-01

With the publication in 2015 of the consensus statement by the perfusion study group of the International Society for Magnetic Resonance in Medicine (ISMRM) and the EU-COST action 'ASL in dementia' on the implementation of arterial spin labelling MRI (ASL) in a clinical setting, the development of ASL can be considered to have become mature and ready for clinical prime-time. In this review article new developments and remaining issues will be discussed, especially focusing on quantification of ASL as well as on new technological developments of ASL for perfusion imaging and flow territory mapping. Uncertainty of the achieved labelling efficiency in pseudo-continuous ASL (pCASL) as well as the presence of arterial transit time artefacts, can be considered the main remaining challenges for the use of quantitative cerebral blood flow (CBF) values. New developments in ASL centre around time-efficient acquisition of dynamic ASL-images by means of time-encoded pCASL and diversification of information content, for example by combined 4D-angiography with perfusion imaging. Current vessel-encoded and super-selective pCASL-methodology have developed into easily applied flow-territory mapping methods providing relevant clinical information with highly similar information content as digital subtraction angiography (DSA), the current clinical standard. Both approaches seem therefore to be ready for clinical use.

Knowledge-Based Image Analysis.

DTIC Science & Technology

1981-04-01

UNCLASSIF1 ED ETL-025s N IIp ETL-0258 AL Ai01319 S"Knowledge-based image analysis u George C. Stockman Barbara A. Lambird I David Lavine Laveen N. Kanal...extraction, verification, region classification, pattern recognition, image analysis . 3 20. A. CT (Continue on rever.. d. It necessary and Identify by...UNCLgSTFTF n In f SECURITY CLASSIFICATION OF THIS PAGE (When Date Entered) .L1 - I Table of Contents Knowledge Based Image Analysis I Preface

Web image retrieval using an effective topic and content-based technique

NASA Astrophysics Data System (ADS)

Lee, Ching-Cheng; Prabhakara, Rashmi

2005-03-01

There has been an exponential growth in the amount of image data that is available on the World Wide Web since the early development of Internet. With such a large amount of information and image available and its usefulness, an effective image retrieval system is thus greatly needed. In this paper, we present an effective approach with both image matching and indexing techniques that improvise on existing integrated image retrieval methods. This technique follows a two-phase approach, integrating query by topic and query by example specification methods. In the first phase, The topic-based image retrieval is performed by using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. This technique consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. In the second phase, we use query by example specification to perform a low-level content-based image match in order to retrieve smaller and relatively closer results of the example image. From this, information related to the image feature is automatically extracted from the query image. The main objective of our approach is to develop a functional image search and indexing technique and to demonstrate that better retrieval results can be achieved.

Projection-based estimation and nonuniformity correction of sensitivity profiles in phased-array surface coils.

PubMed

Yun, Sungdae; Kyriakos, Walid E; Chung, Jun-Young; Han, Yeji; Yoo, Seung-Schik; Park, Hyunwook

2007-03-01

To develop a novel approach for calculating the accurate sensitivity profiles of phased-array coils, resulting in correction of nonuniform intensity in parallel MRI. The proposed intensity-correction method estimates the accurate sensitivity profile of each channel of the phased-array coil. The sensitivity profile is estimated by fitting a nonlinear curve to every projection view through the imaged object. The nonlinear curve-fitting efficiently obtains the low-frequency sensitivity profile by eliminating the high-frequency image contents. Filtered back-projection (FBP) is then used to compute the estimates of the sensitivity profile of each channel. The method was applied to both phantom and brain images acquired from the phased-array coil. Intensity-corrected images from the proposed method had more uniform intensity than those obtained by the commonly used sum-of-squares (SOS) approach. With the use of the proposed correction method, the intensity variation was reduced to 6.1% from 13.1% of the SOS. When the proposed approach was applied to the computation of the sensitivity maps during sensitivity encoding (SENSE) reconstruction, it outperformed the SOS approach in terms of the reconstructed image uniformity. The proposed method is more effective at correcting the intensity nonuniformity of phased-array surface-coil images than the conventional SOS method. In addition, the method was shown to be resilient to noise and was successfully applied for image reconstruction in parallel imaging.

Three-dimensional spatiotemporal features for fast content-based retrieval of focal liver lesions.

PubMed

Roy, Sharmili; Chi, Yanling; Liu, Jimin; Venkatesh, Sudhakar K; Brown, Michael S

2014-11-01

Content-based image retrieval systems for 3-D medical datasets still largely rely on 2-D image-based features extracted from a few representative slices of the image stack. Most 2 -D features that are currently used in the literature not only model a 3-D tumor incompletely but are also highly expensive in terms of computation time, especially for high-resolution datasets. Radiologist-specified semantic labels are sometimes used along with image-based 2-D features to improve the retrieval performance. Since radiological labels show large interuser variability, are often unstructured, and require user interaction, their use as lesion characterizing features is highly subjective, tedious, and slow. In this paper, we propose a 3-D image-based spatiotemporal feature extraction framework for fast content-based retrieval of focal liver lesions. All the features are computer generated and are extracted from four-phase abdominal CT images. Retrieval performance and query processing times for the proposed framework is evaluated on a database of 44 hepatic lesions comprising of five pathological types. Bull's eye percentage score above 85% is achieved for three out of the five lesion pathologies and for 98% of query lesions, at least one same type of lesion is ranked among the top two retrieved results. Experiments show that the proposed system's query processing is more than 20 times faster than other already published systems that use 2-D features. With fast computation time and high retrieval accuracy, the proposed system has the potential to be used as an assistant to radiologists for routine hepatic tumor diagnosis.

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model

PubMed Central

Jabeen, Safia; Mehmood, Zahid; Mahmood, Toqeer; Saba, Tanzila; Rehman, Amjad; Mahmood, Muhammad Tariq

2018-01-01

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques. PMID:29694429

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model.

PubMed

Jabeen, Safia; Mehmood, Zahid; Mahmood, Toqeer; Saba, Tanzila; Rehman, Amjad; Mahmood, Muhammad Tariq

2018-01-01

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.

Mobile visual object identification: from SIFT-BoF-RANSAC to Sketchprint

NASA Astrophysics Data System (ADS)

Voloshynovskiy, Sviatoslav; Diephuis, Maurits; Holotyak, Taras

2015-03-01

Mobile object identification based on its visual features find many applications in the interaction with physical objects and security. Discriminative and robust content representation plays a central role in object and content identification. Complex post-processing methods are used to compress descriptors and their geometrical information, aggregate them into more compact and discriminative representations and finally re-rank the results based on the similarity geometries of descriptors. Unfortunately, most of the existing descriptors are not very robust and discriminative once applied to the various contend such as real images, text or noise-like microstructures next to requiring at least 500-1'000 descriptors per image for reliable identification. At the same time, the geometric re-ranking procedures are still too complex to be applied to the numerous candidates obtained from the feature similarity based search only. This restricts that list of candidates to be less than 1'000 which obviously causes a higher probability of miss. In addition, the security and privacy of content representation has become a hot research topic in multimedia and security communities. In this paper, we introduce a new framework for non- local content representation based on SketchPrint descriptors. It extends the properties of local descriptors to a more informative and discriminative, yet geometrically invariant content representation. In particular it allows images to be compactly represented by 100 SketchPrint descriptors without being fully dependent on re-ranking methods. We consider several use cases, applying SketchPrint descriptors to natural images, text documents, packages and micro-structures and compare them with the traditional local descriptors.

Material classification and automatic content enrichment of images using supervised learning and knowledge bases

NASA Astrophysics Data System (ADS)

Mallepudi, Sri Abhishikth; Calix, Ricardo A.; Knapp, Gerald M.

2011-02-01

In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e.g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

Content based information retrieval in forensic image databases.

PubMed

Geradts, Zeno; Bijhold, Jurrien

2002-03-01

This paper gives an overview of the various available image databases and ways of searching these databases on image contents. The developments in research groups of searching in image databases is evaluated and compared with the forensic databases that exist. Forensic image databases of fingerprints, faces, shoeprints, handwriting, cartridge cases, drugs tablets, and tool marks are described. The developments in these fields appear to be valuable for forensic databases, especially that of the framework in MPEG-7, where the searching in image databases is standardized. In the future, the combination of the databases (also DNA-databases) and possibilities to combine these can result in stronger forensic evidence.

Uses of Metaphors & Imagery in Counseling. Instructor's Manual.

ERIC Educational Resources Information Center

Gladding, Samuel T.

This document presents an instructor's manual designed to accompany the videotape, "Uses of Metaphors and Imagery in Counseling," a tool to teach beginning and experienced counselors how to more efficiently help their clients by focusing on the use of non-literal language and thoughts (i.e., metaphors and images). The format and content of the…

Gender differences in the content of cognitive distraction during sex.

PubMed

Meana, Marta; Nunnink, Sarah E

2006-02-01

This study compared 220 college men and 237 college women on two types of self-reported cognitive distraction during sex, performance- and appearance-based. Affect, psychological distress, sexual knowledge, attitudes, fantasies, experiences, body image, satisfaction, and sexual function were assessed with the Derogatis Sexual Functioning Inventory and the Sexual History Form to determine associations with distraction. Between-gender analyses revealed that women reported higher levels of overall and appearance-based distraction than did men, but similar levels of performance-based distraction. Within-gender analyses revealed that women reported as much of one type of distraction as the other, while men reported more performance- than appearance-based distraction. In women, appearance-based distraction was predicted by negative body image, psychological distress, and not being in a relationship, while performance-based distraction was predicted by negative body image, psychological distress, and sexual dissatisfaction. In men, appearance-based distraction was predicted by negative body image, sexual dissatisfaction and not being in a relationship, while performance-based distraction was predicted by negative body image and sexual dissatisfaction. Investigating the content of cognitive distraction may be useful in understanding gender differences in sexual experience and in refining cognitive components of sex therapy.

Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer.

PubMed

Dhara, Ashis Kumar; Mukhopadhyay, Sudipta; Dutta, Anirvan; Garg, Mandeep; Khandelwal, Niranjan

2017-02-01

Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user. The involvement of radiologists in feature database creation is also reduced, as only a seed point is expected from radiologists instead of manual delineation of the boundary of the nodules. The performance of the retrieval system depends on the accuracy of the segmentation technique. Several 3D features are explored to improve the performance of the proposed retrieval system. A set of relevant shape and texture features are considered for efficient representation of the nodules in the feature space. The proposed CBIR system is evaluated for three configurations such as configuration-1 (composite rank of malignancy "1","2" as benign and "4","5" as malignant), configuration-2 (composite rank of malignancy "1","2", "3" as benign and "4","5" as malignant), and configuration-3 (composite rank of malignancy "1","2" as benign and "3","4","5" as malignant). Considering top 5 retrieved nodules and Euclidean distance metric, the precision achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 82.14, 75.91, and 74.27 %, respectively. The performance of the proposed CBIR system is close to the most recent technique, which is dependent on radiologists for manual segmentation of nodules. A computer-aided diagnosis (CAD) system is also developed based on CBIR paradigm. Performance of the proposed CBIR-based CAD system is close to performance of the CAD system using support vector machine.

On quantification of residual ink content and deinking efficiency in recycling of mixed office waste paper

Treesearch

Bo Li; Gaosheng Wang; Kefu Chen; David W. Vahey; Junyong Zhu

2011-01-01

Although (flotation) deinking has been a common industry practice for several decades, true residual ink content and deinking efficiency have never been quantified. Paper brightness and ERIC (Effective Residual Ink Concentration), based on measurements of the absorption coefficient of deinked pulp, have been used to determine performance of flotation deinking processes...

Performance comparison of denoising filters for source camera identification

NASA Astrophysics Data System (ADS)

Cortiana, A.; Conotter, V.; Boato, G.; De Natale, F. G. B.

2011-02-01

Source identification for digital content is one of the main branches of digital image forensics. It relies on the extraction of the photo-response non-uniformity (PRNU) noise as a unique intrinsic fingerprint that efficiently characterizes the digital device which generated the content. Such noise is estimated as the difference between the content and its de-noised version obtained via denoising filter processing. This paper proposes a performance comparison of different denoising filters for source identification purposes. In particular, results achieved with a sophisticated 3D filter are presented and discussed with respect to state-of-the-art denoising filters previously employed in such a context.

Advanced Image Enhancement Method for Distant Vessels and Structures in Capsule Endoscopy

PubMed Central

Pedersen, Marius

2017-01-01

This paper proposes an advanced method for contrast enhancement of capsule endoscopic images, with the main objective to obtain sufficient information about the vessels and structures in more distant (or darker) parts of capsule endoscopic images. The proposed method (PM) combines two algorithms for the enhancement of darker and brighter areas of capsule endoscopic images, respectively. The half-unit weighted-bilinear algorithm (HWB) proposed in our previous work is used to enhance darker areas according to the darker map content of its HSV's component V. Enhancement of brighter areas is achieved thanks to the novel threshold weighted-bilinear algorithm (TWB) developed to avoid overexposure and enlargement of specular highlight spots while preserving the hue, in such areas. The TWB performs enhancement operations following a gradual increment of the brightness of the brighter map content of its HSV's component V. In other words, the TWB decreases its averaged weights as the intensity content of the component V increases. Extensive experimental demonstrations were conducted, and, based on evaluation of the reference and PM enhanced images, a gastroenterologist (Ø.H.) concluded that the PM enhanced images were the best ones based on the information about the vessels, contrast in the images, and the view or visibility of the structures in more distant parts of the capsule endoscopy images. PMID:29225668

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

PubMed Central

Kurtz, Camille; Depeursinge, Adrien; Napel, Sandy; Beaulieu, Christopher F.; Rubin, Daniel L.

2014-01-01

Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic “soft” prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies. PMID:25036769

Adaptive Chroma Subsampling-binding and Luma-guided Chroma Reconstruction Method for Screen Content Images.

PubMed

Chung, Kuo-Liang; Huang, Chi-Chao; Hsu, Tsu-Chun

2017-09-04

In this paper, we propose a novel adaptive chroma subsampling-binding and luma-guided (ASBLG) chroma reconstruction method for screen content images (SCIs). After receiving the decoded luma and subsampled chroma image from the decoder, a fast winner-first voting strategy is proposed to identify the used chroma subsampling scheme prior to compression. Then, the decoded luma image is subsampled as the identified subsampling scheme was performed on the chroma image such that we are able to conclude an accurate correlation between the subsampled decoded luma image and the decoded subsampled chroma image. Accordingly, an adaptive sliding window-based and luma-guided chroma reconstruction method is proposed. The related computational complexity analysis is also provided. We take two quality metrics, the color peak signal-to-noise ratio (CPSNR) of the reconstructed chroma images and SCIs and the gradient-based structure similarity index (CGSS) of the reconstructed SCIs to evaluate the quality performance. Let the proposed chroma reconstruction method be denoted as 'ASBLG'. Based on 26 typical test SCIs and 6 JCT-VC test screen content video sequences (SCVs), several experiments show that on average, the CPSNR gains of all the reconstructed UV images by 4:2:0(A)-ASBLG, SCIs by 4:2:0(MPEG-B)-ASBLG, and SCVs by 4:2:0(A)-ASBLG are 2.1 dB, 1.87 dB, and 1.87 dB, respectively, when compared with that of the other combinations. Specifically, in terms of CPSNR and CGSS, CSBILINEAR-ASBLG for the test SCIs and CSBICUBIC-ASBLG for the test SCVs outperform the existing state-of-the-art comparative combinations, where CSBILINEAR and CSBICUBIC denote the luma-aware based chroma subsampling schemes by Wang et al.

Gaps in content-based image retrieval

NASA Astrophysics Data System (ADS)

Deserno, Thomas M.; Antani, Sameer; Long, Rodney

2007-03-01

Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potentially strong impact in diagnostics, research, and education. Research successes that are increasingly reported in the scientific literature, however, have not made significant inroads as medical CBIR applications incorporated into routine clinical medicine or medical research. The cause is often attributed without sufficient analytical reasoning to the inability of these applications in overcoming the "semantic gap". The semantic gap divides the high-level scene analysis of humans from the low-level pixel analysis of computers. In this paper, we suggest a more systematic and comprehensive view on the concept of gaps in medical CBIR research. In particular, we define a total of 13 gaps that address the image content and features, as well as the system performance and usability. In addition to these gaps, we identify 6 system characteristics that impact CBIR applicability and performance. The framework we have created can be used a posteriori to compare medical CBIR systems and approaches for specific biomedical image domains and goals and a priori during the design phase of a medical CBIR application. To illustrate the a posteriori use of our conceptual system, we apply it, initially, to the classification of three medical CBIR implementations: the content-based PACS approach (cbPACS), the medical GNU image finding tool (medGIFT), and the image retrieval in medical applications (IRMA) project. We show that systematic analysis of gaps provides detailed insight in system comparison and helps to direct future research.

Breast density quantification with cone-beam CT: A post-mortem study

PubMed Central

Johnson, Travis; Ding, Huanjun; Le, Huy Q.; Ducote, Justin L.; Molloi, Sabee

2014-01-01

Forty post-mortem breasts were imaged with a flat-panel based cone-beam x-ray CT system at 50 kVp. The feasibility of breast density quantification has been investigated using standard histogram thresholding and an automatic segmentation method based on the fuzzy c-means algorithm (FCM). The breasts were chemically decomposed into water, lipid, and protein immediately after image acquisition was completed. The percent fibroglandular volume (%FGV) from chemical analysis was used as the gold standard for breast density comparison. Both image-based segmentation techniques showed good precision in breast density quantification with high linear coefficients between the right and left breast of each pair. When comparing with the gold standard using %FGV from chemical analysis, Pearson’s r-values were estimated to be 0.983 and 0.968 for the FCM clustering and the histogram thresholding techniques, respectively. The standard error of the estimate (SEE) was also reduced from 3.92% to 2.45% by applying the automatic clustering technique. The results of the postmortem study suggested that breast tissue can be characterized in terms of water, lipid and protein contents with high accuracy by using chemical analysis, which offers a gold standard for breast density studies comparing different techniques. In the investigated image segmentation techniques, the FCM algorithm had high precision and accuracy in breast density quantification. In comparison to conventional histogram thresholding, it was more efficient and reduced inter-observer variation. PMID:24254317

Intelligent content fitting for digital publishing

NASA Astrophysics Data System (ADS)

Lin, Xiaofan

2006-02-01

One recurring problem in Variable Data Printing (VDP) is that the existing contents cannot satisfy the VDP task as-is. So there is a strong need for content fitting technologies to support high-value digital publishing applications, in which text and image are the two major types of contents. This paper presents meta-Autocrop framework for image fitting and TextFlex technology for text fitting. The meta-Autocrop framework supports multiple modes: fixed aspect-ratio mode, advice mode, and verification mode. The TextFlex technology supports non-rectangular text wrapping and paragraph-based line breaking. We also demonstrate how these content fitting technologies are utilized in the overall automated composition and layout system.

On-line determination of pork color and intramuscular fat by computer vision

NASA Astrophysics Data System (ADS)

Liao, Yi-Tao; Fan, Yu-Xia; Wu, Xue-Qian; Xie, Li-juan; Cheng, Fang

2010-04-01

In this study, the application potential of computer vision in on-line determination of CIE L*a*b* and content of intramuscular fat (IMF) of pork was evaluated. Images of pork chop from 211 pig carcasses were captured while samples were on a conveyor belt at the speed of 0.25 m•s-1 to simulate the on-line environment. CIE L*a*b* and IMF content were measured with colorimeter and chemical extractor as reference. The KSW algorithm combined with region selection was employed in eliminating the surrounding fat of longissimus dorsi muscle (MLD). RGB values of the pork were counted and five methods were applied for transforming RGB values to CIE L*a*b* values. The region growing algorithm with multiple seed points was applied to mask out the IMF pixels within the intensity corrected images. The performances of the proposed algorithms were verified by comparing the measured reference values and the quality characteristics obtained by image processing. MLD region of six samples could not be identified using the KSW algorithm. Intensity nonuniformity of pork surface in the image can be eliminated efficiently, and IMF region of three corrected images failed to be extracted. Given considerable variety of color and complexity of the pork surface, CIE L*, a* and b* color of MLD could be predicted with correlation coefficients of 0.84, 0.54 and 0.47 respectively, and IMF content could be determined with a correlation coefficient more than 0.70. The study demonstrated that it is feasible to evaluate CIE L*a*b* values and IMF content on-line using computer vision.

Study on High Resolution Membrane-Based Diffractive Optical Imaging on Geostationary Orbit

NASA Astrophysics Data System (ADS)

Jiao, J.; Wang, B.; Wang, C.; Zhang, Y.; Jin, J.; Liu, Z.; Su, Y.; Ruan, N.

2017-05-01

Diffractive optical imaging technology provides a new way to realize high resolution earth observation on geostationary orbit. There are a lot of benefits to use the membrane-based diffractive optical element in ultra-large aperture optical imaging system, including loose tolerance, light weight, easy folding and unfolding, which make it easy to realize high resolution earth observation on geostationary orbit. The implementation of this technology also faces some challenges, including the configuration of the diffractive primary lens, the development of high diffraction efficiency membrane-based diffractive optical elements, and the correction of the chromatic aberration of the diffractive optical elements. Aiming at the configuration of the diffractive primary lens, the "6+1" petal-type unfold scheme is proposed, which consider the compression ratio, the blocking rate and the development complexity. For high diffraction efficiency membrane-based diffractive optical element, a self-collimating method is proposed. The diffraction efficiency is more than 90 % of the theoretical value. For the chromatic aberration correction problem, an optimization method based on schupmann is proposed to make the imaging spectral bandwidth in visible light band reach 100 nm. The above conclusions have reference significance for the development of ultra-large aperture diffractive optical imaging system.

Data Mining and Knowledge Discovery tools for exploiting big Earth-Observation data

NASA Astrophysics Data System (ADS)

Espinoza Molina, D.; Datcu, M.

2015-04-01

The continuous increase in the size of the archives and in the variety and complexity of Earth-Observation (EO) sensors require new methodologies and tools that allow the end-user to access a large image repository, to extract and to infer knowledge about the patterns hidden in the images, to retrieve dynamically a collection of relevant images, and to support the creation of emerging applications (e.g.: change detection, global monitoring, disaster and risk management, image time series, etc.). In this context, we are concerned with providing a platform for data mining and knowledge discovery content from EO archives. The platform's goal is to implement a communication channel between Payload Ground Segments and the end-user who receives the content of the data coded in an understandable format associated with semantics that is ready for immediate exploitation. It will provide the user with automated tools to explore and understand the content of highly complex images archives. The challenge lies in the extraction of meaningful information and understanding observations of large extended areas, over long periods of time, with a broad variety of EO imaging sensors in synergy with other related measurements and data. The platform is composed of several components such as 1.) ingestion of EO images and related data providing basic features for image analysis, 2.) query engine based on metadata, semantics and image content, 3.) data mining and knowledge discovery tools for supporting the interpretation and understanding of image content, 4.) semantic definition of the image content via machine learning methods. All these components are integrated and supported by a relational database management system, ensuring the integrity and consistency of Terabytes of Earth Observation data.

Dependence of Adaptive Cross-correlation Algorithm Performance on the Extended Scene Image Quality

NASA Technical Reports Server (NTRS)

Sidick, Erkin

2008-01-01

Recently, we reported an adaptive cross-correlation (ACC) algorithm to estimate with high accuracy the shift as large as several pixels between two extended-scene sub-images captured by a Shack-Hartmann wavefront sensor. It determines the positions of all extended-scene image cells relative to a reference cell in the same frame using an FFT-based iterative image-shifting algorithm. It works with both point-source spot images as well as extended scene images. We have demonstrated previously based on some measured images that the ACC algorithm can determine image shifts with as high an accuracy as 0.01 pixel for shifts as large 3 pixels, and yield similar results for both point source spot images and extended scene images. The shift estimate accuracy of the ACC algorithm depends on illumination level, background, and scene content in addition to the amount of the shift between two image cells. In this paper we investigate how the performance of the ACC algorithm depends on the quality and the frequency content of extended scene images captured by a Shack-Hatmann camera. We also compare the performance of the ACC algorithm with those of several other approaches, and introduce a failsafe criterion for the ACC algorithm-based extended scene Shack-Hatmann sensors.

Triolein-based polycation lipid nanocarrier for efficient gene delivery: characteristics and mechanism

PubMed Central

Zhang, Zhiwen; Fang, Xiaoling; Hao, Junguo; Li, Yajuan; Sha, Xianyi

2011-01-01

We proposed to develop a polycation lipid nanocarrier (PLN) with higher transfection efficiency than our previously described polycation nanostrucutred lipid nanocarrier (PNLC). PLN was composed of triolein, cetylated low-molecular-weight polyethylenimine, and dioleoyl phosphatidylethanolamine. The physicochemical properties of PLN and the PLN/DNA complexes (PDC) were characterized. The in vitro transfection was performed in human lung adenocarcinoma (SPC-A1) cells, and the intracellular mechanism was investigated as well. The measurements indicated that PLN and PDC are homogenous nanometer-sized particles with a positive charge. The transfection efficiency of PDC significantly increased with the content of triolein and was higher than that of PNLC and commercial Lipofectamine™ 2000. In particular, the transfection of PLN in the presence of 10% serum was more effective than that in its absence. With the help of specific inhibitors of chlorpromazine and filipin, the clathrin-dependent endocytosis pathway was determined to be the main contributor to the successful transfection mediated by PLN in SPC-A1 cells. The captured images verified that the fluorescent PDC was localized in the lysosomes and nuclei after endocytosis. Thus, PLN represents a novel efficient nonviral gene delivery vector. PMID:22114487

Triolein-based polycation lipid nanocarrier for efficient gene delivery: characteristics and mechanism.

PubMed

Zhang, Zhiwen; Fang, Xiaoling; Hao, Junguo; Li, Yajuan; Sha, Xianyi

2011-01-01

We proposed to develop a polycation lipid nanocarrier (PLN) with higher transfection efficiency than our previously described polycation nanostrucutred lipid nanocarrier (PNLC). PLN was composed of triolein, cetylated low-molecular-weight polyethylenimine, and dioleoyl phosphatidylethanolamine. The physicochemical properties of PLN and the PLN/DNA complexes (PDC) were characterized. The in vitro transfection was performed in human lung adenocarcinoma (SPC-A1) cells, and the intracellular mechanism was investigated as well. The measurements indicated that PLN and PDC are homogenous nanometer-sized particles with a positive charge. The transfection efficiency of PDC significantly increased with the content of triolein and was higher than that of PNLC and commercial Lipofectamine 2000. In particular, the transfection of PLN in the presence of 10% serum was more effective than that in its absence. With the help of specific inhibitors of chlorpromazine and filipin, the clathrin-dependent endocytosis pathway was determined to be the main contributor to the successful transfection mediated by PLN in SPC-A1 cells. The captured images verified that the fluorescent PDC was localized in the lysosomes and nuclei after endocytosis. Thus, PLN represents a novel efficient nonviral gene delivery vector.

Adapting the ISO 20462 softcopy ruler method for online image quality studies

NASA Astrophysics Data System (ADS)

Burns, Peter D.; Phillips, Jonathan B.; Williams, Don

2013-01-01

In this paper we address the problem of Image Quality Assessment of no reference metrics, focusing on JPEG corrupted images. In general no reference metrics are not able to measure with the same performance the distortions within their possible range and with respect to different image contents. The crosstalk between content and distortion signals influences the human perception. We here propose two strategies to improve the correlation between subjective and objective quality data. The first strategy is based on grouping the images according to their spatial complexity. The second one is based on a frequency analysis. Both the strategies are tested on two databases available in the literature. The results show an improvement in the correlations between no reference metrics and psycho-visual data, evaluated in terms of the Pearson Correlation Coefficient.

SU-D-206-06: Task-Specific Optimization of Scintillator Thickness for CMOS-Detector Based Cone-Beam Breast CT

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

Vedantham, S; Shrestha, S; Shi, L

Purpose: To optimize the cesium iodide (CsI:Tl) scintillator thickness in a complimentary metal-oxide semiconductor (CMOS)-based detector for use in dedicated cone-beam breast CT. Methods: The imaging task considered was the detection of a microcalcification cluster comprising six 220µm diameter calcium carbonate spheres, arranged in the form of a regular pentagon with 2 mm spacing on its sides and a central calcification, similar to that in ACR-recommended mammography accreditation phantom, at a mean glandular dose of 4.5 mGy. Generalized parallel-cascades based linear systems analysis was used to determine Fourier-domain image quality metrics in reconstructed object space, from which the detectability indexmore » inclusive of anatomical noise was determined for a non-prewhitening numerical observer. For 300 projections over 2π, magnification-associated focal-spot blur, Monte Carlo derived x-ray scatter, K-fluorescent emission and reabsorption within CsI:Tl, CsI:Tl quantum efficiency and optical blur, fiberoptic plate transmission efficiency and blur, CMOS quantum efficiency, pixel aperture function and additive noise, and filtered back-projection to isotropic 105µm voxel pitch with bilinear interpolation were modeled. Imaging geometry of a clinical prototype breast CT system, a 60 kV Cu/Al filtered x-ray spectrum from 0.3 mm focal spot incident on a 14 cm diameter semi-ellipsoidal breast were used to determine the detectability index for 300–600 µm thick (75µm increments) CsI:Tl. The CsI:Tl thickness that maximized the detectability index was considered optimal. Results: The limiting resolution (10% modulation transfer function, MTF) progressively decreased with increasing CsI:Tl thickness. The zero-frequency detective quantum efficiency, DQE(0), in projection space increased with increasing CsI:Tl thickness. The maximum detectability index was achieved with 525µm thick CsI:Tl scintillator. Reduced MTF at mid-to-high frequencies for 600µm thick CsI:Tl lowered the detectability index than 525µm CsI:Tl. Conclusion: For the x-ray spectrum and imaging conditions considered, a 525µm thick CsI:Tl scintillator integrated with the CMOS detector is optimal for detecting microcalcification cluster. Funding support: Supported in part by NIH R01 CA195512. The contents are solely the responsibility of the authors and do not reflect the official views of the NIH or the NCI. Disclosures: SV, GV and AK - Research collaboration, Koning Corp., West Henrietta, NY.« less

Modernization of the International Volcanic Ash Website - a global resource for ashfall preparedness and impact guidance.

NASA Astrophysics Data System (ADS)

Wallace, K.; Leonard, G.; Stewart, C.; Wilson, T. M.; Randall, M.; Stovall, W. K.

2015-12-01

The internationally collaborative volcanic ash website (http://volcanoes.usgs.gov/ash/) has been an important global information resource for ashfall preparedness and impact guidance since 2004. Recent volcanic ashfalls with significant local, regional, and global impacts highlighted the need to improve the website to make it more accessible and pertinent to users worldwide. Recently, the Volcanic Ash Impacts Working Group (Cities and Volcanoes Commission of IAVCEI) redesigned and modernized the website. Improvements include 1) a database-driven back end, 2) reorganized menu navigation, 3) language translation, 4) increased downloadable content, 5) addition of ash-impact case studies, 7) expanded and updated references , 8) an image database, and 9) inclusion of cooperating organization's logos. The database-driven platform makes the website more dynamic and efficient to operate and update. New menus provide information about specific impact topics (buildings, transportation, power, health, agriculture, water and waste water, equipment and communications, clean up) and updated content has been added throughout all topics. A new "for scientists" menu includes information on ash collection and analysis. Website translation using Google translate will significantly increase user base. Printable resources (e.g. checklists, pamphlets, posters) provide information to people without Internet access. Ash impact studies are used to improve mitigation measures during future eruptions, and links to case studies will assist communities' preparation and response plans. The Case Studies menu is intended to be a living topic area, growing as new case studies are published. A database of all images from the website allows users to access larger resolution images and additional descriptive details. Logos clarify linkages among key contributors and assure users that the site is authoritative and science-based.

Improved Resolution Optical Time Stretch Imaging Based on High Efficiency In-Fiber Diffraction.

PubMed

Wang, Guoqing; Yan, Zhijun; Yang, Lei; Zhang, Lin; Wang, Chao

2018-01-12

Most overlooked challenges in ultrafast optical time stretch imaging (OTSI) are sacrificed spatial resolution and higher optical loss. These challenges are originated from optical diffraction devices used in OTSI, which encode image into spectra of ultrashort optical pulses. Conventional free-space diffraction gratings, as widely used in existing OTSI systems, suffer from several inherent drawbacks: limited diffraction efficiency in a non-Littrow configuration due to inherent zeroth-order reflection, high coupling loss between free-space gratings and optical fibers, bulky footprint, and more importantly, sacrificed imaging resolution due to non-full-aperture illumination for individual wavelengths. Here we report resolution-improved and diffraction-efficient OTSI using in-fiber diffraction for the first time to our knowledge. The key to overcome the existing challenges is a 45° tilted fiber grating (TFG), which serves as a compact in-fiber diffraction device offering improved diffraction efficiency (up to 97%), inherent compatibility with optical fibers, and improved imaging resolution owning to almost full-aperture illumination for all illumination wavelengths. 50 million frames per second imaging of fast moving object at 46 m/s with improved imaging resolution has been demonstrated. This conceptually new in-fiber diffraction design opens the way towards cost-effective, compact and high-resolution OTSI systems for image-based high-throughput detection and measurement.

Multi-scale image segmentation and numerical modeling in carbonate rocks

NASA Astrophysics Data System (ADS)

Alves, G. C.; Vanorio, T.

2016-12-01

Numerical methods based on computational simulations can be an important tool in estimating physical properties of rocks. These can complement experimental results, especially when time constraints and sample availability are a problem. However, computational models created at different scales can yield conflicting results with respect to the physical laboratory. This problem is exacerbated in carbonate rocks due to their heterogeneity at all scales. We developed a multi-scale approach performing segmentation of the rock images and numerical modeling across several scales, accounting for those heterogeneities. As a first step, we measured the porosity and the elastic properties of a group of carbonate samples with varying micrite content. Then, samples were imaged by Scanning Electron Microscope (SEM) as well as optical microscope at different magnifications. We applied three different image segmentation techniques to create numerical models from the SEM images and performed numerical simulations of the elastic wave-equation. Our results show that a multi-scale approach can efficiently account for micro-porosities in tight micrite-supported samples, yielding acoustic velocities comparable to those obtained experimentally. Nevertheless, in high-porosity samples characterized by larger grain/micrite ratio, results show that SEM scale images tend to overestimate velocities, mostly due to their inability to capture macro- and/or intragranular- porosity. This suggests that, for high-porosity carbonate samples, optical microscope images would be more suited for numerical simulations.

Recruiting Young Gay and Bisexual Men for a Human Papillomavirus Vaccination Intervention Through Social Media: The Effects of Advertisement Content.

PubMed

Reiter, Paul L; Katz, Mira L; Bauermeister, Jose A; Shoben, Abigail B; Paskett, Electra D; McRee, Annie-Laurie

2017-06-02

Web-based approaches, specifically social media sites, represent a promising approach for recruiting young gay and bisexual men for research studies. Little is known, however, about how the performance of social media advertisements (ads) used to recruit this population is affected by ad content (ie, image and text). The aim of this study was to evaluate the effects of different images and text included in social media ads used to recruit young gay and bisexual men for the pilot test of a Web-based human papillomavirus (HPV) vaccination intervention. In July and September 2016, we used paid Facebook advertisements to recruit men who were aged 18-25 years, self-identified as gay or bisexual, US resident, and had not received HPV vaccine. A 4x2x2 factorial experiment varied ad image (a single young adult male, a young adult male couple, a group of young adult men, or a young adult male talking to a doctor), content focus (text mentioning HPV or HPV vaccine), and disease framing (text mentioning cancer or a sexually transmitted disease [STD]). Poisson regression determined whether these experimental factors affected ad performance. The recruitment campaign reached a total of 35,646 users who viewed ads for 36,395 times. This resulted in an overall unique click-through rate of 2.01% (717/35,646) and an overall conversion rate of 0.66% (241/36,395). Reach was higher for ads that included an image of a couple (incidence rate ratio, IRR=4.91, 95% CI 2.68-8.97, P<.001) or a group (IRR=2.65, 95% CI 1.08-6.50, P=.03) compared with those that included an image of a single person. Ads that included an image of a couple also had a higher conversion rate (IRR=2.56, 95% CI 1.13-5.77, P=.02) than ads that included an image of a single person. Ads with text mentioning an STD had a higher unique click-through rate compared with ads with text mentioning cancer (IRR=1.34, 95% CI 1.06-1.69, P=.01). The campaign cost a total of US $413.72 and resulted in 150 eligible and enrolled individuals (US $2.76 per enrolled participant). Facebook ads are a convenient and cost-efficient strategy for reaching and recruiting young gay and bisexual men for a Web-based HPV vaccination intervention. To help optimize ad performance among this population, researchers should consider the importance of the text and image included in the social media recruitment ads. ©Paul L Reiter, Mira L Katz, Jose A Bauermeister, Abigail B Shoben, Electra D Paskett, Annie-Laurie McRee. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 02.06.2017.

Cell-based flavivirus infection (CFI) assay for the evaluation of dengue antiviral candidates using high-content imaging.

PubMed

Tan, Kah Hin; Ki, Kitti Chan Wing; Watanabe, Satoru; Vasudevan, Subhash G; Krishnan, Manoj

2014-01-01

Large-scale screening of antiviral compounds that target dengue virus life cycle requires a robust cell-based assay that is rapid, easy to conduct, and sensitive enough to be able to assess viral infectivity and cell viability so that antiviral efficacy can be measured. In this chapter we describe a method that uses high-content imaging to evaluate the in vitro antiviral efficacy in a modification to the cell-based flavivirus immunodetection (CFI) assay that was described previously in Wang et al. (Antimicrob Agents Chemother 53(5):1823-1831, 2009).

Geographical classification of apple based on hyperspectral imaging

NASA Astrophysics Data System (ADS)

Guo, Zhiming; Huang, Wenqian; Chen, Liping; Zhao, Chunjiang; Peng, Yankun

2013-05-01

Attribute of apple according to geographical origin is often recognized and appreciated by the consumers. It is usually an important factor to determine the price of a commercial product. Hyperspectral imaging technology and supervised pattern recognition was attempted to discriminate apple according to geographical origins in this work. Hyperspectral images of 207 Fuji apple samples were collected by hyperspectral camera (400-1000nm). Principal component analysis (PCA) was performed on hyperspectral imaging data to determine main efficient wavelength images, and then characteristic variables were extracted by texture analysis based on gray level co-occurrence matrix (GLCM) from dominant waveband image. All characteristic variables were obtained by fusing the data of images in efficient spectra. Support vector machine (SVM) was used to construct the classification model, and showed excellent performance in classification results. The total classification rate had the high classify accuracy of 92.75% in the training set and 89.86% in the prediction sets, respectively. The overall results demonstrated that the hyperspectral imaging technique coupled with SVM classifier can be efficiently utilized to discriminate Fuji apple according to geographical origins.

Distance-Based Opportunistic Mobile Data Offloading

PubMed Central

Lu, Xiaofeng; Lio, Pietro; Hui, Pan

2016-01-01

Cellular network data traffic can be offload onto opportunistic networks. This paper proposes a Distance-based Opportunistic Publish/Subscribe (DOPS) content dissemination model, which is composed of three layers: application layer, decision-making layer and network layer. When a user wants new content, he/she subscribes on a subscribing server. Users having the contents decide whether to deliver the contents to the subscriber based on the distance information. If in the meantime a content owner has traveled further in the immediate past time than the distance between the owner and the subscriber, the content owner will send the content to the subscriber through opportunistic routing. Simulations provide an evaluation of the data traffic offloading efficiency of DOPS. PMID:27314361

Distance-Based Opportunistic Mobile Data Offloading.

PubMed

Lu, Xiaofeng; Lio, Pietro; Hui, Pan

2016-06-15

Cellular network data traffic can be offload onto opportunistic networks. This paper proposes a Distance-based Opportunistic Publish/Subscribe (DOPS) content dissemination model, which is composed of three layers: application layer, decision-making layer and network layer. When a user wants new content, he/she subscribes on a subscribing server. Users having the contents decide whether to deliver the contents to the subscriber based on the distance information. If in the meantime a content owner has traveled further in the immediate past time than the distance between the owner and the subscriber, the content owner will send the content to the subscriber through opportunistic routing. Simulations provide an evaluation of the data traffic offloading efficiency of DOPS.

Constrained Total Generalized p-Variation Minimization for Few-View X-Ray Computed Tomography Image Reconstruction.

PubMed

Zhang, Hanming; Wang, Linyuan; Yan, Bin; Li, Lei; Cai, Ailong; Hu, Guoen

2016-01-01

Total generalized variation (TGV)-based computed tomography (CT) image reconstruction, which utilizes high-order image derivatives, is superior to total variation-based methods in terms of the preservation of edge information and the suppression of unfavorable staircase effects. However, conventional TGV regularization employs l1-based form, which is not the most direct method for maximizing sparsity prior. In this study, we propose a total generalized p-variation (TGpV) regularization model to improve the sparsity exploitation of TGV and offer efficient solutions to few-view CT image reconstruction problems. To solve the nonconvex optimization problem of the TGpV minimization model, we then present an efficient iterative algorithm based on the alternating minimization of augmented Lagrangian function. All of the resulting subproblems decoupled by variable splitting admit explicit solutions by applying alternating minimization method and generalized p-shrinkage mapping. In addition, approximate solutions that can be easily performed and quickly calculated through fast Fourier transform are derived using the proximal point method to reduce the cost of inner subproblems. The accuracy and efficiency of the simulated and real data are qualitatively and quantitatively evaluated to validate the efficiency and feasibility of the proposed method. Overall, the proposed method exhibits reasonable performance and outperforms the original TGV-based method when applied to few-view problems.

Wavelet-based reversible watermarking for authentication

NASA Astrophysics Data System (ADS)

Tian, Jun

2002-04-01

In the digital information age, digital content (audio, image, and video) can be easily copied, manipulated, and distributed. Copyright protection and content authentication of digital content has become an urgent problem to content owners and distributors. Digital watermarking has provided a valuable solution to this problem. Based on its application scenario, most digital watermarking methods can be divided into two categories: robust watermarking and fragile watermarking. As a special subset of fragile watermark, reversible watermark (which is also called lossless watermark, invertible watermark, erasable watermark) enables the recovery of the original, unwatermarked content after the watermarked content has been detected to be authentic. Such reversibility to get back unwatermarked content is highly desired in sensitive imagery, such as military data and medical data. In this paper we present a reversible watermarking method based on an integer wavelet transform. We look into the binary representation of each wavelet coefficient and embed an extra bit to expandable wavelet coefficient. The location map of all expanded coefficients will be coded by JBIG2 compression and these coefficient values will be losslessly compressed by arithmetic coding. Besides these two compressed bit streams, an SHA-256 hash of the original image will also be embedded for authentication purpose.

Evidence-Based Cancer Imaging

PubMed Central

Khorasani, Ramin

2017-01-01

With the advances in the field of oncology, imaging is increasingly used in the follow-up of cancer patients, leading to concerns about over-utilization. Therefore, it has become imperative to make imaging more evidence-based, efficient, cost-effective and equitable. This review explores the strategies and tools to make diagnostic imaging more evidence-based, mainly in the context of follow-up of cancer patients. PMID:28096722

Computer vision in cell biology.

PubMed

Danuser, Gaudenz

2011-11-23

Computer vision refers to the theory and implementation of artificial systems that extract information from images to understand their content. Although computers are widely used by cell biologists for visualization and measurement, interpretation of image content, i.e., the selection of events worth observing and the definition of what they mean in terms of cellular mechanisms, is mostly left to human intuition. This Essay attempts to outline roles computer vision may play and should play in image-based studies of cellular life. Copyright © 2011 Elsevier Inc. All rights reserved.

Collection Fusion Using Bayesian Estimation of a Linear Regression Model in Image Databases on the Web.

ERIC Educational Resources Information Center

Kim, Deok-Hwan; Chung, Chin-Wan

2003-01-01

Discusses the collection fusion problem of image databases, concerned with retrieving relevant images by content based retrieval from image databases distributed on the Web. Focuses on a metaserver which selects image databases supporting similarity measures and proposes a new algorithm which exploits a probabilistic technique using Bayesian…

Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer-aided diagnosis.

PubMed

Welter, Petra; Riesmeier, Jörg; Fischer, Benedikt; Grouls, Christoph; Kuhl, Christiane; Deserno, Thomas M

2011-01-01

It is widely accepted that content-based image retrieval (CBIR) can be extremely useful for computer-aided diagnosis (CAD). However, CBIR has not been established in clinical practice yet. As a widely unattended gap of integration, a unified data concept for CBIR-based CAD results and reporting is lacking. Picture archiving and communication systems and the workflow of radiologists must be considered for successful data integration to be achieved. We suggest that CBIR systems applied to CAD should integrate their results in a picture archiving and communication systems environment such as Digital Imaging and Communications in Medicine (DICOM) structured reporting documents. A sample DICOM structured reporting template adaptable to CBIR and an appropriate integration scheme is presented. The proposed CBIR data concept may foster the promulgation of CBIR systems in clinical environments and, thereby, improve the diagnostic process.

Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer-aided diagnosis

PubMed Central

Riesmeier, Jörg; Fischer, Benedikt; Grouls, Christoph; Kuhl, Christiane; Deserno (né Lehmann), Thomas M

2011-01-01

It is widely accepted that content-based image retrieval (CBIR) can be extremely useful for computer-aided diagnosis (CAD). However, CBIR has not been established in clinical practice yet. As a widely unattended gap of integration, a unified data concept for CBIR-based CAD results and reporting is lacking. Picture archiving and communication systems and the workflow of radiologists must be considered for successful data integration to be achieved. We suggest that CBIR systems applied to CAD should integrate their results in a picture archiving and communication systems environment such as Digital Imaging and Communications in Medicine (DICOM) structured reporting documents. A sample DICOM structured reporting template adaptable to CBIR and an appropriate integration scheme is presented. The proposed CBIR data concept may foster the promulgation of CBIR systems in clinical environments and, thereby, improve the diagnostic process. PMID:21672913

Combining semantic technologies with a content-based image retrieval system - Preliminary considerations

NASA Astrophysics Data System (ADS)

Chmiel, P.; Ganzha, M.; Jaworska, T.; Paprzycki, M.

2017-10-01

Nowadays, as a part of systematic growth of volume, and variety, of information that can be found on the Internet, we observe also dramatic increase in sizes of available image collections. There are many ways to help users browsing / selecting images of interest. One of popular approaches are Content-Based Image Retrieval (CBIR) systems, which allow users to search for images that match their interests, expressed in the form of images (query by example). However, we believe that image search and retrieval could take advantage of semantic technologies. We have decided to test this hypothesis. Specifically, on the basis of knowledge captured in the CBIR, we have developed a domain ontology of residential real estate (detached houses, in particular). This allows us to semantically represent each image (and its constitutive architectural elements) represented within the CBIR. The proposed ontology was extended to capture not only the elements resulting from image segmentation, but also "spatial relations" between them. As a result, a new approach to querying the image database (semantic querying) has materialized, thus extending capabilities of the developed system.

The High Radiosensitizing Efficiency of a Trace of Gadolinium-Based Nanoparticles in Tumors

NASA Astrophysics Data System (ADS)

Dufort, Sandrine; Le Duc, Géraldine; Salomé, Murielle; Bentivegna, Valerie; Sancey, Lucie; Bräuer-Krisch, Elke; Requardt, Herwig; Lux, François; Coll, Jean-Luc; Perriat, Pascal; Roux, Stéphane; Tillement, Olivier

2016-07-01

We recently developed the synthesis of ultrasmall gadolinium-based nanoparticles (GBN), (hydrodynamic diameter <5 nm) characterized by a safe behavior after intravenous injection (renal clearance, preferential accumulation in tumors). Owing to the presence of gadolinium ions, GBN can be used as contrast agents for magnetic resonance imaging (MRI) and as radiosensitizers. The attempt to determine the most opportune delay between the intravenous injection of GBN and the irradiation showed that a very low content of radiosensitizing nanoparticles in the tumor area is sufficient (0.1 μg/g of particles, i.e. 15 ppb of gadolinium) for an important increase of the therapeutic effect of irradiation. Such a promising and unexpected result is assigned to a suited distribution of GBN within the tumor, as revealed by the X-ray fluorescence (XRF) maps.

Intelligent bandwith compression

NASA Astrophysics Data System (ADS)

Tseng, D. Y.; Bullock, B. L.; Olin, K. E.; Kandt, R. K.; Olsen, J. D.

1980-02-01

The feasibility of a 1000:1 bandwidth compression ratio for image transmission has been demonstrated using image-analysis algorithms and a rule-based controller. Such a high compression ratio was achieved by first analyzing scene content using auto-cueing and feature-extraction algorithms, and then transmitting only the pertinent information consistent with mission requirements. A rule-based controller directs the flow of analysis and performs priority allocations on the extracted scene content. The reconstructed bandwidth-compressed image consists of an edge map of the scene background, with primary and secondary target windows embedded in the edge map. The bandwidth-compressed images are updated at a basic rate of 1 frame per second, with the high-priority target window updated at 7.5 frames per second. The scene-analysis algorithms used in this system together with the adaptive priority controller are described. Results of simulated 1000:1 band width-compressed images are presented. A video tape simulation of the Intelligent Bandwidth Compression system has been produced using a sequence of video input from the data base.

Remote sensing image ship target detection method based on visual attention model

NASA Astrophysics Data System (ADS)

Sun, Yuejiao; Lei, Wuhu; Ren, Xiaodong

2017-11-01

The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.

Phase-image-based content-addressable holographic data storage

NASA Astrophysics Data System (ADS)

John, Renu; Joseph, Joby; Singh, Kehar

2004-03-01

We propose and demonstrate the use of phase images for content-addressable holographic data storage. Use of binary phase-based data pages with 0 and π phase changes, produces uniform spectral distribution at the Fourier plane. The absence of strong DC component at the Fourier plane and more intensity of higher order spatial frequencies facilitate better recording of higher spatial frequencies, and improves the discrimination capability of the content-addressable memory. This improves the results of the associative recall in a holographic memory system, and can give low number of false hits even for small search arguments. The phase-modulated pixels also provide an opportunity of subtraction among data pixels leading to better discrimination between similar data pages.

High Content Screening in Zebrafish Speeds up Hazard Ranking of Transition Metal Oxide Nanoparticles

PubMed Central

Lin, Sijie; Zhao, Yan; Xia, Tian; Meng, Huan; Zhaoxia, Ji; Liu, Rong; George, Saji; Xiong, Sijing; Wang, Xiang; Zhang, Haiyuan; Pokhrel, Suman; Mädler, Lutz; Damoiseaux, Robert; Lin, Shuo; Nel, Andre E.

2014-01-01

Zebrafish is an aquatic organism that can be used for high content safety screening of engineered nanomaterials (ENMs). We demonstrate, for the first time, the use of high content bright-field and fluorescence-based imaging to compare the toxicological effect of transition metal oxide (CuO, ZnO, NiO and Co3O4) nanoparticles in zebrafish embryos and larvae. High content bright-field imaging demonstrated potent and dose-dependant hatching interference in the embryos, with the exception of Co3O4 which was relatively inert. We propose that the hatching interference was due to the shedding of Cu and Ni ions, compromising the activity of the hatching enzyme, ZHE1, similar to what we previously proposed for Zn2+. This hypothesis is based on the presence of metal–sensitive histidines in the catalytic center of this enzyme. Co-introduction of a metal ion chelator, diethylene triamine pentaacetic acid (DTPA), reversed the hatching interference of Cu, Zn and Ni. While neither the embryos nor larvae demonstrated morphological abnormalities, high content fluorescence-based imaging demonstrated that CuO, ZnO and NiO could induce increased expression of the heat shock protein 70:enhanced green fluorescence protein (hsp70:eGFP) in transgenic zebrafish larvae. Induction of this response by CuO required a higher nanoparticle dose than the amount leading to hatching interference. This response was also DTPA sensitive. In conclusion, we demonstrate that high content imaging of embryo development, morphological abnormalities and HSP70 expression can be used for hazard ranking and determining the dose-response relationships leading to ENM effects on the development of the zebrafish embryo. PMID:21851096

System Characterizations and Optimized Reconstruction Methods for Novel X-ray Imaging Modalities

NASA Astrophysics Data System (ADS)

Guan, Huifeng

In the past decade there have been many new emerging X-ray based imaging technologies developed for different diagnostic purposes or imaging tasks. However, there exist one or more specific problems that prevent them from being effectively or efficiently employed. In this dissertation, four different novel X-ray based imaging technologies are discussed, including propagation-based phase-contrast (PB-XPC) tomosynthesis, differential X-ray phase-contrast tomography (D-XPCT), projection-based dual-energy computed radiography (DECR), and tetrahedron beam computed tomography (TBCT). System characteristics are analyzed or optimized reconstruction methods are proposed for these imaging modalities. In the first part, we investigated the unique properties of propagation-based phase-contrast imaging technique when combined with the X-ray tomosynthesis. Fourier slice theorem implies that the high frequency components collected in the tomosynthesis data can be more reliably reconstructed. It is observed that the fringes or boundary enhancement introduced by the phase-contrast effects can serve as an accurate indicator of the true depth position in the tomosynthesis in-plane image. In the second part, we derived a sub-space framework to reconstruct images from few-view D-XPCT data set. By introducing a proper mask, the high frequency contents of the image can be theoretically preserved in a certain region of interest. A two-step reconstruction strategy is developed to mitigate the risk of subtle structures being oversmoothed when the commonly used total-variation regularization is employed in the conventional iterative framework. In the thirt part, we proposed a practical method to improve the quantitative accuracy of the projection-based dual-energy material decomposition. It is demonstrated that applying a total-projection-length constraint along with the dual-energy measurements can achieve a stabilized numerical solution of the decomposition problem, thus overcoming the disadvantages of the conventional approach that was extremely sensitive to noise corruption. In the final part, we described the modified filtered backprojection and iterative image reconstruction algorithms specifically developed for TBCT. Special parallelization strategies are designed to facilitate the use of GPU computing, showing demonstrated capability of producing high quality reconstructed volumetric images with a super fast computational speed. For all the investigations mentioned above, both simulation and experimental studies have been conducted to demonstrate the feasibility and effectiveness of the proposed methodologies.

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

Burkholder, Michael B.; Litster, Shawn, E-mail: litster@andrew.cmu.edu

In this study, we analyze the stability of two-phase flow regimes and their transitions using chaotic and fractal statistics, and we report new measurements of dynamic two-phase pressure drop hysteresis that is related to flow regime stability and channel water content. Two-phase flow dynamics are relevant to a variety of real-world systems, and quantifying transient two-phase flow phenomena is important for efficient design. We recorded two-phase (air and water) pressure drops and flow images in a microchannel under both steady and transient conditions. Using Lyapunov exponents and Hurst exponents to characterize the steady-state pressure fluctuations, we develop a new, measurablemore » regime identification criteria based on the dynamic stability of the two-phase pressure signal. We also applied a new experimental technique by continuously cycling the air flow rate to study dynamic hysteresis in two-phase pressure drops, which is separate from steady-state hysteresis and can be used to understand two-phase flow development time scales. Using recorded images of the two-phase flow, we show that the capacitive dynamic hysteresis is related to channel water content and flow regime stability. The mixed-wettability microchannel and in-channel water introduction used in this study simulate a polymer electrolyte fuel cell cathode air flow channel.« less

Optically efficient InAsSb nanowires for silicon-based mid-wavelength infrared optoelectronics.

PubMed

Zhuang, Q D; Alradhi, H; Jin, Z M; Chen, X R; Shao, J; Chen, X; Sanchez, Ana M; Cao, Y C; Liu, J Y; Yates, P; Durose, K; Jin, C J

2017-03-10

InAsSb nanowires (NWs) with a high Sb content have potential in the fabrication of advanced silicon-based optoelectronics such as infrared photondetectors/emitters and highly sensitive phototransistors, as well as in the generation of renewable electricity. However, producing optically efficient InAsSb NWs with a high Sb content remains a challenge, and optical emission is limited to 4.0 μm due to the quality of the nanowires. Here, we report, for the first time, the success of high-quality and optically efficient InAsSb NWs enabling silicon-based optoelectronics operating in entirely mid-wavelength infrared. Pure zinc-blende InAsSb NWs were realized with efficient photoluminescence emission. We obtained room-temperature photoluminescence emission in InAs NWs and successfully extended the emission wavelength in InAsSb NWs to 5.1 μm. The realization of this optically efficient InAsSb NW material paves the way to realizing next-generation devices, combining advances in III-V semiconductors and silicon.

fMR-adaptation indicates selectivity to audiovisual content congruency in distributed clusters in human superior temporal cortex.

PubMed

van Atteveldt, Nienke M; Blau, Vera C; Blomert, Leo; Goebel, Rainer

2010-02-02

Efficient multisensory integration is of vital importance for adequate interaction with the environment. In addition to basic binding cues like temporal and spatial coherence, meaningful multisensory information is also bound together by content-based associations. Many functional Magnetic Resonance Imaging (fMRI) studies propose the (posterior) superior temporal cortex (STC) as the key structure for integrating meaningful multisensory information. However, a still unanswered question is how superior temporal cortex encodes content-based associations, especially in light of inconsistent results from studies comparing brain activation to semantically matching (congruent) versus nonmatching (incongruent) multisensory inputs. Here, we used fMR-adaptation (fMR-A) in order to circumvent potential problems with standard fMRI approaches, including spatial averaging and amplitude saturation confounds. We presented repetitions of audiovisual stimuli (letter-speech sound pairs) and manipulated the associative relation between the auditory and visual inputs (congruent/incongruent pairs). We predicted that if multisensory neuronal populations exist in STC and encode audiovisual content relatedness, adaptation should be affected by the manipulated audiovisual relation. The results revealed an occipital-temporal network that adapted independently of the audiovisual relation. Interestingly, several smaller clusters distributed over superior temporal cortex within that network, adapted stronger to congruent than to incongruent audiovisual repetitions, indicating sensitivity to content congruency. These results suggest that the revealed clusters contain multisensory neuronal populations that encode content relatedness by selectively responding to congruent audiovisual inputs, since unisensory neuronal populations are assumed to be insensitive to the audiovisual relation. These findings extend our previously revealed mechanism for the integration of letters and speech sounds and demonstrate that fMR-A is sensitive to multisensory congruency effects that may not be revealed in BOLD amplitude per se.

Cognitive Support for Learning Computer-Based Tasks Using Animated Demonstration

ERIC Educational Resources Information Center

Chen, Chun-Ying

2016-01-01

This study investigated the influence of cognitive support for learning computer-based tasks using animated demonstration (AD) on instructional efficiency. Cognitive support included (1) segmentation and learner control introducing interactive devices that allow content sequencing through a navigational menu, and content pacing through stop and…

Attention trees and semantic paths

NASA Astrophysics Data System (ADS)

Giusti, Christian; Pieroni, Goffredo G.; Pieroni, Laura

2007-02-01

In the last few decades several techniques for image content extraction, often based on segmentation, have been proposed. It has been suggested that under the assumption of very general image content, segmentation becomes unstable and classification becomes unreliable. According to recent psychological theories, certain image regions attract the attention of human observers more than others and, generally, the image main meaning appears concentrated in those regions. Initially, regions attracting our attention are perceived as a whole and hypotheses on their content are formulated; successively the components of those regions are carefully analyzed and a more precise interpretation is reached. It is interesting to observe that an image decomposition process performed according to these psychological visual attention theories might present advantages with respect to a traditional segmentation approach. In this paper we propose an automatic procedure generating image decomposition based on the detection of visual attention regions. A new clustering algorithm taking advantage of the Delaunay- Voronoi diagrams for achieving the decomposition target is proposed. By applying that algorithm recursively, starting from the whole image, a transformation of the image into a tree of related meaningful regions is obtained (Attention Tree). Successively, a semantic interpretation of the leaf nodes is carried out by using a structure of Neural Networks (Neural Tree) assisted by a knowledge base (Ontology Net). Starting from leaf nodes, paths toward the root node across the Attention Tree are attempted. The task of the path consists in relating the semantics of each child-parent node pair and, consequently, in merging the corresponding image regions. The relationship detected in this way between two tree nodes generates, as a result, the extension of the interpreted image area through each step of the path. The construction of several Attention Trees has been performed and partial results will be shown.

Image restoration for three-dimensional fluorescence microscopy using an orthonormal basis for efficient representation of depth-variant point-spread functions

PubMed Central

Patwary, Nurmohammed; Preza, Chrysanthe

2015-01-01

A depth-variant (DV) image restoration algorithm for wide field fluorescence microscopy, using an orthonormal basis decomposition of DV point-spread functions (PSFs), is investigated in this study. The efficient PSF representation is based on a previously developed principal component analysis (PCA), which is computationally intensive. We present an approach developed to reduce the number of DV PSFs required for the PCA computation, thereby making the PCA-based approach computationally tractable for thick samples. Restoration results from both synthetic and experimental images show consistency and that the proposed algorithm addresses efficiently depth-induced aberration using a small number of principal components. Comparison of the PCA-based algorithm with a previously-developed strata-based DV restoration algorithm demonstrates that the proposed method improves performance by 50% in terms of accuracy and simultaneously reduces the processing time by 64% using comparable computational resources. PMID:26504634

Conversion of a traditional image archive into an image resource on compact disc.

PubMed Central

Andrew, S M; Benbow, E W

1997-01-01

The conversion of a traditional archive of pathology images was organised on 35 mm slides into a database of images stored on compact disc (CD-ROM), and textual descriptions were added to each image record. Students on a didactic pathology course found this resource useful as an aid to revision, despite relative computer illiteracy, and it is anticipated that students on a new problem based learning course, which incorporates experience with information technology, will benefit even more readily when they use the database as an educational resource. A text and image database on CD-ROM can be updated repeatedly, and the content manipulated to reflect the content and style of the courses it supports. Images PMID:9306931

Quantification of sterol-specific response in human macrophages using automated imaged-based analysis.

PubMed

Gater, Deborah L; Widatalla, Namareq; Islam, Kinza; AlRaeesi, Maryam; Teo, Jeremy C M; Pearson, Yanthe E

2017-12-13

The transformation of normal macrophage cells into lipid-laden foam cells is an important step in the progression of atherosclerosis. One major contributor to foam cell formation in vivo is the intracellular accumulation of cholesterol. Here, we report the effects of various combinations of low-density lipoprotein, sterols, lipids and other factors on human macrophages, using an automated image analysis program to quantitatively compare single cell properties, such as cell size and lipid content, in different conditions. We observed that the addition of cholesterol caused an increase in average cell lipid content across a range of conditions. All of the sterol-lipid mixtures examined were capable of inducing increases in average cell lipid content, with variations in the distribution of the response, in cytotoxicity and in how the sterol-lipid combination interacted with other activating factors. For example, cholesterol and lipopolysaccharide acted synergistically to increase cell lipid content while also increasing cell survival compared with the addition of lipopolysaccharide alone. Additionally, ergosterol and cholesteryl hemisuccinate caused similar increases in lipid content but also exhibited considerably greater cytotoxicity than cholesterol. The use of automated image analysis enables us to assess not only changes in average cell size and content, but also to rapidly and automatically compare population distributions based on simple fluorescence images. Our observations add to increasing understanding of the complex and multifactorial nature of foam-cell formation and provide a novel approach to assessing the heterogeneity of macrophage response to a variety of factors.

Parsing Stem Cell Lineage Development Using High Content Image Analysis of Epigenetic Spatial Markers.

PubMed

Kim, Joseph J; Moghe, Prabhas V

2018-06-14

This unit describes a protocol for acquiring and analyzing high-content super-resolution images of human stem cell nuclei for the characterization and classification of the cell differentiation paths based on distinct patterns of epigenetic mark organization. Here, we describe the cell culture, immunocytochemical labeling, super-resolution imaging parameters, and MATLAB-based quantitative image analysis approaches for monitoring human mesenchymal stem cells (hMSCs) and human induced pluripotent stem cells (hiPSCs) as the cells differentiate towards various lineages. Although this protocol uses specific cell types as examples, this approach could be easily extended to a variety of cell types and nuclear epigenetic and mechanosensitive biomarkers that are relevant to specific cell developmental scenarios. © 2018 by John Wiley & Sons, Inc. Copyright © 2018 John Wiley & Sons, Inc.

Integrating Digital Images into the Art and Art History Curriculum.

ERIC Educational Resources Information Center

Pitt, Sharon P.; Updike, Christina B.; Guthrie, Miriam E.

2002-01-01

Describes an Internet-based image database system connected to a flexible, in-class teaching and learning tool (the Madison Digital Image Database) developed at James Madison University to bring digital images to the arts and humanities classroom. Discusses content, copyright issues, ensuring system effectiveness, instructional impact, sharing the…

3D-Holoscopic Imaging: A New Dimension to Enhance Imaging in Minimally Invasive Therapy in Urologic Oncology

PubMed Central

Aggoun, Amar; Swash, Mohammad; Grange, Philippe C.R.; Challacombe, Benjamin; Dasgupta, Prokar

2013-01-01

Abstract Background and Purpose Existing imaging modalities of urologic pathology are limited by three-dimensional (3D) representation on a two-dimensional screen. We present 3D-holoscopic imaging as a novel method of representing Digital Imaging and Communications in Medicine data images taken from CT and MRI to produce 3D-holographic representations of anatomy without special eyewear in natural light. 3D-holoscopic technology produces images that are true optical models. This technology is based on physical principles with duplication of light fields. The 3D content is captured in real time with the content viewed by multiple viewers independently of their position, without 3D eyewear. Methods We display 3D-holoscopic anatomy relevant to minimally invasive urologic surgery without the need for 3D eyewear. Results The results have demonstrated that medical 3D-holoscopic content can be displayed on commercially available multiview auto-stereoscopic display. Conclusion The next step is validation studies comparing 3D-Holoscopic imaging with conventional imaging. PMID:23216303

Estimation of melanin content in iris of human eye: prognosis for glaucoma diagnostics

NASA Astrophysics Data System (ADS)

Bashkatov, Alexey N.; Koblova, Ekaterina V.; Genina, Elina A.; Kamenskikh, Tatyana G.; Dolotov, Leonid E.; Sinichkin, Yury P.; Tuchin, Valery V.

2007-02-01

Based on the experimental data obtained in vivo from digital analysis of color images of human irises, the mean melanin content in human eye irises has been estimated. For registration of the color images a digital camera Olympus C-5060 has been used. The images have been obtained from irises of healthy volunteers as well as from irises of patients with open-angle glaucoma. The computer program has been developed for digital analysis of the images. The result has been useful for development of novel and optimization of already existing methods of non-invasive glaucoma diagnostics.

Hepatic CT image query using Gabor features

NASA Astrophysics Data System (ADS)

Zhao, Chenguang; Cheng, Hongyan; Zhuang, Tiange

2004-07-01

A retrieval scheme for liver computerize tomography (CT) images based on Gabor texture is presented. For each hepatic CT image, we manually delineate abnormal regions within liver area. Then, a continuous Gabor transform is utilized to analyze the texture of the pathology bearing region and extract the corresponding feature vectors. For a given sample image, we compare its feature vector with those of other images. Similar images with the highest rank are retrieved. In experiments, 45 liver CT images are collected, and the effectiveness of Gabor texture for content based retrieval is verified.

Extraction of composite visual objects from audiovisual materials

NASA Astrophysics Data System (ADS)

Durand, Gwenael; Thienot, Cedric; Faudemay, Pascal

1999-08-01

An effective analysis of Visual Objects appearing in still images and video frames is required in order to offer fine grain access to multimedia and audiovisual contents. In previous papers, we showed how our method for segmenting still images into visual objects could improve content-based image retrieval and video analysis methods. Visual Objects are used in particular for extracting semantic knowledge about the contents. However, low-level segmentation methods for still images are not likely to extract a complex object as a whole but instead as a set of several sub-objects. For example, a person would be segmented into three visual objects: a face, hair, and a body. In this paper, we introduce the concept of Composite Visual Object. Such an object is hierarchically composed of sub-objects called Component Objects.

Efficient image enhancement using sparse source separation in the Retinex theory

NASA Astrophysics Data System (ADS)

Yoon, Jongsu; Choi, Jangwon; Choe, Yoonsik

2017-11-01

Color constancy is the feature of the human vision system (HVS) that ensures the relative constancy of the perceived color of objects under varying illumination conditions. The Retinex theory of machine vision systems is based on the HVS. Among Retinex algorithms, the physics-based algorithms are efficient; however, they generally do not satisfy the local characteristics of the original Retinex theory because they eliminate global illumination from their optimization. We apply the sparse source separation technique to the Retinex theory to present a physics-based algorithm that satisfies the locality characteristic of the original Retinex theory. Previous Retinex algorithms have limited use in image enhancement because the total variation Retinex results in an overly enhanced image and the sparse source separation Retinex cannot completely restore the original image. In contrast, our proposed method preserves the image edge and can very nearly replicate the original image without any special operation.

Fast Depiction Invariant Visual Similarity for Content Based Image Retrieval Based on Data-driven Visual Similarity using Linear Discriminant Analysis

NASA Astrophysics Data System (ADS)

Wihardi, Y.; Setiawan, W.; Nugraha, E.

2018-01-01

On this research we try to build CBIRS based on Learning Distance/Similarity Function using Linear Discriminant Analysis (LDA) and Histogram of Oriented Gradient (HoG) feature. Our method is invariant to depiction of image, such as similarity of image to image, sketch to image, and painting to image. LDA can decrease execution time compared to state of the art method, but it still needs an improvement in term of accuracy. Inaccuracy in our experiment happen because we did not perform sliding windows search and because of low number of negative samples as natural-world images.

Developing students’ ideas about lens imaging: teaching experiments with an image-based approach

NASA Astrophysics Data System (ADS)

Grusche, Sascha

2017-07-01

Lens imaging is a classic topic in physics education. To guide students from their holistic viewpoint to the scientists’ analytic viewpoint, an image-based approach to lens imaging has recently been proposed. To study the effect of the image-based approach on undergraduate students’ ideas, teaching experiments are performed and evaluated using qualitative content analysis. Some of the students’ ideas have not been reported before, namely those related to blurry lens images, and those developed by the proposed teaching approach. To describe learning pathways systematically, a conception-versus-time coordinate system is introduced, specifying how teaching actions help students advance toward a scientific understanding.

X3DOM as Carrier of the Virtual Heritage

NASA Astrophysics Data System (ADS)

Jung, Y.; Behr, J.; Graf, H.

2011-09-01

Virtual Museums (VM) are a new model of communication that aims at creating a personalized, immersive, and interactive way to enhance our understanding of the world around us. The term "VM" is a short-cut that comprehends various types of digital creations. One of the carriers for the communication of the virtual heritage at future internet level as de-facto standard is browser front-ends presenting the content and assets of museums. A major driving technology for the documentation and presentation of heritage driven media is real-time 3D content, thus imposing new strategies for a web inclusion. 3D content must become a first class web media that can be created, modified, and shared in the same way as text, images, audio and video are handled on the web right now. A new integration model based on a DOM integration into the web browsers' architecture opens up new possibilities for declarative 3 D content on the web and paves the way for new application scenarios for the virtual heritage at future internet level. With special regards to the X3DOM project as enabling technology for declarative 3D in HTML, this paper describes application scenarios and analyses its technological requirements for an efficient presentation and manipulation of virtual heritage assets on the web.

Developing a NIR multispectral imaging for prediction and visualization of peanut protein content using variable selection algorithms

NASA Astrophysics Data System (ADS)

Cheng, Jun-Hu; Jin, Huali; Liu, Zhiwei

2018-01-01

The feasibility of developing a multispectral imaging method using important wavelengths from hyperspectral images selected by genetic algorithm (GA), successive projection algorithm (SPA) and regression coefficient (RC) methods for modeling and predicting protein content in peanut kernel was investigated for the first time. Partial least squares regression (PLSR) calibration model was established between the spectral data from the selected optimal wavelengths and the reference measured protein content ranged from 23.46% to 28.43%. The RC-PLSR model established using eight key wavelengths (1153, 1567, 1972, 2143, 2288, 2339, 2389 and 2446 nm) showed the best predictive results with the coefficient of determination of prediction (R2P) of 0.901, and root mean square error of prediction (RMSEP) of 0.108 and residual predictive deviation (RPD) of 2.32. Based on the obtained best model and image processing algorithms, the distribution maps of protein content were generated. The overall results of this study indicated that developing a rapid and online multispectral imaging system using the feature wavelengths and PLSR analysis is potential and feasible for determination of the protein content in peanut kernels.

[First clinical experience with extended planning and navigation in an interventional MRI unit].

PubMed

Moche, M; Schmitgen, A; Schneider, J P; Bublat, M; Schulz, T; Voerkel, C; Trantakis, C; Bennek, J; Kahn, T; Busse, H

2004-07-01

To present an advanced concept for patient-based navigation and to report on our first clinical experience with interventions in the cranium, of soft-tissue structures (breast, liver) and in the musculoskeletal system. A PC-based navigation system was integrated into an existing interventional MRI environment. Intraoperatively acquired 3D data were used for interventional planning. The information content of these reference data was increased by integration of additional image modalities (e. g., fMRI, CT) and by color display of areas with early contrast media enhancement. Within 18 months, the system was used in 123 patients undergoing interventions in different anatomic regions (brain: 64, paranasal sinus: 9, breast: 20, liver: 17, bone: 9, muscle: 4). The mean duration of 64 brain interventions was compared with that of 36 procedures using the scanner's standard navigation. In contrast with the continuous scanning mode of the MR system (0.25 fps), the higher quality as well as the real time display (4 fps) of the MR images reconstructed from the 3D reference data allowed adequate hand-eye coordination. With our system, patient movement and tissue shifts could be immediately detected intraoperatively, and, in contrast to the standard procedure, navigation safely resumed after updating the reference data. The navigation system was characterized by good stability, efficient system integration and easy usability. Despite additional working steps still to be optimized, the duration of the image-guided brain tumor resections was not significantly longer. The presented system combines the advantage of intraoperative MRI with established visualization, planning, and real time capabilities of neuronavigation and can be efficiently applied in a broad range of non-neurosurgical interventions.

Problem-Centered Supplemental Instruction in Biology: Influence on Content Recall, Content Understanding, and Problem Solving Ability

ERIC Educational Resources Information Center

Gardner, Joel; Belland, Brian R.

2017-01-01

To address the need for effective, efficient ways to apply active learning in undergraduate biology courses, in this paper, we propose a problem-centered approach that utilizes supplemental web-based instructional materials based on principles of active learning. We compared two supplementary web-based modules using active learning strategies: the…

Improvement of Speckle Contrast Image Processing by an Efficient Algorithm.

PubMed

Steimers, A; Farnung, W; Kohl-Bareis, M

2016-01-01

We demonstrate an efficient algorithm for the temporal and spatial based calculation of speckle contrast for the imaging of blood flow by laser speckle contrast analysis (LASCA). It reduces the numerical complexity of necessary calculations, facilitates a multi-core and many-core implementation of the speckle analysis and enables an independence of temporal or spatial resolution and SNR. The new algorithm was evaluated for both spatial and temporal based analysis of speckle patterns with different image sizes and amounts of recruited pixels as sequential, multi-core and many-core code.

Bone marrow fat content is correlated with hepatic fat content in paediatric non-alcoholic fatty liver disease.

PubMed

Yu, N Y; Wolfson, T; Middleton, M S; Hamilton, G; Gamst, A; Angeles, J E; Schwimmer, J B; Sirlin, C B

2017-05-01

To investigate the relationship between bone marrow fat content and hepatic fat content in children with known or suspected non-alcoholic fatty liver disease (NAFLD). This was an institutional review board-approved, Health Insurance Portability and Accountability Act (HIPAA)-compliant, cross-sectional, prospective analysis of data collected between October 2010 to March 2013 in 125 children with known or suspected NAFLD. Written informed consent was obtained for same-day research magnetic resonance imaging (MRI) of the lumbar spine, liver, and abdominal adiposity. Lumbar spine bone marrow proton density fat fraction (PDFF) and hepatic PDFF were estimated using complex-based MRI (C-MRI) techniques and magnitude-based MRI (M-MRI), respectively. Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SCAT) were quantified using high-resolution MRI. All images were acquired by two MRI technologists. Hepatic M-MRI images were analysed by an image analyst; all other images were analysed by a single investigator. The relationship between lumbar spine bone marrow PDFF and hepatic PDFF was assessed with and without adjusting for the presence of covariates using correlation and regression analysis. Lumbar spine bone marrow PDFF was positively associated with hepatic PDFF in children with known or suspected NAFLD prior to adjusting for covariates (r=0.33, p=0.0002). Lumbar spine bone marrow PDFF was positively associated with hepatic PDFF in children with known or suspected NAFLD (r=0.24, p=0.0079) after adjusting for age, sex, body mass index z-score, VAT, and SCAT in a multivariable regression analysis. Bone marrow fat content is positively associated with hepatic fat content in children with known or suspected NAFLD. Further research is needed to confirm these results and understand their clinical and biological implications. Copyright © 2016 The Royal College of Radiologists. All rights reserved.

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network.

PubMed

Chen, Shuo; Luo, Chenggao; Wang, Hongqiang; Deng, Bin; Cheng, Yongqiang; Zhuang, Zhaowen

2018-04-26

As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. However, there are still two problems in three-dimensional (3D) TCAI. Firstly, the large-scale reference-signal matrix based on meshing the 3D imaging area creates a heavy computational burden, thus leading to unsatisfactory efficiency. Secondly, it is difficult to resolve the target under low signal-to-noise ratio (SNR). In this paper, we propose a 3D imaging method based on matched filtering (MF) and convolutional neural network (CNN), which can reduce the computational burden and achieve high-resolution imaging for low SNR targets. In terms of the frequency-hopping (FH) signal, the original echo is processed with MF. By extracting the processed echo in different spike pulses separately, targets in different imaging planes are reconstructed simultaneously to decompose the global computational complexity, and then are synthesized together to reconstruct the 3D target. Based on the conventional TCAI model, we deduce and build a new TCAI model based on MF. Furthermore, the convolutional neural network (CNN) is designed to teach the MF-TCAI how to reconstruct the low SNR target better. The experimental results demonstrate that the MF-TCAI achieves impressive performance on imaging ability and efficiency under low SNR. Moreover, the MF-TCAI has learned to better resolve the low-SNR 3D target with the help of CNN. In summary, the proposed 3D TCAI can achieve: (1) low-SNR high-resolution imaging by using MF; (2) efficient 3D imaging by downsizing the large-scale reference-signal matrix; and (3) intelligent imaging with CNN. Therefore, the TCAI based on MF and CNN has great potential in applications such as security screening, nondestructive detection, medical diagnosis, etc.

Intelligent bandwidth compression

NASA Astrophysics Data System (ADS)

Tseng, D. Y.; Bullock, B. L.; Olin, K. E.; Kandt, R. K.; Olsen, J. D.

1980-02-01

The feasibility of a 1000:1 bandwidth compression ratio for image transmission has been demonstrated using image-analysis algorithms and a rule-based controller. Such a high compression ratio was achieved by first analyzing scene content using auto-cueing and feature-extraction algorithms, and then transmitting only the pertinent information consistent with mission requirements. A rule-based controller directs the flow of analysis and performs priority allocations on the extracted scene content. The reconstructed bandwidth-compressed image consists of an edge map of the scene background, with primary and secondary target windows embedded in the edge map. The bandwidth-compressed images are updated at a basic rate of 1 frame per second, with the high-priority target window updated at 7.5 frames per second. The scene-analysis algorithms used in this system together with the adaptive priority controller are described. Results of simulated 1000:1 bandwidth-compressed images are presented.

The Socio-Moral Image Database (SMID): A novel stimulus set for the study of social, moral and affective processes.

PubMed

Crone, Damien L; Bode, Stefan; Murawski, Carsten; Laham, Simon M

2018-01-01

A major obstacle for the design of rigorous, reproducible studies in moral psychology is the lack of suitable stimulus sets. Here, we present the Socio-Moral Image Database (SMID), the largest standardized moral stimulus set assembled to date, containing 2,941 freely available photographic images, representing a wide range of morally (and affectively) positive, negative and neutral content. The SMID was validated with over 820,525 individual judgments from 2,716 participants, with normative ratings currently available for all images on affective valence and arousal, moral wrongness, and relevance to each of the five moral values posited by Moral Foundations Theory. We present a thorough analysis of the SMID regarding (1) inter-rater consensus, (2) rating precision, and (3) breadth and variability of moral content. Additionally, we provide recommendations for use aimed at efficient study design and reproducibility, and outline planned extensions to the database. We anticipate that the SMID will serve as a useful resource for psychological, neuroscientific and computational (e.g., natural language processing or computer vision) investigations of social, moral and affective processes. The SMID images, along with associated normative data and additional resources are available at https://osf.io/2rqad/.

Design of Content Based Image Retrieval Scheme for Diabetic Retinopathy Images using Harmony Search Algorithm.

PubMed

Sivakamasundari, J; Natarajan, V

2015-01-01

Diabetic Retinopathy (DR) is a disorder that affects the structure of retinal blood vessels due to long-standing diabetes mellitus. Automated segmentation of blood vessel is vital for periodic screening and timely diagnosis. An attempt has been made to generate continuous retinal vasculature for the design of Content Based Image Retrieval (CBIR) application. The typical normal and abnormal retinal images are preprocessed to improve the vessel contrast. The blood vessels are segmented using evolutionary based Harmony Search Algorithm (HSA) combined with Otsu Multilevel Thresholding (MLT) method by best objective functions. The segmentation results are validated with corresponding ground truth images using binary similarity measures. The statistical, textural and structural features are obtained from the segmented images of normal and DR affected retina and are analyzed. CBIR in medical image retrieval applications are used to assist physicians in clinical decision-support techniques and research fields. A CBIR system is developed using HSA based Otsu MLT segmentation technique and the features obtained from the segmented images. Similarity matching is carried out between the features of query and database images using Euclidean Distance measure. Similar images are ranked and retrieved. The retrieval performance of CBIR system is evaluated in terms of precision and recall. The CBIR systems developed using HSA based Otsu MLT and conventional Otsu MLT methods are compared. The retrieval performance such as precision and recall are found to be 96% and 58% for CBIR system using HSA based Otsu MLT segmentation. This automated CBIR system could be recommended for use in computer assisted diagnosis for diabetic retinopathy screening.

Image-Based Grouping during Binocular Rivalry Is Dictated by Eye-Of-Origin

PubMed Central

Stuit, Sjoerd M.; Paffen, Chris L. E.; van der Smagt, Maarten J.; Verstraten, Frans A. J.

2014-01-01

Prolonged viewing of dichoptically presented images with different content results in perceptual alternations known as binocular rivalry. This phenomenon is thought to be the result of competition at a local level, where local rivalry zones interact to give rise to a single, global dominant percept. Certain perceived combinations that result from this local competition are known to last longer than others, which is referred to as grouping during binocular rivalry. In recent years, the phenomenon has been suggested to be the result of competition at both eye- and image-based processing levels, although the exact contribution from each level remains elusive. Here we use a paradigm designed specifically to quantify the contribution of eye- and image-based processing to grouping during rivalry. In this paradigm we used sine-wave gratings as well as upright and inverted faces, with and without binocular disparity-based occlusion. These stimuli and conditions were used because they are known to result in processing at different stages throughout the visual processing hierarchy. Specifically, more complex images were included in order to maximize the potential contribution of image-based grouping. In spite of this, our results show that increasing image complexity did not lead to an increase in the contribution of image-based processing to grouping during rivalry. In fact, the results show that grouping was primarily affected by the eye-of-origin of the image parts, irrespective of stimulus type. We suggest that image content affects grouping during binocular rivalry at low-level processing stages, where it is intertwined with eye-of-origin information. PMID:24987847

Scalable Coding of Plenoptic Images by Using a Sparse Set and Disparities.

PubMed

Li, Yun; Sjostrom, Marten; Olsson, Roger; Jennehag, Ulf

2016-01-01

One of the light field capturing techniques is the focused plenoptic capturing. By placing a microlens array in front of the photosensor, the focused plenoptic cameras capture both spatial and angular information of a scene in each microlens image and across microlens images. The capturing results in a significant amount of redundant information, and the captured image is usually of a large resolution. A coding scheme that removes the redundancy before coding can be of advantage for efficient compression, transmission, and rendering. In this paper, we propose a lossy coding scheme to efficiently represent plenoptic images. The format contains a sparse image set and its associated disparities. The reconstruction is performed by disparity-based interpolation and inpainting, and the reconstructed image is later employed as a prediction reference for the coding of the full plenoptic image. As an outcome of the representation, the proposed scheme inherits a scalable structure with three layers. The results show that plenoptic images are compressed efficiently with over 60 percent bit rate reduction compared with High Efficiency Video Coding intra coding, and with over 20 percent compared with an High Efficiency Video Coding block copying mode.

Fluorescence imaging of the nanoparticles modified with indocyanine green

NASA Astrophysics Data System (ADS)

Gareev, K. G.; Babikova, K. Y.; Postnov, V. N.; Naumisheva, E. B.; Korolev, D. V.

2017-11-01

The comparative research of silica, the magnetite and magnetite-silica nanoparticles modified with fluorescent dyes using gas-phase and liquid-phase methods was conducted. At the content of fluorescent dye comparable in size a particular spectrophotometric method, nanoparticles with fluorescein have up to 1000 times larger overall luminous efficiency. It is revealed that magnetic nanoparticles are characterized by a smaller light efficiency in comparison with silica particles, at the same time particles of a magnetite are most effective at modification with fluorescein, and magnetite-silica particles - at modification with indocyanine green.

Creation of High Efficient Firefly Luciferase

NASA Astrophysics Data System (ADS)

Nakatsu, Toru

Firefly emits visible yellow-green light. The bioluminescence reaction is carried out by the enzyme luciferase. The bioluminescence of luciferase is widely used as an excellent tool for monitoring gene expression, the measurement of the amount of ATP and in vivo imaging. Recently a study of the cancer metastasis is carried out by in vivo luminescence imaging system, because luminescence imaging is less toxic and more useful for long-term assay than fluorescence imaging by GFP. However the luminescence is much dimmer than fluorescence. Then bioluminescence imaging in living organisms demands the high efficient luciferase which emits near infrared lights or enhances the emission intensity. Here I introduce an idea for creating the high efficient luciferase based on the crystal structure.

Information theoretical assessment of digital imaging systems

NASA Technical Reports Server (NTRS)

John, Sarah; Rahman, Zia-Ur; Huck, Friedrich O.; Reichenbach, Stephen E.

1990-01-01

The end-to-end performance of image gathering, coding, and restoration as a whole is considered. This approach is based on the pivotal relationship that exists between the spectral information density of the transmitted signal and the restorability of images from this signal. The information-theoretical assessment accounts for (1) the information density and efficiency of the acquired signal as a function of the image-gathering system design and the radiance-field statistics, and (2) the improvement in information efficiency and data compression that can be gained by combining image gathering with coding to reduce the signal redundancy and irrelevancy. It is concluded that images can be restored with better quality and from fewer data as the information efficiency of the data is increased. The restoration correctly explains the image gathering and coding processes and effectively suppresses the image-display degradations.

Information theoretical assessment of digital imaging systems

NASA Astrophysics Data System (ADS)

John, Sarah; Rahman, Zia-Ur; Huck, Friedrich O.; Reichenbach, Stephen E.

1990-10-01

The end-to-end performance of image gathering, coding, and restoration as a whole is considered. This approach is based on the pivotal relationship that exists between the spectral information density of the transmitted signal and the restorability of images from this signal. The information-theoretical assessment accounts for (1) the information density and efficiency of the acquired signal as a function of the image-gathering system design and the radiance-field statistics, and (2) the improvement in information efficiency and data compression that can be gained by combining image gathering with coding to reduce the signal redundancy and irrelevancy. It is concluded that images can be restored with better quality and from fewer data as the information efficiency of the data is increased. The restoration correctly explains the image gathering and coding processes and effectively suppresses the image-display degradations.

Perceptual Image Compression in Telemedicine

NASA Technical Reports Server (NTRS)

Watson, Andrew B.; Ahumada, Albert J., Jr.; Eckstein, Miguel; Null, Cynthia H. (Technical Monitor)

1996-01-01

The next era of space exploration, especially the "Mission to Planet Earth" will generate immense quantities of image data. For example, the Earth Observing System (EOS) is expected to generate in excess of one terabyte/day. NASA confronts a major technical challenge in managing this great flow of imagery: in collection, pre-processing, transmission to earth, archiving, and distribution to scientists at remote locations. Expected requirements in most of these areas clearly exceed current technology. Part of the solution to this problem lies in efficient image compression techniques. For much of this imagery, the ultimate consumer is the human eye. In this case image compression should be designed to match the visual capacities of the human observer. We have developed three techniques for optimizing image compression for the human viewer. The first consists of a formula, developed jointly with IBM and based on psychophysical measurements, that computes a DCT quantization matrix for any specified combination of viewing distance, display resolution, and display brightness. This DCT quantization matrix is used in most recent standards for digital image compression (JPEG, MPEG, CCITT H.261). The second technique optimizes the DCT quantization matrix for each individual image, based on the contents of the image. This is accomplished by means of a model of visual sensitivity to compression artifacts. The third technique extends the first two techniques to the realm of wavelet compression. Together these two techniques will allow systematic perceptual optimization of image compression in NASA imaging systems. Many of the image management challenges faced by NASA are mirrored in the field of telemedicine. Here too there are severe demands for transmission and archiving of large image databases, and the imagery is ultimately used primarily by human observers, such as radiologists. In this presentation I will describe some of our preliminary explorations of the applications of our technology to the special problems of telemedicine.

Chip-based generation of carbon nanodots via electrochemical oxidation of screen printed carbon electrodes and the applications for efficient cell imaging and electrochemiluminescence enhancement

NASA Astrophysics Data System (ADS)

Xu, Yuanhong; Liu, Jingquan; Zhang, Jizhen; Zong, Xidan; Jia, Xiaofang; Li, Dan; Wang, Erkang

2015-05-01

A portable lab-on-a-chip methodology to generate ionic liquid-functionalized carbon nanodots (CNDs) was developed via electrochemical oxidation of screen printed carbon electrodes. The CNDs can be successfully applied for efficient cell imaging and solid-state electrochemiluminescence sensor fabrication on the paper-based chips.A portable lab-on-a-chip methodology to generate ionic liquid-functionalized carbon nanodots (CNDs) was developed via electrochemical oxidation of screen printed carbon electrodes. The CNDs can be successfully applied for efficient cell imaging and solid-state electrochemiluminescence sensor fabrication on the paper-based chips. Electronic supplementary information (ESI) available: Experimental section; Fig. S1. XPS spectra of the as-prepared CNDs after being dialyzed for 72 hours; Fig. S2. LSCM images showing time-dependent fluorescence signals of HeLa cells treated by the as-prepared CNDs; Tripropylamine analysis using the Nafion/CNDs modified ECL sensor. See DOI: 10.1039/c5nr01765c

GPR Image and Signal Processing for Pavement and Road Monitoring on Android Smartphones and Tablets

NASA Astrophysics Data System (ADS)

Benedetto, Francesco; Benedetto, Andrea; Tedeschi, Antonio

2014-05-01

Ground Penetrating Radar (GPR) is a geophysical method that uses radar pulses to image the subsurface. This non-destructive method uses electromagnetic radiation and detects the reflected signals from subsurface structures. It can detect objects, changes in material, and voids and cracks. GPR has many applications in a number of fields. In the field of civil engineering one of the most advanced technologies used for road pavement monitoring is based on the deployment of advanced GPR systems. One of the most relevant causes of road pavement damage is often referable to water intrusion in structural layers. In this context, GPR has been recently proposed as a method to estimate moisture content in a porous medium without preventive calibration. Hence, the development of methods to obtain an estimate of the moisture content is a crucial research field involving economic, social and strategic aspects in road safety for a great number of public and private Agencies. In particular, a recent new approach was proposed to estimate moisture content in a porous medium basing on the theory of Rayleigh scattering, showing a shift of the frequency peak of the GPR spectrum towards lower frequencies as the moisture content increases in the soil. Addressing some of these issues, this work proposes a mobile application, for smartphones and tablets, for GPR image and signal processing. Our application has been designed for the Android mobile operating system, since it is open source and android mobile platforms are selling the most smartphones in the world (2013). The GPR map can be displayed in black/white or color and the user can zoom and navigate into the image. The map can be loaded in two different ways: from the local memory of the portable device or from a remote server. This latter possibility can be very useful for real-time and mobile monitoring of road and pavement inspection. In addition, the application allows analyzing the GPR data also in the frequency domain. It is possible to visualize the GPR spectrum, and the application returns the (abscissa of the) frequency peak of the GPR spectrum. It is also possible to visualize more GPR spectra on the same figure, in order to understand if a frequency shift (related to moisture content) has been observed. Finally, the GPR spectra can be exported as a JPEG file. This application has a strategic and innovative potentiality for all the Agencies involved in roads and highway management in order to improve the onsite efficiency and effectiveness of the works. ACKNOWLEDGMENT This work is a contribution to COST Action TU1208 "Civil Engineering Applications of Ground Penetrating Radar."

Efficient single-pixel multispectral imaging via non-mechanical spatio-spectral modulation.

PubMed

Li, Ziwei; Suo, Jinli; Hu, Xuemei; Deng, Chao; Fan, Jingtao; Dai, Qionghai

2017-01-27

Combining spectral imaging with compressive sensing (CS) enables efficient data acquisition by fully utilizing the intrinsic redundancies in natural images. Current compressive multispectral imagers, which are mostly based on array sensors (e.g, CCD or CMOS), suffer from limited spectral range and relatively low photon efficiency. To address these issues, this paper reports a multispectral imaging scheme with a single-pixel detector. Inspired by the spatial resolution redundancy of current spatial light modulators (SLMs) relative to the target reconstruction, we design an all-optical spectral splitting device to spatially split the light emitted from the object into several counterparts with different spectrums. Separated spectral channels are spatially modulated simultaneously with individual codes by an SLM. This no-moving-part modulation ensures a stable and fast system, and the spatial multiplexing ensures an efficient acquisition. A proof-of-concept setup is built and validated for 8-channel multispectral imaging within 420~720 nm wavelength range on both macro and micro objects, showing a potential for efficient multispectral imager in macroscopic and biomedical applications.

Contour-based image warping

NASA Astrophysics Data System (ADS)

Chan, Kwai H.; Lau, Rynson W.

1996-09-01

Image warping concerns about transforming an image from one spatial coordinate to another. It is widely used for the vidual effect of deforming and morphing images in the film industry. A number of warping techniques have been introduced, which are mainly based on the corresponding pair mapping of feature points, feature vectors or feature patches (mostly triangular or quadrilateral). However, very often warping of an image object with an arbitrary shape is required. This requires a warping technique which is based on boundary contour instead of feature points or feature line-vectors. In addition, when feature point or feature vector based techniques are used, approximation of the object boundary by using point or vectors is required. In this case, the matching process of the corresponding pairs will be very time consuming if a fine approximation is required. In this paper, we propose a contour-based warping technique for warping image objects with arbitrary shapes. The novel idea of the new method is the introduction of mathematical morphology to allow a more flexible control of image warping. Two morphological operators are used as contour determinators. The erosion operator is used to warp image contents which are inside a user specified contour while the dilation operation is used to warp image contents which are outside of the contour. This new method is proposed to assist further development of a semi-automatic motion morphing system when accompanied with robust feature extractors such as deformable template or active contour model.

Determination of fat and total protein content in milk using conventional digital imaging.

PubMed

Kucheryavskiy, Sergey; Melenteva, Anastasiia; Bogomolov, Andrey

2014-04-01

The applicability of conventional digital imaging to quantitative determination of fat and total protein in cow's milk, based on the phenomenon of light scatter, has been proved. A new algorithm for extracting features from digital images of milk samples has been developed. The algorithm takes into account spatial distribution of light, diffusely transmitted through a sample. The proposed method has been tested on two sample sets prepared from industrial raw milk standards, with variable fat and protein content. Partial Least-Squares (PLS) regression on the features calculated from images of monochromatically illuminated milk samples resulted in models with high prediction performance when analysed the sets separately (best models with cross-validated R(2)=0.974 for protein and R(2)=0.973 for fat content). However when analysed the sets jointly with the obtained results were significantly worse (best models with cross-validated R(2)=0.890 for fat content and R(2)=0.720 for protein content). The results have been compared with previously published Vis/SW-NIR spectroscopic study of similar samples. Copyright © 2013 Elsevier B.V. All rights reserved.

A fast chaos-based image encryption scheme with a dynamic state variables selection mechanism

NASA Astrophysics Data System (ADS)

Chen, Jun-xin; Zhu, Zhi-liang; Fu, Chong; Yu, Hai; Zhang, Li-bo

2015-03-01

In recent years, a variety of chaos-based image cryptosystems have been investigated to meet the increasing demand for real-time secure image transmission. Most of them are based on permutation-diffusion architecture, in which permutation and diffusion are two independent procedures with fixed control parameters. This property results in two flaws. (1) At least two chaotic state variables are required for encrypting one plain pixel, in permutation and diffusion stages respectively. Chaotic state variables produced with high computation complexity are not sufficiently used. (2) The key stream solely depends on the secret key, and hence the cryptosystem is vulnerable against known/chosen-plaintext attacks. In this paper, a fast chaos-based image encryption scheme with a dynamic state variables selection mechanism is proposed to enhance the security and promote the efficiency of chaos-based image cryptosystems. Experimental simulations and extensive cryptanalysis have been carried out and the results prove the superior security and high efficiency of the scheme.

A new image representation for compact and secure communication

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

Prasad, Lakshman; Skourikhine, A. N.

In many areas of nuclear materials management there is a need for communication, archival, and retrieval of annotated image data between heterogeneous platforms and devices to effectively implement safety, security, and safeguards of nuclear materials. Current image formats such as JPEG are not ideally suited in such scenarios as they are not scalable to different viewing formats, and do not provide a high-level representation of images that facilitate automatic object/change detection or annotation. The new Scalable Vector Graphics (SVG) open standard for representing graphical information, recommended by the World Wide Web Consortium (W3C) is designed to address issues of imagemore » scalability, portability, and annotation. However, until now there has been no viable technology to efficiently field images of high visual quality under this standard. Recently, LANL has developed a vectorized image representation that is compatible with the SVG standard and preserves visual quality. This is based on a new geometric framework for characterizing complex features in real-world imagery that incorporates perceptual principles of processing visual information known from cognitive psychology and vision science, to obtain a polygonal image representation of high fidelity. This representation can take advantage of all textual compression and encryption routines unavailable to other image formats. Moreover, this vectorized image representation can be exploited to facilitate automated object recognition that can reduce time required for data review. The objects/features of interest in these vectorized images can be annotated via animated graphics to facilitate quick and easy display and comprehension of processed image content.« less

Low-power low-noise mixed-mode VLSI ASIC for infinite dynamic range imaging applications

NASA Astrophysics Data System (ADS)

Turchetta, Renato; Hu, Y.; Zinzius, Y.; Colledani, C.; Loge, A.

1998-11-01

Solid state solutions for imaging are mainly represented by CCDs and, more recently, by CMOS imagers. Both devices are based on the integration of the total charge generated by the impinging radiation, with no processing of the single photon information. The dynamic range of these devices is intrinsically limited by the finite value of noise. Here we present the design of an architecture which allows efficient, in-pixel, noise reduction to a practically zero level, thus allowing infinite dynamic range imaging. A detailed calculation of the dynamic range is worked out, showing that noise is efficiently suppressed. This architecture is based on the concept of single-photon counting. In each pixel, we integrate both the front-end, low-noise, low-power analog part and the digital part. The former consists of a charge preamplifier, an active filter for optimal noise bandwidth reduction, a buffer and a threshold comparator, and the latter is simply a counter, which can be programmed to act as a normal shift register for the readout of the counters' contents. Two different ASIC's based on this concept have been designed for different applications. The first one has been optimized for silicon edge-on microstrips detectors, used in a digital mammography R and D project. It is a 32-channel circuit, with a 16-bit binary static counter.It has been optimized for a relatively large detector capacitance of 5 pF. Noise has been measured to be equal to 100 + 7*Cd (pF) electron rms with the digital part, showing no degradation of the noise performances with respect to the design values. The power consumption is 3.8mW/channel for a peaking time of about 1 microsecond(s) . The second circuit is a prototype for pixel imaging. The total active area is about (250 micrometers )**2. The main differences of the electronic architecture with respect to the first prototype are: i) different optimization of the analog front-end part for low-capacitance detectors, ii) in- pixel 4-bit comparator-offset compensation, iii) 15-bit pseudo-random counter. The power consumption is 255 (mu) W/channel for a peaking time of 300 ns and an equivalent noise charge of 185 + 97*Cd electrons rms. Simulation and experimental result as well as imaging results will be presented.

Sparsity-based image monitoring of crystal size distribution during crystallization

NASA Astrophysics Data System (ADS)

Liu, Tao; Huo, Yan; Ma, Cai Y.; Wang, Xue Z.

2017-07-01

To facilitate monitoring crystal size distribution (CSD) during a crystallization process by using an in-situ imaging system, a sparsity-based image analysis method is proposed for real-time implementation. To cope with image degradation arising from in-situ measurement subject to particle motion, solution turbulence, and uneven illumination background in the crystallizer, sparse representation of a real-time captured crystal image is developed based on using an in-situ image dictionary established in advance, such that the noise components in the captured image can be efficiently removed. Subsequently, the edges of a crystal shape in a captured image are determined in terms of the salience information defined from the denoised crystal images. These edges are used to derive a blur kernel for reconstruction of a denoised image. A non-blind deconvolution algorithm is given for the real-time reconstruction. Consequently, image segmentation can be easily performed for evaluation of CSD. The crystal image dictionary and blur kernels are timely updated in terms of the imaging conditions to improve the restoration efficiency. An experimental study on the cooling crystallization of α-type L-glutamic acid (LGA) is shown to demonstrate the effectiveness and merit of the proposed method.

Signal-to-noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky-Golay filters and weighted least squares error.

PubMed

Kiani, M A; Sim, K S; Nia, M E; Tso, C P

2015-05-01

A new technique based on cubic spline interpolation with Savitzky-Golay smoothing using weighted least squares error filter is enhanced for scanning electron microscope (SEM) images. A diversity of sample images is captured and the performance is found to be better when compared with the moving average and the standard median filters, with respect to eliminating noise. This technique can be implemented efficiently on real-time SEM images, with all mandatory data for processing obtained from a single image. Noise in images, and particularly in SEM images, are undesirable. A new noise reduction technique, based on cubic spline interpolation with Savitzky-Golay and weighted least squares error method, is developed. We apply the combined technique to single image signal-to-noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation-based technique requires image details to be correlated over a few pixels, whereas the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In the few test cases involving different images, the efficiency of the developed noise reduction filter is proved to be significantly better than those obtained from the other methods. Noise can be reduced efficiently with appropriate choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

A Content Analysis of College and University Viewbooks (Brochures).

ERIC Educational Resources Information Center

Hite, Robert E.; Yearwood, Alisa

2001-01-01

Systematically examined the content and components of college viewbooks/brochures. Compiled findings on: (1) physical components (e.g., photographs and slogans); (2) message content based on school characteristics such as size, type of school, enrollment, location, etc.; and (3) the type of image schools with different characteristics are seeking…

Image analysis method for the measurement of water saturation in a two-dimensional experimental flow tank

NASA Astrophysics Data System (ADS)

Belfort, Benjamin; Weill, Sylvain; Lehmann, François

2017-04-01

A novel, non-invasive imaging technique that determines 2D maps of water content in unsaturated porous media is presented. This method directly relates digitally measured intensities to the water content of the porous medium. This method requires the classical image analysis steps, i.e., normalization, filtering, background subtraction, scaling and calibration. The main advantages of this approach are that no calibration experiment is needed and that no tracer or dye is injected into the flow tank. The procedure enables effective processing of a large number of photographs and thus produces 2D water content maps at high temporal resolution. A drainage / imbibition experiment in a 2D flow tank with inner dimensions of 40 cm x 14 cm x 6 cm (L x W x D) is carried out to validate the methodology. The accuracy of the proposed approach is assessed using numerical simulations with a state-of-the-art computational code that solves the Richards. Comparison of the cumulative mass leaving and entering the flow tank and water content maps produced by the photographic measurement technique and the numerical simulations demonstrate the efficiency and high accuracy of the proposed method for investigating vadose zone flow processes. Application examples to a larger flow tank with various boundary conditions are finally presented to illustrate the potential of the methodology.

Universal and adapted vocabularies for generic visual categorization.

PubMed

Perronnin, Florent

2008-07-01

Generic Visual Categorization (GVC) is the pattern classification problem which consists in assigning labels to an image based on its semantic content. This is a challenging task as one has to deal with inherent object/scene variations as well as changes in viewpoint, lighting and occlusion. Several state-of-the-art GVC systems use a vocabulary of visual terms to characterize images with a histogram of visual word counts. We propose a novel practical approach to GVC based on a universal vocabulary, which describes the content of all the considered classes of images, and class vocabularies obtained through the adaptation of the universal vocabulary using class-specific data. The main novelty is that an image is characterized by a set of histograms - one per class - where each histogram describes whether the image content is best modeled by the universal vocabulary or the corresponding class vocabulary. This framework is applied to two types of local image features: low-level descriptors such as the popular SIFT and high-level histograms of word co-occurrences in a spatial neighborhood. It is shown experimentally on two challenging datasets (an in-house database of 19 categories and the PASCAL VOC 2006 dataset) that the proposed approach exhibits state-of-the-art performance at a modest computational cost.

Quality assessment of butter cookies applying multispectral imaging

PubMed Central

Andresen, Mette S; Dissing, Bjørn S; Løje, Hanne

2013-01-01

A method for characterization of butter cookie quality by assessing the surface browning and water content using multispectral images is presented. Based on evaluations of the browning of butter cookies, cookies were manually divided into groups. From this categorization, reference values were calculated for a statistical prediction model correlating multispectral images with a browning score. The browning score is calculated as a function of oven temperature and baking time. It is presented as a quadratic response surface. The investigated process window was the intervals 4–16 min and 160–200°C in a forced convection electrically heated oven. In addition to the browning score, a model for predicting the average water content based on the same images is presented. This shows how multispectral images of butter cookies may be used for the assessment of different quality parameters. Statistical analysis showed that the most significant wavelengths for browning predictions were in the interval 400–700 nm and the wavelengths significant for water prediction were primarily located in the near-infrared spectrum. The water prediction model was found to correctly estimate the average water content with an absolute error of 0.22%. From the images it was also possible to follow the browning and drying propagation from the cookie edge toward the center. PMID:24804036

An image understanding system using attributed symbolic representation and inexact graph-matching

NASA Astrophysics Data System (ADS)

Eshera, M. A.; Fu, K.-S.

1986-09-01

A powerful image understanding system using a semantic-syntactic representation scheme consisting of attributed relational graphs (ARGs) is proposed for the analysis of the global information content of images. A multilayer graph transducer scheme performs the extraction of ARG representations from images, with ARG nodes representing the global image features, and the relations between features represented by the attributed branches between corresponding nodes. An efficient dynamic programming technique is employed to derive the distance between two ARGs and the inexact matching of their respective components. Noise, distortion and ambiguity in real-world images are handled through modeling in the transducer mapping rules and through the appropriate cost of error-transformation for the inexact matching of the representation. The system is demonstrated for the case of locating objects in a scene composed of complex overlapped objects, and the case of target detection in noisy and distorted synthetic aperture radar image.

The Precise and Efficient Identification of Medical Order Forms Using Shape Trees

NASA Astrophysics Data System (ADS)

Henker, Uwe; Petersohn, Uwe; Ultsch, Alfred

A powerful and flexible technique to identify, classify and process documents using images from a scanning process is presented. The types of documents can be described to the system as a set of differentiating features in a case base using shape trees. The features are filtered and abstracted from an extremely reduced scanner image of the document. Classification rules are stored with the cases to enable precise recognition and further mark reading and Optical Character Recognition (OCR) process. The method is implemented in a system which actually processes the majority of requests for medical lab procedures in Germany. A large practical experiment with data from practitioners was performed. An average of 97% of the forms were correctly identified; none were identified incorrectly. This meets the quality requirements for most medical applications. The modular description of the recognition process allows for a flexible adaptation of future changes to the form and content of the document’s structures.

DCT-based cyber defense techniques

NASA Astrophysics Data System (ADS)

Amsalem, Yaron; Puzanov, Anton; Bedinerman, Anton; Kutcher, Maxim; Hadar, Ofer

2015-09-01

With the increasing popularity of video streaming services and multimedia sharing via social networks, there is a need to protect the multimedia from malicious use. An attacker may use steganography and watermarking techniques to embed malicious content, in order to attack the end user. Most of the attack algorithms are robust to basic image processing techniques such as filtering, compression, noise addition, etc. Hence, in this article two novel, real-time, defense techniques are proposed: Smart threshold and anomaly correction. Both techniques operate at the DCT domain, and are applicable for JPEG images and H.264 I-Frames. The defense performance was evaluated against a highly robust attack, and the perceptual quality degradation was measured by the well-known PSNR and SSIM quality assessment metrics. A set of defense techniques is suggested for improving the defense efficiency. For the most aggressive attack configuration, the combination of all the defense techniques results in 80% protection against cyber-attacks with PSNR of 25.74 db.

A robust collagen scoring method for human liver fibrosis by second harmonic microscopy.

PubMed

Guilbert, Thomas; Odin, Christophe; Le Grand, Yann; Gailhouste, Luc; Turlin, Bruno; Ezan, Frédérick; Désille, Yoann; Baffet, Georges; Guyader, Dominique

2010-12-06

Second Harmonic Generation (SHG) microscopy offers the opportunity to image collagen of type I without staining. We recently showed that a simple scoring method, based on SHG images of histological human liver biopsies, correlates well with the Metavir assessment of fibrosis level (Gailhouste et al., J. Hepatol., 2010). In this article, we present a detailed study of this new scoring method with two different objective lenses. By using measurements of the objectives point spread functions and of the photomultiplier gain, and a simple model of the SHG intensity, we show that our scoring method, applied to human liver biopsies, is robust to the objective's numerical aperture (NA) for low NA, the choice of the reference sample and laser power, and the spatial sampling rate. The simplicity and robustness of our collagen scoring method may open new opportunities in the quantification of collagen content in different organs, which is of main importance in providing diagnostic information and evaluation of therapeutic efficiency.

The infection algorithm: an artificial epidemic approach for dense stereo correspondence.

PubMed

Olague, Gustavo; Fernández, Francisco; Pérez, Cynthia B; Lutton, Evelyne

2006-01-01

We present a new bio-inspired approach applied to a problem of stereo image matching. This approach is based on an artificial epidemic process, which we call the infection algorithm. The problem at hand is a basic one in computer vision for 3D scene reconstruction. It has many complex aspects and is known as an extremely difficult one. The aim is to match the contents of two images in order to obtain 3D information that allows the generation of simulated projections from a viewpoint that is different from the ones of the initial photographs. This process is known as view synthesis. The algorithm we propose exploits the image contents in order to produce only the necessary 3D depth information, while saving computational time. It is based on a set of distributed rules, which propagate like an artificial epidemic over the images. Experiments on a pair of real images are presented, and realistic reprojected images have been generated.

Constrained Total Generalized p-Variation Minimization for Few-View X-Ray Computed Tomography Image Reconstruction

PubMed Central

Zhang, Hanming; Wang, Linyuan; Yan, Bin; Li, Lei; Cai, Ailong; Hu, Guoen

2016-01-01

Total generalized variation (TGV)-based computed tomography (CT) image reconstruction, which utilizes high-order image derivatives, is superior to total variation-based methods in terms of the preservation of edge information and the suppression of unfavorable staircase effects. However, conventional TGV regularization employs l1-based form, which is not the most direct method for maximizing sparsity prior. In this study, we propose a total generalized p-variation (TGpV) regularization model to improve the sparsity exploitation of TGV and offer efficient solutions to few-view CT image reconstruction problems. To solve the nonconvex optimization problem of the TGpV minimization model, we then present an efficient iterative algorithm based on the alternating minimization of augmented Lagrangian function. All of the resulting subproblems decoupled by variable splitting admit explicit solutions by applying alternating minimization method and generalized p-shrinkage mapping. In addition, approximate solutions that can be easily performed and quickly calculated through fast Fourier transform are derived using the proximal point method to reduce the cost of inner subproblems. The accuracy and efficiency of the simulated and real data are qualitatively and quantitatively evaluated to validate the efficiency and feasibility of the proposed method. Overall, the proposed method exhibits reasonable performance and outperforms the original TGV-based method when applied to few-view problems. PMID:26901410

Error image aware content restoration

NASA Astrophysics Data System (ADS)

Choi, Sungwoo; Lee, Moonsik; Jung, Byunghee

2015-12-01

As the resolution of TV significantly increased, content consumers have become increasingly sensitive to the subtlest defect in TV contents. This rising standard in quality demanded by consumers has posed a new challenge in today's context where the tape-based process has transitioned to the file-based process: the transition necessitated digitalizing old archives, a process which inevitably produces errors such as disordered pixel blocks, scattered white noise, or totally missing pixels. Unsurprisingly, detecting and fixing such errors require a substantial amount of time and human labor to meet the standard demanded by today's consumers. In this paper, we introduce a novel, automated error restoration algorithm which can be applied to different types of classic errors by utilizing adjacent images while preserving the undamaged parts of an error image as much as possible. We tested our method to error images detected from our quality check system in KBS(Korean Broadcasting System) video archive. We are also implementing the algorithm as a plugin of well-known NLE(Non-linear editing system), which is a familiar tool for quality control agent.

Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data

NASA Technical Reports Server (NTRS)

Tilton, James C.; Lawrence, William T.

2005-01-01

NASA's Goddard Space Flight Center has developed a fast and effective method for generating image segmentation hierarchies. These segmentation hierarchies organize image data in a manner that makes their information content more accessible for analysis. Image segmentation enables analysis through the examination of image regions rather than individual image pixels. In addition, the segmentation hierarchy provides additional analysis clues through the tracing of the behavior of image region characteristics at several levels of segmentation detail. The potential for extracting the information content from imagery data based on segmentation hierarchies has not been fully explored for the benefit of the Earth and space science communities. This paper explores the potential of exploiting these segmentation hierarchies for the analysis of multi-date data sets, and for the particular application of change monitoring.

Political leaders and the media. Can we measure political leadership images in newspapers using computer-assisted content analysis?

PubMed

Aaldering, Loes; Vliegenthart, Rens

Despite the large amount of research into both media coverage of politics as well as political leadership, surprisingly little research has been devoted to the ways political leaders are discussed in the media. This paper studies whether computer-aided content analysis can be applied in examining political leadership images in Dutch newspaper articles. It, firstly, provides a conceptualization of political leader character traits that integrates different perspectives in the literature. Moreover, this paper measures twelve political leadership images in media coverage, based on a large-scale computer-assisted content analysis of Dutch media coverage (including almost 150.000 newspaper articles), and systematically tests the quality of the employed measurement instrument by assessing the relationship between the images, the variance in the measurement, the over-time development of images for two party leaders and by comparing the computer results with manual coding. We conclude that the computerized content analysis provides a valid measurement for the leadership images in Dutch newspapers. Moreover, we find that the dimensions political craftsmanship, vigorousness, integrity, communicative performances and consistency are regularly applied in discussing party leaders, but that portrayal of party leaders in terms of responsiveness is almost completely absent in Dutch newspapers.

Ditching the Disc: The Effects of Cloud-Based Image Sharing on Department Efficiency and Report Turnaround Times in Mammography.

PubMed

Morgan, Matthew B; Young, Elizabeth; Harada, Scott; Winkler, Nicole; Riegert, Joanna; Jones, Tony; Hu, Nan; Stein, Matthew

2017-12-01

In screening mammography, accessing prior examination images is crucial for accurate diagnosis and avoiding false-positives. When women visit multiple institutions for their screens, these "outside" examinations must be retrieved for comparison. Traditionally, prior images are obtained by faxing requests to other institutions and waiting for standard mail (film or CD-ROM), which can greatly delay report turnaround times. Recently, advancements in cloud-based image transfer technology have opened up more efficient options for examination transfer between institutions. The objective of this study was to evaluate the effect of cloud-based image transfer on mammography department workflow, time required to obtain prior images, and report turnaround times. Sixty screening examinations requiring prior images were placed into two groups (30 each). The control group used the standard institutional protocol for requesting prior images: faxing requests and waiting for mailed examinations. The experimental group used a cloud-based transfer for both requesting and receiving examinations. The mean number of days between examination request and examination receipt was measured for both groups and compared. The mean number of days from examination request to receipt was 6.08 days (SD 3.50) in the control group compared with 3.16 days (SD 3.95) in the experimental group. Using a cloud-based image transfer to obtain prior mammograms resulted in an average reduction of 2.92 days (P = .0361; 95% confidence interval 0.20-5.65) between examination request and receipt. This improvement in system efficiency is relevant for interpreting radiologists working to improve reporting times and for patients anxious to receive their mammography results. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Resolution analysis of archive films for the purpose of their optimal digitization and distribution

NASA Astrophysics Data System (ADS)

Fliegel, Karel; Vítek, Stanislav; Páta, Petr; Myslík, Jiří; Pecák, Josef; Jícha, Marek

2017-09-01

With recent high demand for ultra-high-definition (UHD) content to be screened in high-end digital movie theaters but also in the home environment, film archives full of movies in high-definition and above are in the scope of UHD content providers. Movies captured with the traditional film technology represent a virtually unlimited source of UHD content. The goal to maintain complete image information is also related to the choice of scanning resolution and spatial resolution for further distribution. It might seem that scanning the film material in the highest possible resolution using state-of-the-art film scanners and also its distribution in this resolution is the right choice. The information content of the digitized images is however limited, and various degradations moreover lead to its further reduction. Digital distribution of the content in the highest image resolution might be therefore unnecessary or uneconomical. In other cases, the highest possible resolution is inevitable if we want to preserve fine scene details or film grain structure for archiving purposes. This paper deals with the image detail content analysis of archive film records. The resolution limit in captured scene image and factors which lower the final resolution are discussed. Methods are proposed to determine the spatial details of the film picture based on the analysis of its digitized image data. These procedures allow determining recommendations for optimal distribution of digitized video content intended for various display devices with lower resolutions. Obtained results are illustrated on spatial downsampling use case scenario, and performance evaluation of the proposed techniques is presented.

Automatic classification for mammogram backgrounds based on bi-rads complexity definition and on a multi content analysis framework

NASA Astrophysics Data System (ADS)

Wu, Jie; Besnehard, Quentin; Marchessoux, Cédric

2011-03-01

Clinical studies for the validation of new medical imaging devices require hundreds of images. An important step in creating and tuning the study protocol is the classification of images into "difficult" and "easy" cases. This consists of classifying the image based on features like the complexity of the background, the visibility of the disease (lesions). Therefore, an automatic medical background classification tool for mammograms would help for such clinical studies. This classification tool is based on a multi-content analysis framework (MCA) which was firstly developed to recognize image content of computer screen shots. With the implementation of new texture features and a defined breast density scale, the MCA framework is able to automatically classify digital mammograms with a satisfying accuracy. BI-RADS (Breast Imaging Reporting Data System) density scale is used for grouping the mammograms, which standardizes the mammography reporting terminology and assessment and recommendation categories. Selected features are input into a decision tree classification scheme in MCA framework, which is the so called "weak classifier" (any classifier with a global error rate below 50%). With the AdaBoost iteration algorithm, these "weak classifiers" are combined into a "strong classifier" (a classifier with a low global error rate) for classifying one category. The results of classification for one "strong classifier" show the good accuracy with the high true positive rates. For the four categories the results are: TP=90.38%, TN=67.88%, FP=32.12% and FN =9.62%.

An Efficient Model-Based Image Understanding Method for an Autonomous Vehicle.

DTIC Science & Technology

1997-09-01

The problem discussed in this dissertation is the development of an efficient method for visual navigation of autonomous vehicles . The approach is to... autonomous vehicles . Thus the new method is implemented as a component of the image-understanding system in the autonomous mobile robot Yamabico-11 at

Uterine fibroids: semiquantitative perfusion MR imaging parameters associated with the intraprocedural and immediate postprocedural treatment efficiencies of MR imaging-guided high-intensity focused ultrasound ablation.

PubMed

Kim, Young-sun; Kim, Byoung-Gie; Rhim, Hyunchul; Bae, Duk-Soo; Lee, Jeong-Won; Kim, Tae-Joong; Choi, Chel Hun; Lee, Yoo-Young; Lim, Hyo Keun

2014-11-01

To determine whether semiquantitative perfusion magnetic resonance (MR) imaging parameters are associated with therapeutic effectiveness of MR imaging-guided high-intensity focused ultrasound ( HIFU high-intensity focused ultrasound ) ablation of uterine fibroids and which semiquantitative perfusion parameters are significant with regard to treatment efficiency. This study was approved by the institutional review board, and informed consent was obtained from all subjects. Seventy-seven women (mean age, 43.3 years) with 119 fibroids (mean diameter, 7.5 cm) treated with MR imaging-guided HIFU high-intensity focused ultrasound ablation were analyzed. The correlation between semiquantitative perfusion MR parameters (peak enhancement, relative peak enhancement, time to peak, wash-in rate, washout rate) and heating and ablation efficiencies (lethal thermal dose volume based on MR thermometry and nonperfused volume based on immediate contrast-enhanced image divided by intended treatment volume) were evaluated by using a linear mixed model on a per-fibroid basis. The specific value of the significant parameter that had a substantial effect on treatment efficiency was determined. The mean peak enhancement, relative peak enhancement, time to peak, wash-in rate, and washout rate of the fibroids were 1293.1 ± 472.8 (range, 570.2-2477.8), 171.4% ± 57.2 (range, 0.6%-370.2%), 137.2 seconds ± 119.8 (range, 20.0-300.0 seconds), 79.5 per second ± 48.2 (range, 12.5-236.7 per second), and 11.4 per second ± 10.1 (range, 0-39.3 per second), respectively. Relative peak enhancement was found to be independently significant for both heating and ablation efficiencies (B = -0.002, P < .001 and B = -0.003, P = .050, respectively). The washout rate was significantly associated with ablation efficiency (B = -0.018, P = .043). Both efficiencies showed the most abrupt transitions at 220% of relative peak enhancement. Relative peak enhancement at semiquantitative perfusion MR imaging was significantly associated with treatment efficiency of MR imaging-guided HIFU high-intensity focused ultrasound ablation of uterine fibroids, and a value of 220% or less is suggested as a screening guideline for more efficient treatment.

An Energy-Efficient Compressive Image Coding for Green Internet of Things (IoT).

PubMed

Li, Ran; Duan, Xiaomeng; Li, Xu; He, Wei; Li, Yanling

2018-04-17

Aimed at a low-energy consumption of Green Internet of Things (IoT), this paper presents an energy-efficient compressive image coding scheme, which provides compressive encoder and real-time decoder according to Compressive Sensing (CS) theory. The compressive encoder adaptively measures each image block based on the block-based gradient field, which models the distribution of block sparse degree, and the real-time decoder linearly reconstructs each image block through a projection matrix, which is learned by Minimum Mean Square Error (MMSE) criterion. Both the encoder and decoder have a low computational complexity, so that they only consume a small amount of energy. Experimental results show that the proposed scheme not only has a low encoding and decoding complexity when compared with traditional methods, but it also provides good objective and subjective reconstruction qualities. In particular, it presents better time-distortion performance than JPEG. Therefore, the proposed compressive image coding is a potential energy-efficient scheme for Green IoT.

Perfect blind restoration of images blurred by multiple filters: theory and efficient algorithms.

PubMed

Harikumar, G; Bresler, Y

1999-01-01

We address the problem of restoring an image from its noisy convolutions with two or more unknown finite impulse response (FIR) filters. We develop theoretical results about the existence and uniqueness of solutions, and show that under some generically true assumptions, both the filters and the image can be determined exactly in the absence of noise, and stably estimated in its presence. We present efficient algorithms to estimate the blur functions and their sizes. These algorithms are of two types, subspace-based and likelihood-based, and are extensions of techniques proposed for the solution of the multichannel blind deconvolution problem in one dimension. We present memory and computation-efficient techniques to handle the very large matrices arising in the two-dimensional (2-D) case. Once the blur functions are determined, they are used in a multichannel deconvolution step to reconstruct the unknown image. The theoretical and practical implications of edge effects, and "weakly exciting" images are examined. Finally, the algorithms are demonstrated on synthetic and real data.

a Hadoop-Based Distributed Framework for Efficient Managing and Processing Big Remote Sensing Images

NASA Astrophysics Data System (ADS)

Wang, C.; Hu, F.; Hu, X.; Zhao, S.; Wen, W.; Yang, C.

2015-07-01

Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing- intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.

Efficient Terahertz Wide-Angle NUFFT-Based Inverse Synthetic Aperture Imaging Considering Spherical Wavefront.

PubMed

Gao, Jingkun; Deng, Bin; Qin, Yuliang; Wang, Hongqiang; Li, Xiang

2016-12-14

An efficient wide-angle inverse synthetic aperture imaging method considering the spherical wavefront effects and suitable for the terahertz band is presented. Firstly, the echo signal model under spherical wave assumption is established, and the detailed wavefront curvature compensation method accelerated by 1D fast Fourier transform (FFT) is discussed. Then, to speed up the reconstruction procedure, the fast Gaussian gridding (FGG)-based nonuniform FFT (NUFFT) is employed to focus the image. Finally, proof-of-principle experiments are carried out and the results are compared with the ones obtained by the convolution back-projection (CBP) algorithm. The results demonstrate the effectiveness and the efficiency of the presented method. This imaging method can be directly used in the field of nondestructive detection and can also be used to provide a solution for the calculation of the far-field RCSs (Radar Cross Section) of targets in the terahertz regime.

Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

NASA Astrophysics Data System (ADS)

Gururaj, C.; Jayadevappa, D.; Tunga, Satish

2018-02-01

Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

NASA Astrophysics Data System (ADS)

Gururaj, C.; Jayadevappa, D.; Tunga, Satish

2018-06-01

Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

Efficient method of image edge detection based on FSVM

NASA Astrophysics Data System (ADS)

Cai, Aiping; Xiong, Xiaomei

2013-07-01

For efficient object cover edge detection in digital images, this paper studied traditional methods and algorithm based on SVM. It analyzed Canny edge detection algorithm existed some pseudo-edge and poor anti-noise capability. In order to provide a reliable edge extraction method, propose a new detection algorithm based on FSVM. Which contains several steps: first, trains classify sample and gives the different membership function to different samples. Then, a new training sample is formed by increase the punishment some wrong sub-sample, and use the new FSVM classification model for train and test them. Finally the edges are extracted of the object image by using the model. Experimental result shows that good edge detection image will be obtained and adding noise experiments results show that this method has good anti-noise.

Biomedical terahertz imaging with a quantum cascade laser

NASA Astrophysics Data System (ADS)

Kim, Seongsin M.; Hatami, Fariba; Harris, James S.; Kurian, Allison W.; Ford, James; King, Douglas; Scalari, Giacomo; Giovannini, Marcella; Hoyler, Nicolas; Faist, Jerome; Harris, Geoff

2006-04-01

We present biomedical imaging using a single frequency terahertz imaging system based on a low threshold quantum cascade laser emitting at 3.7THz (λ=81μm). With a peak output power of 4mW, coherent terahertz radiation and detection provide a relatively large dynamic range and high spatial resolution. We study image contrast based on water/fat content ratios in different tissues. Terahertz transmission imaging demonstrates a distinct anatomy in a rat brain slice. We also demonstrate malignant tissue contrast in an image of a mouse liver with developed tumors, indicating potential use of terahertz imaging for probing cancerous tissues.

Logo image clustering based on advanced statistics

NASA Astrophysics Data System (ADS)

Wei, Yi; Kamel, Mohamed; He, Yiwei

2007-11-01

In recent years, there has been a growing interest in the research of image content description techniques. Among those, image clustering is one of the most frequently discussed topics. Similar to image recognition, image clustering is also a high-level representation technique. However it focuses on the coarse categorization rather than the accurate recognition. Based on wavelet transform (WT) and advanced statistics, the authors propose a novel approach that divides various shaped logo images into groups according to the external boundary of each logo image. Experimental results show that the presented method is accurate, fast and insensitive to defects.

High resolution PET breast imager with improved detection efficiency

DOEpatents

Majewski, Stanislaw

2010-06-08

A highly efficient PET breast imager for detecting lesions in the entire breast including those located close to the patient's chest wall. The breast imager includes a ring of imaging modules surrounding the imaged breast. Each imaging module includes a slant imaging light guide inserted between a gamma radiation sensor and a photodetector. The slant light guide permits the gamma radiation sensors to be placed in close proximity to the skin of the chest wall thereby extending the sensitive region of the imager to the base of the breast. Several types of photodetectors are proposed for use in the detector modules, with compact silicon photomultipliers as the preferred choice, due to its high compactness. The geometry of the detector heads and the arrangement of the detector ring significantly reduce dead regions thereby improving detection efficiency for lesions located close to the chest wall.

Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index.

PubMed

Xue, Wufeng; Zhang, Lei; Mou, Xuanqin; Bovik, Alan C

2014-02-01

It is an important task to faithfully evaluate the perceptual quality of output images in many applications, such as image compression, image restoration, and multimedia streaming. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy, but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The image gradients are sensitive to image distortions, while different local structures in a distorted image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy-the standard deviation of the GMS map-can predict accurately perceptual image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy. MATLAB source code of GMSD can be downloaded at http://www4.comp.polyu.edu.hk/~cslzhang/IQA/GMSD/GMSD.htm.

Nicephor[e]: a web-based solution for teaching forensic and scientific photography.

PubMed

Voisard, R; Champod, C; Furrer, J; Curchod, J; Vautier, A; Massonnet, G; Buzzini, P

2007-04-11

Nicephor[e] is a project funded by "Swiss Virtual Campus" and aims at creating a distant or mixed web-based learning system in forensic and scientific photography and microscopy. The practical goal is to organize series of on-line modular courses corresponding to the educational requirements of undergraduate academic programs. Additionally, this program could be used in the context of continuing educational programs. The architecture of the project is designed to guarantee a high level of knowledge in forensic and scientific photographic techniques, and to have an easy content production and the ability to create a number of different courses sharing the same content. The e-learning system Nicephor[e] consists of three different parts. The first one is a repository of learning objects that gathers all theoretical subject matter of the project such as texts, animations, images, and films. This repository is a web content management system (Typo3) that permits creating, publishing, and administrating dynamic content via a web browser as well as storing it into a database. The flexibility of the system's architecture allows for an easy updating of the content to follow the development of photographic technology. The instructor of a course can decide which modular contents need to be included in the course, and in which order they will be accessed by students. All the modular courses are developed in a learning management system (WebCT or Moodle) that can deal with complex learning scenarios, content distribution, students, tests, and interaction with instructor. Each course has its own learning scenario based on the goals of the course and the student's profile. The content of each course is taken from the content management system. It is then structured in the learning management system according to the pedagogical goals defined by the instructor. The modular courses are created in a highly interactive setting and offer autoevaluating tests to the students. The last part of the system is a digital assets management system (Extensis Portfolio). The practical portion of each course is to produce images of different marks or objects. The collection of all this material produced, indexed by the students and corrected by the instructor is essential to the development of a knowledge base of photographic techniques applied to a specific forensic subject. It represents also an extensible collection of different marks from known sources obtained under various conditions. It allows to reuse these images for creating image-based case files.

High-efficient and high-content cytotoxic recording via dynamic and continuous cell-based impedance biosensor technology.

PubMed

Hu, Ning; Fang, Jiaru; Zou, Ling; Wan, Hao; Pan, Yuxiang; Su, Kaiqi; Zhang, Xi; Wang, Ping

2016-10-01

Cell-based bioassays were effective method to assess the compound toxicity by cell viability, and the traditional label-based methods missed much information of cell growth due to endpoint detection, while the higher throughputs were demanded to obtain dynamic information. Cell-based biosensor methods can dynamically and continuously monitor with cell viability, however, the dynamic information was often ignored or seldom utilized in the toxin and drug assessment. Here, we reported a high-efficient and high-content cytotoxic recording method via dynamic and continuous cell-based impedance biosensor technology. The dynamic cell viability, inhibition ratio and growth rate were derived from the dynamic response curves from the cell-based impedance biosensor. The results showed that the biosensors has the dose-dependent manners to diarrhetic shellfish toxin, okadiac acid based on the analysis of the dynamic cell viability and cell growth status. Moreover, the throughputs of dynamic cytotoxicity were compared between cell-based biosensor methods and label-based endpoint methods. This cell-based impedance biosensor can provide a flexible, cost and label-efficient platform of cell viability assessment in the shellfish toxin screening fields.

Image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing

NASA Astrophysics Data System (ADS)

Zhou, Nanrun; Pan, Shumin; Cheng, Shan; Zhou, Zhihong

2016-08-01

Most image encryption algorithms based on low-dimensional chaos systems bear security risks and suffer encryption data expansion when adopting nonlinear transformation directly. To overcome these weaknesses and reduce the possible transmission burden, an efficient image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing is proposed. The original image is measured by the measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the cycle shift operation controlled by a hyper-chaotic system. Cycle shift operation can change the values of the pixels efficiently. The proposed cryptosystem decreases the volume of data to be transmitted and simplifies the keys distribution simultaneously as a nonlinear encryption system. Simulation results verify the validity and the reliability of the proposed algorithm with acceptable compression and security performance.

Amber light-emitting diode comprising a group III-nitride nanowire active region

DOEpatents

Wang, George T.; Li, Qiming; Wierer, Jr., Jonathan J.; Koleske, Daniel

2014-07-22

A temperature stable (color and efficiency) III-nitride based amber (585 nm) light-emitting diode is based on a novel hybrid nanowire-planar structure. The arrays of GaN nanowires enable radial InGaN/GaN quantum well LED structures with high indium content and high material quality. The high efficiency and temperature stable direct yellow and red phosphor-free emitters enable high efficiency white LEDs based on the RGYB color-mixing approach.

Secure and Efficient Transmission of Hyperspectral Images for Geosciences Applications

NASA Astrophysics Data System (ADS)

Carpentieri, Bruno; Pizzolante, Raffaele

2017-12-01

Hyperspectral images are acquired through air-borne or space-borne special cameras (sensors) that collect information coming from the electromagnetic spectrum of the observed terrains. Hyperspectral remote sensing and hyperspectral images are used for a wide range of purposes: originally, they were developed for mining applications and for geology because of the capability of this kind of images to correctly identify various types of underground minerals by analysing the reflected spectrums, but their usage has spread in other application fields, such as ecology, military and surveillance, historical research and even archaeology. The large amount of data obtained by the hyperspectral sensors, the fact that these images are acquired at a high cost by air-borne sensors and that they are generally transmitted to a base, makes it necessary to provide an efficient and secure transmission protocol. In this paper, we propose a novel framework that allows secure and efficient transmission of hyperspectral images, by combining a reversible invisible watermarking scheme, used in conjunction with digital signature techniques, and a state-of-art predictive-based lossless compression algorithm.

Community detection for fluorescent lifetime microscopy image segmentation

NASA Astrophysics Data System (ADS)

Hu, Dandan; Sarder, Pinaki; Ronhovde, Peter; Achilefu, Samuel; Nussinov, Zohar

2014-03-01

Multiresolution community detection (CD) method has been suggested in a recent work as an efficient method for performing unsupervised segmentation of fluorescence lifetime (FLT) images of live cell images containing fluorescent molecular probes.1 In the current paper, we further explore this method in FLT images of ex vivo tissue slices. The image processing problem is framed as identifying clusters with respective average FLTs against a background or "solvent" in FLT imaging microscopy (FLIM) images derived using NIR fluorescent dyes. We have identified significant multiresolution structures using replica correlations in these images, where such correlations are manifested by information theoretic overlaps of the independent solutions ("replicas") attained using the multiresolution CD method from different starting points. In this paper, our method is found to be more efficient than a current state-of-the-art image segmentation method based on mixture of Gaussian distributions. It offers more than 1:25 times diversity based on Shannon index than the latter method, in selecting clusters with distinct average FLTs in NIR FLIM images.

Electro-optical design for efficient visual communication

NASA Astrophysics Data System (ADS)

Huck, Friedrich O.; Fales, Carl L.; Jobson, Daniel J.; Rahman, Zia-ur

1994-06-01

Visual communication can be regarded as efficient only if the amount of information that it conveys from the scene to the observer approaches the maximum possible and the associated cost approaches the minimum possible. To deal with this problem, Fales and Huck have integrated the critical limiting factors that constrain image gathering into classical concepts of communication theory. This paper uses this approach to assess the electro-optical design of the image gathering device. Design variables include the f-number and apodization of the objective lens, the aperture size and sampling geometry of the photodetection mechanism, and lateral inhibition and nonlinear radiance-to-signal conversion akin to the retinal processing in the human eye. It is an agreeable consequence of this approach that the image gathering device that is designed along the guidelines developed from communication theory behaves very much like the human eye. The performance approaches the maximum possible in terms of the information content of the acquired data, and thereby, the fidelity, sharpness and clarity with which fine detail can be restored, the efficiency with which the visual information can be transmitted in the form of decorrelated data, and the robustness of these two attributes to the temporal and spatial variations in scene illumination.

Research and Analysis of Image Processing Technologies Based on DotNet Framework

NASA Astrophysics Data System (ADS)

Ya-Lin, Song; Chen-Xi, Bai

Microsoft.Net is a kind of most popular program development tool. This paper gave a detailed analysis concluded about some image processing technologies of the advantages and disadvantages by .Net processed image while the same algorithm is used in Programming experiments. The result shows that the two best efficient methods are unsafe pointer and Direct 3D, and Direct 3D used to 3D simulation development, and the others are useful in some fields while these technologies are poor efficiency and not suited to real-time processing. The experiment results in paper will help some projects about image processing and simulation based DotNet and it has strong practicability.

Content-based histopathology image retrieval using CometCloud.

PubMed

Qi, Xin; Wang, Daihou; Rodero, Ivan; Diaz-Montes, Javier; Gensure, Rebekah H; Xing, Fuyong; Zhong, Hua; Goodell, Lauri; Parashar, Manish; Foran, David J; Yang, Lin

2014-08-26

The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance. The CBIR algorithms that were developed can reliably retrieve the candidate image patches exhibiting intensity and morphological characteristics that are most similar to a given query image. The methods described in this paper are able to reliably discriminate among subtle staining differences and spatial pattern distributions. By integrating a newly developed dual-similarity relevance feedback module into the CBIR framework, the CBIR results were improved substantially. By aggregating the computational power of high performance computing (HPC) and cloud resources, we demonstrated that the method can be successfully executed in minutes on the Cloud compared to weeks using standard computers. In this paper, we present a set of newly developed CBIR algorithms and validate them using two different pathology applications, which are regularly evaluated in the practice of pathology. Comparative experimental results demonstrate excellent performance throughout the course of a set of systematic studies. Additionally, we present and evaluate a framework to enable the execution of these algorithms across distributed resources. We show how parallel searching of content-wise similar images in the dataset significantly reduces the overall computational time to ensure the practical utility of the proposed CBIR algorithms.

Intelligent image processing for machine safety

NASA Astrophysics Data System (ADS)

Harvey, Dennis N.

1994-10-01

This paper describes the use of intelligent image processing as a machine guarding technology. One or more color, linear array cameras are positioned to view the critical region(s) around a machine tool or other piece of manufacturing equipment. The image data is processed to provide indicators of conditions dangerous to the equipment via color content, shape content, and motion content. The data from these analyses is then sent to a threat evaluator. The purpose of the evaluator is to determine if a potentially machine-damaging condition exists based on the analyses of color, shape, and motion, and on `knowledge' of the specific environment of the machine. The threat evaluator employs fuzzy logic as a means of dealing with uncertainty in the vision data.

Multiple descriptions based on multirate coding for JPEG 2000 and H.264/AVC.

PubMed

Tillo, Tammam; Baccaglini, Enrico; Olmo, Gabriella

2010-07-01

Multiple description coding (MDC) makes use of redundant representations of multimedia data to achieve resiliency. Descriptions should be generated so that the quality obtained when decoding a subset of them only depends on their number and not on the particular received subset. In this paper, we propose a method based on the principle of encoding the source at several rates, and properly blending the data encoded at different rates to generate the descriptions. The aim is to achieve efficient redundancy exploitation, and easy adaptation to different network scenarios by means of fine tuning of the encoder parameters. We apply this principle to both JPEG 2000 images and H.264/AVC video data. We consider as the reference scenario the distribution of contents on application-layer overlays with multiple-tree topology. The experimental results reveal that our method favorably compares with state-of-art MDC techniques.

Classification of document page images based on visual similarity of layout structures

NASA Astrophysics Data System (ADS)

Shin, Christian K.; Doermann, David S.

1999-12-01

Searching for documents by their type or genre is a natural way to enhance the effectiveness of document retrieval. The layout of a document contains a significant amount of information that can be used to classify a document's type in the absence of domain specific models. A document type or genre can be defined by the user based primarily on layout structure. Our classification approach is based on 'visual similarity' of the layout structure by building a supervised classifier, given examples of the class. We use image features, such as the percentages of tex and non-text (graphics, image, table, and ruling) content regions, column structures, variations in the point size of fonts, the density of content area, and various statistics on features of connected components which can be derived from class samples without class knowledge. In order to obtain class labels for training samples, we conducted a user relevance test where subjects ranked UW-I document images with respect to the 12 representative images. We implemented our classification scheme using the OC1, a decision tree classifier, and report our findings.

An earth imaging camera simulation using wide-scale construction of reflectance surfaces

NASA Astrophysics Data System (ADS)

Murthy, Kiran; Chau, Alexandra H.; Amin, Minesh B.; Robinson, M. Dirk

2013-10-01

Developing and testing advanced ground-based image processing systems for earth-observing remote sensing applications presents a unique challenge that requires advanced imagery simulation capabilities. This paper presents an earth-imaging multispectral framing camera simulation system called PayloadSim (PaySim) capable of generating terabytes of photorealistic simulated imagery. PaySim leverages previous work in 3-D scene-based image simulation, adding a novel method for automatically and efficiently constructing 3-D reflectance scenes by draping tiled orthorectified imagery over a geo-registered Digital Elevation Map (DEM). PaySim's modeling chain is presented in detail, with emphasis given to the techniques used to achieve computational efficiency. These techniques as well as cluster deployment of the simulator have enabled tuning and robust testing of image processing algorithms, and production of realistic sample data for customer-driven image product development. Examples of simulated imagery of Skybox's first imaging satellite are shown.

Liposome-based mucus-penetrating particles (MPP) for mucosal theranostics: demonstration of diamagnetic chemical exchange saturation transfer (diaCEST) magnetic resonance imaging (MRI).

PubMed

Yu, Tao; Chan, Kannie W Y; Anonuevo, Abraham; Song, Xiaolei; Schuster, Benjamin S; Chattopadhyay, Sumon; Xu, Qingguo; Oskolkov, Nikita; Patel, Himatkumar; Ensign, Laura M; van Zjil, Peter C M; McMahon, Michael T; Hanes, Justin

2015-02-01

Mucus barriers lining mucosal epithelia reduce the effectiveness of nanocarrier-based mucosal drug delivery and imaging ("theranostics"). Here, we describe liposome-based mucus-penetrating particles (MPP) capable of loading hydrophilic agents, e.g., the diaCEST MRI contrast agent barbituric acid (BA). We observed that polyethylene glycol (PEG)-coated liposomes containing ≥7 mol% PEG diffused only ~10-fold slower in human cervicovaginal mucus (CVM) compared to their theoretical speeds in water. 7 mol%-PEG liposomes contained sufficient BA loading for diaCEST contrast, and provided improved vaginal distribution compared to 0 and 3mol%-PEG liposomes. However, increasing PEG content to ~12 mol% compromised BA loading and vaginal distribution, suggesting that PEG content must be optimized to maintain drug loading and stability. Non-invasive diaCEST MRI illustrated uniform vaginal coverage and longer retention of BA-loaded 7 mol%-PEG liposomes compared to unencapsulated BA. Liposomal MPP with optimized PEG content hold promise for drug delivery and imaging at mucosal surfaces. This team of authors characterized liposome-based mucus-penetrating particles (MPP) capable of loading hydrophilic agents, such as barbituric acid (a diaCEST MRI contrast agent) and concluded that liposomal MPP with optimized PEG coating enables drug delivery and imaging at mucosal surfaces. Copyright © 2015 Elsevier Inc. All rights reserved.

Informatics in radiology: Efficiency metrics for imaging device productivity.

PubMed

Hu, Mengqi; Pavlicek, William; Liu, Patrick T; Zhang, Muhong; Langer, Steve G; Wang, Shanshan; Place, Vicki; Miranda, Rafael; Wu, Teresa Tong

2011-01-01

Acute awareness of the costs associated with medical imaging equipment is an ever-present aspect of the current healthcare debate. However, the monitoring of productivity associated with expensive imaging devices is likely to be labor intensive, relies on summary statistics, and lacks accepted and standardized benchmarks of efficiency. In the context of the general Six Sigma DMAIC (design, measure, analyze, improve, and control) process, a World Wide Web-based productivity tool called the Imaging Exam Time Monitor was developed to accurately and remotely monitor imaging efficiency with use of Digital Imaging and Communications in Medicine (DICOM) combined with a picture archiving and communication system. Five device efficiency metrics-examination duration, table utilization, interpatient time, appointment interval time, and interseries time-were derived from DICOM values. These metrics allow the standardized measurement of productivity, to facilitate the comparative evaluation of imaging equipment use and ongoing efforts to improve efficiency. A relational database was constructed to store patient imaging data, along with device- and examination-related data. The database provides full access to ad hoc queries and can automatically generate detailed reports for administrative and business use, thereby allowing staff to monitor data for trends and to better identify possible changes that could lead to improved productivity and reduced costs in association with imaging services. © RSNA, 2011.

A Fast, Automatic Segmentation Algorithm for Locating and Delineating Touching Cell Boundaries in Imaged Histopathology

PubMed Central

Qi, Xin; Xing, Fuyong; Foran, David J.; Yang, Lin

2013-01-01

Summary Background Automated analysis of imaged histopathology specimens could potentially provide support for improved reliability in detection and classification in a range of investigative and clinical cancer applications. Automated segmentation of cells in the digitized tissue microarray (TMA) is often the prerequisite for quantitative analysis. However overlapping cells usually bring significant challenges for traditional segmentation algorithms. Objectives In this paper, we propose a novel, automatic algorithm to separate overlapping cells in stained histology specimens acquired using bright-field RGB imaging. Methods It starts by systematically identifying salient regions of interest throughout the image based upon their underlying visual content. The segmentation algorithm subsequently performs a quick, voting based seed detection. Finally, the contour of each cell is obtained using a repulsive level set deformable model using the seeds generated in the previous step. We compared the experimental results with the most current literature, and the pixel wise accuracy between human experts' annotation and those generated using the automatic segmentation algorithm. Results The method is tested with 100 image patches which contain more than 1000 overlapping cells. The overall precision and recall of the developed algorithm is 90% and 78%, respectively. We also implement the algorithm on GPU. The parallel implementation is 22 times faster than its C/C++ sequential implementation. Conclusion The proposed overlapping cell segmentation algorithm can accurately detect the center of each overlapping cell and effectively separate each of the overlapping cells. GPU is proven to be an efficient parallel platform for overlapping cell segmentation. PMID:22526139

Image segmentation by hierarchial agglomeration of polygons using ecological statistics

DOEpatents

Prasad, Lakshman; Swaminarayan, Sriram

2013-04-23

A method for rapid hierarchical image segmentation based on perceptually driven contour completion and scene statistics is disclosed. The method begins with an initial fine-scale segmentation of an image, such as obtained by perceptual completion of partial contours into polygonal regions using region-contour correspondences established by Delaunay triangulation of edge pixels as implemented in VISTA. The resulting polygons are analyzed with respect to their size and color/intensity distributions and the structural properties of their boundaries. Statistical estimates of granularity of size, similarity of color, texture, and saliency of intervening boundaries are computed and formulated into logical (Boolean) predicates. The combined satisfiability of these Boolean predicates by a pair of adjacent polygons at a given segmentation level qualifies them for merging into a larger polygon representing a coarser, larger-scale feature of the pixel image and collectively obtains the next level of polygonal segments in a hierarchy of fine-to-coarse segmentations. The iterative application of this process precipitates textured regions as polygons with highly convolved boundaries and helps distinguish them from objects which typically have more regular boundaries. The method yields a multiscale decomposition of an image into constituent features that enjoy a hierarchical relationship with features at finer and coarser scales. This provides a traversable graph structure from which feature content and context in terms of other features can be derived, aiding in automated image understanding tasks. The method disclosed is highly efficient and can be used to decompose and analyze large images.

A new e-learning platform for radiology education (RadEd).

PubMed

Xiberta, Pau; Boada, Imma

2016-04-01

One of the key elements of e-learning platforms is the content provided to the students. Content creation is a time demanding task that requires teachers to prepare material taking into account that it will be accessed on-line. Moreover, the teacher is restricted by the functionalities provided by the e-learning platforms. In contexts such as radiology where images have a key role, the required functionalities are still more specific and difficult to be provided by these platforms. Our purpose is to create a framework to make teacher's tasks easier, specially when he has to deal with contents where images have a main role. In this paper, we present RadEd, a new web-based teaching framework that integrates a smart editor to create case-based exercises that support image interaction such as changing the window width and the grey scale used to render the image, taking measurements on the image, attaching labels to images and selecting parts of the images, amongst others. It also provides functionalities to prepare courses with different topics, exercises and theory material, and also functionalities to control students' work. Different experts have used RadEd and all of them have considered it a very useful and valuable tool to prepare courses where radiological images are the main component. RadEd provides teachers functionalities to prepare more realistic cases and students the ability to make a more specific diagnosis. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Whole Organism High-Content Screening by Label-Free, Image-Based Bayesian Classification for Parasitic Diseases

PubMed Central

Paveley, Ross A.; Mansour, Nuha R.; Hallyburton, Irene; Bleicher, Leo S.; Benn, Alex E.; Mikic, Ivana; Guidi, Alessandra; Gilbert, Ian H.; Hopkins, Andrew L.; Bickle, Quentin D.

2012-01-01

Sole reliance on one drug, Praziquantel, for treatment and control of schistosomiasis raises concerns about development of widespread resistance, prompting renewed interest in the discovery of new anthelmintics. To discover new leads we designed an automated label-free, high content-based, high throughput screen (HTS) to assess drug-induced effects on in vitro cultured larvae (schistosomula) using bright-field imaging. Automatic image analysis and Bayesian prediction models define morphological damage, hit/non-hit prediction and larval phenotype characterization. Motility was also assessed from time-lapse images. In screening a 10,041 compound library the HTS correctly detected 99.8% of the hits scored visually. A proportion of these larval hits were also active in an adult worm ex-vivo screen and are the subject of ongoing studies. The method allows, for the first time, screening of large compound collections against schistosomes and the methods are adaptable to other whole organism and cell-based screening by morphology and motility phenotyping. PMID:22860151

An authenticated image encryption scheme based on chaotic maps and memory cellular automata

NASA Astrophysics Data System (ADS)

Bakhshandeh, Atieh; Eslami, Ziba

2013-06-01

This paper introduces a new image encryption scheme based on chaotic maps, cellular automata and permutation-diffusion architecture. In the permutation phase, a piecewise linear chaotic map is utilized to confuse the plain-image and in the diffusion phase, we employ the Logistic map as well as a reversible memory cellular automata to obtain an efficient and secure cryptosystem. The proposed method admits advantages such as highly secure diffusion mechanism, computational efficiency and ease of implementation. A novel property of the proposed scheme is its authentication ability which can detect whether the image is tampered during the transmission or not. This is particularly important in applications where image data or part of it contains highly sensitive information. Results of various analyses manifest high security of this new method and its capability for practical image encryption.

Weighted bi-prediction for light field image coding

NASA Astrophysics Data System (ADS)

Conti, Caroline; Nunes, Paulo; Ducla Soares, Luís.

2017-09-01

Light field imaging based on a single-tier camera equipped with a microlens array - also known as integral, holoscopic, and plenoptic imaging - has currently risen up as a practical and prospective approach for future visual applications and services. However, successfully deploying actual light field imaging applications and services will require developing adequate coding solutions to efficiently handle the massive amount of data involved in these systems. In this context, self-similarity compensated prediction is a non-local spatial prediction scheme based on block matching that has been shown to achieve high efficiency for light field image coding based on the High Efficiency Video Coding (HEVC) standard. As previously shown by the authors, this is possible by simply averaging two predictor blocks that are jointly estimated from a causal search window in the current frame itself, referred to as self-similarity bi-prediction. However, theoretical analyses for motion compensated bi-prediction have suggested that it is still possible to achieve further rate-distortion performance improvements by adaptively estimating the weighting coefficients of the two predictor blocks. Therefore, this paper presents a comprehensive study of the rate-distortion performance for HEVC-based light field image coding when using different sets of weighting coefficients for self-similarity bi-prediction. Experimental results demonstrate that it is possible to extend the previous theoretical conclusions to light field image coding and show that the proposed adaptive weighting coefficient selection leads to up to 5 % of bit savings compared to the previous self-similarity bi-prediction scheme.

New development of the image matching algorithm

NASA Astrophysics Data System (ADS)

Zhang, Xiaoqiang; Feng, Zhao

2018-04-01

To study the image matching algorithm, algorithm four elements are described, i.e., similarity measurement, feature space, search space and search strategy. Four common indexes for evaluating the image matching algorithm are described, i.e., matching accuracy, matching efficiency, robustness and universality. Meanwhile, this paper describes the principle of image matching algorithm based on the gray value, image matching algorithm based on the feature, image matching algorithm based on the frequency domain analysis, image matching algorithm based on the neural network and image matching algorithm based on the semantic recognition, and analyzes their characteristics and latest research achievements. Finally, the development trend of image matching algorithm is discussed. This study is significant for the algorithm improvement, new algorithm design and algorithm selection in practice.

TRECVID: the utility of a content-based video retrieval evaluation

NASA Astrophysics Data System (ADS)

Hauptmann, Alexander G.

2006-01-01

TRECVID, an annual retrieval evaluation benchmark organized by NIST, encourages research in information retrieval from digital video. TRECVID benchmarking covers both interactive and manual searching by end users, as well as the benchmarking of some supporting technologies including shot boundary detection, extraction of semantic features, and the automatic segmentation of TV news broadcasts. Evaluations done in the context of the TRECVID benchmarks show that generally, speech transcripts and annotations provide the single most important clue for successful retrieval. However, automatically finding the individual images is still a tremendous and unsolved challenge. The evaluations repeatedly found that none of the multimedia analysis and retrieval techniques provide a significant benefit over retrieval using only textual information such as from automatic speech recognition transcripts or closed captions. In interactive systems, we do find significant differences among the top systems, indicating that interfaces can make a huge difference for effective video/image search. For interactive tasks efficient interfaces require few key clicks, but display large numbers of images for visual inspection by the user. The text search finds the right context region in the video in general, but to select specific relevant images we need good interfaces to easily browse the storyboard pictures. In general, TRECVID has motivated the video retrieval community to be honest about what we don't know how to do well (sometimes through painful failures), and has focused us to work on the actual task of video retrieval, as opposed to flashy demos based on technological capabilities.

Ontology modularization to improve semantic medical image annotation.

PubMed

Wennerberg, Pinar; Schulz, Klaus; Buitelaar, Paul

2011-02-01

Searching for medical images and patient reports is a significant challenge in a clinical setting. The contents of such documents are often not described in sufficient detail thus making it difficult to utilize the inherent wealth of information contained within them. Semantic image annotation addresses this problem by describing the contents of images and reports using medical ontologies. Medical images and patient reports are then linked to each other through common annotations. Subsequently, search algorithms can more effectively find related sets of documents on the basis of these semantic descriptions. A prerequisite to realizing such a semantic search engine is that the data contained within should have been previously annotated with concepts from medical ontologies. One major challenge in this regard is the size and complexity of medical ontologies as annotation sources. Manual annotation is particularly time consuming labor intensive in a clinical environment. In this article we propose an approach to reducing the size of clinical ontologies for more efficient manual image and text annotation. More precisely, our goal is to identify smaller fragments of a large anatomy ontology that are relevant for annotating medical images from patients suffering from lymphoma. Our work is in the area of ontology modularization, which is a recent and active field of research. We describe our approach, methods and data set in detail and we discuss our results. Copyright © 2010 Elsevier Inc. All rights reserved.

On consistent inter-view synthesis for autostereoscopic displays

NASA Astrophysics Data System (ADS)

Tran, Lam C.; Bal, Can; Pal, Christopher J.; Nguyen, Truong Q.

2012-03-01

In this paper we present a novel stereo view synthesis algorithm that is highly accurate with respect to inter-view consistency, thus to enabling stereo contents to be viewed on the autostereoscopic displays. The algorithm finds identical occluded regions within each virtual view and aligns them together to extract a surrounding background layer. The background layer for each occluded region is then used with an exemplar based inpainting method to synthesize all virtual views simultaneously. Our algorithm requires the alignment and extraction of background layers for each occluded region; however, these two steps are done efficiently with lower computational complexity in comparison to previous approaches using the exemplar based inpainting algorithms. Thus, it is more efficient than existing algorithms that synthesize one virtual view at a time. This paper also describes the implementation of a simplified GPU accelerated version of the approach and its implementation in CUDA. Our CUDA method has sublinear complexity in terms of the number of views that need to be generated, which makes it especially useful for generating content for autostereoscopic displays that require many views to operate. An objective of our work is to allow the user to change depth and viewing perspective on the fly. Therefore, to further accelerate the CUDA variant of our approach, we present a modified version of our method to warp the background pixels from reference views to a middle view to recover background pixels. We then use an exemplar based inpainting method to fill in the occluded regions. We use warping of the foreground from the reference images and background from the filled regions to synthesize new virtual views on the fly. Our experimental results indicate that the simplified CUDA implementation decreases running time by orders of magnitude with negligible loss in quality. [Figure not available: see fulltext.

Tantalum Sulfide Nanosheets as a Theranostic Nanoplatform for Computed Tomography Imaging-Guided Combinatorial Chemo-Photothermal Therapy.

PubMed

Liu, Yanlan; Ji, Xiaoyuan; Liu, Jianhua; Tong, Winnie W L; Askhatova, Diana; Shi, Jinjun

2017-10-19

Near-infrared (NIR)-absorbing metal-based nanomaterials have shown tremendous potential for cancer therapy, given their facile and controllable synthesis, efficient photothermal conversion, capability of spatiotemporal-controlled drug delivery, and intrinsic imaging function. Tantalum (Ta) is among the most biocompatible metals and arouses negligible adverse biological responses in either oxidized or reduced forms, and thus Ta-derived nanomaterials represent promising candidates for biomedical applications. However, Ta-based nanomaterials by themselves have not been explored for NIR-mediated photothermal ablation therapy. In this work, we report an innovative Ta-based multifunctional nanoplatform composed of biocompatible tantalum sulfide (TaS 2 ) nanosheets (NSs) for simultaneous NIR hyperthermia, drug delivery, and computed tomography (CT) imaging. The TaS 2 NSs exhibit multiple unique features including (i) efficient NIR light-to-heat conversion with a high photothermal conversion efficiency of 39%. (ii) high drug loading (177% by weight), (iii) controlled drug release triggered by NIR light and moderate acidic pH, (iv) high tumor accumulation via heat-enhanced tumor vascular permeability, (v) complete tumor ablation and negligible side effects, and (vi) comparable CT imaging contrast efficiency to the widely clinically used agent iobitridol. We expect that this multifunctional NS platform can serve as a promising candidate for imaging-guided cancer therapy and selection of cancer patients with high tumor accumulation.

Hyperspectral imaging detection of decayed honey peaches based on their chlorophyll content.

PubMed

Sun, Ye; Wang, Yihang; Xiao, Hui; Gu, Xinzhe; Pan, Leiqing; Tu, Kang

2017-11-15

Honey peach is a very common but highly perishable market fruit. When pathogens infect fruit, chlorophyll as one of the important components related to fruit quality, decreased significantly. Here, the feasibility of hyperspectral imaging to determine the chlorophyll content thus distinguishing diseased peaches was investigated. Three optimal wavelengths (617nm, 675nm, and 818nm) were selected according to chlorophyll content via successive projections algorithm. Partial least square regression models were established to determine chlorophyll content. Three band ratios were obtained using these optimal wavelengths, which improved spatial details, but also integrates the information of chemical composition from spectral characteristics. The band ratio values were suitable to classify the diseased peaches with 98.75% accuracy and clearly show the spatial distribution of diseased parts. This study provides a new perspective for the selection of optimal wavelengths of hyperspectral imaging via chlorophyll content, thus enabling the detection of fungal diseases in peaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Deep eutectic solvent-based valorization of spent coffee grounds.

PubMed

Yoo, Da Eun; Jeong, Kyung Min; Han, Se Young; Kim, Eun Mi; Jin, Yan; Lee, Jeongmi

2018-07-30

Spent coffee grounds (SCGs) are viewed as a valuable resource for useful bioactive compounds, such as chlorogenic acids and flavonoids, and we suggest an eco-friendly and efficient valorization method. A series of choline chloride-based deep eutectic solvents (DESs) were tested as green extraction solvents for use with ultrasound-assisted extraction. Extraction efficiency was evaluated based on total phenolic content (TPC), total flavonoid content, total chlorogenic acids, and/or anti-oxidant activity. A binary DES named HC-6, which was composed of 1,6-hexanediol:choline chloride (molar ratio 7:1) was designed to produce the highest efficiency. Experimental conditions were screened and optimized for maximized efficiency using a two-level fractional factorial design and a central composite design, respectively. As a result, the proposed method presented significantly enhanced TPC and anti-oxidant activity. In addition, phenolic compounds could be easily recovered from extracts at high recovery yields (>90%) by adsorption chromatography. Copyright © 2018 Elsevier Ltd. All rights reserved.

2D Superparamagnetic Tantalum Carbide Composite MXenes for Efficient Breast-Cancer Theranostics

PubMed Central

Liu, Zhuang; Lin, Han; Zhao, Menglong; Dai, Chen; Zhang, Shengjian; Peng, Weijun; Chen, Yu

2018-01-01

Background: The emergence of two-dimensional MXenes has spurred their versatile applications in broad fields, but the exploring of novel MXene-based family members and their potential applications in theranostic nanomedicine (concurrent diagnostic imaging and therapy) have been rarely explored. In this work, we report the construction of a novel superparamagnetic MXene-based theranostic nanoplatform for efficient breast-cancer theranostics, which was based on intriguing tantalum carbide (Ta4C3) MXene and its further rational surface-superparamagnetic iron-oxide functionalization (Ta4C3-IONP-SPs composite MXenes) for efficient breast-cancer theranostic. Methods: The fabrication of ultrathin Ta4C3 nanosheets was based on an exfoliation strategy and superparamagnetic iron oxide nanoparticles were in-situ grown onto the surface of Ta4C3 MXene according to the redox reaction of MXene. Ta4C3-IONP MXenes were modified with soybean phospholipid (SP) to guarantee high stability in physiological conditions. The photothermal therapy, contrast-enhanced CT, T2-weighted magnetic resonance imaging and the high biocompatibility of these composite nanosheets have also been evaluated in vitro at cellular level and in vivo on mice breast tumor allograft tumor model. Results: The Ta component of Ta4C3-IONP-SPs exhibits high performance for contrast-enhanced CT imaging because of its high atomic number and high X-ray attenuation coefficient, and the integrated superparamagnetic IONPs act as excellent contrast agents for T2-weighted magnetic resonance imaging. Especially, these Ta4C3-IONP-SPs composite nanosheets with high photothermal-conversion efficiency (η: 32.5%) has achieved complete tumor eradication without reoccurrence, verifying their highly efficient breast-tumor photo-ablation performance. Conclusion: This work not only significantly broadens the biomedical applications of MXene-based nanoplatforms (Ta4C3 MXene) by exploring their novel family members and further functionalization strategies (magnetic functionalization in this work), but also provides a novel and efficient theranostic nanoplatform for efficient breast-cancer theranostics. PMID:29556347

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

PubMed

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

2017-12-01

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

Visualization and understanding of the granulation liquid mixing and distribution during continuous twin screw granulation using NIR chemical imaging.

PubMed

Vercruysse, Jurgen; Toiviainen, Maunu; Fonteyne, Margot; Helkimo, Niko; Ketolainen, Jarkko; Juuti, Mikko; Delaet, Urbain; Van Assche, Ivo; Remon, Jean Paul; Vervaet, Chris; De Beer, Thomas

2014-04-01

Over the last decade, there has been increased interest in the application of twin screw granulation as a continuous wet granulation technique for pharmaceutical drug formulations. However, the mixing of granulation liquid and powder material during the short residence time inside the screw chamber and the atypical particle size distribution (PSD) of granules produced by twin screw granulation is not yet fully understood. Therefore, this study aims at visualizing the granulation liquid mixing and distribution during continuous twin screw granulation using NIR chemical imaging. In first instance, the residence time of material inside the barrel was investigated as function of screw speed and moisture content followed by the visualization of the granulation liquid distribution as function of different formulation and process parameters (liquid feed rate, liquid addition method, screw configuration, moisture content and barrel filling degree). The link between moisture uniformity and granule size distributions was also studied. For residence time analysis, increased screw speed and lower moisture content resulted to a shorter mean residence time and narrower residence time distribution. Besides, the distribution of granulation liquid was more homogenous at higher moisture content and with more kneading zones on the granulator screws. After optimization of the screw configuration, a two-level full factorial experimental design was performed to evaluate the influence of moisture content, screw speed and powder feed rate on the mixing efficiency of the powder and liquid phase. From these results, it was concluded that only increasing the moisture content significantly improved the granulation liquid distribution. This study demonstrates that NIR chemical imaging is a fast and adequate measurement tool for allowing process visualization and hence for providing better process understanding of a continuous twin screw granulation system. Copyright © 2013 Elsevier B.V. All rights reserved.

Recognition of pornographic web pages by classifying texts and images.

PubMed

Hu, Weiming; Wu, Ou; Chen, Zhouyao; Fu, Zhouyu; Maybank, Steve

2007-06-01

With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages.

Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.

Supervised graph hashing for histopathology image retrieval and classification.

PubMed

Shi, Xiaoshuang; Xing, Fuyong; Xu, KaiDi; Xie, Yuanpu; Su, Hai; Yang, Lin

2017-12-01

In pathology image analysis, morphological characteristics of cells are critical to grade many diseases. With the development of cell detection and segmentation techniques, it is possible to extract cell-level information for further analysis in pathology images. However, it is challenging to conduct efficient analysis of cell-level information on a large-scale image dataset because each image usually contains hundreds or thousands of cells. In this paper, we propose a novel image retrieval based framework for large-scale pathology image analysis. For each image, we encode each cell into binary codes to generate image representation using a novel graph based hashing model and then conduct image retrieval by applying a group-to-group matching method to similarity measurement. In order to improve both computational efficiency and memory requirement, we further introduce matrix factorization into the hashing model for scalable image retrieval. The proposed framework is extensively validated with thousands of lung cancer images, and it achieves 97.98% classification accuracy and 97.50% retrieval precision with all cells of each query image used. Copyright © 2017 Elsevier B.V. All rights reserved.

Combined multi-spectrum and orthogonal Laplacianfaces for fast CB-XLCT imaging with single-view data

NASA Astrophysics Data System (ADS)

Zhang, Haibo; Geng, Guohua; Chen, Yanrong; Qu, Xuan; Zhao, Fengjun; Hou, Yuqing; Yi, Huangjian; He, Xiaowei

2017-12-01

Cone-beam X-ray luminescence computed tomography (CB-XLCT) is an attractive hybrid imaging modality, which has the potential of monitoring the metabolic processes of nanophosphors-based drugs in vivo. Single-view data reconstruction as a key issue of CB-XLCT imaging promotes the effective study of dynamic XLCT imaging. However, it suffers from serious ill-posedness in the inverse problem. In this paper, a multi-spectrum strategy is adopted to relieve the ill-posedness of reconstruction. The strategy is based on the third-order simplified spherical harmonic approximation model. Then, an orthogonal Laplacianfaces-based method is proposed to reduce the large computational burden without degrading the imaging quality. Both simulated data and in vivo experimental data were used to evaluate the efficiency and robustness of the proposed method. The results are satisfactory in terms of both location and quantitative recovering with computational efficiency, indicating that the proposed method is practical and promising for single-view CB-XLCT imaging.

A computer vision for animal ecology.

PubMed

Weinstein, Ben G

2018-05-01

A central goal of animal ecology is to observe species in the natural world. The cost and challenge of data collection often limit the breadth and scope of ecological study. Ecologists often use image capture to bolster data collection in time and space. However, the ability to process these images remains a bottleneck. Computer vision can greatly increase the efficiency, repeatability and accuracy of image review. Computer vision uses image features, such as colour, shape and texture to infer image content. I provide a brief primer on ecological computer vision to outline its goals, tools and applications to animal ecology. I reviewed 187 existing applications of computer vision and divided articles into ecological description, counting and identity tasks. I discuss recommendations for enhancing the collaboration between ecologists and computer scientists and highlight areas for future growth of automated image analysis. © 2017 The Author. Journal of Animal Ecology © 2017 British Ecological Society.

Efficient OCT Image Enhancement Based on Collaborative Shock Filtering

PubMed Central

2018-01-01

Efficient enhancement of noisy optical coherence tomography (OCT) images is a key task for interpreting them correctly. In this paper, to better enhance details and layered structures of a human retina image, we propose a collaborative shock filtering for OCT image denoising and enhancement. Noisy OCT image is first denoised by a collaborative filtering method with new similarity measure, and then the denoised image is sharpened by a shock-type filtering for edge and detail enhancement. For dim OCT images, in order to improve image contrast for the detection of tiny lesions, a gamma transformation is first used to enhance the images within proper gray levels. The proposed method integrating image smoothing and sharpening simultaneously obtains better visual results in experiments. PMID:29599954

Efficient OCT Image Enhancement Based on Collaborative Shock Filtering.

PubMed

Liu, Guohua; Wang, Ziyu; Mu, Guoying; Li, Peijin

2018-01-01

Efficient enhancement of noisy optical coherence tomography (OCT) images is a key task for interpreting them correctly. In this paper, to better enhance details and layered structures of a human retina image, we propose a collaborative shock filtering for OCT image denoising and enhancement. Noisy OCT image is first denoised by a collaborative filtering method with new similarity measure, and then the denoised image is sharpened by a shock-type filtering for edge and detail enhancement. For dim OCT images, in order to improve image contrast for the detection of tiny lesions, a gamma transformation is first used to enhance the images within proper gray levels. The proposed method integrating image smoothing and sharpening simultaneously obtains better visual results in experiments.

Using object-based image analysis to guide the selection of field sample locations

USDA-ARS?s Scientific Manuscript database

One of the most challenging tasks for resource management and research is designing field sampling schemes to achieve unbiased estimates of ecosystem parameters as efficiently as possible. This study focused on the potential of fine-scale image objects from object-based image analysis (OBIA) to be u...

Improved integral images compression based on multi-view extraction

NASA Astrophysics Data System (ADS)

Dricot, Antoine; Jung, Joel; Cagnazzo, Marco; Pesquet, Béatrice; Dufaux, Frédéric

2016-09-01

Integral imaging is a technology based on plenoptic photography that captures and samples the light-field of a scene through a micro-lens array. It provides views of the scene from several angles and therefore is foreseen as a key technology for future immersive video applications. However, integral images have a large resolution and a structure based on micro-images which is challenging to encode. A compression scheme for integral images based on view extraction has previously been proposed, with average BD-rate gains of 15.7% (up to 31.3%) reported over HEVC when using one single extracted view. As the efficiency of the scheme depends on a tradeoff between the bitrate required to encode the view and the quality of the image reconstructed from the view, it is proposed to increase the number of extracted views. Several configurations are tested with different positions and different number of extracted views. Compression efficiency is increased with average BD-rate gains of 22.2% (up to 31.1%) reported over the HEVC anchor, with a realistic runtime increase.

Devices for monitoring content of microparticles and bacterium in injection solutions in pharmaceutical production

NASA Astrophysics Data System (ADS)

Bilyi, Olexander I.; Getman, Vasyl B.; Konyev, Fedir A.; Sapunkov, Olexander; Sapunkov, Pavlo G.

2001-06-01

The devices for monitoring of parameters of efficiency of water solutions filtration, which are based on the analysis of scattered light by microparticles are considered in this article. The efficiency of using of devices in pharmaceutics in technological processes of manufacturing medical injection solutions is shown. The examples of monitoring of contents of bacterial cultures Pseudomonas aeruginosa, Escherichia coli, and Micrococcus luteus in water solutions of glucose are indicated.

Use of a Machine Learning-Based High Content Analysis Approach to Identify Photoreceptor Neurite Promoting Molecules.

PubMed

Fuller, John A; Berlinicke, Cynthia A; Inglese, James; Zack, Donald J

2016-01-01

High content analysis (HCA) has become a leading methodology in phenotypic drug discovery efforts. Typical HCA workflows include imaging cells using an automated microscope and analyzing the data using algorithms designed to quantify one or more specific phenotypes of interest. Due to the richness of high content data, unappreciated phenotypic changes may be discovered in existing image sets using interactive machine-learning based software systems. Primary postnatal day four retinal cells from the photoreceptor (PR) labeled QRX-EGFP reporter mice were isolated, seeded, treated with a set of 234 profiled kinase inhibitors and then cultured for 1 week. The cells were imaged with an Acumen plate-based laser cytometer to determine the number and intensity of GFP-expressing, i.e. PR, cells. Wells displaying intensities and counts above threshold values of interest were re-imaged at a higher resolution with an INCell2000 automated microscope. The images were analyzed with an open source HCA analysis tool, PhenoRipper (Rajaram et al., Nat Methods 9:635-637, 2012), to identify the high GFP-inducing treatments that additionally resulted in diverse phenotypes compared to the vehicle control samples. The pyrimidinopyrimidone kinase inhibitor CHEMBL-1766490, a pan kinase inhibitor whose major known targets are p38α and the Src family member lck, was identified as an inducer of photoreceptor neuritogenesis by using the open-source HCA program PhenoRipper. This finding was corroborated using a cell-based method of image analysis that measures quantitative differences in the mean neurite length in GFP expressing cells. Interacting with data using machine learning algorithms may complement traditional HCA approaches by leading to the discovery of small molecule-induced cellular phenotypes in addition to those upon which the investigator is initially focusing.

Efficient scatter model for simulation of ultrasound images from computed tomography data

NASA Astrophysics Data System (ADS)

D'Amato, J. P.; Lo Vercio, L.; Rubi, P.; Fernandez Vera, E.; Barbuzza, R.; Del Fresno, M.; Larrabide, I.

2015-12-01

Background and motivation: Real-time ultrasound simulation refers to the process of computationally creating fully synthetic ultrasound images instantly. Due to the high value of specialized low cost training for healthcare professionals, there is a growing interest in the use of this technology and the development of high fidelity systems that simulate the acquisitions of echographic images. The objective is to create an efficient and reproducible simulator that can run either on notebooks or desktops using low cost devices. Materials and methods: We present an interactive ultrasound simulator based on CT data. This simulator is based on ray-casting and provides real-time interaction capabilities. The simulation of scattering that is coherent with the transducer position in real time is also introduced. Such noise is produced using a simplified model of multiplicative noise and convolution with point spread functions (PSF) tailored for this purpose. Results: The computational efficiency of scattering maps generation was revised with an improved performance. This allowed a more efficient simulation of coherent scattering in the synthetic echographic images while providing highly realistic result. We describe some quality and performance metrics to validate these results, where a performance of up to 55fps was achieved. Conclusion: The proposed technique for real-time scattering modeling provides realistic yet computationally efficient scatter distributions. The error between the original image and the simulated scattering image was compared for the proposed method and the state-of-the-art, showing negligible differences in its distribution.

Image analysis method for the measurement of water saturation in a two-dimensional experimental flow tank

NASA Astrophysics Data System (ADS)

Belfort, Benjamin; Weill, Sylvain; Lehmann, François

2017-07-01

A novel, non-invasive imaging technique is proposed that determines 2D maps of water content in unsaturated porous media. This method directly relates digitally measured intensities to the water content of the porous medium. This method requires the classical image analysis steps, i.e., normalization, filtering, background subtraction, scaling and calibration. The main advantages of this approach are that no calibration experiment is needed, because calibration curve relating water content and reflected light intensities is established during the main monitoring phase of each experiment and that no tracer or dye is injected into the flow tank. The procedure enables effective processing of a large number of photographs and thus produces 2D water content maps at high temporal resolution. A drainage/imbibition experiment in a 2D flow tank with inner dimensions of 40 cm × 14 cm × 6 cm (L × W × D) is carried out to validate the methodology. The accuracy of the proposed approach is assessed using a statistical framework to perform an error analysis and numerical simulations with a state-of-the-art computational code that solves the Richards' equation. Comparison of the cumulative mass leaving and entering the flow tank and water content maps produced by the photographic measurement technique and the numerical simulations demonstrate the efficiency and high accuracy of the proposed method for investigating vadose zone flow processes. Finally, the photometric procedure has been developed expressly for its extension to heterogeneous media. Other processes may be investigated through different laboratory experiments which will serve as benchmark for numerical codes validation.

In situ nondestructive imaging of functional pigments in Micro-Tom tomato fruits by multi spectral imaging based on Wiener estimation method

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Ooe, Shintaro; Todoroki, Shinsuke; Asamizu, Erika

2013-05-01

To evaluate the functional pigments in the tomato fruits nondestructively, we propose a method based on the multispectral diffuse reflectance images estimated by the Wiener estimation for a digital RGB image. Each pixel of the multispectral image is converted to the absorbance spectrum and then analyzed by the multiple regression analysis to visualize the contents of chlorophyll a, lycopene and β-carotene. The result confirms the feasibility of the method for in situ imaging of chlorophyll a, β-carotene and lycopene in the tomato fruits.

Fundamental limits of image registration performance: Effects of image noise and resolution in CT-guided interventions.

PubMed

Ketcha, M D; de Silva, T; Han, R; Uneri, A; Goerres, J; Jacobson, M; Vogt, S; Kleinszig, G; Siewerdsen, J H

2017-02-11

In image-guided procedures, image acquisition is often performed primarily for the task of geometrically registering information from another image dataset, rather than detection / visualization of a particular feature. While the ability to detect a particular feature in an image has been studied extensively with respect to image quality characteristics (noise, resolution) and is an ongoing, active area of research, comparatively little has been accomplished to relate such image quality characteristics to registration performance. To establish such a framework, we derived Cramer-Rao lower bounds (CRLB) for registration accuracy, revealing the underlying dependencies on image variance and gradient strength. The CRLB was analyzed as a function of image quality factors (in particular, dose) for various similarity metrics and compared to registration accuracy using CT images of an anthropomorphic head phantom at various simulated dose levels. Performance was evaluated in terms of root mean square error (RMSE) of the registration parameters. Analysis of the CRLB shows two primary dependencies: 1) noise variance (related to dose); and 2) sum of squared image gradients (related to spatial resolution and image content). Comparison of the measured RMSE to the CRLB showed that the best registration method, RMSE achieved the CRLB to within an efficiency factor of 0.21, and optimal estimators followed the predicted inverse proportionality between registration performance and radiation dose. Analysis of the CRLB for image registration is an important step toward understanding and evaluating an intraoperative imaging system with respect to a registration task. While the CRLB is optimistic in absolute performance, it reveals a basis for relating the performance of registration estimators as a function of noise content and may be used to guide acquisition parameter selection (e.g., dose) for purposes of intraoperative registration.

A similarity measure method combining location feature for mammogram retrieval.

PubMed

Wang, Zhiqiong; Xin, Junchang; Huang, Yukun; Li, Chen; Xu, Ling; Li, Yang; Zhang, Hao; Gu, Huizi; Qian, Wei

2018-05-28

Breast cancer, the most common malignancy among women, has a high mortality rate in clinical practice. Early detection, diagnosis and treatment can reduce the mortalities of breast cancer greatly. The method of mammogram retrieval can help doctors to find the early breast lesions effectively and determine a reasonable feature set for image similarity measure. This will improve the accuracy effectively for mammogram retrieval. This paper proposes a similarity measure method combining location feature for mammogram retrieval. Firstly, the images are pre-processed, the regions of interest are detected and the lesions are segmented in order to get the center point and radius of the lesions. Then, the method, namely Coherent Point Drift, is used for image registration with the pre-defined standard image. The center point and radius of the lesions after registration are obtained and the standard location feature of the image is constructed. This standard location feature can help figure out the location similarity between the image pair from the query image to each dataset image in the database. Next, the content feature of the image is extracted, including the Histogram of Oriented Gradients, the Edge Direction Histogram, the Local Binary Pattern and the Gray Level Histogram, and the image pair content similarity can be calculated using the Earth Mover's Distance. Finally, the location similarity and content similarity are fused to form the image fusion similarity, and the specified number of the most similar images can be returned according to it. In the experiment, 440 mammograms, which are from Chinese women in Northeast China, are used as the database. When fusing 40% lesion location feature similarity and 60% content feature similarity, the results have obvious advantages. At this time, precision is 0.83, recall is 0.76, comprehensive indicator is 0.79, satisfaction is 96.0%, mean is 4.2 and variance is 17.7. The results show that the precision and recall of this method have obvious advantage, compared with the content-based image retrieval.

Image databases: Problems and perspectives

NASA Technical Reports Server (NTRS)

Gudivada, V. Naidu

1989-01-01

With the increasing number of computer graphics, image processing, and pattern recognition applications, economical storage, efficient representation and manipulation, and powerful and flexible query languages for retrieval of image data are of paramount importance. These and related issues pertinent to image data bases are examined.

Simultenious binary hash and features learning for image retrieval

NASA Astrophysics Data System (ADS)

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.

Imaging of endogenous exchangeable proton signals in the human brain using frequency labeled exchange transfer imaging.

PubMed

Yadav, Nirbhay N; Jones, Craig K; Hua, Jun; Xu, Jiadi; van Zijl, Peter C M

2013-04-01

To image endogenous exchangeable proton signals in the human brain using a recently reported method called frequency labeled exchange transfer (FLEX) MRI. As opposed to labeling exchangeable protons using saturation (i.e., chemical exchange saturation transfer, or CEST), FLEX labels exchangeable protons with their chemical shift evolution. The use of short high-power frequency pulses allows more efficient labeling of rapidly exchanging protons, while time domain acquisition allows removal of contamination from semi-solid magnetization transfer effects. FLEX-based exchangeable proton signals were detected in human brain over the 1-5 ppm frequency range from water. Conventional magnetization transfer contrast and the bulk water signal did not interfere in the FLEX spectrum. The information content of these signals differed from in vivo CEST data in that the average exchange rate of these signals was 350-400 s(-1) , much faster than the amide signal usually detected using direct saturation (∼30 s(-1) ). Similarly, fast exchanging protons could be detected in egg white in the same frequency range where amide and amine protons of mobile proteins and peptides are known to resonate. FLEX MRI in the human brain preferentially detects more rapidly exchanging amide/amine protons compared to traditional CEST experiments, thereby changing the information content of the exchangeable proton spectrum. This has the potential to open up different types of endogenous applications as well as more easy detection of rapidly exchanging protons in diaCEST agents or fast exchanging units such as water molecules in paracest agents without interference of conventional magnetization transfer contrast. Copyright © 2013 Wiley Periodicals, Inc.

Phaedra, a protocol-driven system for analysis and validation of high-content imaging and flow cytometry.

PubMed

Cornelissen, Frans; Cik, Miroslav; Gustin, Emmanuel

2012-04-01

High-content screening has brought new dimensions to cellular assays by generating rich data sets that characterize cell populations in great detail and detect subtle phenotypes. To derive relevant, reliable conclusions from these complex data, it is crucial to have informatics tools supporting quality control, data reduction, and data mining. These tools must reconcile the complexity of advanced analysis methods with the user-friendliness demanded by the user community. After review of existing applications, we realized the possibility of adding innovative new analysis options. Phaedra was developed to support workflows for drug screening and target discovery, interact with several laboratory information management systems, and process data generated by a range of techniques including high-content imaging, multicolor flow cytometry, and traditional high-throughput screening assays. The application is modular and flexible, with an interface that can be tuned to specific user roles. It offers user-friendly data visualization and reduction tools for HCS but also integrates Matlab for custom image analysis and the Konstanz Information Miner (KNIME) framework for data mining. Phaedra features efficient JPEG2000 compression and full drill-down functionality from dose-response curves down to individual cells, with exclusion and annotation options, cell classification, statistical quality controls, and reporting.

Digital hologram transformations for RGB color holographic display with independent image magnification and translation in 3D.

PubMed

Makowski, Piotr L; Zaperty, Weronika; Kozacki, Tomasz

2018-01-01

A new framework for in-plane transformations of digital holograms (DHs) is proposed, which provides improved control over basic geometrical features of holographic images reconstructed optically in full color. The method is based on a Fourier hologram equivalent of the adaptive affine transformation technique [Opt. Express18, 8806 (2010)OPEXFF1094-408710.1364/OE.18.008806]. The solution includes four elementary geometrical transformations that can be performed independently on a full-color 3D image reconstructed from an RGB hologram: (i) transverse magnification; (ii) axial translation with minimized distortion; (iii) transverse translation; and (iv) viewing angle rotation. The independent character of transformations (i) and (ii) constitutes the main result of the work and plays a double role: (1) it simplifies synchronization of color components of the RGB image in the presence of mismatch between capture and display parameters; (2) provides improved control over position and size of the projected image, particularly the axial position, which opens new possibilities for efficient animation of holographic content. The approximate character of the operations (i) and (ii) is examined both analytically and experimentally using an RGB circular holographic display system. Additionally, a complex animation built from a single wide-aperture RGB Fourier hologram is presented to demonstrate full capabilities of the developed toolset.

Monitoring Pest Insect Traps by Means of Low-Power Image Sensor Technologies

PubMed Central

López, Otoniel; Rach, Miguel Martinez; Migallon, Hector; Malumbres, Manuel P.; Bonastre, Alberto; Serrano, Juan J.

2012-01-01

Monitoring pest insect populations is currently a key issue in agriculture and forestry protection. At the farm level, human operators typically must perform periodical surveys of the traps disseminated through the field. This is a labor-, time- and cost-consuming activity, in particular for large plantations or large forestry areas, so it would be of great advantage to have an affordable system capable of doing this task automatically in an accurate and a more efficient way. This paper proposes an autonomous monitoring system based on a low-cost image sensor that it is able to capture and send images of the trap contents to a remote control station with the periodicity demanded by the trapping application. Our autonomous monitoring system will be able to cover large areas with very low energy consumption. This issue would be the main key point in our study; since the operational live of the overall monitoring system should be extended to months of continuous operation without any kind of maintenance (i.e., battery replacement). The images delivered by image sensors would be time-stamped and processed in the control station to get the number of individuals found at each trap. All the information would be conveniently stored at the control station, and accessible via Internet by means of available network services at control station (WiFi, WiMax, 3G/4G, etc.). PMID:23202232

Monitoring pest insect traps by means of low-power image sensor technologies.

PubMed

López, Otoniel; Rach, Miguel Martinez; Migallon, Hector; Malumbres, Manuel P; Bonastre, Alberto; Serrano, Juan J

2012-11-13

Monitoring pest insect populations is currently a key issue in agriculture and forestry protection. At the farm level, human operators typically must perform periodical surveys of the traps disseminated through the field. This is a labor-, time- and cost-consuming activity, in particular for large plantations or large forestry areas, so it would be of great advantage to have an affordable system capable of doing this task automatically in an accurate and a more efficient way. This paper proposes an autonomous monitoring system based on a low-cost image sensor that it is able to capture and send images of the trap contents to a remote control station with the periodicity demanded by the trapping application. Our autonomous monitoring system will be able to cover large areas with very low energy consumption. This issue would be the main key point in our study; since the operational live of the overall monitoring system should be extended to months of continuous operation without any kind of maintenance (i.e., battery replacement). The images delivered by image sensors would be time-stamped and processed in the control station to get the number of individuals found at each trap. All the information would be conveniently stored at the control station, and accessible via Internet by means of available network services at control station (WiFi, WiMax, 3G/4G, etc.).

The Effects of Changing Water Content, Relaxation Times, and Tissue Contrast on Tissue Segmentation and Measures of Cortical Anatomy in MR Images

PubMed Central

Bansal, Ravi; Hao, Xuejun; Liu, Feng; Xu, Dongrong; Liu, Jun; Peterson, Bradley S.

2013-01-01

Water content is the dominant chemical compound in the brain and it is the primary determinant of tissue contrast in magnetic resonance (MR) images. Water content varies greatly between individuals, and it changes dramatically over time from birth through senescence of the human life span. We hypothesize that the effects that individual- and age-related variations in water content have on contrast of the brain in MR images also has important, systematic effects on in vivo, MRI-based measures of regional brain volumes. We also hypothesize that changes in water content and tissue contrast across time may account for age-related changes in regional volumes, and that differences in water content or tissue contrast across differing neuropsychiatric diagnoses may account for differences in regional volumes across diagnostic groups. We demonstrate in several complementary ways that subtle variations in water content across age and tissue compartments alter tissue contrast, and that changing tissue contrast in turn alters measures of the thickness and volume of the cortical mantle: (1) We derive analytic relations describing how age-related changes in tissue relaxation times produce age-related changes in tissue gray-scale intensity values and tissue contrast; (2) We vary tissue contrast in computer-generated images to assess its effects on tissue segmentation and volumes of gray matter and white matter; and (3) We use real-world imaging data from adults with either Schizophrenia or Bipolar Disorder and age- and sex-matched healthy adults to assess the ways in which variations in tissue contrast across diagnoses affects group differences in tissue segmentation and associated volumes. We conclude that in vivo MRI-based morphological measures of the brain, including regional volumes and measures of cortical thickness, are a product of, or at least are confounded by, differences in tissue contrast across individuals, ages, and diagnostic groups, and that differences in tissue contrast in turn likely derive from corresponding differences in water content of the brain across individuals, ages, and diagnostic groups. PMID:24055410

48 CFR 323.7100 - Policy.

Code of Federal Regulations, 2014 CFR

2014-10-01

... ENVIRONMENT, ENERGY AND WATER EFFICIENCY, RENEWABLE ENERGY TECHNOLOGIES, OCCUPATIONAL SAFETY, AND DRUG-FREE... the acquisition and use of designated recycled content, and Energy Star ®, Electronic Product Environmental Assessment Tool (EPEAT)-registered, energy-efficient, bio-based, and environmentally preferable...

Thiophene dendrimer-based low donor content solar cells

NASA Astrophysics Data System (ADS)

Stoltzfus, Dani M.; Ma, Chang-Qi; Nagiri, Ravi C. R.; Clulow, Andrew J.; Bäuerle, Peter; Burn, Paul L.; Gentle, Ian R.; Meredith, Paul

2016-09-01

Low donor content solar cells containing polymeric and non-polymeric donors blended with fullerenes have been reported to give rise to efficient devices. In this letter, we report that a dendrimeric donor can also be used in solution-processed low donor content devices when blended with a fullerene. A third generation dendrimer containing 42 thiophene units (42T) was found to give power conversion efficiencies of up to 3.5% when blended with PC70BM in optimized devices. The best efficiency was measured with 10 mole percent (mol. %) of 42T in PC70BM and X-ray reflectometry showed that the blends were uniform. Importantly, while 42T comprised 10 mol. % of the film, it made up 31% of the film by volume. Finally, it was found that solvent annealing was required to achieve the largest open circuit voltage and highest device efficiencies.

Steganalysis based on reducing the differences of image statistical characteristics

NASA Astrophysics Data System (ADS)

Wang, Ran; Niu, Shaozhang; Ping, Xijian; Zhang, Tao

2018-04-01

Compared with the process of embedding, the image contents make a more significant impact on the differences of image statistical characteristics. This makes the image steganalysis to be a classification problem with bigger withinclass scatter distances and smaller between-class scatter distances. As a result, the steganalysis features will be inseparate caused by the differences of image statistical characteristics. In this paper, a new steganalysis framework which can reduce the differences of image statistical characteristics caused by various content and processing methods is proposed. The given images are segmented to several sub-images according to the texture complexity. Steganalysis features are separately extracted from each subset with the same or close texture complexity to build a classifier. The final steganalysis result is figured out through a weighted fusing process. The theoretical analysis and experimental results can demonstrate the validity of the framework.

X-Ray Phase Imaging for Breast Cancer Detection

DTIC Science & Technology

2012-09-01

the Gerchberg-Saxton algorithm in the Fresnel diffraction regime, and is much more robust against image noise than the TIE-based method. For details...developed efficient coding with the software modules for the image registration, flat-filed correction , and phase retrievals. In addition, we...X, Liu H. 2010. Performance analysis of the attenuation-partition based iterative phase retrieval algorithm for in-line phase-contrast imaging

Using Blur to Affect Perceived Distance and Size

PubMed Central

HELD, ROBERT T.; COOPER, EMILY A.; O’BRIEN, JAMES F.; BANKS, MARTIN S.

2011-01-01

We present a probabilistic model of how viewers may use defocus blur in conjunction with other pictorial cues to estimate the absolute distances to objects in a scene. Our model explains how the pattern of blur in an image together with relative depth cues indicates the apparent scale of the image’s contents. From the model, we develop a semiautomated algorithm that applies blur to a sharply rendered image and thereby changes the apparent distance and scale of the scene’s contents. To examine the correspondence between the model/algorithm and actual viewer experience, we conducted an experiment with human viewers and compared their estimates of absolute distance to the model’s predictions. We did this for images with geometrically correct blur due to defocus and for images with commonly used approximations to the correct blur. The agreement between the experimental data and model predictions was excellent. The model predicts that some approximations should work well and that others should not. Human viewers responded to the various types of blur in much the way the model predicts. The model and algorithm allow one to manipulate blur precisely and to achieve the desired perceived scale efficiently. PMID:21552429

Application of Scion image software to the simultaneous determination of curcuminoids in turmeric (Curcuma longa).

PubMed

Sotanaphun, Uthai; Phattanawasin, Panadda; Sriphong, Lawan

2009-01-01

Curcumin, desmethoxycurcumin and bisdesmethoxycurcumin are bioactive constituents of turmeric (Curcuma longa). Owing to their different potency, quality control of turmeric based on the content of each curcuminoid is more reliable than that based on total curcuminoids. However, to perform such an assay, high-cost instrument is needed. To develop a simple and low-cost method for the simultaneous quantification of three curcuminoids in turmeric using TLC and the public-domain software Scion Image. The image of a TLC chromatogram of turmeric extract was recorded using a digital scanner. The density of the TLC spot of each curcuminoid was analysed by the Scion Image software. The density value was transformed to concentration by comparison with the calibration curve of standard curcuminoids developed on the same TLC plate. The polynomial regression data for all curcuminoids showed good linear relationship with R(2) > 0.99 in the concentration range of 0.375-6 microg/spot. The limits of detection and quantitation were 43-73 and 143-242 ng/spot, respectively. The method gave adequate precision, accuracy and recovery. The contents of each curcuminoid determined using this method were not significantly different from those determined using the TLC densitometric method. TLC image analysis using Scion Image is shown to be a reliable method for the simultaneous analysis of the content of each curcuminoid in turmeric.

Efficient Time-Domain Imaging Processing for One-Stationary Bistatic Forward-Looking SAR Including Motion Errors

PubMed Central

Xie, Hongtu; Shi, Shaoying; Xiao, Hui; Xie, Chao; Wang, Feng; Fang, Qunle

2016-01-01

With the rapid development of the one-stationary bistatic forward-looking synthetic aperture radar (OS-BFSAR) technology, the huge amount of the remote sensing data presents challenges for real-time imaging processing. In this paper, an efficient time-domain algorithm (ETDA) considering the motion errors for the OS-BFSAR imaging processing, is presented. This method can not only precisely handle the large spatial variances, serious range-azimuth coupling and motion errors, but can also greatly improve the imaging efficiency compared with the direct time-domain algorithm (DTDA). Besides, it represents the subimages on polar grids in the ground plane instead of the slant-range plane, and derives the sampling requirements considering motion errors for the polar grids to offer a near-optimum tradeoff between the imaging precision and efficiency. First, OS-BFSAR imaging geometry is built, and the DTDA for the OS-BFSAR imaging is provided. Second, the polar grids of subimages are defined, and the subaperture imaging in the ETDA is derived. The sampling requirements for polar grids are derived from the point of view of the bandwidth. Finally, the implementation and computational load of the proposed ETDA are analyzed. Experimental results based on simulated and measured data validate that the proposed ETDA outperforms the DTDA in terms of the efficiency improvement. PMID:27845757

Optimal focal-plane restoration

NASA Technical Reports Server (NTRS)

Reichenbach, Stephen E.; Park, Stephen K.

1989-01-01

Image restoration can be implemented efficiently by calculating the convolution of the digital image and a small kernel during image acquisition. Processing the image in the focal-plane in this way requires less computation than traditional Fourier-transform-based techniques such as the Wiener filter and constrained least-squares filter. Here, the values of the convolution kernel that yield the restoration with minimum expected mean-square error are determined using a frequency analysis of the end-to-end imaging system. This development accounts for constraints on the size and shape of the spatial kernel and all the components of the imaging system. Simulation results indicate the technique is effective and efficient.

Fast global image smoothing based on weighted least squares.

PubMed

Min, Dongbo; Choi, Sunghwan; Lu, Jiangbo; Ham, Bumsub; Sohn, Kwanghoon; Do, Minh N

2014-12-01

This paper presents an efficient technique for performing a spatially inhomogeneous edge-preserving image smoothing, called fast global smoother. Focusing on sparse Laplacian matrices consisting of a data term and a prior term (typically defined using four or eight neighbors for 2D image), our approach efficiently solves such global objective functions. In particular, we approximate the solution of the memory-and computation-intensive large linear system, defined over a d-dimensional spatial domain, by solving a sequence of 1D subsystems. Our separable implementation enables applying a linear-time tridiagonal matrix algorithm to solve d three-point Laplacian matrices iteratively. Our approach combines the best of two paradigms, i.e., efficient edge-preserving filters and optimization-based smoothing. Our method has a comparable runtime to the fast edge-preserving filters, but its global optimization formulation overcomes many limitations of the local filtering approaches. Our method also achieves high-quality results as the state-of-the-art optimization-based techniques, but runs ∼10-30 times faster. Besides, considering the flexibility in defining an objective function, we further propose generalized fast algorithms that perform Lγ norm smoothing (0 < γ < 2) and support an aggregated (robust) data term for handling imprecise data constraints. We demonstrate the effectiveness and efficiency of our techniques in a range of image processing and computer graphics applications.

Melanin-Based Contrast Agents for Biomedical Optoacoustic Imaging and Theranostic Applications.

PubMed

Longo, Dario Livio; Stefania, Rachele; Aime, Silvio; Oraevsky, Alexander

2017-08-07

Optoacoustic imaging emerged in early 1990s as a new biomedical imaging technology that generates images by illuminating tissues with short laser pulses and detecting resulting ultrasound waves. This technique takes advantage of the spectroscopic approach to molecular imaging, and delivers high-resolution images in the depth of tissue. Resolution of the optoacoustic imaging is scalable, so that biomedical systems from cellular organelles to large organs can be visualized and, more importantly, characterized based on their optical absorption coefficient, which is proportional to the concentration of absorbing chromophores. Optoacoustic imaging was shown to be useful in both preclinical research using small animal models and in clinical applications. Applications in the field of molecular imaging offer abundant opportunities for the development of highly specific and effective contrast agents for quantitative optoacoustic imaging. Recent efforts are being made in the direction of nontoxic biodegradable contrast agents (such as nanoparticles made of melanin) that are potentially applicable in clinical optoacoustic imaging. In order to increase the efficiency and specificity of contrast agents and probes, they need to be made smart and capable of controlled accumulation in the target cells. This review was written in recognition of the potential breakthroughs in medical optoacoustic imaging that can be enabled by efficient and nontoxic melanin-based optoacoustic contrast agents.

Melanin-Based Contrast Agents for Biomedical Optoacoustic Imaging and Theranostic Applications

PubMed Central

Longo, Dario Livio; Aime, Silvio

2017-01-01

Optoacoustic imaging emerged in early 1990s as a new biomedical imaging technology that generates images by illuminating tissues with short laser pulses and detecting resulting ultrasound waves. This technique takes advantage of the spectroscopic approach to molecular imaging, and delivers high-resolution images in the depth of tissue. Resolution of the optoacoustic imaging is scalable, so that biomedical systems from cellular organelles to large organs can be visualized and, more importantly, characterized based on their optical absorption coefficient, which is proportional to the concentration of absorbing chromophores. Optoacoustic imaging was shown to be useful in both preclinical research using small animal models and in clinical applications. Applications in the field of molecular imaging offer abundant opportunities for the development of highly specific and effective contrast agents for quantitative optoacoustic imaging. Recent efforts are being made in the direction of nontoxic biodegradable contrast agents (such as nanoparticles made of melanin) that are potentially applicable in clinical optoacoustic imaging. In order to increase the efficiency and specificity of contrast agents and probes, they need to be made smart and capable of controlled accumulation in the target cells. This review was written in recognition of the potential breakthroughs in medical optoacoustic imaging that can be enabled by efficient and nontoxic melanin-based optoacoustic contrast agents. PMID:28783106

Indocyanine Green-Loaded Liposomes for Light-Triggered Drug Release.

PubMed

Lajunen, Tatu; Kontturi, Leena-Stiina; Viitala, Lauri; Manna, Moutusi; Cramariuc, Oana; Róg, Tomasz; Bunker, Alex; Laaksonen, Timo; Viitala, Tapani; Murtomäki, Lasse; Urtti, Arto

2016-06-06

Light-triggered drug delivery systems enable site-specific and time-controlled drug release. In previous work, we have achieved this with liposomes containing gold nanoparticles in the aqueous core. Gold nanoparticles absorb near-infrared light and release the energy as heat that increases the permeability of the liposomal bilayer, thus releasing the contents of the liposome. In this work, we replaced the gold nanoparticles with the clinically approved imaging agent indocyanine green (ICG). The ICG liposomes were stable at storage conditions (4-22 °C) and at body temperature, and fast near-infrared (IR) light-triggered drug release was achieved with optimized phospholipid composition and a 1:50 ICG-to-lipid molar ratio. Encapsulated small molecular calcein and FITC-dextran (up to 20 kDa) were completely released from the liposomes after light exposure for 15 s. Location of ICG in the PEG layer of the liposomes was simulated with molecular dynamics. ICG has important benefits as a light-triggering agent in liposomes: fast content release, improved stability, improved possibility of liposomal size control, regulatory approval to use in humans, and the possibility of imaging the in vivo location of the liposomes based on the fluorescence of ICG. Near-infrared light used as a triggering mechanism has good tissue penetration and safety. Thus, ICG liposomes are an attractive option for light-controlled and efficient delivery of small and large drug molecules.