Computed tomography image (CTI) sequence is essentially a time-series data that typically consists of a large amount of nearby and similar CTIs. Due to the high communication and computational costs, it is difficult to perform a progressive distributed similarity retrieval of the large CTI sequence (CTIS)s, particularly in resource-constraint mobile telemedicine network (MTN)s. In this paper, we present a Dprs method—progressive distributed and parallel similarity retrieval scheme for the CTISs in the MTN. To the best of our knowledge, there is little research on the Dprs processing, especially in the MTN. Four supporting techniques (i.e., (1) PCTI-based similarity measurement, (2) lightweight privacy-preserving strategy, (3) SSL-based data distribution scheme, and (4) the UDI framework) are developed. The experimental evaluation indicates that our proposed Dprs method is more progressive than the state of the art, with a significant reduction in response time.

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Progressive Distributed and Parallel Similarity Retrieval of Large CT Image Sequences in Mobile Telemedicine Networks

This paper proposes a general framework that allows to identify a video in real time using perceptual image hashing algorithms. In order to evaluate the versatility and performance of the framework, it was coupled for a use case about ads tv monitoring. Four Perceptual Image Hashing (PIH) algorithms were subject to a benchmarking process in order to identify the best one for the use case. This process was focused on analyze differences in terms of discriminability (D), robustness (R), time processing (Tp) and efficiency (E). A truth table was used to obtain information about discriminability and robustness, while processing time was directly measured. An efficiency metric based on time processing and identification capacity was proposed. In general terms, DHASH and PHASH algorithms have higher identification capacities than AHASH and WHASH in order to identify a video using only one frame. Moreover, a progressive decrease in robustness with the increment of the Hamming distance is observed in all cases. However, in a specific case of tv monitoring where speed is critical, the processing time becomes the most discriminatory parameter for the selection of the algorithm. So, for this case, a particular type of PIH (Average Hash) is highlighted as the most efficient one among other techniques, reaching an accuracy of 100% and frame rates on processing average of 108 fps with a Hamming Distance of 1. At the end, the proposed framework has remarkable identification skills, and presents an efficient search. Furthermore, presents the steps to select the best algorithm and its more adequate parameters, according to the requirements of each particular case.

A robust video identification framework using perceptual image hashing

In the current era of digital communication, the use of images is growing exponentially since they are one of the best ways of expressing, sharing and memorizing knowledge. In fact, images can be used in various real-world applications, like biology, medical diagnosis, space research, remote sensing, etc. However, finding the most relevant images that meet the users’ needs is a challenging task, especially when the search is performed over gigantic amounts of images. This has led to the emergence of several image retrieval studies during the past two decades. Typically, research studies in this area were focused on the Content-based Image Retrieval (CBIR). However, extensive research have proved that there is a ‘semantic gap’ between the visual information captured by the imaging devices and the image semantics understandable by humans. As an alternative, researchers’ efforts have been oriented towards the Text-based Image Retrieval (TBIR). Indeed, TBIR is a typical method that helps bridge the issue of ‘semantic gap’ between the low-level image features and the high-level image semantics. Its policy consists in associating textual descriptions with the images, which constitute the focus of the research queries later on. In this paper, we analyze various image annotation methods, namely: Visual Content-based and Users’ Tags-based Image Annotation Methods. In particular, we focus on the visual content-based image annotation techniques since they are one of the dynamic research fields nowadays.

A review on visual content-based and users’ tags-based image annotation: methods and techniques

Interactive advertising based on multiple devices opens new possibilities for mobile applications, where users can search, select, or expand the information provided in advertising commercials by incorporating interactivity-friendly companion devices such as smart phones and tablets. In this paper, we derive the basic requirements for a flexible infrastructure that can support interactive ad applications. The infrastructure comprises a set of components and their externally visible properties, and the relationships between them. The main contributions of this work are first, establishing a desirable set of requirements for a suitable working scenario on which the different interactivity supporting systems people use in a regular basis can be easily integrated, and second, the definition of a platform which considers the different stages and requirements identified from different works in related areas.

Towards a multi-screen interactive ad delivery platform

This article presents an analysis of public policy regarding the obligatory diffusion of Ecuadorian music on Ecuadorian commercial radio stations in accordance with communications laws passed in 2013. The research focuses on mass diffusion of the music, as well as commercial radio in Ecuador. In this article, the researcher aims to define “Ecuadorian music” according to national law, the role of commercial radio in Ecuadorian-created music, the difficulties experienced by local musicians in diffusing their product on local radio, and how the new laws have increased professional satisfaction among Ecuadorian musicians.

http://www.revistarazonypalabra.com/index.php/ryp/article/download/1095/1096

Ley Orgánica de Comunicación en la programación de la radio comercial. Análisis de la Ley del 1x1 en el Ecuador

This article follows step by step a general framework for fingerprint extraction in order to develop a system for advertisements' monitoring. The parameterization process uses some spatial and spectral characteristics measured over 600 advertisements that contain various types of sounds. Key factors such as accuracy, process time, and granularity are analyzed together in order to enhance the system performance. At the end, the algorithm shows an accuracy of 99% using three seconds of granularity samples, and also the best compromise between processing time and performance is achieved. This study suggests a set of parameterization steps which could be successfully implemented in other related audio applications.

Audio fingerprint parameterization for multimedia advertising identification

Advances in medical imaging have fostered medical diagnosis based on digital images. Consequently, the number of studies by medical images diagnosis increases, thus, collaborative work and tele-radiology systems are required to effectively scale up to this diagnosis trend. We tackle the problem of the collaborative access of medical images, and present WebMedSA, a framework to manage large datasets of medical images. WebMedSA relies on a PACS and supports the ontological annotation, as well as segmentation and visualization of the images based on their semantic description. Ontological annotations can be performed directly on the volumetric image or at different image planes (e.g., axial, coronal, or sagittal); furthermore, annotations can be complemented after applying a segmentation technique. WebMedSA is based on three main steps: (1) RDF-ization process for extracting, anonymizing, and serializing metadata comprised in DICOM medical images into RDF/XML; (2) Integration of different biomedical ontologies (using L-MOM library), making this approach ontology independent; and (3) segmentation and visualization of annotated data which is further used to generate new annotations according to expert knowledge, and validation. Initial user evaluations suggest that WebMedSA facilitates the exchange of knowledge between radiologists, and provides the basis for collaborative work among them.

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WebMedSA: a web-based framework for segmenting and annotating medical images using biomedical ontologies

Teleradiology systems tackle the problem of transferring radiological images between medical image workstations for facilitating different medical activities, e.g., diagnosis, treatment and follow up a patient, medical training, or consulting second opinion. Nowadays, m-Health (aka mobile health) is becoming popular because of high quality of mobile displays, although remains a work in progress. In this paper a mobile teleradiology system is reported, which main contribution is the development of a platform: (1) supported by a Grid infrastructure, (2) using biomedical ontologies for adding semantic annotations on medical images, and (3) supporting semantic and content-based image retrieval. Images are located physically in different repositories like; hospitals and diagnostic imaging centers. All these features make the system ubiquitous, portable, and suitable for m-Health services.

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Francisco Vega

Papers

A robust video identification framework using perceptual image hashing

Audio fingerprint parameterization for multimedia advertising identification

Towards a multi-screen interactive ad delivery platform

WebMedSA: a web-based framework for segmenting and annotating medical images using biomedical ontologies

Mobile teleradiology system suitable for m-health services supporting content and semantic based image retrieval on a grid infrastructure