A medical image retrieval scheme through a medical social network

Ayadi, Mouhamed Gaith; Bouslimi, Riadh; Akaichi, Jalel

doi:10.1007/s13721-016-0130-9

Cited by 5 publications

(3 citation statements)

References 66 publications

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“…We conducted many experiments to determine the framework's accuracy, proven and investigated by a comparison study with other CBMIR models. In the context of our research continuity, our work presents an evolution our existing works . Thus, we keep the same protocol of evaluation and experimentation.…”

Section: Methodsmentioning

confidence: 99%

Medical social network content analysis for medical image retrieval purpose

Ayadi¹,

Bouslimi²,

Akaichi

2019

Softw Pract Exp

Self Cite

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Background and objective: Medical social networking platforms provide virtual spaces ensuring the interaction between different healthcare participants. As a part of the exchange, these spaces allow subscribers to upload medical images, describing different medical cases for an analysis or an interpretation proposal. Facing this expected huge amount of uploaded images generated daily, it is needed to engage new mechanisms to effectively deal with this circumstance, for enhancing the search function process of medical images, based on what is uploaded. To overcome this issue, setting up of images visual searching based on a content-based medical image retrieval scheme is the solution.More clearly, such mechanism will help and motivate medical social networking subscribers to find visually similar stored images.Methods: To ensure this task, the development of this mechanism, technically, is based mainly on a fusion of three visual features, which offers a flexible and more precision. It is reinforced by a weighted distance approach through attributing weights for feature vectors to scale up the performance. Indeed, the displayed results of this system can be updated based on user's intention by a user interactive feedback mechanism to indicate the truly relevant images. Results: We provide the theoretical performance of our scheme. Extensive experiments were conducted on a categorically classified collection containing 500 images. We conduct a practical evaluation on this dataset classes, putting returned results in a comparative study with other models results, existing in the literature. Conclusions:The proposed scheme preserves the efficiency of the search task. As theoretically and experimentally established, our scheme offers an effective image retrieval model that can support different subscribers' expectations.The relevance feedback mechanism can keep the dynamism of the system, thus offering a continuous searching result evolution. Experimentation outcomes indicate better findings compared with the other models.

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Section: Methodsmentioning

confidence: 99%

Medical social network content analysis for medical image retrieval purpose

Ayadi¹,

Bouslimi²,

Akaichi

2019

Softw Pract Exp

Self Cite

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“…The developed OntoCAIM ontology not only fulfills SNA functionality but also defines an annotation mechanism for pictures with an underlying ontology. Ayadi et al 181 have proposed an approach in which content‐based image acquisition based on combining low‐level visual image features (color, shape, and texture properties) effectively works with medical social network. Content‐based medical image acquisition system has provided an opportunity to store a visually similar recorded image with a new image, to allow specialists and patients to check their past diagnostic examinations and other physicians' annotations, resulting in a new diagnosis or a new report for the imaging exam.…”

Section: Semantic Analysis In Social Networkmentioning

confidence: 99%

Semantic analysis on social networks: A survey

Bayrakdar

Yücedağ

Şimşek

et al. 2020

Int J Communication

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Summary As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.

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“…Online social networking is attracting more and more attention in today's Internet, where users can share and consume all kinds of multimedia contents. The contents range from 3D models, images, and videos, to text available on the Web or kept in storage as a collection of big data [2]. Information retrieval has been developed from a single text object to 2D image or other multimedia information, and even more realistic 3D models or 3D scene.…”

Section: Introductionmentioning

confidence: 99%

Feature Matching of Multi-View 3d Models Based on Hash Binary Encoding

Li¹,

Zhao²,

Li³

et al. 2017

NNW

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Technology has undergone rapid development in the past several decades and we are now at a point where many technologies are available to help create smart cities. Many technology companies and research institutions as well as political organizations are currently discussing this field with the highest priority. One can say that the biggest challenge to smart cities is not technologies themselves, but the merging of all available technologies into one symbiotic unit that fulfills the expected objectives. Smart cities are about connecting subsystems, sharing and evaluating data, and providing quality of life and satisfaction to citizens. We have various models of transportation systems, optimizations of energy usage, street lighting systems, building management systems, urban transport optimizations, however currently, such models are dealt with separately. In this paper, we provide an overview of the smart city concept and discuss why Multi-agent systems are the right tool for the modeling of smart cities. The biggest challenge is in connecting and linking particular subsystems within a smart city. In this paper, a modeling of a smart city building blocks is provided and demonstrated with one particular example -a smart street lighting system. Focus will be on the decomposition of the system into subsystems as well as a description of particular modules. We propose to build models and since each individual entity can be modeled as an agent with its beliefs, desires and intentions, we suggest using Multi-agent systems as a tool for modeling systems' connections within the smart city and assessing how best to use the data from those systems.

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A medical image retrieval scheme through a medical social network

Cited by 5 publications

References 66 publications

Medical social network content analysis for medical image retrieval purpose

Medical social network content analysis for medical image retrieval purpose

Semantic analysis on social networks: A survey

Feature Matching of Multi-View 3d Models Based on Hash Binary Encoding

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