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Information
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Information-Centered Healthcare
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Information-Centered Healthcare

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (10 May 2018) | Viewed by 14777

Special Issue Editors

Prof. Dr. Adel S. Elmaghraby

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40292, USA
Interests: network and information forensics; biomedical imaging; multimedia and virtual reality systems; artificial intelligence; performance evaluation; computer modeling and simulation; human–machine systems; logistics; automation and manufacturing; distributed systems; bioinformatics applications
Special Issues, Collections and Topics in MDPI journals
Dr. Daniel Sierra-Sosa

E-Mail Website
Guest Editor
Department of Computer Science & Information Technology, Hood College Frederick, MD 21701, USA
Interests: image and signal processing; computer imaging; machine learning; quantum computing
Prof. Dr. Begoña Garcia-Zapirain

E-Mail Website
Guest Editor
Departament of Computer Science, Electronics and Telecommunications, University of Deusto, 48007 Bilbao, Spain
Interests: eHealth; biomedical engineering; computer games for health; biofeedback
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The IEEE Symposium in Signal Processing and Information 2017 has been held over the period 18–20 December in Bilbao, Spain. For seventeen years this series of international symposia has congregated researchers from around the world, covering topics that relate to signal processing and information technology in a wide range of applications. Given the extremely rapid pace of development in information technologies applied to healthcare, in this year’s Symposium we will discuss topics related with information-centered healthcare. With this Special Issue we look forward to summarize remarkable contributions made by the academic community attending to the Symposium in health analytics, technology for active aging, mobile-health, signal processing for healthcare, integrated cancer, medical imaging and games for health and wellbeing.

Prof. Adel Elmaghraby
Dr. Daniel Sierra-Sosa
Prof. Begoña Garcia-Zapirain
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • health analytics
  • information technologies for health
  • mobile-health
  • medical signal processing
  • medical imaging processing
  • active aging and wellbeing

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Published Papers (2 papers)

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Research

14 pages, 456 KiB  
Article
Efficient Low-Resource Compression of HIFU Data
by Petr Kleparnik, David Barina, Pavel Zemcik and Jiri Jaros
Information 2018, 9(7), 155; https://doi.org/10.3390/info9070155 - 26 Jun 2018
Cited by 3 | Viewed by 4780
Abstract
Large-scale numerical simulations of high-intensity focused ultrasound (HIFU), important for model-based treatment planning, generate large amounts of data. Typically, it is necessary to save hundreds of gigabytes during simulation. We propose a novel algorithm for time-varying simulation data compression specialised for HIFU. Our [...] Read more.
Large-scale numerical simulations of high-intensity focused ultrasound (HIFU), important for model-based treatment planning, generate large amounts of data. Typically, it is necessary to save hundreds of gigabytes during simulation. We propose a novel algorithm for time-varying simulation data compression specialised for HIFU. Our approach is particularly focused on on-the-fly parallel data compression during simulations. The algorithm is able to compress 3D pressure time series of linear and non-linear simulations with very acceptable compression ratios and errors (over 80% of the space can be saved with an acceptable error). The proposed compression enables significant reduction of resources, such as storage space, network bandwidth, CPU time, and so forth, enabling better treatment planning using fast volume data visualisations. The paper describes the proposed method, its experimental evaluation, and comparisons to the state of the arts. Full article
(This article belongs to the Special Issue Information-Centered Healthcare)
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26 pages, 3023 KiB  
Article
Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network
by Abdullah-Al Nahid and Yinan Kong
Information 2018, 9(1), 19; https://doi.org/10.3390/info9010019 - 16 Jan 2018
Cited by 99 | Viewed by 9338
Abstract
Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes [...] Read more.
Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD) techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist’s decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN) technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC) images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information—for example, the Local Binary Pattern (LBP) represents the effective textural information, Histogram represent the pixel strength distribution, Contourlet Transform (CT) gives much detailed information about the smoothness about the edges, and Discrete Fourier Transform (DFT) derives frequency-domain information from the image. Utilizing these advantages, along with our proposed novel CNN model, we have examined the performance of the novel CNN model as Histopathological image classifier. To do so, we have introduced five cases: (a) Convolutional Neural Network Raw Image (CNN-I); (b) Convolutional Neural Network CT Histogram (CNN-CH); (c) Convolutional Neural Network CT LBP (CNN-CL); (d) Convolutional Neural Network Discrete Fourier Transform (CNN-DF); (e) Convolutional Neural Network Discrete Cosine Transform (CNN-DC). We have performed our experiments on the BreakHis image dataset. The best performance is achieved when we utilize the CNN-CH model on a 200× dataset that provides Accuracy, Sensitivity, False Positive Rate, False Negative Rate, Recall Value, Precision and F-measure of 92.19%, 94.94%, 5.07%, 1.70%, 98.20%, 98.00% and 98.00%, respectively. Full article
(This article belongs to the Special Issue Information-Centered Healthcare)
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