Special Issue "Medical Imaging & Image Processing Ⅱ"

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: closed (28 February 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Yudong Zhang

Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
Website | E-Mail
Interests: magnetic resonance imaging; computer vision; machine learning; pattern recognition; machine vision; artificial neural network; support vector machine; swarm intelligence; global optimization
Guest Editor
Dr. Zhengchao Dong

Professor, Columbia University, New York, NY 10032, USA, Research Scientist, New York State Psychiatric Institute, New York, NY 10032, USA
Website | E-Mail
Phone: +1-212-543-5997
Fax: +1-212-543 6660
Interests: magnetic resonance spectroscopy imaging

Special Issue Information

Dear Colleagues,

Medical Imaging is becoming an essential component in various fields of bio-medical research and clinical practice: Neuroscientists detect regional metabolic brain activity from positron emission tomography (PET), functional magnetic resonance imaging (MRI), and magnetic resonance spectrum imaging (MRSI) scans, biologists study cells and generate 3D confocal microscopy data sets, virologists generate 3D reconstructions of viruses from micrographs, and radiologists identify and quantify tumors from MRI and computed tomography (CT) scans.

On the other hand, Image Processing includes the analysis, enhancement and display of biomedical images. Image reconstruction and modeling techniques allow instant processing of 2D signals to create 3D images. Image processing and analysis can be used to determine the diameter, volume and vasculature of a tumor or organ, flow parameters of blood or other fluids and microscopic changes that have yet to raise any otherwise discernible flags. Image classification techniques help to detect subjects suffered from particular diseases and to detect disease-related regions.

The relevant Special Issue can be found here: https://www.mdpi.com/journal/technologies/special_issues/medical_imaging

Prof. Dr. Yudong Zhang
Prof. Dr. Zhengchao Dong
Guest Editors

 

Manuscript Submission Information

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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. Technologies is an international peer-reviewed open access quarterly 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 350 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

  • Biomedical imaging

  • Magnetic resonance imaging

  • Neuroimaging

  • X-ray

  • Computerized tomography

  • Mammography

  • Image processing and analysis

  • Computer vision

  • Machine learning

Published Papers (6 papers)

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Editorial

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Open AccessEditorial Special Issue on “Medical Imaging & Image Processing II”
Technologies 2018, 6(2), 39; https://doi.org/10.3390/technologies6020039
Received: 26 March 2018 / Revised: 27 March 2018 / Accepted: 28 March 2018 / Published: 30 March 2018
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Abstract
Medical Imaging is becoming an essential component in various fields of bio-medical research and clinical practice: Neuroscientists detect regional metabolic brain activity from positron emission tomography (PET), functional magnetic resonance imaging (MRI), and magnetic resonance spectrum imaging (MRSI) scans; biologists study cells and
[...] Read more.
Medical Imaging is becoming an essential component in various fields of bio-medical research and clinical practice: Neuroscientists detect regional metabolic brain activity from positron emission tomography (PET), functional magnetic resonance imaging (MRI), and magnetic resonance spectrum imaging (MRSI) scans; biologists study cells and generate 3D confocal microscopy data sets; virologists generate 3D reconstructions of viruses from micrographs; and radiologists identify and quantify tumors from MRI and computed tomography (CT) scans [...]
Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing Ⅱ)

Research

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Open AccessArticle Validation of Various Filters and Sampling Parameters for a COP Analysis
Technologies 2018, 6(2), 56; https://doi.org/10.3390/technologies6020056
Received: 16 April 2018 / Revised: 10 June 2018 / Accepted: 11 June 2018 / Published: 13 June 2018
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Abstract
The center of pressure (CoP) is one of the most utilized quantitative measurements describing postural competency. Due to the complexity and biological variability of postural regulatory systems, a myriad of different methods and parameters have been established describing the CoP trajectory. Besides procedural
[...] Read more.
The center of pressure (CoP) is one of the most utilized quantitative measurements describing postural competency. Due to the complexity and biological variability of postural regulatory systems, a myriad of different methods and parameters have been established describing the CoP trajectory. Besides procedural variables, such as foot position, visual condition, and sampling duration, the method of data collection itself has a relevant effect on the result of the measurement. Furthermore, different methods for recording the measured data have been developed, which differ regarding the filters, frequencies, and test durations used. The goal of this study was the methodical comparison of various filters, measurement frequencies, and measurement duration, with respect to their effects on the CoP trajectory. Based on the results presented, we demonstrate that the Butterworth and Bessel filters can be recommended for analysis of CoP data, and at the very least, a second-order filter should be chosen for the process. For assessment of the cutoff frequency, a technical pendulum was used to show that a cutoff frequency of 13 Hz provided reliable data and it can be inferred that a 100 Hz sampling rate would be the minimum requirement. Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing Ⅱ)
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Open AccessArticle An Algorithm for Data Hiding in Radiographic Images and ePHI/R Application
Received: 7 October 2017 / Revised: 5 January 2018 / Accepted: 8 January 2018 / Published: 11 January 2018
Cited by 1 | PDF Full-text (2433 KB) | HTML Full-text | XML Full-text
Abstract
Telemedicine is the use of Information and Communication Technology (ICT) for clinical health care from a distance. The exchange of radiographic images and electronic patient health information/records (ePHI/R) for diagnostic purposes has the risk of confidentiality, ownership identity, and authenticity. In this paper,
[...] Read more.
Telemedicine is the use of Information and Communication Technology (ICT) for clinical health care from a distance. The exchange of radiographic images and electronic patient health information/records (ePHI/R) for diagnostic purposes has the risk of confidentiality, ownership identity, and authenticity. In this paper, a data hiding technique for ePHI/R is proposed. The color information in the cover image is used for key generation, and stego-images are produced with ideal case. As a result, the whole stego-system is perfectly secure. This method includes the features of watermarking and steganography techniques. The method is applied to radiographic images. For the radiographic images, this method resembles watermarking, which is an ePHI/R data system. Experiments show promising results for the application of this method to radiographic images in ePHI/R for both transmission and storage purpose. Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing Ⅱ)
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Open AccessArticle A Novel Kernel-Based Regularization Technique for PET Image Reconstruction
Technologies 2017, 5(2), 37; https://doi.org/10.3390/technologies5020037
Received: 12 April 2017 / Revised: 11 June 2017 / Accepted: 16 June 2017 / Published: 19 June 2017
Cited by 1 | PDF Full-text (971 KB) | HTML Full-text | XML Full-text
Abstract
Positron emission tomography (PET) is an imaging technique that generates 3D detail of physiological processes at the cellular level. The technique requires a radioactive tracer, which decays and releases a positron that collides with an electron; consequently, annihilation photons are emitted, which can
[...] Read more.
Positron emission tomography (PET) is an imaging technique that generates 3D detail of physiological processes at the cellular level. The technique requires a radioactive tracer, which decays and releases a positron that collides with an electron; consequently, annihilation photons are emitted, which can be measured. The purpose of PET is to use the measurement of photons to reconstruct the distribution of radioisotopes in the body. Currently, PET is undergoing a revamp, with advancements in data measurement instruments and the computing methods used to create the images. These computer methods are required to solve the inverse problem of “image reconstruction from projection”. This paper proposes a novel kernel-based regularization technique for maximum-likelihood expectation-maximization ( κ -MLEM) to reconstruct the image. Compared to standard MLEM, the proposed algorithm is more robust and is more effective in removing background noise, whilst preserving the edges; this suppresses image artifacts, such as out-of-focus slice blur. Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing Ⅱ)
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Open AccessArticle Wireless Accelerometer for Neonatal MRI Motion Artifact Correction
Received: 9 November 2016 / Revised: 10 January 2017 / Accepted: 17 January 2017 / Published: 22 January 2017
Cited by 1 | PDF Full-text (5254 KB) | HTML Full-text | XML Full-text
Abstract
A wireless accelerometer has been used in conjunction with a dedicated 3T neonatal MRI system installed on a Neonatal Intensive Care Unit to measure in-plane rotation which is a common problem with neonatal MRI. Rotational data has been acquired in real-time from phantoms
[...] Read more.
A wireless accelerometer has been used in conjunction with a dedicated 3T neonatal MRI system installed on a Neonatal Intensive Care Unit to measure in-plane rotation which is a common problem with neonatal MRI. Rotational data has been acquired in real-time from phantoms simultaneously with MR images which shows that the wireless accelerometer can be used in close proximity to the MR system. No artifacts were observed on the MR images from the accelerometer or from the MR system on the accelerometer output. Initial attempts to correct the raw data using the measured rotational angles have been performed, but further work will be required to make a robust correction algorithm. Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing Ⅱ)
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Review

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Open AccessReview Review of Computational Methods on Brain Symmetric and Asymmetric Analysis from Neuroimaging Techniques
Technologies 2017, 5(2), 16; https://doi.org/10.3390/technologies5020016
Received: 26 November 2016 / Revised: 16 April 2017 / Accepted: 17 April 2017 / Published: 18 April 2017
Cited by 1 | PDF Full-text (492 KB) | HTML Full-text | XML Full-text
Abstract
The brain is the most complex organ in the human body and it is divided into two hemispheres—left and right. The left hemisphere is responsible for control of the right side of our body, whereas the right hemisphere is responsible for control of
[...] Read more.
The brain is the most complex organ in the human body and it is divided into two hemispheres—left and right. The left hemisphere is responsible for control of the right side of our body, whereas the right hemisphere is responsible for control of the left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts of clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal planes, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer’s, autism, schizophrenia, lesions and epilepsy. Therefore, in this paper, we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in different neuroimaging techniques such as the magnetic resonance (MR), computed tomography (CT), positron emission tomography (PET), single-photon emission computed tomography (SPECT), which are utilized for detecting various brain related disorders. Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing Ⅱ)
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