Special Issue "Medical Imaging & Image Processing"

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A special issue of Technologies (ISSN 2227-7080).

Deadline for manuscript submissions: closed (31 March 2015)

Special Issue Editors

Guest Editor
Prof. Dr. Yudong Zhang

1 School of information science and technology, Nanjing Normal University, Nanjing, China
2 Columbia University, New York, NY, USA
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 has become an essential component in many fields of bio-medical research and clinical practice. Biologists study cells and generate 3D confocal microscopy data sets, virologists generate 3D reconstructions of viruses from micrographs, radiologists identify and quantify tumors from MRI and CT scans, and neuroscientists detect regional metabolic brain activity from PET and functional MRI scans.

Image Processing includes the analysis, enhancement and display of images captured via various medical imaging technologies. Image reconstruction and modeling techniques allow instant processing of 2D signals to create 3D images. In addition, 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.

Dr. Yudong Zhang
Dr. Zhengchao Dong
Guest Editors

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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. For the first couple of issues the Article Processing Charge (APC) will be waived for well-prepared manuscripts. English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.


Keywords

  • medical imaging
  • biological imaging
  • magnetic resonance imaging
  • neuroimaging
  • computerized tomography
  • image processing & analysis
  • machine learning
  • pattern recognition

Published Papers (5 papers)

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Editorial

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Open AccessEditorial Special Issue on “Medical Imaging and Image Processing”
Technologies 2014, 2(4), 164-165; doi:10.3390/technologies2040164
Received: 8 December 2014 / Accepted: 18 December 2014 / Published: 19 December 2014
PDF Full-text (604 KB) | HTML Full-text | XML Full-text
Abstract
Over the last decade, Medical Imaging has become an essential component in many fields of bio-medical research and clinical practice. Biologists study cells and generate 3D confocal microscopy data sets, virologists generate 3D reconstructions of viruses from micrographs, radiologists identify and quantify tumors
[...] Read more.
Over the last decade, Medical Imaging has become an essential component in many fields of bio-medical research and clinical practice. Biologists study cells and generate 3D confocal microscopy data sets, virologists generate 3D reconstructions of viruses from micrographs, radiologists identify and quantify tumors from MRI and CT scans, and neuroscientists detect regional metabolic brain activity from PET and functional MRI scans. On the other hand, Image Processing includes the analysis, enhancement, and display of images captured via various medical imaging technologies. Image reconstruction and modeling techniques allow instant processing of 2D signals to create 3D images. In addition, 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 not previously been discernible.[...] Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing)

Research

Jump to: Editorial

Open AccessArticle A New Compton Camera Imaging Model to Mitigate the Finite Spatial Resolution of Detectors and New Camera Designs for Implementation
Technologies 2015, 3(4), 219-237; doi:10.3390/technologies3040219
Received: 26 June 2015 / Revised: 19 October 2015 / Accepted: 21 October 2015 / Published: 27 October 2015
PDF Full-text (273 KB) | HTML Full-text | XML Full-text
Abstract
An intrinsic limitation of the accuracy that can be achieved with Compton cameras results from the inevitable fact that the detectors, which comprise the camera, cannot have infinitely-accurate spatial resolution. To mitigate this loss of accuracy, a new imaging model is proposed. The
[...] Read more.
An intrinsic limitation of the accuracy that can be achieved with Compton cameras results from the inevitable fact that the detectors, which comprise the camera, cannot have infinitely-accurate spatial resolution. To mitigate this loss of accuracy, a new imaging model is proposed. The implementation of the new imaging model, however, requires new camera designs. The results of a computer simulation indicate that the new imaging model can produce reasonable images, at least when noiseless simulated data are used. In the future, more work is needed to determine if the use of the new imaging model will improve the imaging capabilities of Compton cameras despite the loss of sensitivity caused by the use of the new camera designs. Regardless of the outcome of this work, the results presented here illustrate that new models for imaging from Compton scatters are possible and motivate the development of further models that could be more advantageous than the ones already developed. Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing)
Open AccessArticle Automated Segmentation of MS Lesions in MR Images Based on an Information Theoretic Clustering and Contrast Transformations
Technologies 2015, 3(2), 142-161; doi:10.3390/technologies3020142
Received: 31 March 2015 / Revised: 27 May 2015 / Accepted: 28 May 2015 / Published: 5 June 2015
PDF Full-text (2261 KB) | HTML Full-text | XML Full-text
Abstract
Magnetic Resonance Imaging (MRI) plays a significant role in the current characterization and diagnosis of multiple sclerosis (MS) in radiological imaging. However, early detection of MS lesions from MRI still remains a challenging problem. In the present work, an information theoretic approach to
[...] Read more.
Magnetic Resonance Imaging (MRI) plays a significant role in the current characterization and diagnosis of multiple sclerosis (MS) in radiological imaging. However, early detection of MS lesions from MRI still remains a challenging problem. In the present work, an information theoretic approach to cluster the voxels in MS lesions for automatic segmentation of lesions of various sizes in multi-contrast (T1, T2, PD-weighted) MR images, is applied. For accurate detection of MS lesions of various sizes, the skull-stripped brain data are rescaled and histogram manipulated prior to mapping the multi-contrast data to pseudo-color images. For automated segmentation of multiple sclerosis (MS) lesions in multi-contrast MRI, the improved jump method (IJM) clustering method has been enhanced via edge suppression for improved segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF) and MS lesions if present. From this preliminary clustering, a pseudo-color to grayscale conversion is designed to equalize the intensities of the normal brain tissues, leaving the MS lesions as outliers. Binary discrete and 8-bit fuzzy labels are then assigned to segment the MS lesions throughout the full brain. For validation of the proposed method, three brains, with mild, moderate and severe hyperintense MS lesions labeled as ground truth, were selected. The MS lesions of mild, moderate and severe categories were detected with a sensitivity of 80%, and 96%, and 94%, and with the corresponding Dice similarity coefficient (DSC) of 0.5175, 0.8739, and 0.8266 respectively. The MS lesions can also be clearly visualized in a transparent pseudo-color computer rendered 3D brain. Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing)
Open AccessArticle Medical Image Processing for Fully Integrated Subject Specific Whole Brain Mesh Generation
Technologies 2015, 3(2), 126-141; doi:10.3390/technologies3020126
Received: 16 April 2015 / Revised: 14 May 2015 / Accepted: 15 May 2015 / Published: 21 May 2015
Cited by 5 | PDF Full-text (1965 KB) | HTML Full-text | XML Full-text
Abstract
Currently, anatomically consistent segmentation of vascular trees acquired with magnetic resonance imaging requires the use of multiple image processing steps, which, in turn, depend on manual intervention. In effect, segmentation of vascular trees from medical images is time consuming and error prone due
[...] Read more.
Currently, anatomically consistent segmentation of vascular trees acquired with magnetic resonance imaging requires the use of multiple image processing steps, which, in turn, depend on manual intervention. In effect, segmentation of vascular trees from medical images is time consuming and error prone due to the tortuous geometry and weak signal in small blood vessels. To overcome errors and accelerate the image processing time, we introduce an automatic image processing pipeline for constructing subject specific computational meshes for entire cerebral vasculature, including segmentation of ancillary structures; the grey and white matter, cerebrospinal fluid space, skull, and scalp. To demonstrate the validity of the new pipeline, we segmented the entire intracranial compartment with special attention of the angioarchitecture from magnetic resonance imaging acquired for two healthy volunteers. The raw images were processed through our pipeline for automatic segmentation and mesh generation. Due to partial volume effect and finite resolution, the computational meshes intersect with each other at respective interfaces. To eliminate anatomically inconsistent overlap, we utilized morphological operations to separate the structures with a physiologically sound gap spaces. The resulting meshes exhibit anatomically correct spatial extent and relative positions without intersections. For validation, we computed critical biometrics of the angioarchitecture, the cortical surfaces, ventricular system, and cerebrospinal fluid (CSF) spaces and compared against literature values. Volumina and surface areas of the computational mesh were found to be in physiological ranges. In conclusion, we present an automatic image processing pipeline to automate the segmentation of the main intracranial compartments including a subject-specific vascular trees. These computational meshes can be used in 3D immersive visualization for diagnosis, surgery planning with haptics control in virtual reality. Subject-specific computational meshes are also a prerequisite for computer simulations of cerebral hemodynamics and the effects of traumatic brain injury. Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing)
Open AccessArticle A Hybrid Feature Extractor using Fast Hessian Detector and SIFT
Technologies 2015, 3(2), 103-110; doi:10.3390/technologies3020103
Received: 3 March 2015 / Revised: 12 May 2015 / Accepted: 12 May 2015 / Published: 15 May 2015
PDF Full-text (824 KB) | HTML Full-text | XML Full-text
Abstract
This paper addresses a new hybrid feature extractor algorithm, which in essence integrates a Fast-Hessian detector into the SIFT (Scale Invariant Feature Transform) algorithm. Feature extractors mainly consist of two essential parts: feature detector and descriptor extractor. This study proposes to integrate (Speeded-Up
[...] Read more.
This paper addresses a new hybrid feature extractor algorithm, which in essence integrates a Fast-Hessian detector into the SIFT (Scale Invariant Feature Transform) algorithm. Feature extractors mainly consist of two essential parts: feature detector and descriptor extractor. This study proposes to integrate (Speeded-Up Robust Features) SURF’s hessian detector into the SIFT algorithm so as to boost the total number of true matched pairs. This is a critical requirement in image processing and widely used in various corresponding fields from image stitching to object recognition. The proposed hybrid algorithm has been tested under different experimental conditions and results are quite encouraging in terms of obtaining higher matched pairs and precision score. Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing)

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

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