sensors-logo

Journal Browser

Journal Browser

Special Issue "Medical and Biomedical Sensing and Imaging"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 30 April 2021.

Special Issue Editor

Dr. Canan Schumann
Website
Guest Editor
Health Sciences University, Portland OR, United States
Interests: imaging; nanotechnology; RNA therapeutics; theranostic; lipid particle synthesis and design

Special Issue Information

Dear Colleagues,

Medical imaging is the forefront of early detection of cancer, morphological tissue changes, and other deleterious disease states. The use of contrast agents, and photosensitive dyes delivered either through nanoparticle, lipid, or polymeric delivery platforms provides the means to detect small subtleties and elucidate structural abnormalities of the tissue of interest. Therefore, it is imperative that imaging stay on the cutting edge of design, sensitivity, and reproducibility. The scope of biomedical imaging is vast and should remain as novel as the problems it is trying to solve.

Dr. Canan Schumann
Guest Editor

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 papers will be 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. Sensors is an international peer-reviewed open access semimonthly 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 2000 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
  • Flourececne 
  • Nanoparticle
  • Theranostics
  • Contrast agents
  • Dyes
  • Ultrasound
  • Cancer
  • Nanotechnology
  • Imaging

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
A Shape Approximation for Medical Imaging Data
Sensors 2020, 20(20), 5879; https://doi.org/10.3390/s20205879 - 17 Oct 2020
Abstract
This study proposes a shape approximation approach to portray the regions of interest (ROI) from medical imaging data. An effective algorithm to achieve an optimal approximation is proposed based on the framework of Particle Swarm Optimization. The convergence of the proposed algorithm is [...] Read more.
This study proposes a shape approximation approach to portray the regions of interest (ROI) from medical imaging data. An effective algorithm to achieve an optimal approximation is proposed based on the framework of Particle Swarm Optimization. The convergence of the proposed algorithm is derived under mild assumptions on the selected family of shape equations. The issue of detecting Parkinson’s disease (PD) based on the Tc-99m TRODAT-1 brain SPECT/CT images of 634 subjects, with 305 female and an average age of 68.3 years old from Kaohsiung Chang Gung Memorial Hospital, Taiwan, is employed to demonstrate the proposed procedure by fitting optimal ellipse and cashew-shaped equations in the 2D and 3D spaces, respectively. According to the visual interpretation of 3 experienced board-certified nuclear medicine physicians, 256 subjects are determined to be abnormal, 77 subjects are potentially abnormal, 174 are normal, and 127 are nearly normal. The coefficients of the ellipse and cashew-shaped equations, together with some well-known features of PD existing in the literature, are employed to learn PD classifiers under various machine learning approaches. A repeated hold-out with 100 rounds of 5-fold cross-validation and stratified sampling scheme is adopted to investigate the classification performances of different machine learning methods and different sets of features. The empirical results reveal that our method obtains 0.88 ±0.04 classification accuracy, 0.87 ±0.06 sensitivity, and 0.88 ±0.08 specificity for test data when including the coefficients of the ellipse and cashew-shaped equations. Our findings indicate that more constructive and useful features can be extracted from proper mathematical representations of the 2D and 3D shapes for a specific ROI in medical imaging data, which shows their potential for improving the accuracy of automated PD identification. Full article
(This article belongs to the Special Issue Medical and Biomedical Sensing and Imaging)
Back to TopTop