Journal of Healthcare Engineering

Advancements of Medical Image Enhancement in Healthcare Applications


Publishing date
22 Sep 2017
Status
Published
Submission deadline
05 May 2017

Lead Editor

1Shandong University, Jinan, China

2Tufts University, Boston, USA

3Beijing Institute of Technology, Beijing, China

4Emory University, Atlanta, USA


Advancements of Medical Image Enhancement in Healthcare Applications

Description

As one of the world’s largest and fastest-growing industries, healthcare engineering refers to all aspects of the prevention, diagnosis, treatment, and management of illness, as well as the preservation and improvement of physical and mental health and wellbeing through medical services.

Medical imaging technologies play more and more important roles not only in the diagnosis and treatment of diseases, but also in disease prevention, health checkup, major disease screening, health management, early diagnosis, disease severity evaluation, choice of treatment methods, treatment effect evaluation, and rehabilitation. The status of medical imaging technologies has increased continuously in healthcare applications.

Due to its ability to make the diagnosis and treatment of diseases, image guided surgery, and other medical links more timely, accurate, and efficient, medical image enhancement has become a routine task. Through producing excellent tissue uniformity, optimized contrast, edge enhancement, artifact elimination, intelligent noise reduction, and so forth, cutting-edge image enhancement helps doctors to accurately interpret medical images, a crucial foundation for better diagnosis and treatment.

The purpose of this special issue is to publish high-quality research articles as well as

reviews that seek to address recent development on medical image enhancement, denoising, edge sharpening, artifact elimination, and resolution enhancement, as well as relevant prospects on opportunities and challenges.

Potential topics include but are not limited to the following:

  • Algorithms for medical image denoising: ultrasound, MRI, X-ray imaging, fundus photography, OCT, mammography, and so forth
  • New methods of image sharpening and contrast enhancement
  • Algorithms for artifact elimination: MRI, X-ray, CT, and so forth
  • 2D/3D/4D image filtering and visualization: ultrasound, MRI, X-ray, CT, and OCT
  • Algorithms for medical image resolution enhancement: interpolation and superresolution
  • Molecular imaging filtering and fusion: PET, fMRI, ultrasound, and SPECT
  • Optical microscopy and fluorescent imaging and enhancement

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 7035264
  • - Editorial

Advancements of Medical Image Enhancement in Healthcare Applications

Shujun Fu | Ming Zhang | ... | Xiaohong Shen
  • Special Issue
  • - Volume 2018
  • - Article ID 7329548
  • - Research Article

Efficient OCT Image Enhancement Based on Collaborative Shock Filtering

Guohua Liu | Ziyu Wang | ... | Peijin Li
  • Special Issue
  • - Volume 2017
  • - Article ID 8536206
  • - Research Article

Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

Jean Marie Vianney Kinani | Alberto Jorge Rosales Silva | ... | Alfonso Arellano
  • Special Issue
  • - Volume 2017
  • - Article ID 5817970
  • - Research Article

Semiautomatic Epicardial Fat Segmentation Based on Fuzzy c-Means Clustering and Geometric Ellipse Fitting

Vladimir Zlokolica | Lidija Krstanović | ... | Danilo Babin
  • Special Issue
  • - Volume 2017
  • - Article ID 1417270
  • - Research Article

Frame-Based CT Image Reconstruction via the Balanced Approach

Weifeng Zhou | Hua Xiang
  • Special Issue
  • - Volume 2017
  • - Article ID 2727686
  • - Research Article

Automatic Radiographic Position Recognition from Image Frequency and Intensity

Ning-ning Ren | An-ran Ma | ... | Jian-feng Qiu
  • Special Issue
  • - Volume 2017
  • - Article ID 3978410
  • - Research Article

The Edge Detectors Suitable for Retinal OCT Image Segmentation

Su Luo | Jing Yang | ... | Chang’an A. Zhan
  • Special Issue
  • - Volume 2017
  • - Article ID 5859727
  • - Research Article

Deep Learning- and Transfer Learning-Based Super Resolution Reconstruction from Single Medical Image

YiNan Zhang | MingQiang An

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