Journal of Healthcare Engineering

Computer Vision in Healthcare Applications


Status
Published

Lead Editor

1Jiangxi University of Finance and Economics, Nanchang, China

2Chonbuk National University, Jeonju, Republic of Korea

3Laureate Institute for Brain Research, Tulsa, USA

4South-Central University for Nationalities, Wuhan, China


Computer Vision in Healthcare Applications

Description

Medical imaging has attracted increasing attention in recent years due to its vital component in healthcare applications. The advancement in computer vision, such as multimodal image fusion, medical image segmentation, image registration, computer-aided diagnosis, image annotation, and image-guided therapy, has opened up many new possibilities for revolutionizing healthcare. Such areas include mobile healthcare, computer vision for predictive analytics and therapy, medical imaging, population health applications, and mobile devices as biometric sensors.

With this scope in mind, this special issue focuses on recent advances in the applications of computer vision techniques for healthcare. For this purpose, we solicit submission of original research contributions that advance computer vision methods for healthcare engineering, as well as review articles that will stimulate the continuing efforts to understand the problems usually encountered in this field.

Potential topics include but are not limited to the following:

  • Medical image analysis for healthcare (such as noninvasive diagnosis, image-guided radiotherapy, and treatment planning)
  • Computer vision for predictive analytics and therapy
  • Fundamental algorithms for medical images in healthcare applications, such as segmentation, registration, fusion, and classification
  • Scalable, robust, data-driven, ensemble learning, deep learning algorithms for medical images
  • Visualization, mining, and analysis of medical image collections
  • Visualization for healthcare big data

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 5157020
  • - Editorial

Computer Vision in Healthcare Applications

Junfeng Gao | Yong Yang | ... | Dong Sun Park
  • Special Issue
  • - Volume 2018
  • - Article ID 5098973
  • - Research Article

Leukocyte Image Segmentation Using Novel Saliency Detection Based on Positive Feedback of Visual Perception

Chen Pan | Wenlong Xu | ... | Yong Yang
  • Special Issue
  • - Volume 2018
  • - Article ID 4098237
  • - Research Article

An Elderly Care System Based on Multiple Information Fusion

Zhiwei He | Dongwei Lu | ... | Mingyu Gao
  • Special Issue
  • - Volume 2017
  • - Article ID 6506049
  • - Research Article

An Improved Random Walker with Bayes Model for Volumetric Medical Image Segmentation

Chunhua Dong | Xiangyan Zeng | ... | Yen-Wei Chen
  • Special Issue
  • - Volume 2017
  • - Article ID 9271251
  • - Research Article

Digital Path Approach Despeckle Filter for Ultrasound Imaging and Video

Marek Szczepański | Krystian Radlak
  • Special Issue
  • - Volume 2017
  • - Article ID 8314740
  • - Research Article

Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images

QingZeng Song | Lei Zhao | ... | XueChen Dou
  • Special Issue
  • - Volume 2017
  • - Article ID 9856058
  • - Research Article

Low-Rank and Sparse Decomposition Model for Accelerating Dynamic MRI Reconstruction

Junbo Chen | Shouyin Liu | Min Huang
  • Special Issue
  • - Volume 2017
  • - Article ID 4037190
  • - Research Article

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

David Vázquez | Jorge Bernal | ... | Aaron Courville
  • Special Issue
  • - Volume 2017
  • - Article ID 3090343
  • - Review Article

A Review on Human Activity Recognition Using Vision-Based Method

Shugang Zhang | Zhiqiang Wei | ... | Zhen Li
  • Special Issue
  • - Volume 2017
  • - Article ID 4128183
  • - Research Article

An Interactive Care System Based on a Depth Image and EEG for Aged Patients with Dementia

Xin Dang | Bingbing Kang | ... | Guangyu Cui

Article of the Year Award: Impactful research contributions of 2022, as selected by our Chief Editors. Discover the winning articles.