scholarly journals 3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review

2021 ◽  
Author(s):  
Daria Kern ◽  
Andre Mastmeyer
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Medical Image ◽  
Image Data ◽  
Bounding Box ◽  
Medical Image Data

scholarly journals 3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review

2020 ◽  
Author(s):  
Daria Kern ◽  
Andre Mastmeyer
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Medical Image ◽  
Image Data ◽  
Random Regression ◽  
Feature Engineering ◽  
Anatomical Structures ◽  
Bounding Box ◽  
Medical Image Data ◽  
2D And 3D

This paper discusses current methods and trends for 3D bounding box detection in volumetric medical image data. For this purpose, an overview of relevant papers from recent years is given. 2D and 3D implementations are discussed and compared. Multiple identified approaches for localizing anatomical structures are presented. The results show that most research recently focuses on Deep Learning methods, such as Convolutional Neural Networks vs. methods with manual feature engineering, e.g. Random-Regression-Forests. An overview of bounding box detection options is presented and helps researchers to select the most promising approach for their target objects.<br>

scholarly journals 3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review

2020 ◽  
Author(s):  
Daria Kern ◽  
Andre Mastmeyer
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Medical Image ◽  
Image Data ◽  
Random Regression ◽  
Feature Engineering ◽  
Anatomical Structures ◽  
Bounding Box ◽  
Medical Image Data ◽  
2D And 3D

This paper discusses current methods and trends for 3D bounding box detection in volumetric medical image data. For this purpose, an overview of relevant papers from recent years is given. 2D and 3D implementations are discussed and compared. Multiple identified approaches for localizing anatomical structures are presented. The results show that most research recently focuses on Deep Learning methods, such as Convolutional Neural Networks vs. methods with manual feature engineering, e.g. Random-Regression-Forests. An overview of bounding box detection options is presented and helps researchers to select the most promising approach for their target objects.<br>

scholarly journals Design and Implementation of Compressed Medical Data over an Authenticated Secured

2020 ◽  
Vol 9 (3) ◽  
pp. 1543-1548
Keyword(s):  
Medical Image ◽  
Research Work ◽  
Health Sector ◽  
Image Data ◽  
Medical Data ◽  
Information Network ◽  
Design And Implementation ◽  
Simulation Results ◽  
Medical Image Data ◽  
Secure Transmission

Ideally, secure transmission of medical image data is one of the major challenges in health sector. The National Health Information Network has to protect the data in confidential manner. Storage is also one of the basic concern along with secure transmission. In this paper we propose an algorithm that supports confidentiality, authentication and integrity implementation of the scrambled data before transmitting on the communication medium. Before communication the data is compressed while keeping data encrypted. The research work demonstrate with simulation results. The results shows that the proposed work effectively maintains confidentiality, authentication and integrity. The experimental results evaluated medical image quality like PSNR, MSE, SC, and NAEetc.

Similarity Searching of Medical Image Data in Distributed Systems

2012 ◽  
pp. 58-77
Author(s):  
Amalia Charisi ◽  
Panagiotis Korvesis ◽  
Vasileios Megalooikonomou
Keyword(s):  
Distributed Systems ◽  
Image Retrieval ◽  
Medical Image ◽  
Image Data ◽  
Medical Data ◽  
Similarity Searching ◽  
Network Bandwidth ◽  
Local Site ◽  
Distributed Index ◽  
Medical Image Data

In this paper, the authors propose a method for medical image retrieval in distributed systems to facilitate telemedicine. The proposed framework can be used by a network of healthcare centers, where some can be remotely located, assisting in diagnosis without the necessary transfer of patients. Security and confidentiality issues of medical data are expected, which are handled at the local site following the procedures and protocols of each institution. To make the search more effective, the authors introduce a distributed index based on features that are extracted from each image. Considering network bandwidth limitations and other restrictions that are associated with handling medical data, the images are processed locally and a pointer is distributed in the network. For the distribution of this pointer, the authors propose a function that maps the pointer of each image to a node with similar contents.

Sign in / Sign up

Export Citation Format

Share Document