Computational 2D and 3D Medical Image Data Compression Models

2021 ◽  
Author(s):  
S. Boopathiraja ◽  
V. Punitha ◽  
P. Kalavathi ◽  
V. B. Surya Prasath
Keyword(s):  
Data Compression ◽  
Medical Image ◽  
Image Data ◽  
Medical Image Data ◽  
Image Data Compression ◽  
2D And 3D

A Study of Medical Image Data Compression

SMPTE Journal ◽  
1993 ◽  
Vol 102 (1) ◽  
pp. 9-13 ◽  
Author(s):  
M. A. Wondrow ◽  
P. J. Wegwerth ◽  
M. P. Mitchell ◽  
B. K. Gilbert
Keyword(s):  
Data Compression ◽  
Medical Image ◽  
Image Data ◽  
Medical Image Data ◽  
Image Data Compression

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>

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