Compression and Extraction of the Region of Interest from Medical Images through Deep Learning Methods
- Resource Type
- Conference
- Authors
- Gupta, Manali; Sharma, Sanjay Kumar; Saxena, Roshi; Arora, Sonia; Singh, Yaduvir
- Source
- 2021 First International Conference on Advances in Computing and Future Communication Technologies (ICACFCT) Advances in Computing and Future Communication Technologies (ICACFCT), 2021 First International Conference on. :243-247 Dec, 2021
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Deep learning
Neuroimaging
Image quality
Image coding
Bit rate
Communications technology
Image restoration
brain extraction
medical image compression
deep learning approach
- Language
The objective of any medical image compression strategy include reducing the bit rate and increasing compression efficiency while trying to maintain the quality of diagnostic images. A partial region-based compression method is applied in order to achieve high compression rate and at the same time maintaining the image quality. Most of the medical images contain regions of interest which are critical for making the diagnoses. These regions, however, need to be removed and recreated in high quality. The present paper aims at describing and exploring deep learning techniques which can be used to extract regions of interest in MR brain imaging. The restored and the recreated high quality regions are then compressed through both lossless and lossy techniques.