Visual enhancement of brain cancer MRI using multiscale dyadic filter and Hilbert transformation.
Ankit VidyarthiPublished in: Biomedizinische Technik. Biomedical engineering (2020)
The quality of the medical image plays a major role in decision making by the radiologists. There exists a visual differentiation between the normal scene color images and medical images. Due to the low illumination and unavailability of the color parameter, medical images require more attention by radiologists for decision making. In this paper a new approach is proposed that enhances the quality of the Magnetic Resonance (MR) images. Proposed approach uses the spectral information present in form of Amplitude and Frequency within the MR image slices for an enhancement. The extracted enhanced spectral information gives better visualization as compared with original signal image generated from MR scanner. The quantitative analysis of the proposed approach suggests that the new method is far better than the traditional state-of-art image enhancement methods.
Keyphrases
- deep learning
- artificial intelligence
- magnetic resonance
- contrast enhanced
- optical coherence tomography
- convolutional neural network
- healthcare
- machine learning
- magnetic resonance imaging
- decision making
- resting state
- papillary thyroid
- health information
- high resolution
- hiv infected
- white matter
- young adults
- dual energy
- antiretroviral therapy
- image quality
- functional connectivity
- social media
- mass spectrometry
- blood brain barrier