Two-Dimensional Wavelet based Medical Videos using Hidden Markov Tree Model

Abstract

Wavelet based statistical image denoising is vital preprocessing technique in real world imaging. Most of the medical videos inherit system introduced noise during acquisition as a result of additional image capturing techniques, resulting in the poor quality of video for examination. So, we needed a trade-off between preservation and noise reduction of the actual image content to retains all the necessary information. The existing techniques are based on time-frequency domain where the wavelet coefficients need to be independent or jointly Gaussian. In denoising arena there is a need to exploit the temporal dependencies of wavelet coefficients with non-Gaussian nature. Here we present a YCbCr (Luminance-Chrominance) based denoising strategy on Hidden Markov Model (HMM) based on Multiresolution Analysis in the framework of Expectation-Maximization algorithm. Proposed algorithm applies denoising technique independently on each frame of the video. It models Non-Gaussian statistics of each wavelet coefficient and captures the statistical dependencies between coefficients. Denoised frames are restored inversely by processing the wavelet coefficients. Significant results are visualized through objective as well as subjective analysis. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) like parameters are used for the quality assessment of proposed method in comparison with Gray scale and Red, Green, Blue (RGB) scale videos coefficients.