Optical coherence tomography (OCT) allows for non-invasive 3D visualization of biological tissue at cellular level resolution. Often hindered by speckle noise, the visualization of important biological tissue details in OCT that can aid disease diagnosis can be improved by speckle noise compensation. A challenge with handling speckle noise is its inherent non-stationary nature, where the underlying noise characteristics vary with the spatial location. In this study, an innovative speckle noise compensation method is presented for handling the non-stationary traits of speckle noise in OCT imagery. The proposed approach centers on a non-stationary spline-based speckle noise modeling strategy to characterize the speckle noise. The novel method was applied to ultra high-resolution OCT (UHROCT) images of the human retina and corneo-scleral limbus acquired in-vivo that vary in tissue structure and optical properties. Test results showed improved performance of the proposed novel algorithm compared to a number of previously published speckle noise compensation approaches in terms of higher signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and better overall visual assessment.
Fluorescence microscopy is an essential part of a biologist’s toolkit, allowing assaying of many parameters like subcellular localization of proteins, changes in cytoskeletal dynamics, protein-protein interactions, and the concentration of specific cellular ions. A fundamental challenge with using fluorescence microscopy is the presence of noise. This study introduces a novel approach to reducing noise in fluorescence microscopy images. The noise reduction problem is posed as a Maximum A Posteriori estimation problem, and solved using a novel random field model called stochastically-connected random field (SRF), which combines random graph and field theory. Experimental results using synthetic and real fluorescence microscopy data show the proposed approach achieving strong noise reduction performance when compared to several other noise reduction algorithms, using quantitative metrics. The proposed SRF approach was able to achieve strong performance in terms of signal-to-noise ratio in the synthetic results, high signal to noise ratio and contrast to noise ratio in the real fluorescence microscopy data results, and was able to maintain cell structure and subtle details while reducing background and intra-cellular noise.
Improving the spatial resolution of Optical Coherence Tomography (OCT) images is important for the visualization and analysis of small morphological features in biological tissue such as blood vessels, membranes, cellular layers, etc. In this paper, we propose a novel reconstruction approach to obtaining super-resolved OCT tomograms from multiple lower resolution images. The proposed Multi-Penalty Conditional Random Field (MPCRF) method combines four different penalty factors (spatial proximity, first and second order intensity variations, as well as a spline-based smoothness of fit) into the prior model within a Maximum A Posteriori (MAP) estimation framework. Test carried out in retinal OCT images illustrate the effectiveness of the proposed MPCRF reconstruction approach in terms of spatial resolution enhancement, as compared to previously published super resolved image reconstruction methods. Visual assessment of the MPCRF results demonstrate the potential of this method in better preservation of fine details and structures of the imaged sample, as well as retaining the sharpness of biological tissue boundaries while reducing the effects of speckle noise inherent to OCT. Quantitative evaluation using imaging metrics such as Signal-to-Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Equivalent Number of Looks (ENL), and Edge Preservation Parameter show significant visual quality improvement with the MPCRF approach. Therefore, the proposed MPCRF reconstruction approach is an effective tool for enhancing the spatial resolution of OCT images without the necessity for significant imaging hardware modifications.
Abstract-Diffusion weighted magnetic resonance imaging (DW-MR) is a powerful tool in imaging-based prostate cancer screening and detection. Endorectal coils are commonly used in DW-MR imaging to improve the signal-to-noise ratio (SNR) of the acquisition, at the expense of significant intensity inhomogeneities (bias field) that worsens as we move away from the endorectal coil. The presence of bias field can have a significant negative impact on the accuracy of different image analysis tasks, as well as prostate tumor localization, thus leading to increased inter-and intra-observer variability. Retrospective bias correction approaches are introduced as a more efficient way of bias correction compared to the prospective methods such that they correct for both of the scanner and anatomy-related bias fields in MR imaging. Previously proposed retrospective bias field correction methods suffer from undesired noise amplification that can reduce the quality of bias-corrected DW-MR image. Here, we propose a unified data reconstruction approach that enables joint compensation of bias field as well as data noise in DW-MR imaging. The proposed noise-compensated, biascorrected (NCBC) data reconstruction method takes advantage of a novel stochastically fully connected joint conditional random field (SFC-JCRF) model to mitigate the effects of data noise and bias field in the reconstructed MR data. The proposed NCBC reconstruction method was tested on synthetic DW-MR data, physical DW-phantom as well as real DW-MR data all acquired using endorectal MR coil. Both qualitative and quantitative analysis illustrated that the proposed NCBC method can achieve improved image quality when compared to other tested bias correction methods. As such, the proposed NCBC method may have potential as a useful retrospective approach for improving the consistency of image interpretations.
White matter fibre tractography is a non-invasive method for reconstructing three dimensional trajectories of fibre pathways. Fast Marching is one of fibre tracking methods in which co-linearity of principal eigenvectors determines the speed of front's evolution. In this algorithm effect of tensor's eigenvalues are not considered. In the current work, the speed function of standard fast marching was modified by considering the strength of tensor's eigenvectors. The proposed speed function has an adaptive Fractional Anisotropy (FA) weighted factor which can be set by type of brain's environments (i.e. isotropic and anisotropic regions). This modification was found to have high accuracy for detecting fibres by reducing false pathways. The proposed method has performed high accuracy in detection of fibre crossing.
The performance ofICA algorithms in correct separation sequences such as system noise, patient motion, physiological ofindependent sources can be highly affected by existence ofnoises noise (breathing and heart beat), ghost artifact in fMRI in the observation data. In this paper a hybrid Wavelet-ICA method images, long-term instability of the scanner baseline, local for improving the functionality of noise free ICA algorithms in changes in magnetic field due to short term scanner noisy environment is proposed. At first the robustness of two most instabilities, and slow phase variation in MR images has made frequent ICA algorithms, named Fast ICA and Information . ' maximization ICA, for extracting true activated spatial and the detectlon of bram acivations a challengig task by temporal sources offMRI signals in the presence of different noise different algorithms. Many different methods have been levels are evaluated. These algorithms are applied on simulated proposed for analyzing fMRI signals which can be categorized fMRI datasets consisting of different activated sources with various into two approaches: model based analysis such as General temporal patterns, different levels of activation, trend and noise. linear model, GLM and data driven analysis such as principal Then, a hybrid wavelet-Fast ICA model to transform the signals component analysis, PCA, Factor analysis, FA and into a domain, allowing for simultaneous un-mixing and wavelet Independent component analysis (ICA) [1 ]. Among data based de-noising is proposed. As the results show this combination * * t has significantly improved the sensitivity of extracted sources in drivesndmetds, the mowrf estCa method whIc different SNR levels, in particular in low SNR s. To measure the assumes idep enddcy of the sources that constitute feitRI accuracy of source separation, the correlation coefficients between signal. ICA has been used for fMRI analysis to separate either extracted activation signals and simulated temporal patterns are spatially independent brain maps or their activation time also measured. As the results suggest the proposed hybrid method is courses (Spatial ICA) and temporally independent time more robust in comparison with noise free ICA for noisy courses and the corresponding spatial maps (Temporal ICA) observation in extracting more accurate independent sources. from the fMRI signal. There have been many algorithms proposed to implement the ICA method but the most Index Term: Functional magnetic resonance imaging, frequently used for analyzing of them are Fast ICA and Independent Component Analysis, Infomax, Wavelet, Infomax [1,2]. One of the main factors which influence the Wiener Filter Fast ICA, PICA, SNR performances of different ICA algorithms is the amount of noise in the observation data, here fMRI datasets, which I. INTRODUCTION makes the estimation of un-mixing matrix more complicated. s a noninvasive technique to measure brain activity, Furthermore, the estimation of true dimension of independent fMRI based on Blood Oxygenation Le...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.