Ameneh Boroomand

Stochastic speckle noise compensation in optical coherence tomography using non-stationary spline-based speckle noise modelling.

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.

Multi-penalty conditional random field approach to super-resolved reconstruction of optical coherence tomography images.

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.

Fluorescence microscopy image noise reduction using a stochastically-connected random field model.

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.

Saliency-guided projection geometric correction using a projector-camera system

Projecting an image onto an arbitrary non-flat screen surface leads to undesired geometric distortions in the images projection. Geometric correction pre-distorts the image being projected such that the images projection appears geometrically correct. In this work, we propose a novel saliency-guided projection geometric correction (SPGC) method that leverages calibration parameters along with 3D surface geometry captured by the projector-camera system to compensate for the geometric distortions created by non-flat screen surfaces. The proposed SPGC method incorporates a novel sampling scheme that selects a small set of surface points for geometric correction estimation based on local surface saliency, which greatly reduces the computational complexity of geometric correction estimation process. Experimental results using a test non-flat screen surface with abrupt edges and curve showed that the proposed SPGC approach achieved superior distortion compensation performance both quantitatively and qualitatively when compared to an unguided projection geometric correction method, while requiring just 3% of the samples used by a conventional densely-sampled projection geometric correction method.

Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model

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, bias-corrected (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.

Lateral resolution enhancement via imbricated spectral domain optical coherence tomography in a maximum-a-posterior reconstruction framework

The lateral resolution of a Spectral Domain Optical Coherence Tomography (SD-OCT) image is limited by the focusing properties of the OCT imaging probe optics, the wavelength range which SD-OCT system operates at, spherical and chromatic aberrations induced by the imaging optics, the optical properties of the imaged object, and in the special case of in-vivo retinal imaging by the optics of the eye. This limitation often results in challenges with resolving fine details and structures of the imaged sample outside of the Depth-Of-Focus (DOF) range. We propose a novel technique for generating Laterally Resolved OCT (LR-OCT) images using OCT measurements acquired with intentional imbrications. The proposed, novel method is based on a Maximum A Posteriori (MAP) reconstruction framework which takes advantage of a Stochastic Fully Connected Conditional Random Field (SFCRF) model to compensate for the artifacts and noise when reconstructing a LR-OCT image from imbricated OCT measurement. The proposed lateral resolution enhancement method was tested on synthetic OCT measurement as well as on a human cornea SDOCT image to evaluate the usefulness of the proposed approach in lateral resolution enhancement. Experimental results show that applying this method to OCT images, noticeably improves the sharpness of morphological features in the OCT image and in lateral direction, thus demonstrating better delineation of fine dot shape details in the synthetic OCT test, as well as better delineation of the keratocyte cells in the human corneal OCT test image.

Axial resolution improvement in spectral domain optical coherence tomography using a depth-adaptive maximum-a-posterior framework

The axial resolution of Spectral Domain Optical Coherence Tomography (SD-OCT) images degrades with scanning depth due to the limited number of pixels and the pixel size of the camera, any aberrations in the spectrometer optics and wavelength dependent scattering and absorption in the imaged object [1]. Here we propose a novel algorithm which compensates for the blurring effect of these factors of the depth-dependent axial Point Spread Function (PSF) in SDOCT images. The proposed method is based on a Maximum A Posteriori (MAP) reconstruction framework which takes advantage of a Stochastic Fully Connected Conditional Random Field (SFCRF) model. The aim is to compensate for the depth-dependent axial blur in SD-OCT images and simultaneously suppress the speckle noise which is inherent to all OCT images. Applying the proposed depth-dependent axial resolution enhancement technique to an OCT image of cucumber considerably improved the axial resolution of the image especially at higher imaging depths and allowed for better visualization of cellular membrane and nuclei. Comparing the result of our proposed method with the conventional Lucy-Richardson deconvolution algorithm clearly demonstrates the efficiency of our proposed technique in better visualization and preservation of fine details and structures in the imaged sample, as well as better speckle noise suppression. This illustrates the potential usefulness of our proposed technique as a suitable replacement for the hardware approaches which are often very costly and complicated.

A stochastically fully connected conditional random field framework for super resolution OCT

A number of factors can degrade the resolution and contrast of OCT images, such as: (1) changes of the OCT pointspread function (PSF) resulting from wavelength dependent scattering and absorption of light along the imaging depth (2) speckle noise, as well as (3) motion artifacts. We propose a new Super Resolution OCT (SR OCT) imaging framework that takes advantage of a Stochastically Fully Connected Conditional Random Field (SF-CRF) model to generate a Super Resolved OCT (SR OCT) image of higher quality from a set of Low-Resolution OCT (LR OCT) images. The proposed SF-CRF SR OCT imaging is able to simultaneously compensate for all of the factors mentioned above, that degrade the OCT image quality, using a unified computational framework. The proposed SF-CRF SR OCT imaging framework was tested on a set of simulated LR human retinal OCT images generated from a high resolution, high contrast retinal image, and on a set of in-vivo, high resolution, high contrast rat retinal OCT images. The reconstructed SR OCT images show considerably higher spatial resolution, less speckle noise and higher contrast compared to other tested methods. Visual assessment of the results demonstrated the usefulness of the proposed approach in better preservation of fine details and structures of the imaged sample, retaining biological tissue boundaries while reducing speckle noise using a unified computational framework. Quantitative evaluation using both Contrast to Noise Ratio (CNR) and Edge Preservation (EP) parameter also showed superior performance of the proposed SF-CRF SR OCT approach compared to other image processing approaches.

A unified Bayesian-based compensated magnetic resonance imaging

Magnetic resonance (MR) images of higher quality is demanded for helping with more accurate and earlier diagnosis of different diseases. The overall quality of MR images is limited due to the existence of different degradation factors such as (1) MR aberrations due to intrinsic properties of the MR scanner, (2) magnetic field inhomogeneity, and (3) inherent MRI noise. Correcting each MRI degradation factor could be solely useful for the quality enhancement of MR imaging with a limited impact. Here, we propose a unified Bayesian based compensated MR imaging (CMRI) system which jointly corrects for the different aforementioned MR aberrations as well as MR noise and hence generates compensated MR (CMR) images with a higher quality. Testing the proposed CMRI system on both MR physical phantom as well as diffusion weighted and T2 weighted MR imaging data resulted in producing MR images with an overall higher quality that better represents different structures of tissue. The quantitative performance analysis shows a higher Signal to Noise (SNR) and Contrast to Noise (CNR) ratios as well as less Coefficient of Variation (CV) for reconstructed images using the proposed CMRI system compared to the Blind Deconvolution Compensation (BDC) method as state-of-the-art. As such, the proposed CMRI system has potential in improving MR image quality, which is important for accurate and consistent clinical interpretation.Magnetic resonance (MR) images of higher quality is demanded for helping with more accurate and earlier diagnosis of different diseases. The overall quality of MR images is limited due to the existence of different degradation factors such as (1) MR aberrations due to intrinsic properties of the MR scanner, (2) magnetic field inhomogeneity, and (3) inherent MRI noise. Correcting each MRI degradation factor could be solely useful for the quality enhancement of MR imaging with a limited impact. Here, we propose a unified Bayesian based compensated MR imaging (CMRI) system which jointly corrects for the different aforementioned MR aberrations as well as MR noise and hence generates compensated MR (CMR) images with a higher quality. Testing the proposed CMRI system on both MR physical phantom as well as diffusion weighted and T2 weighted MR imaging data resulted in producing MR images with an overall higher quality that better represents different structures of tissue. The quantitative performance analysis shows a higher Signal to Noise (SNR) and Contrast to Noise (CNR) ratios as well as less Coefficient of Variation (CV) for reconstructed images using the proposed CMRI system compared to the Blind Deconvolution Compensation (BDC) method as state-of-the-art. As such, the proposed CMRI system has potential in improving MR image quality, which is important for accurate and consistent clinical interpretation.

Bayesian Compensated Microscopy

We present a novel Bayesian compensated microscopy (BCM) technique designed for enhancing microscopy image quality. The proposed BCM technique provides a computational approach to jointly compensate for microscopy image degradations due to (1) optical aberrations, (2) illumination non-uniformities, and (3) imaging noise within a probabilistic framework. Experimental results based on a stained pathology sample of spleen tissue with leukemia demonstrate the effectiveness of the proposed BCM technique for the quality enhancement in microscopy imaging. The proposed BCM technique can lead to improved visualization of fine tissue structures as well as a more consistent visualization across the entire sample, which can be beneficial for accurate analysis and better interpretation of microscopy samples.