Optical coherence tomography angiography (OCTA) is a novel noninvasive imaging modality for visualization of retinal blood flow in the human retina. Using specific OCTA imaging biomarkers for the identification of pathologies, automated image segmentations of the blood vessels can improve subsequent analysis and diagnosis. We present a novel method for the vessel density identification based on frequency representations of the image, in particular, using so-called Gabor filter banks. The algorithm is evaluated qualitatively and quantitatively on an OCTA image in-house data set from 10 eyes acquired by a Cirrus HD-OCT device. Qualitatively, the segmentation outcomes received very good visual evaluation feedback by experts. Quantitatively, we compared resulting vessel density values with the automated in-built values provided by the device. The results underline the visual evaluation. Furthermore, for the evaluation of the substep of FAZ identification manual annotations of 2 expert graders were used, showing that our results coincide well in visual and quantitative manners. Lastly, we suggest the computation of adaptive local vessel density maps that allow straightforward analysis of retinal blood flow in a local manner.
Iterative polynomial fitting along image rows and columns has recently been used to remove curvature bias in multispectral image sets of the human forearm and phantoms. However, this method is only applicable if foreground and background features satisfy strong separation conditions. In this method, we verify that the iterative polynomial approach converges toward bivariate polynomial fitting, and, hence, the resulting fit corresponds to low-pass filtering the image. In contrast to the iterative fitting, the bivariate polynomial fit can be performed on images with missing or excluded parts. Indeed, our observation enables us to modify the scheme and significantly weaken the required assumptions on foreground/background separation allowing a wider range of application.
This paper discusses a variational method of processing the scanning laser ophthalmoscope(cSLO) image sequences
in the context of extracting the local rhodopsin density and modeling the bleaching kinetics. This work
supports the characterization and detection of early pathological changes in clinical retinal data. Our goals include
providing automated tools for tracing early pathological changes over time, in particular rhodopsin density
variations and local lesion progression.
Our computational approach is a variational technique that approximates measured cSLO image sets optimally
within the range of the bleaching model. The characterizing parameters of the approximating curves are
computed locally and their spatial changes reflect variations in bleaching kinetics and hence changes in the local
rhodopsin density.
The curve fitting in the temporal direction of the image stack can be also viewed as a denoising/enhancement
routine. The advantages of the temporal correction include a better fit of the image intensity function to the
model and the avoidance of local averaging that would impair the spatial resolution.
Localized rod photoreceptor and rhodopsin losses have been observed in post mortem histology both in normal
aging and in age-related maculopathy. We propose to noninvasively map local rod rhodopsin density through
analysis of the brightening of the underlying lipofuscin autofluorescence (LAF) in confocal scanning laser ophthalmoscopy
(cSLO) imaging sequences starting in the dark adapted eye. The detected LAF increases as rhodopsin is
bleached (time constant ≈ 25sec) by the average retinal irradiance of the cSLO 488nm laser beam. We fit parameters
of analytical expressions for the kinetics of rhodopsin bleaching that Lamb validated using electroretinogram
recordings in human. By performing localized (≈ 100μm) kinetic analysis, we create high resolution maps of the
rhodopsin density. This new noninvasive imaging and analysis approach appears well-suited for measuring localized
changes in the rod photoreceptors and correlating them at high spatial resolution with localized pathological
changes of the retinal pigment epithelium (RPE) seen in steady-state LAF images.
Noncontact optical imaging of curved objects can result in strong artifacts due to the object's shape, leading to curvature biased intensity distributions. This artifact can mask variations due to the object's optical properties, and makes reconstruction of optical/physiological properties difficult. In this work we demonstrate a curvature correction method that removes this artifact and recovers the underlying data, without the necessity of measuring the object's shape. This method is applicable to many optical imaging modalities that suffer from shape-based intensity biases. By separating the spatially varying data (e.g., physiological changes) from the background signal (dc component), we show that the curvature can be extracted by either averaging or fitting the rows and columns of the images. Numerical simulations show that our method is equivalent to directly removing the curvature, when the object's shape is known, and accurately recovers the underlying data. Experiments on phantoms validate the numerical results and show that for a given image with 16.5% error due to curvature, the method reduces that error to 1.2%. Finally, diffuse multispectral images are acquired on forearms in vivo. We demonstrate the enhancement in image quality on intensity images, and consequently on reconstruction results of blood volume and oxygenation distributions.
KEYWORDS: Blood, Principal component analysis, Skin, Data modeling, Chromophores, Multispectral imaging, Associative arrays, Absorption, RGB color model, Biological research
Multispectral images of skin contain information on the spatial distribution of biological chromophores, such as blood and melanin. From this, parameters such as blood volume and blood oxygenation can be retrieved using reconstruction algorithms. Most such approaches use some form of pixelwise or volumetric reconstruction code. We explore the use of principal component analysis (PCA) of multispectral images to access blood volume and blood oxygenation in near real time. We present data from healthy volunteers under arterial occlusion of the forearm, experiencing ischemia and reactive hyperemia. Using a two-layered analytical skin model, we show reconstruction results of blood volume and oxygenation and compare it to the results obtained from our new spectral analysis based on PCA. We demonstrate that PCA applied to multispectral images gives near equivalent results for skin chromophore mapping and quantification with the advantage of being three orders of magnitude faster than the reconstruction algorithm.
State of the art dimension reduction and classification schemes in multi- and
hyper-spectral imaging rely primarily on the information contained in the spectral
component. To better capture the joint spatial and spectral data distribution we
combine the Wavelet Packet Transform with the linear dimension reduction method
of Principal Component Analysis. Each spectral band is decomposed by means of the
Wavelet Packet Transform and we consider a joint entropy across all the spectral
bands as a tool to exploit the spatial information. Dimension reduction is then
applied to the Wavelet Packets coefficients. We present examples of this technique
for hyper-spectral satellite imaging. We also investigate the role of various shrinkage
techniques to model non-linearity in our approach.
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