Patent Description:
Coherent imaging modalities (e.g., optical coherence tomography (OCT)) are subject to noise, which can degrade final image quality. In particular with ophthalmological imaging, the presence of noise can significantly degrade imaging fidelity and can lead to errors during segmentation of retinal layers (e.g., during segmentation of B-scans or like images including a depth dimension). These segmentation errors can further lead to errors in analysis of the images based on the segmentation, and thus ultimately, errors in diagnosis and treatment of an imaged subject.

Currently, noise is treated by applying a common filter to an underlying set of data or images produced from that data. However, these filters are unable to remove all noise at appropriate levels. Further, noise reduction systems, and methods that utilize a large number of sample data and images to normalize noise are still subject to noises and errors that persist throughout the sample sets. Therefore, the images can still lack quality, and segmentation and analysis can still be prone to errors.

<NPL>, relates to an adaptive multiple columne stacked sparse denoising autoencoders.

<CIT> discloses a signal processing apparatus for performing a filtering process on an input image using a plurality of filters to generate an output image includes the following elements. A region variance value calculating unit calculates a region variance value for a region around a predetermined pixel used as a pixel of interest on the input image. A filter processing unit applies a filtering process to a pixel value of the pixel of interest using the filters. A reflection amount determining unit determines a reflection amount based on the region variance value. A combining unit calculates a pixel value of a pixel of the output image corresponding to the pixel of interest on the basis of respective filter output values obtained by filtering processes applied by a plurality of filter processing units, respective reflection amounts determined with respect to the filter output values, and the pixel value of the pixel of interest.

According to the invention, an image processing method is provided according to claim <NUM>.

In various embodiments of a first example, the first noise-reduced image and the second noise-reduced images are combined by weighted averaging, the first noise-reduced image being weighted according to a level of the first type of noise in the input image and the second noise-reduced image being weighted according to a level of the second type of noise in the input image; the first noise-reduced image and the second noise-reduced images are combined by a machine learning system; the input image is an optical coherence tomography (OCT) or OCT-angiography B-scan image; the input image is an en face optical coherence tomography (OCT) or OCT-angiography image; the input image is obtained by an optical coherence tomography imaging system configured to operate at least at a <NUM> A-line rate; the input image is an image of a retina; the method further comprises segmenting the input image as a result of applying the first filter and the second filter; and/or the method further comprises displaying the final noise-reduced image in real-time with the capturing of the input image.

According to a second example, an image processing method comprises: filtering a first input image; filtering a second input image; and combining the filtered first input image and the filtered second input image, thereby generating a final noise-reduced volume, wherein the first input image and the second input image are different 2D images of a 3D volume, and wherein the first input image and the second input image are from different planes and/or en face images of different reference layers of the 3D volume.

In view of the above, the present disclosure is directed to improvements in noise reduction for coherent imaging modalities. It is now recognized that more than one type of noise exists in images from coherent imaging modalities, and the types and levels of the noise can vary between pixels of an image. These types of noise include: <NUM>) random noise from the system; and <NUM>) speckle variation (noise) caused by the coherent imaging modalities or objects being imaged (e.g., a subject's eye or other biological tissue). The speckle noise may arise from interference of light waves having random phases, for example, as light scattered from various points of turbid object being imaged. As suggested above, while existing noise reductions methods and systems exist they are directed to treating the different types of noise with the same filter. And other machine learning based methods improperly rely on a substantial number of averaged images that can induce errors in the filtering process.

The aforementioned two types of noise can be seen with respect to <FIG>, which illustrates an image showing the absolute difference between each pixel of two OCT B-scan images generated from scans of essentially the same location of a subject's eye at different times. It is noted that the reference to OCT images is merely an example and that the systems and methods described by the present application are applicable to images generated by any coherent imaging technique. Because the general structure at the location is unchanged, the difference between the two images represents noise, where the brightness of each pixel corresponds to the level of noise.

Three regions of noise are identified in <FIG>. The first region <NUM> is an area above the retina, which includes random noise from the imaging system. The second region <NUM> in the retinal tissue includes speckle variation due to blood flow as well as the random noise in the system. The third region <NUM> in the retinal tissue similarly includes other speckle variation due to live biological tissues (such as blood flow) as well as the random noise in the system.

Because the different types of noise, due to their difference in source, follow different statistical patterns, they should be processed separately. This process is illustrated in the flow chart of <FIG>, showing an example noise reduction method according to the present disclosure. As seen therein, a plurality of noise reduction filters <NUM> (<NUM> to N) are applied to a noisy input image <NUM>. Each noise reduction filter <NUM> produces a corresponding noise reduced image <NUM> (<NUM> to N). The filters <NUM> may be applied in parallel such that each noise reduced image <NUM> only has the noise reduced that corresponds to the applied filter <NUM>, and thus may still include the other types of noise not reduced by the corresponding filter <NUM>. Of course, actual processing may occur sequentially in time while retaining the parallel nature of the application. The plurality of noise reduced images <NUM> can then combined <NUM> to produce a final noise reduced image <NUM>. The combination of noise reduced images results in the final noise reduced image <NUM> having each type of noise reduced.

In other examples such as those corresponding to <FIG>, however, the filters <NUM>-<NUM> to <NUM>-N may be applied sequentially such that a first type of noise is removed by a first filter <NUM>-<NUM> to produce a first noise-reduced image <NUM>-<NUM> in which the first type of noise is removed but other types of noise remain. A second type of filter <NUM>-<NUM> may then be applied to the first noise-reduced image <NUM>-<NUM> to further remove a second type of noise, while still other types of noise remain. This process can be repeated with additional noise reduction filters <NUM>-N until all desired noise is removed and a final noise-reduced image <NUM> produced.

Each filter may be designed and applied based on the probability distribution for the relative intensity for the pixels in an image without a given type of noise. For example, the intensity of each pixel in an image, when subject to random noise, can be modeled as shown in <FIG>. The "true value" (represented by the dashed vertical line) corresponds to the actual intensity of the pixel without any noise. In the probability distribution example of <FIG>, the 'true value' may be determined as the intensity where probability is a maximum. Each pixel may have its own unique probability distribution. In other words, the noise (type and level) may not be uniform throughout an entire image. For example, as seen in <FIG>, the intensity probability distribution <NUM> for a pixel <NUM> above the retina (having only random system noise) in a B-scan image <NUM> is skewed toward zero relative to the intensity probability distribution <NUM> for a pixel <NUM> in a retinal layer (having random system noise and speckle noise) of the image <NUM>. The 'true value' intensity at the maximum of the probability distribution is shown in a corresponding noise-reduced B-scan image <NUM> where the intensity of the pixel <NUM> above the retina is much lower (darker) than that of the retinal pixel <NUM>.

These probability distributions may be different for each type of noise. For example, the intensity probability distribution for pixels without many types of noise may be Gaussian; however, some removal of other noises may cause the intensity probability distributions to follow different statistical models. For noises resulting in the same type of distribution model, removal of each type of noise may result in probability distributions having a different mean, sigma value, or like statistical property.

Therefore, if the intensity probability distribution is understood for the types of noise in each pixel of an image, the noise-free value of each pixel can be reconstructed. For example, the application of each filter may set each pixel of an image to have its true intensity value (e.g., the intensity corresponding to the maximum probability).

Artificial intelligence systems such as deep learning (and other machine learning) models/systems can be used to determine the intensity probability distributions for each pixel (e.g., each location of the retina) to more accurately estimate the most probable intensity value of each pixel. The deep learning systems of each filter are particularly designed and trained to estimate the intensity probability distribution of each pixel. The design and training of the deep learning systems are based on an understanding of the fundamental physics in OCT imaging (or the other coherent imaging methods) so that the systems can be trained with images demonstrating the correct intensity probability distributions.

For example, the deep learning systems can be trained with data sets having two or more images. In each set, the pixel intensity at the same corresponding location among different images is used to construct the intensity probability distribution of that pixel. Put another way, a perfectly large set will include at least one image having every possible intensity for a given pixel location, and the relative number of images in the set having a given intensity represents the corresponding probability of that intensity. Thus, the relative number of images for every intensity value will produce the intensity probability distribution. For sets of fewer images, the same process can be used to generate a probability distribution by fitting the relative number of images of each intensity to a known type of distribution curve. However, as the above description derives probability distribution models based on pixel intensities that retain noise information, the approach alone does not necessarily suppress the different types of noise separately.

To account for the different types of noise, differences between the images of a set can be compared, and adjusted based on the comparison, prior to developing the probability distribution. For example, the effect of random noise can be derived by considering differences between images of a set of B-scan images taken at the same cross-sectional location through the retina. This is because the difference between images of the set will primarily represent random noise, since the underlying structure at the same cross-sectional location generally remains unchanged. Adjusting each image based on these differences has the effect of removing the random noise from the adjusted images. As a result, a probability distribution derived based on the adjusted images is a probability distribution for the image where random noise is suppressed but speckle is largely preserved. In these embodiments, the difference may be determined according to any statistical method, such as by simple subtraction, an average of the differences between each successive scan, or the like; and the adjustments may be made in any statistical manner, such as by subtracting the average difference from each image.

Similarly, the effect of speckle noise can be found from sets of images of B-scans taken from adjacent or nearby locations within a transverse resolution of the imaging system. The differences between the images of such sets will primarily represent speckle noise. Again, the images may be adjusted according to any statistical determination of the difference, so that a resulting intensity probability distribution is one in which the pixel intensity at the maximum probability represents the most probable intensity without speckle noise (e.g., where both speckle noise and random noise are suppressed).

Referring back to <FIG> and <FIG>, a deep learning system trained to suppress random noise may correspond to noise reduction filter <NUM> (a sharp filter) <NUM>-<NUM>, <NUM>-<NUM> and a deep learning system trained to suppress speckle noise may correspond to noise reduction filter <NUM> (a smooth filter) <NUM>-<NUM>, <NUM>-<NUM>. Where deep learning systems are the filters, the original noisy image is input to the trained system (filter), and the output of the trained system is an image in which each pixel is set to the intensity corresponding to the maximum probability. In other words, a trained deep learning system may be one that has been trained to know the intensity probability distribution (or otherwise knows a 'true value' intensity) for each pixel without one type of noise.

Such a configuration where separately trained deep learning systems filter an original input B-scan image to produce a combined noise-reduced image is illustrated in <FIG>. More particularly, an original input image (e.g., an OCT B-scan) <NUM> is input to both a trained machine learning system <NUM> providing a sharp filter and a trained machine learning system <NUM> providing a smooth filter. The sharp filter <NUM> produces an image <NUM> with reduced random noise and the smooth filter produces an image <NUM> with reduced speckle noise. These reduced noise images <NUM>, <NUM> are then combined to produce a final noise-reduced image <NUM> with both random and speckle noises reduced.

In still other embodiments, any type of filter capable of reducing a desired type of noise may be used. Such filters may include median filters, Gaussian filters, spectral domain filters, and the like.

The noise-reduced images output by each filter can be combined according to any statistical combination method. For example, each noise-reduced image can be combined through a weighted average technique where the weight of each image is adjusted to match the relative noise level of the corresponding noise type. In other words, each noise-reduced image can be weighted according to the amount of the corresponding noise in the original input image. Considering the example of <FIG>, the output weights are <NUM> for the sharp filter (meaning <NUM>% of the noise is random) <NUM> and <NUM> for the smooth filter (meaning <NUM>% of the noise is speckle noise) <NUM>.

By way of comparison, combining the outputs of each filter can produce a final noise-reduced image comparable to an image produced by averaging <NUM> images taken at the same location (a traditional technique for suppressing noise). <FIG> shows such a comparison, where an original input image (without any noise reduction) <NUM>, an image produced by averaging <NUM> images from substantially the same location (including the original input image) <NUM>, and an a noise reduced image <NUM> resulting from the above-described combination of outputs of a smooth filter and a sharp filter. As can be seen in the entire B-scan, and in the enlarged portions, separately applying different types noise reduction and then combining the outputs of the filters produces comparable or better results to the averaging, both clearly having less noise than the input image. However, with the method described herein, only one B-scan image is needed to be filtered, rather than the many needed if reducing noise by averaging or the perpetuated errors introduced if reducing noise based on a system trained by averaging.

Other embodiments may use additional filters, or use the filters individually. For example, the noise-reduced output image <NUM> of the smooth filter <NUM> (removing speckle noise) has smoother retinal boundaries than image <NUM> output by the sharp filter <NUM>. Therefore, the smoothed image <NUM> (rather than the output from the sharp filter, or the combined output image) may be best used in tasks such as visualization/display and layer segmentation. <FIG> illustrates this concept with respect to <NUM>-pixel depth en-face images of the lamina cribrosa, where the primary noise component is speckle noise. As seen in <FIG>, the noise-reduced image <NUM> filtered only with a smooth filter to reduce speckle noise is smoother and more visually appealing than the original image <NUM>. Thus, the speckle noise-reduced image <NUM> output from the smooth filter may be beneficial for improving visualization applications.

Similarly, because the noise reduced output image <NUM> of the sharp filter <NUM> preserves speckle information, it may be best used in tasks such as abnormality detection (identifying inflammatory cells) or OCT angiography (which relies on differences between images at a common location to indicate blood flow). For example, <FIG> illustrates an original B-scan image <NUM> and a noise-reduced B-scan image <NUM> output by a sharp filter to remove random noise. In both images <NUM>, <NUM>, an inflammatory cell(s) (circled) <NUM> is visible. However, the cell <NUM> is more identifiable in the random noise-reduced image <NUM> as it is against a clearer background.

During the training of the deep learning systems for noise-reduction filtering, additional information such as the retinal layers, are implicitly learned. This results from the different statistical properties of noise associated with pixels of each retinal layer. As the deep learning system is trained to reduce the different noises (types and levels) in different retinal layers, it thus also learns which pixels are associated with each layer. Put another way, for the deep learning systems to remove proper noise types and levels from each retinal layer, it should know to which layer each pixel belongs. With such knowledge, layer boundaries can be identified, and segmentation realized, by determining the pixels at which the associated layers change.

Thus, in some embodiments, noise-reduction filters and segmentation detectors can be trained simultaneously as part of the same deep learning system. Such an output is illustrated in <FIG>, where an original image <NUM> is input into a trained deep learning system <NUM>, which outputs a noise-reduced image <NUM> and a retinal segmentation <NUM> of the image. The segmentation <NUM> includes (from top to bottom) boundaries of the inner limiting membrane (ILM), retinal pigment epithelium (RPE), and Bruch's membrane layers.

While 2D images such as B-scans have generally been described, the above method and corresponding system could be applied to any type of image. For example, the input may be a single optical coherence tomography (OCT) (or like coherent imaging modality) B-scan, a 3D volumetric scan, an en face image or C-scan, an angiographic B-scan image, an angiography en face image or C-scan, or like images from other imaging modalities.

When using the methods described herein to reduce noise for 3D volumetric data (e.g., 3D OCT structural data, 3D OCT-angiography data), the noise reduction can be performed in different planes of 2D images that collectively form the 3D volumes, as shown in <FIG>. In these embodiments, the noise (types and levels) may be different depending on the location in the volume (either at different depths, or different locations). Accordingly, different noise reduction filters may be applied to different portions of the volume. These filters, if trained machine learning systems, may be trained for different types of noise and/or the different effects of noise at different locations in the volume. Weights used to combine differently filtered images, or other combination techniques, may also be variable and change depending on where in the volume the images are located.

Any of the volumetric data may be noise reduced according to the above-described methods. For example, collections of B-scans in the X-Z plane <NUM> as described above can be individually noise reduced to reduce noise of an entire volume. In other embodiments, the data may be sampled (or re-sampled) in the Y-Z plane and noise reduction as described above applied on Y-Z B-scans <NUM>. Similarly, the noise reduction process can be applied on X-Y plane C-scans or en face images flattened over any depth/thickness and/or with respect to any reference layer. Such a reference layer may be the separation between two retinal tissue layers, for example, the ILM.

In some embodiments, filtering can be applied to different planes of 2D images of a 3D volume or other collection of 2D images (e.g., from non-adjacent locations of a larger 3D volume). Just as with the application of individual filters to the 2D images, the application of filters to different planes can be in parallel and combined, or sequential. <FIG> illustrates a first example of sequential noise reduction of a 3D volume (e.g., an OCT-angiography (OCT-A) volume). According to the example of <FIG>, a first step of noise reducing a 3D OCT-A volume <NUM> involves noise reduction of OCT-angiography images in X-Z plane <NUM> of the volume <NUM>, and a second step involves noise reduction of en face images <NUM> of the volume <NUM>, to produce a final noise-reduced 3D volume <NUM>. <FIG> illustrates a comparison of an en face image <NUM> of the deep plexiform layer taken from the original OCT-A volume <NUM>, and of a en face image <NUM> of deep plexiform layer noise-reduced by the method of <FIG>. As can be seen in <FIG>, vessel connectivity is greatly enhanced after noise reduction.

<FIG> illustrate similar sequential methods with other combinations of planes. In the example of <FIG>, X-Z plane images <NUM> from an original 3D volume <NUM> are first filtered, followed by Y-Z plane images <NUM> and then X-Y plane images <NUM>, to produce a final noise-reduced volume <NUM>. And in the example of <FIG>, X-Y plane images <NUM> from an original 3D volume <NUM> are first filtered, and then X-Z plane images <NUM> are filtered to produce a final noise-reduced volume <NUM>. As above, the different filterings may be directed to a type of noise and/or the plane in which it is applied.

Each filtering step may be performed by a filter trained or otherwise designed to remove a different type of noise and/or to identify noise in the respective plane in which it is applied. Removing a different type of noise in each filter step may be beneficial where each type of noise in more apparent in a different plane, whereas removing the same type of noise in multiple steps may be beneficial where one type of noise expresses itself differently in different planes. Where different types of noise are removed, the images of the plane in a subsequent filtering step may be generated from the original volume after a preceding noise reduction step. Accordingly, the subsequently generated images may already have the preceding type of noise removed. By sequentially applying the filters, a volume reconstructed from the finally filtered images represents a noise-reduced volume.

While each of <FIG> represent the sequential application of filters where subsequent filters are applied to images in which some noise has already been reduced, other embodiments may apply filters in parallel. An example of noise reduction for different planes in parallel is shown in <FIG>. Therein, noise reduction is separately performed in parallel on images of the X-Z plane <NUM> and the Y-Z plane <NUM> of an original 3D volume. As above, performing noise reduction in parallel refers to performing the noise-reduction on images of a 3D volume not subject to the other noise-reduction application. Accordingly, the actual application of the noise-reduction may still be performed sequentially in time. The resulting noise-reduced volumes <NUM>, <NUM> may be averaged (e.g., by weighted averaging or other combination techniques as described above) to produce a final noise-reduced volume <NUM>. Of course, different planes and combinations may be used in parallel in other embodiments.

The present disclosure provides various improvements compared to traditional noise reduction techniques (such as averaging <NUM> images taken from a single location). For example, it can take a relatively long time to obtain <NUM> images (or any number of images being averaged), which limits the total imaging speed and makes the variously averaged images more susceptible to motion artifact. Additionally, it is practically difficult to take so many images at a single location; therefore, each image of the average may include different information. Put another way, an imaging target at that location may not present in every image, or other elements may be confused as the imaging target. Further, to the extent any other machine learning systems are trained with the above-described average images as supervisory training data, the inherent limitations of averaging are imputed into the trained machine learning system.

In addition, it has been empirically observed that the presently described noise reduction preserves the quantitative information available in the original images. Thus, the present noise reduction technique enables applications which were previously only achievable with high-speed devices (e.g. variable interscan time analysis (VISTA) systems), where the high-speed is used to take multiple repeated scans to suppress noise, possible on low-speed devices with fewer repeats. In particular such applications include angiographic imaging (e.g., OCT-A) and the like where imaging is of, or based on, dynamic (non-static) structures (e.g., movement of cells, such as red blood cells). Further in this vein, the noise reduction described herein may be used in at or near real-time imaging applications. In some embodiments, the input images may be obtained with high speed OCT imaging (e.g., operating at a <NUM> A-line scanning rate), such that the filtering according to the present disclosure provides high fidelity images at that high speed. As a result, noise-reduced 3D volumes, cross-sectional images (B-scan or C-scan images), or en face images can be displayed concurrent (or nearly concurrently) with their imaging.

A system not covered by the claims for executing the above-described methods is also contemplated within the scope of the present disclosure. Such a system may include a computer having one or more processors (e.g., in the form of an integrated circuit(s), discrete circuitry, or the like) for executing the method, storage (such as a hard disk, memory, RAM, or the like) and an input/output interface (e.g., display, keyboard, mouse, and the like). The above-described filters may be implemented via software executed by the processor(s) or hardware (e.g., via circuitry, optical filters, or the like) depending on the desired filtering. The storage may be located locally with the computer, or remotely, for example at a centralized database, and store the software instructions for executing the method and filtering, the filtered images and/or volumes, and/or the resulting noise-reduced images and/or volumes. The system may also be integrated or separate from a system used to obtain the images of the object to be processed. For example, the computer may be the same as that used to control an OCT system.

Claim 1:
An image processing method for images generated by a coherent imaging technique comprising:
applying a first filter (<NUM>, <NUM>-<NUM>) to an input image (<NUM>, <NUM>, <NUM>, <NUM>), thereby generating a first noise-reduced image (<NUM>-<NUM>, <NUM>);
applying a second filter (<NUM>, <NUM>-<NUM>) to the input image, thereby generating a second noise-reduced image (<NUM>-<NUM>, <NUM>); and
combining (<NUM>) the first noise-reduced image (<NUM>) and the second noise-reduced image (<NUM>-<NUM>, <NUM>), thereby generating a final noise-reduced image (<NUM>, <NUM>, <NUM>),
wherein the first filter (<NUM>, <NUM>-<NUM>) is configured to suppress a first type of noise from the input image and the second filter (<NUM>, <NUM>-<NUM>) is configured to suppress a second type of noise from the input image, the first and second types of noise being different, wherein the first type of noise is random noise or noise caused by an imaging system that captured the input image, wherein the second type of noise is speckle noise, and
wherein the first filter (<NUM>, <NUM>-<NUM>) is a machine learning system trained with images taken from the same or substantially the same location of a subject's biological tissue as the input image, wherein the second filter (<NUM>, <NUM>-<NUM>) is a machine learning system (<NUM>) trained with images taken from locations of a subject's biological tissue adjacent to a location of the input image.