Image exposure technique for ophthalmic microscope equipment.

Abstract

The image exposure of the ophthalmic microscope apparatus is controlled by first recording a camera image (M) with a first exposure parameter in the microscope's camera (22). Then, edge detection, e.g. based on convolution using a discrete differential operator, is performed to identify areas of high and low edge density in the image. A brightness parameter of the camera image (M) is calculated by weighting the brightness of pixels in areas with higher edge density than other areas. This brightness parameter is used to control the exposure parameter of the microscope.

Description

[Technical field]

[0001] The present invention relates to a method for controlling image exposure in an ophthalmic microscope apparatus, and to an ophthalmic microscope apparatus comprising a control unit adapted to carry out this method. [Background technology]

[0002] Ophthalmic microscope devices such as slit lamp microscopes and fundus cameras include an illumination light source such as a slit lamp for illuminating the subject's eye and a microscope for observing the subject's eye. In many cases, the microscope also includes a camera.

[0003] When recording an image with a camera, exposure correction may be performed by automatically adjusting the exposure time, aperture width, gain, illumination time, etc. For example, the average luminance value of the recorded image is calculated, and the exposure parameters are changed so that this average luminance value becomes a desired value. Summary of the Invention

[0004] The problem that the invention aims to solve is to provide a method and a microscope of the above type which provide improved image quality.

[0005] This problem is solved by the method and microscope of the independent claims.

[0006] Thus, a method for controlling image exposure in an ophthalmic microscope apparatus having a camera and an illumination source includes at least the following steps.

[0007] - Recording a camera image with a camera using the first exposure parameters: the "camera image" can be an original image recorded by the camera itself or an image derived therefrom, for example by modifying the resolution and / or compression and / or applying a first image processing step.

[0008] - using edge detection to identify at least one first sub-region and at least one second sub-region of the camera image (M), the first sub-region having a higher edge density than the second sub-region, the sub-regions corresponding, for example, to a particular subset of the (possibly downscaled) pixels of the camera image;

[0009] - Calculate the brightness parameter of the camera image by weighting the brightness of pixels in the first sub-region more strongly than the brightness of pixels in the second sub-region. In other words, if pixel A of the camera image is a pixel of the first sub-region and pixel B of the camera image is a pixel of the second sub-region, then the brightness of pixel A will influence the brightness parameter more strongly than the brightness of pixel B. For example, the brightness of pixel B may be completely ignored or may be scaled down so that it has a weaker influence on the brightness parameter.

[0010] Depending on the brightness parameter the first exposure parameter is adjusted to obtain the second exposure parameter. For example, if the brightness parameter is too low the exposure time, the illumination intensity, the aperture diameter and / or the camera gain can be increased. At least one of the parameters or some of them can be adjusted.

[0011] - Using the second exposure parameters, an adjusted image is recorded by the camera.

[0012] This method is based on the understanding that ophthalmic microscopy has a shallow depth of field that typically covers only the region of interest or a portion of it: a significant portion of the scene is typically out of focus and therefore does not show sharp pixel intensity changes in the corresponding image regions.

[0013] Therefore, by weighting edge-heavy subregions of the camera image more heavily than uniform subregions for the purposes of calculating the brightness parameters, better exposure can be achieved for the subregion of interest, which may result in more "uniform" parts of the image being under- or over-exposed, but which are generally of no interest.

[0014] The steps of the method are preferably performed automatically, for example by a microscope control unit.

[0015] Determining the first and second sub-regions may include calculating an edge strength image having pixel values ​​indicative of the presence of an edge at each pixel.

[0016] Such an edge strength image can be calculated, for example, using a discrete convolution operator, such as a Sobel filter or a Laplace filter. This operation can be performed, for example, on the camera image or an image derived therefrom, such as a grayscale version of the camera image and / or a downscaled version of the camera image. The edge strength image can then be spatially averaged, for example, by spatial smoothing or downscaling, to obtain an image reflecting both the strength and density of edges.

[0017] In the step of determining the first and second sub-regions, different weights may be assigned to different predetermined parts of the image. For example, parts closer to the center of the image may be weighted more strongly than parts further from the center. Then, before identifying the first and second sub-regions, the edge density is weighted differently. In other words, different parts get different importance. For example, by weighting the center of the image more strongly, the calculated brightness parameters are more sensitive to edge structures in the center of the image.

[0018] The method allows focusing exposure control on areas that are in focus, i.e. areas with a high density of edges. Other areas may be larger, but are more or less uniform, and therefore are weighted less strongly or not at all when calculating the brightness parameters. This is in contrast to conventional exposure control methods, where large uniform areas can dominate the exposure parameters.

[0019] The invention also relates to an ophthalmic microscope apparatus, in particular a slit lamp, having an illumination source, a camera, and a control unit, the control unit being connected to the illumination source and the camera and adapted (e.g. by programming and / or hardware) to carry out the method of the invention. [Brief description of the drawings]

[0020] The invention will be better understood, and further objects thereof will become apparent, from a consideration of the following detailed description, which refers to the accompanying drawings, in which: [Figure 1] FIG. 1 is a perspective view of a slit lamp microscope. [Diagram 2] FIG. 2 is a functional block circuit diagram of the microscope. [Diagram 3] FIG. 3 is a schematic diagram of a portion of the optical system of the microscope. [Figure 4] Figure 4 is a grayscale version of the image recorded by the camera. [Diagram 5] Figure 5 is a weight image calculated by the method. Note: Figures 4 and 5 are dithered for printing. The actual image is, for example, a true scalar image. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] definition By "multiplying" two pixel-based images it is understood that each pixel value of the first image is multiplied with the corresponding pixel value of the second image to calculate each pixel value of the resulting image. Optionally, the images may be rescaled before such an operation so that each pixel of the first image is assigned to a defined pixel of the second image, or vice versa.

[0022] If one of the images has multi-component pixel values ​​(e.g. RGB values) and the second image has scalar pixel values, the multiplication may for example consist of multiplying each multi-component pixel value of the first image with a pixel value of the second image, resulting in an image again with multi-component pixel values. In another variant, the multiplication may first be to combine the multi-component pixel values ​​of the pixels of the first image into a single value, for example using an averaging operation, and then multiply the single value by the pixel value of the second image, in which case the resulting image has scalar pixel values.

[0023] The term "edge density" is any measure that correlates to the number and magnitude of discontinuities in pixel intensity in a given subregion of an image, such as the value of the discrete Laplace transform as described below, or the power in the 2D Fourier spectrum above a given frequency.

[0024] Hardware Figure 1 shows a slit lamp microscope as an example of an ophthalmic microscopy device. The device comprises a microscope 2 and an illumination source 4, such as a slit lamp. These elements can be rotated, for example, about a pivot axis 6, and are used to observe the eye of a subject.

[0025] A headrest 8 may be provided for the subject to rest his / her head on.

[0026] The device may further comprise an external display 10, which is preferably a touch screen.

[0027] A user of the microscope apparatus can view an image of the subject's eye on a display 10 or directly using an eyepiece 12 of the microscope 2 .

[0028] 2 is a functional block circuit diagram of the microscope apparatus. The apparatus includes a control unit 20, which may be, for example, a microprocessor with appropriate memory and programmed to provide the functions of the apparatus. The control unit 20 may be built into the apparatus itself or may be provided externally to the apparatus.

[0029] The control unit 20 is connected to further parts 24 of the apparatus, such as an illumination source 4 , an electronic camera 22 of the microscope 2 and an adjustable aperture of the imaging optics of the microscope 2 .

[0030] The control unit 20 may also be connected to an external display 10 and / or an internal display 26 (described below).

[0031] 3 is a schematic diagram of the optical system of the microscope 2. The microscope 2 includes an objective optical system 30 for capturing an image of a subject's eyeball 32. The objective optical system 30 can have a variable magnification and / or a variable aperture controlled by the control unit 20.

[0032] Light from objective optics 30 passes through beam splitter 34 and a portion of it is directed to eyepiece 12 for direct viewing by a user 36 .

[0033] A beam splitter 34 reflects a portion of the light from the objective optics 30 to the camera 22, which may be, for example, a CCD camera.

[0034] Additionally, the microscope apparatus 2 may include the internal display 26 described above. Light from the internal display 26 is sent to the beam splitter 34, and a portion of the light is reflected back to the eyepiece 12, allowing the information displayed on the internal display 26 to be viewed.

[0035] Exposure Control The control unit 20 is adapted and programmed to automatically select appropriate exposure parameters when recording an image with the camera 22. These parameters may be one or more of the following:

[0036] the brightness of the illumination source 2: this brightness can be adjusted, for example, by adjusting the current through the illumination source 2;

[0037] - Pulse length of the illumination light source 2: the illumination light source 2 can be operated in pulsed mode during the recording of the image, i.e. it can emit one or several light pulses during the recording of one frame by the camera 22. In that case the total duration of the light pulses during the recording of the frame can be adjusted to obtain an adequate exposure.

[0038] - Microscope aperture diameter: if the microscope 2 is equipped with an adjustable aperture as described above, the control 20 can vary the diameter of the aperture to decrease or increase the exposure dose.

[0039] - Camera gain.

[0040] - The sampling time of the camera, i.e. the integration time between taking one image.

[0041] To determine the appropriate exposure parameters, the control unit 20 first captures a "camera image" with the camera 22. From this camera image, a brightness parameter B is calculated as described below. If the brightness parameter B indicates that the image is not sufficiently bright, the control unit 20 increases one or more of the exposure parameters described above and captures an "adjusted image" with the camera 22.

[0042] In particular, the control unit 20 may implement a control loop that attempts to adjust the brightness parameter B of multiple successive images captured by the camera 22 to a desired value by modifying one or more exposure parameters.

[0043] Calculation of luminance parameters The method is based on measuring at least one brightness parameter B from the images recorded by the camera. In the following, the procedure for calculating this parameter is explained in detail.

[0044] Step 1: Recording camera images In a first step, a camera image M is recorded by the camera 22. The camera image is a pixel-based representation of the image recorded by the camera, e.g. a colour image, a greyscale image, a pseudo-colour image etc. As mentioned above, it may be the raw image taken by the camera 22 or may be derived from this raw image by a first image processing step.

[0045] Step 2: Calculate the grayscale image In the next step, the camera image M, i.e., each pixel G ij A grayscale image G can be computed from an image where (i=1...N, j=1...M) are scalar values ​​indicating the "brightness" in a given pixel region. If the camera image M is a color image, the grayscale image G can be computed, for example, from a weighted average of the RGB values ​​of the camera image M.

[0046] Step 3: Calculate the edge strength image In a next step, an edge strength image E is calculated, for example from the grayscale image G (if a grayscale image has been calculated) or directly from the camera image M. The pixel values ​​E of the edge strength image E ij is G ij indicates the presence of a discontinuity between and its adjacent pixel.

[0047] For example, an edge strength image E is calculated from a greyscale image G by convolving it with a two-dimensional discrete Laplacian operator D and taking the absolute value of the result. E ij =|(D*G) ij | (1)

[0048] The discrete Laplace operator D is calculated, for example, using a convolution with an appropriate kernel, which calculates the weighted difference between the value of a pixel and its neighboring pixels. See, for example, https: / / en.wikipedia.org / wiki / Discrete_Laplace_operator. The Laplacian is a 2D isotropic measure of the second spatial derivative of an image, which emphasizes areas with abrupt changes in intensity.

[0049] Instead of using the absolute discrete Laplace operator D, other edge-sensitive filters such as Prewitt, Roberts, Sobel, Scharr, etc. can also be used. Currently, Scharr is preferred. Also, the edge strength image can be spatially averaged, for example by downscaling, to obtain a filtered image that reflects both the strength and density of edges.

[0050] Note that an edge strength image can also be computed directly from the camera image M, allowing, for example, to detect hue and saturation edges that are not apparent in grayscale space.

[0051] Instead of calculating a complete edge strength image, edge detection can generally be used to identify at least one region of the camera image that has a high edge density and another region that has a low edge density, for example by dividing the camera image into multiple sub-regions, calculating the Fourier transform of each sub-region, and identifying the one with the strongest high frequency spectral content.

[0052] Step 4: Offset the edge strength image In optional step 4, the edge strength image is offset by a fixed value. E´=E+off (2)

[0053] This ensures that areas of low edge density or intensity have minimal impact on the final exposure.

[0054] In optional step 5, the edge strength image E (or E') is multiplied pixel-wise by a filter image F that has higher values ​​in the center of the image than at the edges. The result of this step is a weights image W. W ij =E´ ij F ij (4)

[0055] The filter image F may for example be a Gaussian distribution of values ​​centred on the image.

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[0056] Alternatively, the filter image F can be any other pixel distribution that prioritizes certain predefined parts of the image and reduces the importance of other parts. Preferably, the highest pixel value F in the center of the image is ij where pixel values ​​decrease monotonically with increasing distance from the center of the image.

[0057] Step 6: Normalization As a result of the previous step, the weight image W may be normalized (unless normalization is performed in another step of the method), i.e. its pixel values ​​are scaled so that their sum is equal to a certain predefined value (i.e. a value independent of the camera image), in particular equal to 1. For example, the normalized weight image W′ can be calculated as follows:

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[0058] Step 7: Calculate the luminance parameters Using the normalized weighting image W', the brightness parameter B can be calculated as the weighted sum of all the pixels of the grayscale image G, with the weights given by the weighting image. B=Σ i,j W ij G ij (7)

[0059] The brightness parameter B is a weighted sum of the brightness of the pixels in the grayscale image G, with higher weights for pixels that are at edge structures and (if vignetting is used) closer to the center of the image, etc.

[0060] Instead of calculating the brightness parameter B from the greyscale image G, it may for example be calculated from a direct multiplication with the camera image M and again by weighting its pixel values ​​with the pixel values ​​of the weighted image W.

[0061] Image resolution In the above example calculation of luminance values, it was assumed that all images had the same resolution.

[0062] However, it should be noted that the resolution of the various images may differ. Mapping operations may be used between the individual steps and / or in mathematically combining the individual images.

[0063] For example, the resolution of the camera image M is not necessarily the same as the resolution of the camera, for example, if a portion of the image recorded by the camera is cut off (e.g., if only a square region of a non-square camera is used) and / or the resolution of the camera image may be downscaled to derive the camera image M for fast processing.

[0064] Similarly, the edge strength image E may be downscaled before the calculation of offset, vignetting, normalization and / or brightness parameters. This can be seen as a method of spatial averaging that turns the edge strengths into a combined edge strength / density information. The advantage of such downscaling is twofold: on the one hand, the processing becomes faster; on the other hand, pixels close to edge structures in the camera image M are given a higher weight, even if they are not in the immediate vicinity of the edge structure.

[0065] example Figure 4 shows a (dithered) grayscale image G taken with a slit lamp microscope, showing edge-rich regions corresponding to a cross-section of the cornea enhanced by slit illumination.

[0066] Figure 5 shows the weight image W calculated by the above procedure from the grayscale image in Figure 4. As can be seen, the edges in the edge-heavy structures in Figure 4 have high (bright) values ​​in the center.

[0067] Precautions In steps 3-5 above, edge detection is used to identify at least one "first sub-region" of the camera image having a high edge density. This first sub-region corresponds, for example, to pixels of the edge strength image E or E' having a high value or, if edge detection based on a 2D Fourier transform is used, to the sub-region having the strongest high frequency spectral content. Similarly, at least one "second sub-region" having a low edge density is identified. This second sub-region corresponds, for example, to pixels of the edge strength image E or E' having a low value or, if edge detection based on a 2D Fourier transform is used, to the sub-region having the lowest high frequency spectral content.

[0068] The identification process of the first and second sub-regions may use a second set of weights, as in step 5, that are assigned to a predetermined portion of the image and multiplied with the weights from step 4, where the "predetermined portion" is a portion whose location does not depend on the content of the camera image M.

[0069] In the above example, the pixels of the filtered image F are indicative of these predetermined portions in that they have high values ​​in those portions that should be weighted more strongly. In calculating equation (4), the edge density embodied by the edge density image E or E' is weighted with different weights for the predetermined portions of the image with the weights embodied by the filtered image F. In a specific example, portions closer to the center of the image are weighted more strongly than portions further from the center.

[0070] In particular, the discrimination between the first and second sub-regions may give preference to the sub-region closer to the center of the image as the first sub-region, in other words parts closer to the center of the image are weighted more strongly than parts further from the center.

[0071] Steps 6 and 7 then calculate a brightness parameter B of the camera image by weighting the pixel intensities of the first sub-region more strongly than the pixel intensities of the second sub-region, in this example calculating the average of a weighted sum of the pixels of the greyscale image.

[0072] In a more general embodiment, equation (7) can be replaced by: B = f1(Σ i,j f2(W ij G ij )) (7) where f1 and f2 are monotonic functions. In other words, the brightness parameter is the product W ij G ij may depend nonlinearly on

[0073] While the presently preferred embodiments of the invention have been shown and described, the invention is not limited thereto. It is to be clearly understood that the present invention is not limited to the disclosed embodiments, and may be variously embodied and implemented within the scope of the appended claims. sea ​​bream. [Configuration 1] A method for controlling image exposure in an ophthalmic microscope apparatus, the ophthalmic microscope apparatus having a camera (22) and an illumination source (4), the method comprising: recording a camera image (M) with said camera (22) using first exposure parameters; using edge detection to identify at least one first sub-region and at least one second sub-region of the camera image (M), the first sub-region having a higher edge density than the second sub-region; - calculating a brightness parameter (B) of said camera image (M) by weighting the pixel brightness of said first sub-region more strongly than the pixel brightness of said second sub-region; adjusting the first exposure parameter according to the brightness parameter (B) to obtain a second exposure parameter; recording an adjusted image with the camera (22) using the second exposure parameters; The method includes: [Configuration 2] 2. The method of claim 1, wherein determining the first and second sub-regions comprises calculating an edge strength image (E, E') having pixel values ​​indicative of the presence of a discontinuity at each pixel. [Configuration 3] 3. The method of claim 2, wherein the edge strength image (E, E') is calculated by applying a discrete convolution operator to the camera image (M) or an image derived from the camera image (M). [Configuration 4] 4. A method according to any one of the preceding claims, wherein in the step of identifying the first and second sub-regions, different weights are assigned to different predetermined parts of the image, and before identifying the first and second sub-regions, the edge density is weighted with the different weights, in particular parts closer to a centre of the image are weighted more strongly than parts further from the centre. [Configuration 5] A method according to any one of configurations 4 and 2 or 3, comprising calculating a weight image (W, W') by multiplying the edge strength image (E, E') with a filter image (F), the filter image (F) having pixel values ​​indicative of the predetermined portion of the image. [Configuration 6] 6. The method of claim 5, comprising multiplying the weighted image (W, W') with the camera image (M) or an image derived from the camera image (W), in particular a grayscale image (G) of the camera image (M). [Configuration 7] prior to multiplying said weighted image (W, W') with said camera image (M), normalizing said weighted image by scaling it such that the sum of said pixel values ​​is equal to a predetermined predefined value; B=f 1 (Σ i,j f 2 (W ij ·G ij )) Calculate the brightness parameter B from where f 1 and f 2 is a monotonic function, The method according to configuration 6. [Configuration 8] 8. The method of any one of configurations 1 to 7, comprising the step of calculating a greyscale image (G) of the camera image (M), wherein the first and second sub-regions are identified using the greyscale image (G). [Configuration 9] Depending on the brightness parameter (B), the brightness of the illumination light source (4); the pulse length of the illumination light source (4); the aperture diameter of the microscope; The gain of the camera (22) the sampling time of the camera (22); 9. The method of any one of the preceding claims, comprising adjusting at least one of the parameters. [Configuration 10] An illumination source (4); A camera (22); a control unit (20) connected to the illumination source (4) and to the camera (22), the control unit (20) being adapted to perform the method according to any one of the preceding claims; An ophthalmic microscope apparatus comprising:

Claims

1. A method for controlling image exposure in an ophthalmic microscope apparatus, the ophthalmic microscope apparatus having a camera (22) and an illumination source (4), the method comprising the steps of: recording a camera image (M) with said camera (22) using first exposure parameters; using edge detection to identify at least one first sub-region and at least one second sub-region of the camera image (M), the first sub-region having a higher edge density than the second sub-region; - calculating a brightness parameter (B) of said camera image (M) by weighting the pixel brightness of said first sub-region more strongly than the pixel brightness of said second sub-region; adjusting the first exposure parameter according to the brightness parameter (B) to obtain a second exposure parameter; recording an adjusted image with said camera (22) using said second exposure parameters; The method includes:

2. determining the first and second sub-regions includes calculating a pixel value Eij of a pixel (i,j) of an edge strength image (E,E'), the pixel value Eij being indicative of the presence of a discontinuity at each pixel (i,j) of the camera image or a greyscale image (G) calculated from the camera image; the edge strength image (E, E') having pixel values ​​indicative of the presence of discontinuities at each pixel; The method of claim 1.

3. 3. The method of claim 2, wherein the edge strength image (E, E') is calculated by applying a discrete convolution operator to the camera image (M) or to an image derived from the camera image (M).

4. 4. The method of claim 1, wherein in the step of identifying the first and second sub-regions, different weights are assigned to different predetermined portions of the image, and the edge density is weighted with the different weights before identifying the first and second sub-regions.

5. in the step of identifying the first and second sub-regions, different weights are assigned to different predetermined portions of the image, and before identifying the first and second sub-regions, the edge densities are weighted with the different weights; calculating pixel values ​​W ij of a weighted image (W, W') by multiplying pixel values ​​E' ij of the edge strength image (E, E') by pixel values ​​F ij of a filtered image (F); W ij = E' ij ・F ij The method according to claim 2 or 3, wherein the pixel values ​​F ij of the filtered image (F) are indicative of the predetermined portion of the image.

6. 6. A method according to claim 4 or 5, wherein the edge density is weighted more strongly in areas closer to a centre of the image than in areas further from the centre.

7. The method of claim 5, comprising the step of multiplying the weighted image (W, W') with the camera image (M) or with an image derived from the camera image (W).

8. The method of claim 7 , wherein the derived image is a grayscale image (G) of the camera image (M).

9. prior to multiplying said weight image (W,W') with said camera image (M), normalizing said weight image by scaling it such that the sum of said pixel values ​​is equal to a predetermined predefined value; B=f １ (Σ ｉ，ｊ f ２ (W ｉｊ ・G ｉｊ )) Calculate the brightness parameter B from Here, f １ and f ２ is a monotonic function, The method according to claim 7.

10. 10. The method of claim 1, further comprising the step of calculating a greyscale image (G) of the camera image (M), wherein the first and second sub-regions are identified using the greyscale image (G).

11. Depending on the brightness parameter (B), the brightness of the illumination light source (4), the pulse length of the illumination source (4); the aperture diameter of the ophthalmic microscope device; The gain of the camera (22) the sampling time of the camera (22); 11. The method according to claim 1, further comprising adjusting at least one of the exposure parameters of said first exposure step.

12. An illumination source (4); A camera (22); a control unit (20) connected to said illumination source (4) and to said camera (22), said control unit (20) being adapted to perform a method according to any one of claims 1 to 11; An ophthalmic microscope apparatus comprising:

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