Patent Publication Number: US-2023156320-A1

Title: Apparatus and method for imaging fundus of eye

Description:
FIELD 
     Various embodiments relate to an apparatus for imaging a fundus of an eye, and to a method for imaging the fundus of the eye. 
     BACKGROUND 
     The fundus of the eye is the rear interior surface of the eye opposite the lens. The fundus comprises the retina, optic disc (or optic nerve head), macula (or macula lutea), fovea, and posterior pole. Traditionally, the fundus is examined by ophthalmoscopy, but nowadays fundus photography is also used. With the fundus photography, the central and peripheral retina, optic disc, and macula may be examined. The applicant, medical technology company Optomed, is the leading manufacturer of handheld fundus cameras globally. Optomed Aurora® IQ is an example of a handheld fundus camera. Although it is easy to use, correct operation and practice is required especially in aiming the camera correctly to capture a still image or a video of the fundus. 
     BRIEF DESCRIPTION 
     According to an aspect, there is provided an apparatus for imaging a fundus of an eye comprising: an optical system; an image sensor to capture a still image or a video through the optical system; a user interface including a display to display data to a user of the apparatus; one or more processors to cause performance of at least the following: setting the image sensor to capture an aiming video; detecting a retina of the eye in the aiming video; setting the display to display the aiming video with a highlight of the detected retina; and setting the image sensor to capture one or more final still images of the fundus or a final video of the fundus. 
     According to an aspect, there is provided a method for imaging a fundus of an eye comprising: setting an image sensor to capture an aiming video through an optical system; detecting a retina of the eye in the aiming video; setting a display to display the aiming video with a highlight of the detected retina; and setting the image sensor to capture one or more final still images of the fundus or a final video of the fundus through the optical system. 
     In an embodiment, the one or more processors cause performance of detecting the retina comprises using a machine vision algorithm trained with images of eyes with annotated fundi. 
     In an embodiment, the one or more processors cause performance of detecting the retina comprises using an Adaptive Boosting, AdaBoost, statistical classification meta-algorithm or one of its variants, which construct a strong classifier by combining results of a sequence of weak classifiers. 
     In an embodiment, the one or more processors cause performance of determining, by the weak classifiers, a probability of a pixel of interest in a single frame of the aiming video belonging to the retina by comparing either an average luminosity of an area in the single frame relative to the pixel of interest to a first constant, or a difference of averages of luminosities of two areas in the single frame relative to the pixel of interest to a second constant. 
     In an embodiment, the one or more processors cause performance of using, by the weak classifiers, also one or more results of comparisons from previous weak classifiers in the sequence of the classifiers to improve the accuracy of a next weak classifier in the sequence of the classifiers. 
     In an embodiment, the one or more processors cause performance of comparing, by the weak classifiers, also average probabilities of pixels in areas in the single frame belonging to the retina as determined by an already executed weak classifier as follows: comparing either an average probability of an area in the single frame relative to the pixel of interest to a third constant, or a difference of averages of probabilities of two areas in the single frame relative to the pixel of interest to a fourth constant. 
     One or more examples of implementations are set forth in more detail in the accompanying drawings and the description of embodiments. 
    
    
     
       LIST OF DRAWINGS 
       Some embodiments will now be described with reference to the accompanying drawings, in which 
         FIG.  1    and  FIG.  2    illustrate embodiments of an apparatus for imaging a fundus of an eye; 
         FIG.  3   ,  FIG.  4   , and  FIG.  5    illustrate various light sources in the apparatus and their use in the imaging of the fundus of the eye; 
         FIG.  6 A ,  FIG.  6 B ,  FIG.  7   ,  FIG.  8   ,  FIG.  9 A ,  FIG.  9 B ,  FIG.  9 C ,  FIG.  9 D ,  FIG.  9 E , and  FIG.  9 F  illustrate various user interface details in the apparatus and their use in aiming of the apparatus to the fundus of the eye; and 
         FIG.  10    is a flow chart illustrating embodiments of a method for imaging the fundus of the eye. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     The following embodiments are only examples. Although the specification may refer to “an” embodiment in several locations, this does not necessarily mean that each such reference is to the same embodiment(s), or that the feature only applies to a single embodiment. Single features of different embodiments may also be combined to provide other embodiments. Furthermore, words “comprising” and “including” should be understood as not limiting the described embodiments to consist of only those features that have been mentioned and such embodiments may contain also features/structures that have not been specifically mentioned. 
     Reference numbers, both in the description of the embodiments and in the claims, serve to illustrate the embodiments with reference to the drawings, without limiting it to these examples only. 
     The embodiments and features, if any, disclosed in the following description that do not fall under the scope of the independent claims are to be interpreted as examples useful for understanding various embodiments of the invention. 
     Let us study simultaneously  FIG.  1    and  FIG.  2   , which illustrate embodiments of an apparatus  100  for imaging a fundus of an eye, and  FIG.  10   , which is a flow chart illustrating embodiments of a method, performed by the apparatus  100 , for imaging the fundus of the eye. 
     The apparatus  100  for imaging the fundus of the eye comprises an optical system  116 , an image sensor  114  to capture a still image or a video through the optical system  116 , a user interface  102  including a display  104  to display data to a user of the apparatus  100 , and one or more processors  106 . 
     In an embodiment, the apparatus  100  is a handheld apparatus for imaging the fundus of the eye. However, the embodiments are also applicable to tabletop or stationary apparatuses for imaging the fundus of the eye. 
     In an embodiment, the apparatus  100  is a handheld Optomed Aurora® IQ fundus camera, but the embodiments are applicable to other models and brands with similar features. 
     Optomed Aurora®  100  is a modular ophthalmic camera that is designed for use in a medical environment. It is intended to capture digital images and video of the fundus of the eye and surface of the eye for documentation, screening, and consultation. It is used with interchangeable optics modules Optomed Aurora® Retinal Module and Optomed Aurora® Anterior Module. Optics modules are attached to the camera  100  with bayonet connectors. Optomed Aurora® Retinal Module is intended for non-mydriatic fundus imaging. In non-mydriatic imaging no mydriasis is needed because infrared light is used for targeting the fundus and white light is flashed when an image is taken. The pupil does not respond to the infrared light, so examination is convenient for the patient. Mydriatic drops are needed when recording a video. Mydriatic drops are also recommended when pupil diameter is small. Optomed Aurora® Retinal Module has nine internal fixation targets for the patient to fixate on during imaging. The middle fixation target provides a macula-centred image. Optomed Aurora® Anterior Module is intended for imaging the surface of the eye and the surrounding areas. 
     As shown in  FIG.  2   , the optical system  116  may comprise single lenses,  202 , mirrors  204 ,  206 , lens groups  214 , motor adjustable lenses  216 , but depending on the structure, also other such optical elements and also other types of optical elements used in imaging. 
     The image sensor  114  may be an active-pixel sensor (or CMOS sensor), but also a charge-coupled device (CCD) may be used. Optomed Aurora®  100  uses a five megapixel CMOS sensor  114 . 
     The user interface  102  may include, besides the display  104 , a touch pad (that may be integrated with the display  104  to form a touch screen), and various knobs, switches and other electrical, mechanical, or electromechanical user interface elements. As shown in  FIG.  2   , the user interface  102  may include a shutter button  226  and a rotary knob  228 . Optomed Aurora®  100  uses a four inch TFT-LCD display with a resolution of 800×480 pixels. 
     The apparatus  100  may also comprise other parts, such as a WLAN module  218 , which enables wireless data transfer to an external apparatus (such as a laptop or another computing device, or even a computing cloud). In addition to WLAN, captured images and recorded videos may also be transferred to the computing device via a wired connection such as an USB interface  224  when the camera  100  is placed on a charging station. The apparatus  100  may comprise one or more (rechargeable) batteries  222 . The apparatus  100  may comprise an insertable SD memory card  220 . The apparatus  100  may comprises numerous other parts, but as their operation is not essential for understanding the embodiments, their description will be omitted. However, some additional parts, such as leds  208 ,  210 ,  212  and a soft eye cup  200  will be explained later in relation to some optional embodiments. 
     In an embodiment illustrated in  FIG.  1   , the one or more processors  106  comprise one or more memories  110  including computer program code  112 , and one or more microprocessors  108  configured to execute the computer program code  112  to cause the performance of the apparatus  100 . 
     In an alternative embodiment, the one or more processors  106  comprise a circuitry configured to cause the performance of the apparatus  100 . 
     A non-exhaustive list of implementation techniques for the one or more microprocessors  108  and the one or more memories  110 , or the circuitry includes, but is not limited to: logic components, standard integrated circuits, application-specific integrated circuits (ASIC), system-on-a-chip (SoC), application-specific standard products (ASSP), microprocessors, microcontrollers, digital signal processors, special-purpose computer chips, field-programmable gate arrays (FPGA), and other suitable electronics structures. 
     The term ‘memory’  110  refers to a device that is capable of storing data run-time (=working memory) or permanently (=non-volatile memory). The working memory and the non-volatile memory may be implemented by a random-access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), a flash memory (such as a NAND flash or a NOR flash), a solid state disk (SSD), PROM (programmable read-only memory), a suitable semiconductor, or any other means of implementing an electrical computer memory. 
     The computer program code (or software)  112  may be written by a suitable programming language (such as C, C++, assembler, or machine language, for example), and the resulting executable code may be stored in the one or more memories  110  and run by the one or more microprocessors  108 . In an embodiment, the computer program code  112  may be stored in a flash memory (such as in the NAND flash)  110 , and loaded by a bootloader also residing in the flash memory to the RAM  110 . The computer program code implements the method/algorithm illustrated in  FIG.  10   . The computer program code  112  may be stored in a source code form, object code form, executable form, or in some intermediate form, but for use in the one or more microprocessors  108  it is in the executable form. There are many ways to structure the computer program code  112 : the operations may be divided into modules, sub-routines, methods, classes, objects, applets, macros, etc., depending on the software design methodology and the programming language used. In modern programming environments, there are software libraries, i.e., compilations of ready-made functions, which may be utilized by the computer program code  112  for performing a wide variety of standard operations. In addition, an operating system (such as a general-purpose operating system or a real-time operating system) may provide the computer program code  112  with system services. 
     An embodiment provides a computer-readable medium  120  storing the computer program code  112 , which, when loaded into the one or more microprocessors  108  and executed by the one or more microprocessors  108 , causes the performance of the computer-implemented method/algorithm for imaging the fundus of the eye. The computer-readable medium  120  may comprise at least the following: any entity or device capable of carrying the computer program code  112  to the one or more microprocessors  108 , a record medium, a computer memory, a read-only memory, an electrical carrier signal, a telecommunications signal, and a software distribution medium. In some jurisdictions, depending on the legislation and the patent practice, the computer-readable medium  120  may not be the telecommunications signal. In an embodiment, the computer-readable medium  120  is a computer-readable storage medium. In an embodiment, the computer-readable medium  120  is a non-transitory computer-readable storage medium. 
     Now that the basic structure of the apparatus  100  and its operating environment have been described, let us study the dynamics of the method/algorithm with reference to  FIG.  10    for the main sequence and its optional embodiments. The method starts in  1000  and ends in  1040 . The operations are not strictly in chronological order and some of the operations may be performed simultaneously or in an order differing from the given ones. Other functions may also be executed between the operations or within the operations and other data exchanged between the operations. Some of the operations or part of the operations may also be left out or replaced by a corresponding operation or part of the operation. It should be noted that no special order of operations is required, except where necessary due to the logical requirements for the processing order. 
     When imaging with the apparatus  100 , the examination room should be as dim as possible. It is recommended that both a patient and a user operating the apparatus  100  are seated during the examination. It is also possible to perform the examination when the patient is lying down. 
     As illustrated in  FIG.  3   , a target light source  210  (such as a red led) shines a light  300  through the optical system  116  (such as reflected by the two mirrors  206 ,  204  through the lens  202 .) 
     As illustrated in  FIG.  4   , the patient  400  is asked to keep the eye  402  aligned with the target light  300  and to cover the other eye but to keep the covered eye open. The user approaches the pupil of the eye  402  with the apparatus  100  and stabilizes the apparatus  100  by supporting the apparatus  100  on his/her thumb and places fingers on the forehead of the patient  400 . The soft eye cup  200  is pressed firmly around the examined eye  402 . The user makes micro adjustments with the supporting hand to fine tune the alignment. The pupil is approached until a reflection from the fundus of the eye  402  is seen. The right imaging distance may be about two centimetres. 
     It is exactly this alignment that is difficult to perform, especially for a less experienced user. The aligning may be eased with a sequence of four operations described next. 
       FIG.  4    and  FIG.  6 A  illustrate an aiming phase, and  FIG.  5    and  FIG.  6 B  illustrate a capture phase. 
     In  1002 , the image sensor  114  is set to capture an aiming video. The aiming is illustrated in the  FIG.  4   : the image sensor  114  captures the aiming video  410  through  408  the optical system  116  (comprising the lenses  202 ,  214 ,  216 ). 
     In  1006 , a retina of the eye  402  is detected in the aiming video. As shown in  FIG.  5   , the fundus  500  is imaged through the pupil  502 . 
     In  1020 , the display  104  is set to display the aiming video with a highlight of the detected retina. As shown in  FIG.  6 A , the aiming video  410  illustrates the vicinity of the eye  402 , and the detected retina is shown with a highlight  600  on the display  104 . 
     In an embodiment of  1020 , the display  104  is set to highlight the detected retina in the aiming video as a highlighted area  600 ,  1022  marking the detected retina. The highlighted area  600 ,  1022  may be coloured with a suitable colour that is clearly distinguishable from the surrounding area (such as the iris of the eye, the sclera of the eye, and the skin surrounding the eye). The highlighted area  600 ,  1022  may also be shown with a clearly visible borderline around the area, or with a suitable pattern fill covering the area. 
     In  1036 , the image sensor  114  is set to capture one or more final still images of the fundus  500  or a final video of the fundus  500 . The capture is illustrated in the  FIG.  5   : the image sensor  114  captures the final still image(s)  512  or the final video  512  through  510  the optical system  116  (comprising the lenses  202 ,  214 ,  216 ). 
     In an embodiment, detecting the retina in  1006  comprises using  1008  a machine vision algorithm trained with images of eyes with annotated fundi. 
     In an embodiment, detecting the retina in  1006  comprises using  1012  an Adaptive Boosting, AdaBoost, statistical classification meta-algorithm or one of its variants, which construct a strong classifier  1014  by combining results of a sequence of weak classifiers  1016 ,  1018 . 
     In an embodiment, a probability of a pixel of interest in a single frame of the aiming video belonging to the retina is determined, by the weak classifiers  1016 ,  1018 , by comparing either an average luminosity of an area in the single frame relative to the pixel of interest to a first constant, or a difference of averages of luminosities of two areas in the single frame relative to the pixel of interest to a second constant. 
     In an embodiment, also one or more results of comparisons are used, by the weak classifiers  1016 ,  1018 , from previous weak classifiers in the sequence of the classifiers to improve the accuracy of a next weak classifier in the sequence of the classifiers. 
     In an embodiment, also average probabilities of pixels in areas in the single frame belonging to the retina are compared by the weak classifiers  1016 ,  1018  as determined by an already executed weak classifier as follows: comparing either an average probability of an area in the single frame relative to the pixel of interest to a third constant, or a difference of averages of probabilities of two areas in the single frame relative to the pixel of interest to a fourth constant. 
     Recognizing objects, such as faces, pedestrians or in our case retinas in the image has for decades been regarded as a machine learning task: the developer collects representative images and annotates the objects to be detected in them. A machine learning algorithm then uses these training examples to detect the objects of interest in yet unseen images. Since these unseen images may not exist in the training examples, the machine learning algorithm must do its best to generalize the information included in the training examples. 
     While “deep learning” of artificial neural networks has without a doubt shown the most accurate image detection and segmentation results for the past decade, they are computationally expensive to a degree that they may have to be excluded from such a small and battery-powered device  100 . Therefore, for example, the task of detecting a human face in an image and finding the smallest rectangle containing the whole face is still typically performed with “classic” machine learning algorithms predating the success of deep learning. The best known of such algorithms is called the Viola-Jones face detection algorithm, and it is based on the more general idea of the AdaBoost  1012 , which builds an accurate “strong” machine learning algorithm  1014  by employing a sequence of simpler and more inaccurate “weak” machine learning algorithms  1016 ,  1018  and performing a weighted voting or weighted summation of the weak learners&#39; results. The rich mathematical foundation of AdaBoost and its dozens of variants have been well documented in the scientific literature. 
     Practitioners of the AdaBoost use most commonly decision trees of various kinds as the weak learners  1016 ,  1018 . The simplest possible decision tree, a decision stub, performs a simple comparison of a feature value to a decision boundary value. While this, combined with a good choice of image features as in the Viola-Jones algorithm, has been shown to perform well in face detection tasks, a more difficult image detection task may require prohibitively many weak learners thereby respectively slowing down the image detector. On the other extreme one may be tempted to build a very deep decision tree, possibly one where each leaf corresponds to a single training example. While such a decision tree would indeed be fully accurate on training data, it would also generalize poorly to other inputs than the given training examples. Practitioners therefore choose an a priori maximum height, say six, for the decision tree in their attempt to find a compromise between the higher performance of fewer weak learners  1016 ,  1018  and good generalization characteristics of more numerous weak learners  1016 ,  1018 . 
     Decision trees contain several aspects we consider suboptimal: Firstly, following the then- and else-branches of the decision is highly likely to cause pipeline hazards in the processor resulting in both excessive energy (battery) consumption and a significant performance reduction. Secondly, a decision tree of any significant height will result in an exponential increase in program size. Our contributions include increasing the accuracy of decision stubs with the ability to refer to the result of earlier (not necessarily immediately preceding) decision stubs. In other words, if the N&#39;th decision stump is implemented conventionally in pseudo-code as 
       cmp N =1 if then-branch taken in N&#39;th decision stump, else 0 
       sum+=weight_table N [cmp N ] 
     where sum represents the weighted sum of the AdaBoost or its variants, then we propose to use a reasonable number, here five, of earlier comparisons for example as follows: 
       cmp N =1 if then-branch taken in N&#39;th decision stump, else 0 
       sum+=weight_table N [cmp N ][cmp N-a ][cmp N-b ][cmp N-c ][cmp N-d ] 
     where the distinct positive values a to d refer to the distances to the previous comparison from N. Note that even though the weight table grows similarly exponentially as caused by the depth of the decision tree discussed earlier, the table contains typically smallish integer or floating-point values and consumes overall significantly less memory than a decision tree would. Note also that this added functionality does not entail any more branching and pipeline stalls causes by branching. Furthermore, note that in a practical application the list of values cmp N  could be implemented cheaply using a bit vector. 
     For a while, consider the retina detection algorithm working by applying the AdaBoosted classifier in turn for each pixel (“pixel of interest”) in a reduced resolution grayscale aiming image of the fundus camera  100 . The Viola-Jones algorithm for face detection uses “Haar-like features” as the values to compare in the conditions of the weak classifiers. A Haar-like feature considers adjacent rectangular regions of equal size at a specific location relative to the pixel of interest, sums up the pixel luminosities in each region and calculates the difference between these sums. The difference is then compared to a boundary value to yield the weak classifier. Haar-like features have the benefit of being efficiently computable by precomputing an “integral image” (also known as “summed-area table”) for the image. Some systems use also Haar-like features tilted by 45 degrees, with corresponding precomputed integral images. 
     We have found that at least for retina detection strict Haar-like features result in somewhat too inaccurate weak classifiers. Particularly, we have found it beneficial to lift the limitation that the rectangles, or tilted rectangles, must be adjacent and that they must be of equal size. This choice allows a well-chosen weak learner to detect regions inside other regions, such as the darker pupil and iris inside the white sclera, or the reflection of an infrared light of an aiming light source  212  (explained later) inside the region of the pupil. 
     Consequently, instead of using the sum of pixel luminosities we use the average of the pixel luminosities in the compared rectangles. At first this would seem to imply a costly floating-point division, but in fact we may multiply the compared averages to the lowest common multiple of the sizes of the rectangles. Furthermore, because in our implementation these multipliers are compile-time constants in the retina detector&#39;s source code, the compiler will often find strength-reduction optimizations resulting in negligible computational overhead. 
     The retina is a relatively textureless object and may in the aiming image easily be confused with, say, the cheek or forehead of the patient  400 . However, if the aiming image (or video)  410  contains, for example, the optic disc, then one is certain that the adjacent area is indeed of the retina, and conversely, if the image contains the nose, eyebrows, or members of the outer eye, then indicating a retina detection is premature. In other words, given a pixel of interest in a textureless surrounding, it being part of the retina correlates most strongly with whether nearby pixels have been determined to belong to the retina. 
     The above observation has led us to restructure the overall retina detection algorithm as follows: 
     1. Downscale the aiming image to a suitable resolution. 
     2. Compute integral images for a fast computation of the average luminosity of rectangular and possibly tilted rectangular areas. 
     3. For each pixel in the image, execute, say, k first weak classifiers  1016 ,  1018  resulting in a sum-value for each of the pixels of interest. These first weak classifiers  1016 ,  1018  may only use pixel and average rectangle luminosities. 
     4. Construct a two-dimensional array of the pixelwise sum-values. This array is of identical dimensions as the downscaled aiming image and may thus be treated as a second image to be used by weak classifiers  1016 ,  1018 . To facilitate this, compute also the integral images for this “sum-image”. 
     5. For each pixel in the image, execute the next, say, j weak classifiers  1016 ,  1018  updating the sum-values for each pixel. These weak classifiers  1016 ,  1018  may use not only features found in the downscaled aiming image, but also in the sum-image. 
     6. If a sufficient accuracy of retina detection has been found, as in original the AdaBoost, the sign of each value in the sum-image indicates whether the pixel is part of the retina or something else. Alternatively, repeat these steps  4 - 6  for the next weak classifiers  1106 ,  1018 . 
     In practice we have found that the accuracy or the performance of the retina detection algorithm depend little on the exact choice of k and j above. Overly small values result in some waste of performance to needlessly recomputing the integral of sum-images, and too large values of k and j result in needlessly weak weak classifiers  1016 ,  1018 . We typically use values around five fork and j. 
     Next, let us study the user interface  102 , especially the display  104  during the aiming and capture in more detail. 
       FIG.  7    illustrates the display  104  showing the aiming video  410 , wherein the detected retina is shown with the highlight  600 . 
       FIG.  8    illustrates an embodiment, wherein the display  104  is set to display the aiming video  410  with an align aid  800 ,  1024  related to a position of the highlight  600  of the detected retina on the display  104 . The align aid  800  may be a sight symbol as illustrated in  FIG.  8   , but it may also be another kind of align aid, such as a transparent overlay symbol in the form of the pupil. In an embodiment, the display  104  is set to display the align aid  1024  with an aiming target area  1032  in a middle of the display  104  and in relation to the highlight  600  of the detected retina in the display  104  while the image sensor  114  is capturing the aiming video  410 . 
     The idea is that the user will move the apparatus  100  so that the align aid  800  becomes overlapped with the highlight  600  of the detected retina. To achieve the overlapping, the user may have to cause a movement of the optical system  116  in a planar direction (such as in x and y directions along the surface of the face and iris) and in a depth direction (such as in z direction towards the iris or away from the iris). 
       FIG.  9 A  illustrates an embodiment, wherein the display  104  is set to display the align aid  800 ,  1024  with a planar indicator  900 ,  902 ,  1026  of the x and y direction alignment of the highlight  600  of the detected retina in the display  104 , and a depth indicator  904 ,  1028  of the z direction alignment of the apparatus  100  in relation to the highlight  600  of the detected retina in the display  104 . The display  104  is also set to display the align aid  800 ,  1024  with an instruction  906 ,  1030  instructing the user to move the optical system  116  nearer to the eye or further off the eye. Note that besides the arrow symbols  900 ,  902 ,  904 ,  906  other kinds of symbols may to used to indicate the various alignments and instructions. 
     Note also that the display  104  may also be set to display the align aid  800 ,  1024  with an instruction instructing the user to move the optical system  116  in the x and/or y direction, this is actually shown in  FIG.  9 A  so that the x direction planar indicator  900  shows with the direction of the arrow that the optical system  116  should be moved to the left so that the align aid  800  becomes aligned in the x direction with the highlight  600 . Also, the y direction planar indicator  902  shows with the direction of the arrow that the optical system  116  should be moved to the up so that the align aid  800  becomes aligned in the y direction with the highlight  600 . 
     In  FIG.  9 B , the x direction alignment has been reached, and the x direction planar indicator  900  is changed to a round symbol. 
     In  FIG.  9 C , the x direction planar is no more shown, and also the y direction alignment has been reached, and the y direction planar indicator  902  is changed to a round symbol. 
     In  FIG.  9 D , as the x and y direction alignments have been achieved no planar indicators  900 ,  902  are shown, but the z direction (or depth) indicator  904  is still shown with the instruction  906 .  FIG.  9 D  also illustrates that as the user has moved the optical system  116  nearer, the align aid  800  is also enlarged. 
     In  FIG.  9 E , the instruction  906  still instructs to move closer, and the align aid  800  has also grown larger. 
     Finally, in  FIG.  9 F , the highlight  600  of the detected retina fills the align aid  800 . As the align aid  800  is now of the correct size filling the display  104 , the depth indicator  904  is changed to a round symbol, and the instruction  906  highlights the indicator  904 , thereby signalling that the alignment is now perfect, and the final still image(s) or the final video may be captured. Note that the z direction (or depth indicator)  904  may pause in this full view, so that the final still image or the final video may be captured, but after this, it may further instruct the user to move the optical system nearer  116  to take more final still images or additional final video. This may be required as there may still be reflections caused by the retina in the full view still images/video. 
     In an embodiment, the sequence of operations in  FIG.  10    may be augmented by an optional test in  1034 . The test may check whether the detected retina fulfils a predetermined condition, such as whether the detected retina fills the align aid as shown in  FIG.  9   , or whether the detected retina in some other way indicates that an adequately good image or video of the fundus  500  may be captured. If the detected retina fulfils a predetermined condition (the test in  1034  evaluates “YES”), the image sensor  114  is set in  1036  to capture the one or more final still images  512  or the final video  512 , or else (the test in  1034  evaluates “NO”), the aiming video  410  is continued to be captured. Besides such automatic capture, the user may of course capture the final still image(s) or video by operating an appropriate user interface control, such as pressing the shutter button  226 . 
     In an embodiment, the optical system  116  is set to autofocus  1010  on the detected retina while the image sensor  114  is capturing the aiming video  410 . The autofocus range may be from −15 to +10 dioptres, for example. If the refractive error (hyperopia or myopia) of the patient is known, the dioptre value may also be manually entered with an appropriate user interface control, such as a touch screen  104  and/or the rotary button  228 . In an embodiment, the apparatus  100  comprises a mechanism  200 ,  216  to adjust the optical system  116  in relation to the eye and the fundus of the eye while the image sensor  114  is capturing the aiming video  410  and the one or more final still images  512  or the final video  512 . The mechanism may comprise the soft eye cup  200 , with which the distance and direction of the foremost lens  202  may be adjusted in relation to the eye  402 . The mechanism may also comprise an adjustment within the optical system  116 , such as the motor adjustable lenses  216 , with which the focus may be adjusted. 
     In an embodiment, the apparatus  100  comprises an aiming light source  212  to illuminate  404 ,  406  the eye  402  through the optical system  116 , and an imaging light source  208  to illuminate  504 ,  506 ,  508  the fundus  500  through the optical system  116 . As shown in  FIG.  4    and  FIG.  5   , the light sources  208 ,  212  may be alternatively lit and their light directed via the mirrors  206 ,  204  through the foremost lens  202 . The one or more processors  106  cause setting  1004  of the aiming light source  212  to illuminate  406  the eye  402  while the image sensor  114  is capturing the aiming video  410 , and setting  1038  of the imaging light source  208  to illuminate  508  the fundus  500  while the image sensor  114  is capturing the one or more final still images  512  or the final video  512 . In an embodiment, the aiming light source ( 212 ) comprises an infrared (IR) light emitting diode (LED), and the imaging light source  208  comprises a white light emitting diode (LED). The infrared led does not cause discomfort during the (possibly relatively long) aiming, whereas the white light led provides neutral lighting during the (possibly relatively brief) capture. The infrared led may be a near infrared (NIR) led. 
     Even though the invention has been described with reference to one or more embodiments according to the accompanying drawings, it is clear that the invention is not restricted thereto but can be modified in several ways within the scope of the appended claims. All words and expressions should be interpreted broadly, and they are intended to illustrate, not to restrict, the embodiments. It will be obvious to a person skilled in the art that, as technology advances, the inventive concept can be implemented in various ways.