CN110060311B - Image processing device of retina stimulator - Google Patents
Image processing device of retina stimulator Download PDFInfo
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- CN110060311B CN110060311B CN201910291332.2A CN201910291332A CN110060311B CN 110060311 B CN110060311 B CN 110060311B CN 201910291332 A CN201910291332 A CN 201910291332A CN 110060311 B CN110060311 B CN 110060311B
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N1/00—Electrotherapy; Circuits therefor
- A61N1/02—Details
- A61N1/04—Electrodes
- A61N1/05—Electrodes for implantation or insertion into the body, e.g. heart electrode
- A61N1/0526—Head electrodes
- A61N1/0543—Retinal electrodes
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N1/00—Electrotherapy; Circuits therefor
- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
- A61N1/36046—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation of the eye
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N1/00—Electrotherapy; Circuits therefor
- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
- A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
- A61N1/36057—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for stimulating afferent nerves
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding
Abstract
The present disclosure relates to an image processing apparatus of a retinal stimulator with an ultra-large compression ratio, which includes: the device comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring an initial image with a preset pixel number; the gray processing module is used for carrying out gray processing on the initial image to obtain a gray image; and a pixel processing module which performs compression processing on the gray image based on a predetermined number of pixels and a target number of pixels having a prescribed number to obtain a target image having the target number of pixels, wherein the compression processing includes determining a total compression ratio based on the predetermined number of pixels and the target number of pixels; determining the number of compression steps and the compression rate of each step according to the total compression rate and the target pixel number, wherein the total compression rate is equal to the product of the compression rates of each step; in the compression processing of each step, a compression window of a predetermined size is determined based on the number of pixels and the compression rate of each step, and gradation conversion is performed on each pixel in the compression window. The present disclosure is able to accommodate low resolution retinal stimulators.
Description
The application is filed as09 month 09 year 2018Application No. is201811047459.1The invention is named asVision net Image processing method and device of membrane stimulator and retina stimulatorDivisional application of the patent application.
Technical Field
The present disclosure relates specifically to an image processing apparatus of a retinal stimulator.
Background
Retinal diseases such as RP (retinitis pigmentosa), AMD (age-related macular degeneration), and the like are important blinding diseases, and patients suffer from visual deterioration or blindness due to obstruction of the light-sensing pathway.
With the research and development of the technology, there has appeared a technical means for repairing the above-mentioned retinal diseases using a retinal stimulator or the like. The existing retina stimulators generally comprise a camera device arranged outside the patient, a video processing device and an intra-ocular implant placed in the patient. The external camera device captures images and converts the obtained images into visual signals, the video processing device processes the visual signals and sends the processed visual signals to the implant, the implant converts the received processed visual signals into electrical stimulation signals, and the electrical stimulation signals stimulate ganglion cells or bipolar cells on retina to generate excitation response so as to generate light sensation.
However, the initial image captured by the camera device of the retinal stimulator is usually millions of pixels, and the resolution of the retinal stimulator, i.e. the number of stimulation electrodes of the implant, is limited (for example, 60 electrodes), which is far lower than the number of pixels of the initial image. In this case, the general image processing method for the initial image captured by the camera device is not applicable to such a low-resolution retina stimulator.
Disclosure of Invention
The present disclosure has been made in view of the above circumstances, and an object thereof is to provide an image processing method and apparatus that can accommodate a retinal stimulator with a low resolution, and a retinal stimulator.
To this end, a first aspect of the present disclosure provides an image processing method for a retinal stimulator having a predetermined number of stimulation electrodes, the image processing method comprising: the method comprises the following steps: acquiring an initial image with a preset pixel number; carrying out graying processing on the initial image to obtain a grayscale image; and performing compression processing on the gray-scale image based on the predetermined number of pixels and the number of target pixels having the predetermined number to obtain a target image having the target number of pixels, the compression processing including: determining a total compression ratio based on the predetermined pixel number and the target pixel number; determining the number of compression steps and each step of compression ratio according to the total compression ratio and the target pixel number, wherein the total compression ratio is equal to the product of each step of compression ratio; in each step of compression processing, determining a compression window with a specified size according to the number of pixels and each step of compression ratio, and performing gray level conversion on each pixel in the compression window; calculating the average gray value of each pixel of the gray image in the compression window by utilizing the sliding of the compression window, generating pixel points with the average gray value, and combining the generated pixel points in sequence to form an image with a specified number of pixels; and repeating the above-mentioned compression processing of each step according to the number of the compression steps.
In the present disclosure, an initial image having a predetermined number of pixels is subjected to a graying and compression process in which a compression step number and a compression rate of each step are determined based on the predetermined number of pixels and a target number of pixels to obtain a target image through multi-step compression. In each step of compression, each pixel in a compression window is subjected to gray scale conversion, the average gray scale value of each pixel of a gray scale image in the compression window is calculated by utilizing sliding of the compression window, pixel points with the average gray scale value are generated, and the generated pixel points are sequentially combined to form an image with a specified pixel number. Therefore, the compression ratio of the initial image and the target image can be improved, the quality of the target image can be improved to the maximum extent, and the method is suitable for a retina stimulator with low resolution.
In the image processing method according to the first aspect of the present disclosure, the grayscale transform may include dividing a grayscale level for each pixel within the compression window, and assigning different weights to pixels having different grayscale levels. This makes it possible to effectively highlight effective information in the image, such as an obstacle.
In the image processing method according to the first aspect of the present disclosure, optionally, a gray scale value of each pixel of the gray scale image in the compression window is obtained to obtain a pixel average gray scale value of each pixel; and dividing the gray level of each pixel in the compression window based on the pixel average gray value and the gray value of each pixel of the gray image in the compression window. Thereby, the gray level of a pixel within the compression window can be determined based on the pixel average gray value.
In the image processing method according to the first aspect of the present disclosure, optionally, the pixel average grayscale value is compared with grayscale values of respective pixels of the grayscale image in the compression window, and a pixel greater than or equal to the pixel average grayscale value is made to be a first grayscale level, and a pixel smaller than the second average grayscale value is made to be a second grayscale level. Thereby, each pixel can be divided into respective gray levels based on the pixel average gray value.
In the image processing method according to the first aspect of the present disclosure, optionally, for the grayscale image within the compression window, gradient value calculation is performed on the grayscale image along a preset direction; and dividing the gray level of each pixel in the compression window based on the gradient value in the preset direction and a preset gradient. Thereby, the gray level of each pixel along the preset direction within the compression window can be determined.
In the image processing method according to the first aspect of the present disclosure, optionally, the gradient value of the grayscale image in the preset direction is compared with a preset gradient value, a pixel of the grayscale image with the gradient value in the preset direction greater than or equal to the preset gradient value is made to be a third grayscale level, and a pixel of the grayscale image with the gradient value in the preset direction less than the preset gradient value is made to be a fourth grayscale level. Thereby, each pixel along the preset direction within the compression window can be divided into the corresponding gray levels.
A second aspect of the present disclosure provides an image processing apparatus for a retinal stimulator having a predetermined number of stimulation electrodes, the image processing apparatus comprising: the device comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring an initial image with a preset pixel number; the gray processing module is used for carrying out gray processing on the initial image to obtain a gray image; and a pixel processing module, which performs compression processing on the gray image based on the predetermined number of pixels and the target number of pixels with the specified number to obtain a target image with the target number of pixels, wherein the compression processing includes determining a total compression ratio based on the predetermined number of pixels and the target number of pixels; determining the number of compression steps and each step of compression ratio according to the total compression ratio and the target pixel number, wherein the total compression ratio is equal to the product of each step of compression ratio; in each step of compression processing, determining a compression window with a specified size according to the number of pixels and each step of compression ratio, and performing gray level conversion on each pixel in the compression window; calculating the average gray value of each pixel of the gray image in the compression window by utilizing the sliding of the compression window, generating pixel points with the average gray value, and combining the generated pixel points in sequence to form an image with a specified number of pixels; and repeating the above-mentioned compression processing of each step according to the number of the compression steps.
In the present disclosure, the gray processing module performs a graying process on the initial image having the predetermined number of pixels acquired by the acquisition module to obtain a gray image, and the pixel processing module performs a compression process on the gray image in which the number of compression steps and the compression rate of each step are determined based on the predetermined number of pixels and the target number of pixels to obtain the target image through multi-step compression. In each step of compression, each pixel in a compression window is subjected to gray scale conversion, the average gray scale value of each pixel of a gray scale image in the compression window is calculated by utilizing sliding of the compression window, pixel points with the average gray scale value are generated, and the generated pixel points are sequentially combined to form an image with a specified pixel number. Therefore, the compression ratio of the initial image and the target image can be improved, the quality of the target image can be improved to the maximum extent, and the method is suitable for a retina stimulator with low resolution.
In the image processing apparatus according to the second aspect of the present disclosure, the pixel processing module may further include a gray scale conversion unit configured to divide a gray scale level for each pixel in the compression window and to assign different weights to pixels having different gray scale levels. This makes it possible to effectively highlight effective information in the image, such as an obstacle.
In the image processing apparatus according to the second aspect of the present disclosure, optionally, a gray scale value of each pixel of the gray scale image in the compression window is acquired to obtain a pixel average gray scale value of each pixel; and dividing the gray level of each pixel in the compression window based on the pixel average gray value and the gray value of each pixel of the gray image in the compression window. Thereby, the gray level of the pixel within the compression window can be determined based on the pixel average gray value.
In the image processing apparatus according to the second aspect of the present disclosure, the average gray-scale value of the pixel may be compared with gray-scale values of respective pixels of the gray-scale image in the compression window, and pixels greater than or equal to the average gray-scale value of the pixel may be set to a first gray-scale level, and pixels smaller than the second average gray-scale value may be set to a second gray-scale level. Thereby, each pixel can be divided into respective gray levels based on the pixel average gray value.
In the image processing apparatus according to the second aspect of the present disclosure, optionally, the grayscale conversion unit is configured to perform gradient value calculation on the grayscale image in a preset direction with respect to the grayscale image in the compression window; and dividing the gray level of each pixel in the compression window based on the gradient value in the preset direction and a preset gradient. Thereby, the gray level of each pixel along the preset direction within the compression window can be determined by the gray converting unit.
In the image processing apparatus according to the second aspect of the present disclosure, optionally, the gradient value of the grayscale image in the preset direction is compared with a preset gradient value, a pixel of the grayscale image having a gradient value in the preset direction that is greater than or equal to the preset gradient value is set as a third grayscale level, and a pixel of the grayscale image having a gradient value in the preset direction that is less than the preset gradient value is set as a fourth grayscale level. Thereby, each pixel along the preset direction within the compression window can be divided into the corresponding gray levels.
A third aspect of the present disclosure provides a retinal stimulator comprising: a camera device for capturing a video image and converting the video image into a visual signal; video processing means connected to the camera means and comprising at least the image processing means of any one of claims 7 to 12 for processing the visual signal to generate a visual compressed signal; and an implant device for receiving the visual compression signal and converting the visual compression signal into an electrical stimulation signal to deliver the electrical stimulation signal to the retina.
According to the present disclosure, it is possible to provide an image processing method and apparatus for a retinal stimulator, which can improve the compression ratio between an initial image and a target image, improve the quality of the target image to the maximum extent, and be suitable for a low-resolution retinal stimulator, and a retinal stimulator.
Drawings
Fig. 1 is a schematic view of a retina stimulator according to the present disclosure.
Fig. 2 is a schematic configuration diagram of an image processing apparatus of a retinal stimulator according to the present disclosure.
Fig. 3 is a schematic configuration diagram of an image processing device of a retinal stimulator according to the present disclosure.
Fig. 4 is a flowchart illustrating an image processing method of the retinal stimulator according to the present disclosure.
Fig. 5 is a schematic flow chart of a compression processing method in the image processing method of the retinal stimulator according to the present disclosure.
Fig. 6 is a schematic flow chart of a gray scale change method in the image processing method of the retinal stimulator according to the present disclosure.
Fig. 7 is a flowchart illustrating an example of a gray scale division method in the image processing method of the retinal stimulator according to the present disclosure.
Fig. 8 is a flowchart illustrating a modification of the gray scale division method in the image processing method of the retinal stimulator according to the present disclosure.
Detailed Description
Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the disclosure, the same components are denoted by the same reference numerals, and redundant description thereof is omitted. The drawings are schematic and the ratio of the dimensions of the components and the shapes of the components may be different from the actual ones.
In addition, the headings and the like referred to in the following description of the present disclosure are not intended to limit the content or scope of the present disclosure, but merely serve as a reminder for reading. Such a subtitle should neither be understood as a content for segmenting an article, nor should the content under the subtitle be limited to only the scope of the subtitle.
Fig. 1 is a schematic view of a retina stimulator according to the present disclosure. The retinal stimulator of the present disclosure may be suitable for patients who have blindness due to retinopathy, but whose visual pathways remain intact, such as bipolar cells, ganglion cells, etc. In the present disclosure, the retinal stimulator 1 is also sometimes referred to as "artificial retina", "artificial retinal system", or the like.
In some examples, as shown in fig. 1, the retinal stimulator 1 may include a camera device 10, a video processing device 20, and an implant device 30. Implant device 30 may receive the visual compression signal and generate an electrical stimulation signal based on the visual compression signal. The visual compression signal can be obtained by the video processing device 20 processing the video image captured by the camera device 10.
In some examples, the camera 10 may be used to capture video images and convert the video images into visual signals. For example, the camera 10 may capture video images of the environment in which the patient is located.
In some examples, the image capture apparatus 10 may be a device having an image capture function, such as a video camera, a still camera, or the like. For ease of use, a camera of smaller volume may be designed on (e.g., embedded in) the eyewear.
In other examples, the patient may also capture video images by wearing lightweight camera-enabled glasses as the camera 10. The imaging device 10 may be implemented by google glasses or the like. In addition, the imaging device 10 may be mounted on smart wearable devices such as smart glasses, smart head wears, and smart bracelets.
In some examples, the camera device 10 may be connected with a video processing device 20. The image pickup device 10 and the video processing device 20 may be connected by wire or wirelessly.
In some examples, the wired connection may be a data line connection, the wireless connection may be a bluetooth connection, a WiFi connection, an infrared connection, an NFC connection, or a radio frequency connection, among others.
In addition, in some examples, the camera device 10 and the video processing device 20 may be configured outside the patient's body. For example, the patient may wear the imaging device 10 on glasses. The patient may also wear the camera device 10 on a wearable accessory such as a headgear, hair band, or brooch. In addition, the patient can wear the video processing device 20 on the waist, and the patient can wear the video processing device 20 on the arm, leg, or the like. Examples of the present disclosure are not limited thereto, and for example, the patient may also place the video processing device 20 in, for example, a handbag or backpack that is carried around.
In some examples, video processing apparatus 20 may be used to process visual signals to generate visual compressed signals. Specifically, the video processing apparatus 20 may receive the visual signal generated by the image pickup apparatus 10, and the video processing apparatus 20 may process the visual signal to generate a visual compression signal. Video processing device 20 may send the visually compressed signal to implanted device 30 via a transmitting antenna.
Additionally, in some examples, video processing device 20 may include an image processing device for processing images.
In some examples, implant device 30 may be configured to receive the visual compression signal and convert the visual compression signal into an electrical stimulation signal to deliver the electrical stimulation signal to the retina.
In some examples, implant device 30 may include a prescribed number of stimulation electrodes. Stimulation electrodes (sometimes simply referred to as "electrodes") may generate electrical stimulation signals based on the visual signals. In particular, the implanted device 30 may receive visual signals and the stimulation electrodes convert the received visual signals into bi-directional pulsed current signals as electrical stimulation signals, thereby delivering bi-directional pulsed current signals to ganglion cells or bipolar cells of the retina to produce light sensation. Alternatively, implant device 30 may be implanted within a human body, such as an eyeball.
Fig. 2 is a schematic configuration diagram of an image processing apparatus of a retinal stimulator according to the present disclosure. The image processing apparatus 200 of the retinal stimulator according to the present disclosure (which may be simply referred to as the image processing apparatus 200) may be used for the retinal stimulator 1 as a functional block of image processing. The image processing device 200 may be included in the video processing device 20 of the retinal stimulator 1.
In some examples, as shown in fig. 2, the image processing apparatus 200 may include an acquisition module 210. The acquisition module 210 may be used to acquire an initial image having a predetermined number of pixels.
In some examples, the initial image may be captured by the camera 10. The acquisition module 210 may acquire an initial image captured by the camera 10. The predetermined number of pixels of the initial image may be determined by the pixels of the imaging lens of the imaging device 10. For example, the number of pixels of the imaging lens may be 30 ten thousand, 50 ten thousand, 100 ten thousand, 500 ten thousand, 1200 ten thousand, etc., and the predetermined number of pixels of the initial image may be correspondingly a number of pixels matching the imaging lens, for example, 30 ten thousand, 50 ten thousand, 100 ten thousand, 500 ten thousand, 1200 ten thousand, etc.
In some examples, the initial image may be an image captured by the camera 10 without any processing. The initial image typically taken of the surrounding environment by the camera 10 may be a color image. In some examples, the color image may be considered an HSI image. Color images can also be viewed as RGB images. However, examples of the present disclosure are not limited thereto, and the initial image captured by the image capture device 10 may be a grayscale image.
In some examples, the image processing apparatus 200 may further include a grayscale processing module 220. The gray processing module 220 may be configured to perform a graying process on the initial image to obtain a gray image. A grayscale image can be considered as a special color image of R, G, B in which the three components have the same size (i.e., R ═ G ═ B).
In some examples, the graying process primarily processes color information of the image. For example, the graying process may change the color information of the initial image and retain the morphological feature information of the initial image (particularly, the middle object or the obstacle of the image).
In some examples, the graying processing method may be a component method, i.e., selecting R, G, B a value of any one of the three components as a grayscale value. The graying processing method may also be a maximum value method, i.e., a maximum value of R, G, B three components is selected as the grayscale value. The graying processing method may also be an average value method, that is, an average value of R, G, B three components is selected as the grayscale value. The graying processing method can also be a weighting method, namely R, G, B three components are weighted according to different weighting coefficients to obtain the gray value.
In some examples, the graying process can reduce the data amount of the initial image, facilitate the subsequent processing of the image, and help to highlight useful information in the image during the subsequent processing.
In some examples, the image processing apparatus 200 may further include a pixel processing module 230. The pixel processing module 230 may perform compression processing on the gray-scale image based on a predetermined number of pixels and a target number of pixels having a prescribed number, to obtain a target image having the target number of pixels.
In some examples, the compression process may be a multi-step compression process. That is, the pixel processing module 230 may perform multi-step compression processing on the grayscale image to obtain a target image with a target number of pixels. Wherein the target number of pixels may have a prescribed number. The prescribed number may be determined based on the number of stimulation electrodes of the implant device 30.
In some examples, in a multi-step compression process, a total compression ratio may be determined from the initial image and the target image. Specifically, the pixel processing module 230 may determine the total compression ratio based on a predetermined number of pixels and a target number of pixels. Wherein, the total compression ratio may be a ratio of a predetermined number of pixels to a target number of pixels. The predetermined number of pixels may be the number of pixels of the initial image acquired by the acquisition module 210, and the target number of pixels may be based on the number of stimulation electrodes of the implant device 30.
In some examples, the pixel processing module 230 may also determine the number of compression steps and the per-step compression ratio according to the total compression ratio and the target number of pixels. The number of compression steps may include 1 st, 2 nd, 3 rd, … … th, mth, where m may be a natural number greater than or equal to 2. The compression ratio may be the same or different for each step. In addition, the total compression ratio is equal to the product of the compression ratios of the steps.
In some examples, in the step-by-step compression process, the pixel processing module 230 may determine a compression window of a prescribed size according to the number of pixels and the step-by-step compression rate. The number of pixels may be the number of pixels of an image obtained by the previous step of the current step of the compression process. For example, in the first step compression process, the pixel processing module 230 may determine a first compression window of a prescribed size according to the number of pixels of the grayscale image obtained by the grayscale processing module 220 and the first step compression rate. The number of pixels of the gray image obtained by the gray processing module 220 may be a predetermined number of pixels of the initial image. Accordingly, the prescribed size of the first compression window can be obtained based on the predetermined number of pixels and the first-step compression rate. In the second-step compression process, the pixel processing module 230 may determine a second compression window of a prescribed size according to the number of pixels of the image obtained by the first-step compression process and the second-step compression rate.
In some examples, in each step of the compression process, the pixel processing module 230 may perform a grayscale transformation on each pixel within the compression window. Specifically, the pixel processing module 230 may include a gray scale conversion unit (not shown) for dividing gray scale levels for respective pixels within the compression window and giving different weights to pixels having different gray scale levels. In this case, the recognizability of the target image by the gradation converting unit is improved to some extent as compared with the target image obtained without the gradation conversion, whereby the image quality of the target image can be improved.
In some examples, there may be 1 gray scale conversion unit of the pixel processing module 230. At each step of the compression process, each pixel in the corresponding compression window may be subjected to gray scale conversion by the gray scale conversion unit, respectively. However, the examples of the present disclosure are not limited thereto, and the gray scale conversion unit of the pixel processing module 230 may be plural, for example, m gray scale conversion units. That is, each compression process may correspond to one gray scale conversion unit. Thereby, the respective pixels in the corresponding compression windows can be subjected to gradation conversion by the corresponding gradation conversion units.
In some examples, the grayscale transforming unit may obtain grayscale values of respective pixels of the grayscale image within the compression window to obtain a pixel average grayscale value of the respective pixels. That is, the pixel average gray level value may be an average gray level value of each pixel of the gray image obtained by the gray processing module 220 within the compression window.
In some examples, the grayscale transform unit may divide the grayscale levels of the respective pixels within the compression window based on the pixel average grayscale value and the grayscale values of the respective pixels of the grayscale image within the compression window. Thereby, the gray level of the pixel within the compression window can be determined by the gray scale conversion unit.
In some examples, the grayscale transform unit may compare the pixel average grayscale value with the grayscale values of the individual pixels of the grayscale image within the compression window. And setting the pixels with the average gray value larger than or equal to the average gray value of the pixels as a first gray level, and setting the pixels with the average gray value smaller than the second gray level as a second gray level. Thereby, the gray level of the pixel within the compression window can be further determined. The gray scale conversion unit may assign different weights to the pixels of the first gray scale level and the second gray scale level.
In other examples, the grayscale transform unit may be configured to perform gradient value calculation on the grayscale image along a preset direction with respect to the grayscale image within the compression window. The gray scale conversion unit may further divide the gray scale level of each pixel within the compression window based on the gradient value in the preset direction and the preset gradient. Thereby, the gray level of the pixel within the compression window can be determined by the gray scale conversion unit.
In some examples, the gray converting unit may compare the gradient value of the gray image in a preset direction with a preset gradient value. And setting the pixel of which the gradient value in the preset direction of the gray image is greater than or equal to the preset gradient value as a third gray level, and setting the pixel of which the gradient value in the preset direction of the gray image is less than the preset gradient value as a fourth gray level. Thereby, the gray level of the pixel within the compression window can be further determined. The gray scale conversion unit may assign different weights to the pixels of the third gray scale level and the fourth gray scale level.
Examples of the present disclosure are not limited thereto, and for example, the gradation conversion unit may also arrange the gradation values of the respective pixels of the gradation image within the compression window in ascending order from small to large (or descending order from large to small), and select the median or mode as the preset gradation value.
In some examples, the gray scale converting unit may further make the gray scale value of each pixel within the compression window greater than or equal to a fifth gray scale level of the pixel of the preset gray scale value, and make the gray scale value of each pixel within the compression window less than a sixth gray scale level of the pixel of the preset gray scale value. The gray scale conversion unit may assign different weights to the pixels of the fifth gray scale level and the sixth gray scale level.
In addition, the gray scale conversion unit may also divide each pixel within the compression window into three or more gray scale levels. For example, the gradation conversion unit may also determine a concentration distribution section of the gradation values of the respective pixels of the gradation image within the compression window. The gray value of each pixel in the compression window is smaller than the seventh gray level of the distribution interval threshold value, the gray value of each pixel in the compression window is positioned at the eighth gray level of the distribution interval threshold value, and the gray value of each pixel in the compression window is larger than the ninth gray level of the distribution interval threshold value. The gray scale conversion unit may assign different weights to the pixels of the seventh gray scale level, the eighth gray scale level, and the ninth gray scale level. In addition, the sum of the weights corresponding to the pixels of different gray levels may be 1.
In some examples, in each step of the compression process, the pixel processing module 230 may calculate an average gray value of each pixel of the gray image within the compression window by using the compression window sliding, and generate a pixel point having the average gray value. Specifically, the pixel processing module 230 may calculate an average gray value of each pixel of the gray image processed by the gray transforming unit within the compression window by using the compression window sliding. And the pixel processing module 230 may generate a pixel point having the average gray value.
In some examples, in each step of the compression process, the pixel processing module 230 may combine the generated individual pixel points in order to form an image having a prescribed number of pixels. Specifically, the pixel processing module 230 may combine the respective pixel points generated during the sliding of the compression window in order and form an image having a prescribed number of pixels. For example, in the first step compression process, the pixel processing module 230 may sequentially combine the respective pixel points generated during the sliding of the first compression window and form a first intermediate image having a first prescribed number of pixels. Wherein the first prescribed number of pixels may be obtained from the first-step compression rate and the number of pixels of the initial image. The first intermediate image is a compressed image obtained by the first step of compression processing.
In some examples, the pixel processing module 230 may repeat the above-described compression processes of each step according to the number of compression steps to obtain a target image having a target number of pixels. Specifically, after the m-step compression process, the pixel processing module 230 may generate a target image having a target number of pixels. Then, video processing apparatus 20 may generate and transmit a visual compression signal based on the target image generated by image processing apparatus 200, and implant apparatus 30 may receive the visual compression signal and convert the visual compression signal into an electrical stimulation signal to deliver the electrical stimulation signal to the retina. In this case, the compression ratio between the initial image and the target image can be increased, and the quality of the target image can be improved to the maximum extent, so that the method is suitable for a retina stimulator with low resolution.
In the present disclosure, the gray processing module 220 may perform a graying process on the initial image having the predetermined number of pixels acquired by the acquisition module 210 to obtain a gray image. The pixel processing module 230 may perform compression processing on the grayscale image. In the compression process, the number of compression steps and the compression rate of each step may be determined based on a predetermined pixel number and a target pixel number to obtain a target image by multi-step compression. In each step of compression, a gray scale transform may be performed on each pixel within the compression window. The average gray value of each pixel of the gray image in the compression window can be calculated by utilizing the sliding of the compression window, and a pixel point with the average gray value is generated. The generated pixel points may be combined in order to form an image having a predetermined number of pixels. Thereby, the visual perception of the patient can be improved and the image quality of the target image can be optimized.
Here, the functions of the above-mentioned units of the image processing apparatus 200, including the acquisition module 210, the gradation processing module 220, and the pixel processing module 230, can be realized by the image processing apparatus 200 of fig. 3 described below. This is explained in detail below.
Fig. 3 is a schematic configuration diagram of an image processing device of a retinal stimulator according to the present disclosure. In some examples, as shown in fig. 3, image processing apparatus 200 may include a processor 410, a memory 420, and a communication interface 430.
In some examples, the processor 410 may be used to control and manage actions performed by the image processing apparatus 200. For example, the processor 410 may be used to support the functions of the various modules of the image processing apparatus 200 described above. In addition, the processor 410 may also be used to support the image processing apparatus 300 to perform steps S10-S30 in fig. 4 described later and/or other processes for the techniques described herein.
In some examples, Processor 410 may be a Central Processing Unit (CPU), a general purpose Processor, a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, transistor logic, hardware components, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure of the disclosure. The processor 410 may also be a combination of computing functions, e.g., comprising one or more microprocessors, a combination of a DSP and a microprocessor, or the like.
In some examples, the communication interface 430 may be used to support communication of the image processing apparatus 200 with other devices (e.g., the camera apparatus 10).
Additionally, in some examples, communication interface 430 may be a communication interface, a transceiver, a transceiving circuit, and/or the like. The communication interface 430 is a generic term and may include one or more interfaces.
In some examples, memory 420 may be used to store program codes and data for image processing apparatus 200.
In addition, in some examples, the image processing apparatus 200 may further include a communication bus 440, and the communication bus 440 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus 440 may also be divided into an address bus, a data bus, a control bus, etc. There may be one or more communication buses 440. For ease of illustration, only one line is shown in FIG. 3, but this does not represent only one bus or one type of bus.
The image processing device 200 of the retinal stimulator to which the present disclosure relates has been described above. Hereinafter, the image processing method of the retinal stimulator according to the present disclosure is described in detail with reference to a flowchart.
Fig. 4 is a flowchart illustrating an image processing method of the retinal stimulator according to the present disclosure. The image processing method of the retinal stimulator is applied to the image processing apparatus 200 in the retinal stimulator 1. The retinal stimulator 1 has a prescribed number of stimulating electrodes. In the present embodiment, the image processing method of the retinal stimulator may be simply referred to as an image processing method.
In some examples, as shown in fig. 4, the image processing method may include acquiring an initial image having a predetermined number of pixels (step S10).
In step S10, the predetermined number of pixels of the initial image may be decided by the pixels of the imaging lens of the imaging device 10. In addition, the initial image may be a color image, for example, an HSI image or an RGB image. But examples of the present disclosure are not limited thereto and the initial image may be a grayscale image.
In some examples, as shown in fig. 4, the image processing method may further include performing a graying process on the initial image to obtain a grayscale image (step S20).
In step S20, the grayscale image can be regarded as a special color image in which the sizes of the three components R, G, B are the same (i.e., R ═ G ═ B values). The graying process mainly processes color information of an image. For example, the graying process may change the color information of the initial image, and retain the morphological feature information of the initial image (particularly, the middle object or the obstacle of the image). Therefore, the data volume of the initial image can be reduced, the subsequent processing of the image is facilitated, and the useful information in the image is highlighted during the subsequent processing.
In some examples, the graying processing method may employ one of a component method, a maximum value method, an average value method, or a weighting method.
In some examples, as shown in fig. 4, the image processing method may further include performing compression processing on the gray-scale image based on a predetermined number of pixels and a target number of pixels having a prescribed number, obtaining a target image having the target number of pixels (step S30).
In step S30, the predetermined number of pixels may be the number of pixels of the initial image acquired in step S10, and the target number of pixels may be based on the number of stimulation electrodes of the implant device 30.
In some examples, the compression process may be a multi-step compression process. Fig. 5 is a schematic flow chart of a compression processing method in the image processing method of the retinal stimulator according to the present disclosure.
In some examples, as shown in fig. 5, the compression process may include determining a total compression ratio based on a predetermined number of pixels and a target number of pixels (step S31). Wherein, the total compression ratio may be a ratio of a predetermined number of pixels to a target number of pixels.
In some examples, as shown in fig. 5, the compression process may further include determining the number of compression steps and the compression rate of each step in accordance with a total compression rate and the target pixel number, the total compression rate being equal to a product of the compression rates of each step (step S32).
In step S32, the number of compression steps may include step 1, step 2, step 3, step … …, and step m, where m may be a natural number greater than or equal to 2. The compression ratio may be the same or different for each step.
In some examples, as shown in fig. 5, the compression process may further include determining a compression window of a prescribed size according to the number of pixels and the compression rate of each step in the compression process of each step, and performing a gray-scale conversion on each pixel within the compression window (step S33).
In step S33, the number of pixels may be the number of pixels of the image resulting from the previous compression process of the current step. For example, in the first step of compression processing, the number of pixels may be the number of pixels of the gray-scale image obtained by the gray-scale processing module 220. In the second compression process, the pixel processing module 230 may compress the number of pixels of the resulting image according to the first compression process.
In step S33, a grayscale transform may be performed on each pixel within the compression window. Fig. 6 is a schematic flow chart of a gray scale change method in the image processing method of the retinal stimulator according to the present disclosure. As shown in fig. 6, the gray scale transformation may include dividing gray scale levels for respective pixels within the compression window, and giving different weights to pixels having different gray scale levels (step S330). The recognizability of the target image obtained by the gradation converting unit is improved to some extent compared to the target image obtained without the gradation conversion, in which case the image quality of the target image can be improved by the step S330. The gradation level division method for each step may be the same or different.
Fig. 7 is a flowchart illustrating a gray scale dividing method in an image processing method of a retinal stimulator according to the present disclosure. Fig. 8 is a flowchart illustrating a gray scale division method in another image processing method of a retinal stimulator according to the present disclosure.
In some examples, as shown in fig. 7, the dividing of the gray scale level in step S330 may include acquiring gray scale values of respective pixels of the gray scale image within the compression window to obtain a pixel average gray scale value of the respective pixels (step S3311). The pixel average gradation value may be an average gradation value of each pixel of the gradation image obtained through step S20 within the compression window.
In some examples, as shown in fig. 7, the dividing of the gray levels in step S330 may further include dividing the gray levels of the respective pixels within the compression window based on the pixel average gray value and the gray values of the respective pixels of the gray image within the compression window (step S3312). For example, the pixel average grayscale value may be compared to the grayscale values of the individual pixels of the grayscale image within the compression window. And setting the pixels with the average gray value larger than or equal to the average gray value of the pixels as a first gray level, and setting the pixels with the average gray value smaller than the second gray level as a second gray level. Thereby, the gray level of the pixel within the compression window can be further determined.
In some examples, as shown in fig. 8, the dividing of the gray level in step S330 may further include performing gradient value calculation on the gray image along a preset direction with respect to the gray image within the compression window (step S3321).
In some examples, as shown in fig. 8, the dividing of the gray levels in step S330 may further include dividing the gray levels of the respective pixels within the compression window based on the gradient values in the preset direction and the preset gradient (step S3322). Thereby, the gray level of each pixel along the preset direction within the compression window can be determined. For example, the gradient value of the gray image in a preset direction may be compared with a preset gradient value. And setting the pixel of which the gradient value in the preset direction of the gray image is greater than or equal to the preset gradient value as a third gray level, and setting the pixel of which the gradient value in the preset direction of the gray image is less than the preset gradient value as a fourth gray level. Thereby, the gray level of each pixel along the preset direction within the compression window can be further determined.
Examples of the present disclosure are not limited thereto, and for example, the dividing of the gray levels in step S330 may further include arranging the gray values of the respective pixels of the gray image within the compression window in ascending order from small to large (or descending order from large to small), and selecting a median or a mode as the preset gray value. And enabling the gray value of each pixel in the compression window to be greater than or equal to the fifth gray level of the pixel with the preset gray value, and enabling the gray value of each pixel in the compression window to be less than the sixth gray level of the pixel with the preset gray value.
In some examples, three or more gray levels may also be divided for each pixel within the compression window in step S330. For example, the dividing of the gray levels may further include determining a concentrated distribution interval of the gray values of the pixels of the gray image in the compression window, and making the seventh gray level that the gray value of each pixel in the compression window is smaller than the threshold of the distribution interval, making the eighth gray level that the gray value of each pixel in the compression window is located at the threshold of the distribution interval, and making the ninth gray level that the gray value of each pixel in the compression window is larger than the threshold of the distribution interval.
In addition, corresponding weights may be given to the divided gray levels in step S330. In the above-described various methods of dividing the gray scale, the sum of the weights corresponding to the pixels of different gray scales may be one.
In some examples, as shown in fig. 5, the compression process may further include calculating an average gray value of each pixel of the gray image within the compression window using a compression window sliding, generating a pixel point having the average gray value, and combining the generated pixel points in order to form an image having a prescribed number of pixels (step S34).
In step S34, the average gradation value may be an average gradation value of each pixel of the gradation image subjected to the gradation conversion process within the compression window. Further, the respective pixel points generated during the sliding of the compression window may be combined in order to form an image having a prescribed number of pixels. For example, in the first-step compression process, the respective pixel points generated during the sliding of the first compression window may be combined in order and form a first intermediate image having a first prescribed number of pixels. Wherein the first prescribed number of pixels may be obtained from the first-step compression rate and the number of pixels of the initial image. The first intermediate image is a compressed image obtained by the first step of compression processing.
In some examples, as shown in fig. 5, the compression process may further include and repeat the above-described each-step compression process according to the number of compression steps (step S35).
In step S35, after the m-step compression process, a target image having a target number of pixels may be generated.
In the present disclosure, the graying and compression processing may be performed on an initial image having a predetermined number of pixels. In the compression process, the number of compression steps and the compression rate of each step may be determined based on a predetermined pixel number and a target pixel number to obtain a target image by multi-step compression. In each step of compression, a gray scale transform may be performed on each pixel within the compression window. The average gray value of each pixel of the gray image in the compression window can be calculated by utilizing the sliding of the compression window, and a pixel point with the average gray value is generated. The generated pixel points may be combined in order to form an image having a predetermined number of pixels. Thereby, the visual effect of the patient can be improved, and the image quality of the target image can be improved.
While the invention has been specifically described above in connection with the drawings and examples, it will be understood that the above description is not intended to limit the invention in any way. Those skilled in the art can make modifications and variations to the present invention as needed without departing from the true spirit and scope of the invention, and such modifications and variations are within the scope of the invention.
Claims (10)
1. An image processing apparatus for a retinal stimulator having a predetermined number of stimulation electrodes, characterized in that,
the method comprises the following steps:
the device comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring an initial image with a preset pixel number;
the gray processing module is used for carrying out gray processing on the initial image to obtain a gray image; and
a pixel processing module which performs compression processing on the gray image based on the predetermined number of pixels and the target number of pixels having the predetermined number of pixels to obtain a target image having the target number of pixels,
wherein the compression process includes determining a total compression ratio based on the predetermined number of pixels and the target number of pixels; determining the number of compression steps and each step of compression ratio according to the total compression ratio and the target pixel number, wherein the total compression ratio is equal to the product of each step of compression ratio; in each step of compression processing, a compression window with a specified size is determined according to the number of pixels and each step of compression ratio, and each pixel in the compression window is subjected to gray scale conversion, wherein the number of pixels is the number of pixels of an image obtained by the previous step of compression processing of the current step, and the gray scale conversion is used for dividing gray scale levels for each pixel in the compression window and endowing different weights to the pixels with different gray scale levels.
2. The image processing apparatus according to claim 1,
the pixel processing module includes a gradation conversion unit for performing the gradation conversion.
3. The image processing apparatus according to claim 2,
acquiring the gray value of each pixel of the gray image in the compression window to obtain the pixel average gray value of each pixel;
and dividing the gray level of each pixel in the compression window based on the pixel average gray value and the gray value of each pixel of the gray image in the compression window.
4. The image processing apparatus according to claim 3,
and comparing the average gray value of the pixels with the gray value of each pixel of the gray image in the compression window, wherein the pixels which are larger than or equal to the average gray value of the pixels are in a first gray level, and the pixels which are smaller than the average gray value of the pixels are in a second gray level.
5. The image processing apparatus according to claim 2,
the gray level conversion unit is used for calculating the gradient value of the gray level image in the compression window along a preset direction;
and dividing the gray level of each pixel in the compression window based on the gradient value in the preset direction and a preset gradient value.
6. The image processing apparatus according to claim 5,
and comparing the gradient value of the gray image in the preset direction with the preset gradient value, enabling the pixel of the gray image in the preset direction with the gradient value larger than or equal to the preset gradient value to be a third gray level, and enabling the pixel of the gray image in the preset direction with the gradient value smaller than the preset gradient value to be a fourth gray level.
7. The image processing apparatus according to claim 2,
the gray level conversion unit arranges the gray level values of all pixels of the gray level image in the compression window in an ascending order from small to large, and selects a median or a mode as a preset gray level value.
8. The image processing apparatus according to claim 2,
the gray level conversion unit arranges the gray level values of all pixels of the gray level image in the compression window in a descending order from big to small, and selects a median or a mode as a preset gray level value.
9. The image processing apparatus according to claim 7 or 8,
and setting the pixel with the gray value of each pixel in the compression window being greater than or equal to the preset gray value as a fifth gray level, and setting the pixel with the gray value of each pixel in the compression window being smaller than the preset gray value as a sixth gray level.
10. A retinal stimulator, characterized in that,
the method comprises the following steps:
a camera device for capturing a video image and converting the video image into a visual signal;
a video processing device connected with the camera device and comprising at least the image processing device of any one of claims 1 to 9, the video processing device for processing the visual signal to generate a visual compressed signal; and
an implant device for receiving the visual compression signal and converting the visual compression signal to an electrical stimulation signal to deliver the electrical stimulation signal to the retina.
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