CN118446945B - Eye image enhancement method for strabismus detection - Google Patents
Eye image enhancement method for strabismus detection Download PDFInfo
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Abstract
The invention relates to the field of image enhancement, in particular to an eye image enhancement method for strabismus detection. According to the method, firstly, a gray level image of eyes of a person to be detected is obtained, an initial iris region in the gray level image is extracted, boundary pixel points on an edge line are obtained according to the difference of pixel point gray level values in a preset neighborhood range of each pixel point on the edge line of the initial iris region, the boundary pixel point edge line is divided into different edge curve segments by utilizing the boundary pixel point edge line, real circular arc edge lines of the initial iris region are screened out from all the edge curve segments according to the gradient direction change and gradient values of the pixel points on each edge curve segment, the circle center and the radius which are fitted by each real circular arc edge line are adjusted, a complete iris region is obtained, and then the pixel point gray level values in the complete iris region are filled and enhanced, so that the real iris region is obtained. The invention can extract the real iris region of the eyes of the person to be detected and improve the enhancement effect on the eye images.
Description
Technical Field
The invention relates to the field of image enhancement, in particular to an eye image enhancement method for strabismus detection.
Background
Strabismus is a visual disorder of eyes of a human body, when an eye image of a person to be detected is acquired, a real iris area cannot be extracted due to factors such as noise or eyelid shielding, and accuracy of strabismus detection is reduced, so that enhancement of the eye image of the person to be detected is significant for strabismus detection.
In the related art, denoising and enhancing treatment is generally only carried out on an eye image of a person to be tested, and an iris region of the eye is extracted by utilizing an image segmentation technology, but in the process of acquiring the eye image, partial pixel points are missing in the iris region because the iris region of the eye is often shielded by eyelids, and the real iris region of the eye of the person to be tested cannot be extracted only by the prior art, so that the enhancing effect on the eye image is reduced.
Disclosure of Invention
In order to solve the technical problem that the real iris region of eyes of a person to be detected cannot be extracted in the prior art, so that the enhancement effect on an eye image is reduced, the invention aims to provide an eye image enhancement method for strabismus detection, and the adopted technical scheme is as follows:
the invention provides an eye image enhancement method for strabismus detection, which comprises the following steps:
Acquiring an eye image of a person to be detected, performing color space conversion on the eye image to acquire a gray level image and a color space image, and extracting an initial iris region in the gray level image based on brightness component values of all pixel points in the color space image;
obtaining a boundary pixel point on an initial iris region by edge detection, obtaining an initial edge line according to the difference of pixel point gray values in a preset neighborhood range taking each pixel point as a center on the initial edge line, dividing the initial edge line into different edge curve segments by using the boundary pixel point, and obtaining the real edge probability of each edge curve segment according to the gradient direction change and gradient values of all pixel points on each edge curve segment;
Performing circle fitting on each real circular arc edge line to obtain a fitting circle center and a fitting radius of each real circular arc edge line;
and enhancing the gray value of the pixel point in the complete iris region based on the gray value of the pixel point in the initial iris region to obtain a real iris region of the person to be detected.
Further, the obtaining the demarcation pixel point on the initial edge line according to the difference of the gray values of the pixel points in the preset neighborhood range with each pixel point as the center on the initial edge line includes:
Clustering all pixel points in a preset neighborhood range based on the gray values of the pixel points to obtain first clustering clusters, wherein the number of the first clustering clusters is 3;
Taking the absolute value of the difference value between the maximum value and the next largest value of the cluster centers in all the first clusters as a first difference value, and taking the absolute value of the difference value between the minimum value and the next largest value of the cluster centers in all the first clusters as a second difference value;
normalizing the minimum value in the first difference value and the second difference value to obtain the possibility of the demarcation point of each pixel point on the initial edge line;
and screening out boundary pixel points from all pixel points of the initial edge line according to the boundary point possibility.
Further, the screening the demarcation pixel points from all the pixel points of the initial edge line according to the demarcation point likelihood includes:
Clustering all pixel points on the initial edge line based on the possibility of the demarcation point to obtain second clustering clusters, wherein the number of the second clustering clusters is 2;
And taking the pixel point in the second cluster corresponding to the maximum value of the cluster center in the second cluster as a demarcation pixel point.
Further, the obtaining the true edge probability of each edge curve segment according to the gradient direction change and the gradient value of all the pixel points on each edge curve segment includes:
Taking the absolute value of the difference value of the gradient directions of any two adjacent pixel points on each edge curve section as the gradient direction variation quantity of the adjacent pixel points;
carrying out negative correlation normalization on variances of gradient direction variation amounts of all adjacent pixel points to obtain the degree of quasi-circles of each edge curve segment;
Normalizing the gradient value of each pixel point on each edge curve segment to obtain a standard gradient value of each pixel point; taking the average value of the standard gradient values of all pixel points on each edge curve segment as the integral gradient of each edge curve segment;
and taking the product value of the degree of the quasi-circle and the integral gradient as the real edge probability of each edge curve segment.
Further, the filtering the true circular arc edge line of the initial iris region from all edge curve segments based on the true edge likelihood comprises:
And taking the edge curve section with the real edge probability larger than a preset probability threshold as a real circular arc edge line of the initial iris region.
Further, performing circle fitting on each real circular arc edge line to obtain a fitting circle center and a fitting radius of each real circular arc edge line includes:
In the gray level image, a connecting line between two end points of each real arc edge line is used as a chord line of the corresponding real arc edge line, and the distance between the midpoint of each real arc edge line and the midpoint of the corresponding chord line is used as the camber of the corresponding real arc edge line;
inputting the length of the chord line and the camber into a preset radius calculation equation and solving to obtain a fitting radius corresponding to the edge line of the real arc;
And (3) making a ray pointing to the middle point of the corresponding chord line from the middle point of each real circular arc edge line, and taking the position, which is on the ray and is away from the middle point of the real circular arc edge line and is equal to the fitting radius, as the fitting circle center of the corresponding real circular arc edge line.
Further, the adjusting the fitting center and the fitting radius based on the real edge probability respectively includes:
the fitting circle center comprises a fitting circle center abscissa and a fitting circle center ordinate;
Normalizing the real edge probability of each real arc edge line to obtain a weight coefficient of the corresponding real arc edge line, wherein the accumulated value of the weight coefficients of all the real arc edge lines is equal to 1;
the method comprises the steps of carrying out weighted summation on the fitted circle center abscissa corresponding to the real circular arc edge line based on the weight coefficient to obtain the real circle center abscissa, carrying out weighted summation on the fitted circle center ordinate corresponding to the real circular arc edge line based on the weight coefficient to obtain the real circle center ordinate;
And taking the positions determined by the abscissa of the true circle center and the ordinate of the true circle center as the true circle center and taking the circular area determined by the true circle center and the true radius as the complete iris area.
Further, the enhancing the gray value of the pixel point in the complete iris area based on the gray value of the pixel point in the initial iris area, and obtaining the real iris area of the person to be detected includes:
obtaining filling gray values according to gray values of all pixel points in the initial iris region;
Taking the pixel points which are in the complete iris area and are not in the initial iris area as missing pixel points of the complete iris area;
and replacing the gray value of the missing pixel point with the filling gray value, and taking the replaced complete iris area as the real iris area of the person to be detected.
Further, the obtaining the filling gray value according to the gray values of all the pixel points in the initial iris region includes:
And taking the average value of the gray values of all pixel points in the initial iris area as a filling gray value.
Further, the extracting the initial iris region in the gray-scale image based on the brightness component value of each pixel point in the color space image includes:
Marking the pixel points of which the brightness component values are smaller than a preset brightness threshold value in the color space image, and marking the pixel points of corresponding positions in the gray level image;
and performing a closing operation on the region formed by the marked pixel points in the gray level image to obtain an initial iris region.
The invention has the following beneficial effects:
In consideration of the fact that the prior art cannot acquire the real iris area of eyes of a person to be detected, the enhancement effect on the eye image is poor, the accuracy of subsequent strabismus detection is reduced, therefore, the eye image is firstly converted into the gray image and the color space image, the brightness difference of the iris, the sclera and the eyelid is considered to be large, the initial iris area in the gray image can be initially extracted based on the brightness component values of pixel points in the color space image, the edge of the initial iris area is not complete due to the shielding of the eyelid, the false edge formed by shielding of the eyelid exists on the edge line of the initial iris area, and the real edge formed between the iris and the sclera is required to be extracted, the boundary point between the false edge and the real edge is considered to be at the position of the iris, the sclera and the eyelid boundary point with large local gray difference, the boundary point of the false edge is divided into a plurality of edge curve sections through the boundary pixel points, the shape of the iris is considered to be approximate to be circular, the gradient change of the pixel points on the real edge is uniform, the gray difference on two sides is large, the edge of the initial iris area can be screened out through the possibility of the real edge, namely the edge line of the real iris is more complete, the full-circle-radius of the iris area is obtained, the full-circle-fitting effect of the iris area is obtained after the full-circle-fitting of the iris area is obtained.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an eye image enhancement method for strabismus detection according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description refers to specific implementation, structure, characteristics and effects of an eye image enhancement method for strabismus detection according to the invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
An embodiment of an eye image enhancement method for strabismus detection:
the following specifically describes a specific scheme of an eye image enhancement method for strabismus detection provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of an eye image enhancement method for strabismus detection according to an embodiment of the present invention is shown, where the method includes:
Step S1, acquiring an eye image of a person to be detected, performing color space conversion on the eye image to acquire a gray level image and a color space image, and extracting an initial iris region in the gray level image based on brightness component values of all pixel points in the color space image.
The detection of the strabismus of the eyes needs to be carried out by analyzing an iris image of the eyes to obtain a detection result, in the prior art, the denoising-processed iris image is usually carried out by image segmentation to extract an iris region of the eyes, but partial pixel points are missing in the iris region because the iris region of the eyes is often shielded by eyelids, and the real iris region of the eyes of a person to be detected cannot be extracted only by the prior art, so that the enhancement effect of the eye image is reduced.
According to the embodiment of the invention, eye imaging equipment such as a fundus camera is used for collecting eye images of a person to be detected, head stability and moderate ambient light of the person to be detected are ensured in the shooting process, so that a clearer eye image is obtained, the eye images which are collected initially are RGB images, in order to reduce the calculation amount of subsequent image processing and improve the processing speed, the collected eye images are subjected to gray processing and converted into single-channel gray images, meanwhile, the color of an iris area in the eye images is considered to be close to black, the brightness of the iris area is smaller, the brightness difference among the iris area, a sclera area and an eyelid area is larger, in order to obtain the iris area in the gray images more accurately, the iris area in the gray images is extracted initially based on the brightness component values of all pixel points in the color space images, namely the initial iris area is a technical means which is well known to a person skilled in the art, and details are omitted.
Preferably, in one embodiment of the present invention, an HSV color space is selected, because the brightness of the iris area is smaller, the pixel points in the HSV image with the brightness component value smaller than the preset brightness threshold value can be marked, and the pixel points in the corresponding position in the gray image can be marked, wherein the brightness component of the pixel points in the HSV image is a V component, the value of the V component is in the range of [0,1], and due to the light reflection phenomenon in the iris area, holes exist in the area formed by the marked pixel points in the gray image, therefore, the area formed by the marked pixel points in the gray image can be subjected to a closing operation, and the holes exist in the area can be removed, so that the initial iris area in the gray image can be obtained, wherein the preset brightness threshold value can be set to 0.25, the specific value of the preset brightness threshold value can be set by an operator according to the specific implementation scene, the closing operation is not limited, and the closing operation is a technical means well known to those skilled in the art.
It should be noted that other color spaces containing luminance components, such as HSI color space or HSB color space, etc., may be used in other embodiments of the present invention, which are not limited herein.
It should be noted that, when the pixel points are marked, a marking value may be set for the pixel points, and a specific value of the marking value may be set by an implementer, in an embodiment of the present invention, marking values of all the pixel points may be initialized to 0, and marking values of the pixel points to be marked are set to 1, and then the pixel points with marking values of 1 represent the marked pixel points.
Step S2, carrying out edge detection on an initial iris region to obtain an initial edge line, obtaining boundary pixel points on the initial edge line according to the difference of pixel point gray values in a preset neighborhood range taking each pixel point as a center on the initial edge line, dividing the initial edge line into different edge curve segments by utilizing the boundary pixel points, obtaining the real edge probability of each edge curve segment according to the gradient direction change and gradient values of all pixel points on each edge curve segment, and screening out the real arc edge line of the initial iris region from all the edge curve segments based on the real edge probability.
The eyelid of the person to be tested can shade a part of the iris area, so that the initial iris area acquired in the process is incomplete, part of pixel points are missing, and the edge of the iris is considered to be close to a circle.
Preferably, in one embodiment of the present invention, the initial edge line of the initial iris region is extracted by using a Canny edge detection algorithm, which is a technical means well known to those skilled in the art, and will not be described herein.
The existence of a part of the edge line in the obtained initial edge line is formed by the shielding of the eyelid, the part of the edge line is actually the edge of the eyelid and is not the real edge of the iris, namely the false edge, while the other part of the edge line is formed between the iris and the sclera, is positioned at the junction of the iris and the sclera and is the real edge of the iris, and the real edge of the iris is obtained by carrying out circle fitting by using the real edge of the iris in the follow-up process, so that the real edge of the iris needs to be extracted from the initial edge line, and the boundary point between the false edge and the real edge, namely the boundary pixel point needs to be determined because the obtained initial edge line is a whole, and the boundary pixel point is considered to be positioned at the junction of the iris, the sclera and the eyelid, the difference of local gray scales at the boundary positions of the three is larger, so that boundary pixel points on the initial edge line can be obtained according to the difference of gray scale values of the pixel points in a preset neighborhood range taking each pixel point as a center on the initial edge line, the initial edge line can be divided through the boundary points in the follow-up process, the false edge and the real edge on the initial edge line are separated conveniently, the false edge and the real edge can be distinguished conveniently, the real edge of the iris can be extracted from the initial edge line more accurately, the size of the preset neighborhood range is set to be 5 multiplied by 5, and the specific size of the preset neighborhood range can be set by an operator according to specific implementation scenes without limitation.
Preferably, in an embodiment of the present invention, the method for acquiring the boundary pixel point on the initial edge line specifically includes:
The method comprises the steps of clustering all pixel points in a preset neighborhood range according to a pixel point gray value to obtain three first cluster clusters, taking the absolute value of the difference between the maximum value and the secondary maximum value of the cluster centers in all first cluster clusters as a first difference value, taking the absolute value of the difference between the minimum value and the secondary maximum value of the cluster centers in all first cluster clusters as a second difference value, taking the cluster center of each first cluster as the average value of the gray values of all pixel points in the corresponding cluster, carrying out normalization on the minimum value in the first difference value and the second difference value to obtain the boundary point possibility of each pixel point on an initial edge line, and taking the maximum value of the corresponding pixel points as the boundary point between a pseudo edge and a real edge, wherein the embodiment of the method considers that the boundary point between the two types of the pixel points exist on the initial edge line, namely the boundary point between the two pixel points is the secondary maximum value, taking the cluster center as the average value of the k=of all pixel points in the corresponding cluster, taking the second cluster center as the average value of the gray value of all pixel points in the corresponding cluster, carrying out the clustering cluster center, taking the average value of the k=of the second cluster as the boundary point between the two pixel points in the initial edge line, and carrying out the clustering center-like, and taking the average value of the second cluster as the boundary point between the two pixel points in the second cluster as the threshold value, and will not be described in detail herein. The expression of the demarcation possibility may specifically be, for example:
Dz＝norm[min(|Az-Bz|,|Cz-Bz|)]
Wherein D z represents the demarcation possibility of the z-th pixel point on the initial edge line, A z represents the maximum value of the clustering centers in all the first clustering clusters obtained after the pixel points in the preset neighborhood range of the z-th pixel point on the initial edge line are clustered, B z represents the next maximum value of the clustering centers in all the first clustering clusters obtained after the pixel points in the preset neighborhood range of the z-th pixel point on the initial edge line are clustered, C z represents the minimum value of the clustering centers in all the first clustering clusters obtained after the pixel points in the preset neighborhood range of the z-th pixel point on the initial edge line are clustered, wherein A z≥Bz≥Cz is min () represents a minimum function, and norm [ ] represents a normalization function.
In the process of acquiring the demarcation possibility of each pixel point on the initial edge line, the greater the demarcation possibility D z is, the more likely the corresponding pixel point on the initial edge line is a demarcation point between the pseudo edge and the real edge, namely, the demarcation pixel point is; because the boundary pixel points are positioned at the junctions of the iris, the sclera and the eyelid, and the gray values of the pixel points in the iris, the sclera and the eyelid are larger, three gray types of pixel points exist in the preset neighborhood range of the boundary pixel points, when the pixel points in the preset neighborhood range of the boundary pixel points are clustered, the difference of the cluster centers among the three first clusters is larger, the first difference value |A z-Bz | of the cluster center and the maximum value is larger, the second difference value |C z-Bz | of the cluster center and the minimum value is larger, and the non-boundary pixel points are positioned at the junctions of the iris and the sclera or the junctions of the iris and the eyelid, therefore, only two gray-scale type pixel points exist in the preset neighborhood range of the non-demarcation pixel point, when the pixel points in the preset neighborhood range of the non-demarcation pixel point are clustered, the first difference value |A z-Bz | between the next largest value and the largest value of the clustering center or the second difference value |C z-Bz | between the next largest value and the smallest value of the clustering center in the three obtained first clustering clusters may be smaller, so that the min (|A z-Bz|,|Cz-Bz |) can be utilized to reflect the possibility that the pixel point on the initial edge line is the demarcation pixel point, the larger the min (|A z-Bz|,|Cz-Bz |) is, the more likely that the pixel point is located at the juncture of the iris, the sclera and the eyelid, and the more likely that the pixel point is the demarcation pixel point is, the greater the demarcation probability D z, the normalization process is performed on min (|A z-Bz|,|Cz-Bz |) to limit the demarcation probability D z to the [0,1] range for facilitating subsequent evaluation analysis.
In one embodiment of the present invention, the normalization process may specifically be, for example, maximum and minimum normalization processes, and the normalization in the subsequent steps may be performed by using the maximum and minimum normalization processes, and in other embodiments of the present invention, other normalization methods may be selected according to a specific range of values, which will not be described herein.
In other embodiments of the present invention, a pixel point with a demarcation probability greater than a preset demarcation threshold on the initial edge line may be used as a demarcation pixel point, where the preset demarcation probability may be set to 0.8, and a specific value of the preset demarcation probability may also be set by an implementer according to a specific implementation scenario, which is not limited herein.
After the demarcation pixel points are extracted from the initial edge line, the demarcation pixel points may be used as segmentation points on the initial edge line, so that the initial edge line is divided into a plurality of curve segments, i.e., edge curve segments, by using a plurality of demarcation pixel points, wherein the edge curve segments are partial curves of the initial edge line, and the edge curve segments comprise real edges and pseudo edges of the iris, and the pseudo edges are not actually edges of the iris, so that in order to obtain a more accurate and real iris region in the following, the embodiment of the invention needs to extract the real edges of the iris, i.e., the real circular arc edge line, from the edge curve segments, and in consideration of the shape of the iris to be close to a circle, therefore in a gray scale image, compared with the pseudo edge of the iris, the change of the gradient directions of the pixel points on the real edge is more uniform, the pixel points on the real edge are positioned at the junction of the iris and the sclera, the difference of the gray values of the pixel points on two sides of the real edge is larger, and the gradient values of the pixel points on the real edge are larger, so that the change of the gradient directions and the gradient values of all the pixel points on each edge curve section can be analyzed, the obtained possibility of the real edge of each edge curve section is reflected by the obtained possibility of the real edge, and the real edge of the iris, namely the real circular arc edge line, can be accurately extracted according to the obtained possibility of the real edge.
Preferably, in one embodiment of the present invention, the method for obtaining the true edge probability of each edge curve segment specifically includes:
The method comprises the steps of firstly, calculating a gradient value and a gradient direction of each pixel point on each edge curve segment by utilizing a sobel gradient operator, taking an absolute value of a difference value of the gradient directions of any two adjacent pixel points on each edge curve segment as a gradient direction variation quantity of the adjacent pixel points, carrying out inversely related normalization on variances of the gradient direction variation quantities of all the adjacent pixel points to obtain a quasi-circle degree of each edge curve segment, carrying out normalization on the gradient value of each pixel point on each edge curve segment to obtain a standard gradient value of each pixel point, taking an average value of the standard gradient values of all the pixel points on each edge curve segment as an integral gradient of each edge curve segment, and taking a product value of the quasi-circle degree and the integral gradient as a real edge possibility of each edge curve segment. The expression of the true edge likelihood may specifically be, for example:
Yi＝{Δθ(i,j)|Δθ(i,j)＝|θ(i,j)-θ(i,j+1)|,j＝1,2...wi-1}
Wherein E i represents the real edge probability of the ith edge curve segment, Y i represents the set of gradient direction change amounts of all adjacent pixels on the ith edge curve segment, w i represents the number of pixels on the ith edge curve segment, F (i,j) represents the gradient value of the jth pixel on the ith edge curve segment, delta theta (i,j) represents the gradient change amount of the jth adjacent pixel on the ith edge curve segment, theta (i,j) represents the gradient direction of the jth pixel on the ith edge curve segment, theta (i,j+1) represents the gradient direction of the jth+1th pixel on the ith edge curve segment, wherein the jth pixel and the jth+1th pixel are the adjacent pixels exp [ ] represents an exponential function based on a natural constant E, norm represents a normalization function, var () represents a difference function { | } represents a set symbol, and| represents an absolute value.
In the process of acquiring the real edge possibility of each edge curve segment, the larger the real edge possibility E i is, the more likely the edge curve segment is the real edge of the iris, the shape of the iris is close to a circle, so that in a gray scale image, relative to the pseudo edge of the iris, the change of the gradient directions of pixel points on the real edge is more uniform, the difference between the gradient direction change amounts of all adjacent pixel points on the real edge is smaller, so that the smaller the variance Var (Y i) of the set Y i of all gradient direction change amounts of the real edge is, the larger the degree of quasi-circle exp-Var (Y i) is, the closer the edge curve segment is to a circular arc, and the more likely the edge curve segment is the real edge of the iris is, the larger the real edge possibility E i is; and the pixel points on the real edge are positioned at the junction of the iris and the sclera, the difference of the gray values of the pixel points on two sides of the real edge is larger, so that the gradient value F (i,j) of each pixel point on the edge curve segment is larger, which means that the more likely the pixel point on the edge curve segment is positioned at the junction of the iris and the sclera, further means that the more likely the edge curve segment is the real edge of the iris, the larger the real edge probability E i is, so that the real edge probability E i is limited in the range of [0,1], the subsequent evaluation analysis is facilitated, the gradient value of each pixel point on the edge curve segment is normalized in one embodiment of the invention, and the standard gradient value norm (F (i,j)) is obtained, and taking the average value of the standard gradient values of all pixel points on the edge curve segment as the integral gradient of each edge curve segment
Because the greater the real edge probability is, the more likely the corresponding edge curve segment is the real edge of the iris, the real edge of the initial iris region, namely the real circular arc edge line, can be screened from all the edge curve segments based on the real edge probability, so that the subsequent circular fitting of the real circular arc edge line is facilitated, the more complete and accurate iris region is obtained, and the enhancement effect on the eye image is improved.
Preferably, in one embodiment of the present invention, an edge curve segment with a real edge likelihood greater than a preset likelihood threshold is used as a real circular arc edge line of the initial iris region, the preset likelihood threshold is set to 0.8, and a specific value of the preset likelihood threshold may also be set by an implementer according to a specific implementation scenario, which is not limited herein.
After the real circular arc edge line in the initial edge line of the initial iris region is obtained, the real circular arc edge line is the real edge of the iris, so that the real circular arc edge line can be subjected to circle fitting in the follow-up process to obtain the complete iris region.
And step S3, performing circle fitting on each real circular arc edge line to obtain a fitting circle center and a fitting radius of each real circular arc edge line, and respectively adjusting the fitting circle center and the fitting radius based on the real edge probability to obtain a complete iris region.
Because the shape of the iris is close to a circle, the obtained real circular arc edge lines are more close to a circular arc, and in order to determine a more complete iris region, the circle fitting can be carried out on the real circular arc edge lines, the fitting circle center and the fitting radius of each real circular arc edge line are preliminarily obtained, the fitting circle centers and the fitting radii of all the real circular arc edge lines are conveniently combined, and the circle centers and the radii corresponding to the complete iris region are further obtained.
Preferably, in one embodiment of the present invention, the method for obtaining the fitting center and the fitting radius of each real circular arc edge line specifically includes:
The method comprises the steps of taking the lower boundary of a gray image as a horizontal axis and the left boundary of the gray image as a vertical axis, establishing a two-dimensional coordinate system, taking a connecting line between two end points of each real arc edge line as a chord line of the corresponding real arc edge line in the gray image, taking the distance between the midpoint of each real arc edge line and the midpoint of the corresponding chord line as the camber of the corresponding real arc edge line, inputting the length of the chord line and the camber into a preset radius calculation equation, solving to obtain a fitting radius of the corresponding real arc edge line, taking a ray pointing to the midpoint direction of the corresponding chord line from the midpoint of each real arc edge line, and taking the position of the midpoint of the ray, which is far from the real arc edge line, as the fitting circle center of the corresponding real arc edge line.
The preset radius calculation equation is as follows:
Wherein r k represents the fitting radius of the kth real circular arc edge line, r k is an unknown number, G k represents the length of the chord line of the kth real circular arc edge line, and H k represents the camber of the kth real circular arc edge line.
Substituting the length G k and the arch height H k of the chord line of each real circular arc edge line into the preset radius calculation equation, and solving the equation to obtain the fitting radius r k of each real circular arc edge line.
Based on the obtained fitting radius and the fitting circle center, the fitting circle corresponding to each real circular arc edge line can be uniquely determined in the two-dimensional coordinate system, and because the fitting radius and the fitting circle center of different real circular arc edge lines have certain difference, the fitting circle corresponding to different real circular arc edge lines has certain deviation, in order to obtain the region corresponding to the iris more accurately, the fitting radius and the fitting circle center of all the real circular arc edge lines are required to be analyzed, and because the real edge probability can reflect the degree of the corresponding real circular arc edge line approaching to the circle, the larger the real edge probability is, the smaller the influence of noise points on the corresponding real circular arc edge line is, the circle fitted through the real circular arc edge line is closer to the complete iris region, so that the fitting circle center and the fitting radius can be respectively adjusted based on the real edge probability, the complete iris region can be obtained, the pixel points in the complete iris region can be further filled and enhanced, and the enhancement effect on the eye image can be improved.
Preferably, in one embodiment of the present invention, the method for acquiring the complete iris region specifically includes:
In a two-dimensional coordinate system, each fitting circle center comprises a fitting circle center abscissa and a fitting circle center ordinate, the real edge probability of each real circular arc edge line is normalized to obtain a weight coefficient corresponding to the real circular arc edge line, wherein the accumulated value of the weight coefficients of all the real circular arc edge lines is equal to 1, the fitting circle center abscissas corresponding to the real circular arc edge lines are weighted and summed based on the weight coefficient to obtain a real circle center abscissa, the fitting circle center abscissas corresponding to the real circular arc edge lines are weighted and summed based on the weight coefficient to obtain a real circle center ordinate, the fitting radius corresponding to the real circular arc edge lines is weighted and summed based on the weight coefficient to obtain a real radius, the position determined by the real circle center abscissa and the real circle center ordinate is used as a real circle center, and a circular area determined by the real circle center and the real radius is used as a complete iris area. The expressions of the true circle center abscissa, the true circle center ordinate, and the true radius may specifically be, for example:
Wherein x ' represents the abscissa of the true circle center, E k represents the true edge probability of the kth true arc edge line, E n represents the true edge probability of the nth true arc edge line, x k represents the abscissa of the fitted circle center of the kth true arc edge line, N represents the number of the true arc edge lines, y ' represents the ordinate of the true circle center, y k represents the ordinate of the fitted circle center of the kth true arc edge line, r ' represents the true radius, and r k represents the fitted radius of the kth true arc edge line.
In the process of acquiring the horizontal coordinate of the real circle center, the vertical coordinate of the real circle center and the real radius, as the real edge probability E k can reflect the degree of approaching the circle of the corresponding real circular arc edge line, the larger the real edge probability E k is, which shows that the smaller the corresponding real circular arc edge line is affected by noise points, the circle fitted by the real circular arc edge line is more approximate to the complete iris region, therefore, the real edge probability of each real circular arc edge line can be normalized, and the weight coefficient of the corresponding real circular arc edge line is obtainedWherein the accumulated value of the true edge likelihoods of all the true circular arc edge linesThe method is used for normalizing E k, the larger the weight coefficient of the real circular arc edge line is, the more accurate the fitting circle center abscissa corresponding to the real circular arc edge line is, so that the weighting and summing are carried out on the fitting circle center abscissa corresponding to the real circular arc edge line by using the weight coefficient to obtain a more accurate real circle center abscissa x ', and the analysis of the real circle center ordinate y' and the real radius r 'can refer to the analysis of the real circle center abscissa x' by the same method, and is not repeated herein.
And S4, enhancing the gray value of the pixel point in the complete iris region based on the gray value of the pixel point in the initial iris region to obtain the real iris region of the person to be detected.
The complete iris region obtained by the process only determines the complete region range of the iris due to the fact that partial pixel points are missing in the iris caused by the shielding of the eyelid, and the gray value of the partial pixel points in the complete iris region is greatly different from the gray value of the initial iris region, so that the gray value of the pixel points in the complete iris region is filled and enhanced based on the gray value of the pixel points in the initial iris region, the real iris region of a person to be detected is obtained, and the enhancement effect of the eye image of the person to be detected can be improved.
Preferably, in one embodiment of the present invention, the method for acquiring the real iris region specifically includes:
The method comprises the steps of taking an average value of gray values of all pixel points in an initial iris area as a filling gray value, taking pixel points which are in a complete iris area and are not in the initial iris area as missing pixel points of the complete iris area, replacing the gray values of the missing pixel points with the filling gray value, and taking the replaced complete iris area as a real iris area of a person to be detected, so that enhancement of an eye iris image of the person to be detected is realized.
In the embodiment of the invention, the obtained real iris area of the person to be detected is used for carrying out subsequent strabismus detection on the person to be detected.
In summary, the embodiment of the invention firstly carries out color space conversion on an eye image of a person to be detected to obtain a gray image and a color space image, extracts an initial iris region in the gray image based on brightness component values of all pixel points in the color space image, extracts initial edge lines of the initial iris region, obtains boundary pixel points on the initial edge lines according to differences of pixel point gray values in a preset neighborhood range of each pixel point on the initial edge lines, further divides the initial edge lines into different edge curve segments by using the boundary pixel points, obtains real edge probability of each edge curve segment according to gradient direction changes of the pixel points on each edge curve segment and gradient values, screens out real circular arc edge lines of the initial iris region based on the real edge probability, carries out circle fitting on each real circular arc edge line to obtain fitting circle centers and radius of each real circular arc edge line, respectively adjusts the circle centers and the fitting radius based on the real edge probability to obtain a complete iris region, carries out the gray scale enhancement of pixel points in the complete iris region based on the pixel point missing in the initial iris region, and obtains the real iris region to be detected.
An embodiment of an eye iris complete image extraction method:
In the related art, an iris image of an eye of a person to be detected is generally extracted by using an image segmentation technology, but when the eye image is shot and acquired, a partial area of the iris of the eye is blocked by an eyelid of the person to be detected, so that a complete iris area of the eye cannot be extracted by using the existing image segmentation technology.
In order to solve the problem, the embodiment provides a method for extracting an eye iris complete image, which comprises the following steps:
Step S1, acquiring an eye image of a person to be detected, performing color space conversion on the eye image to acquire a gray level image and a color space image, and extracting an initial iris region in the gray level image based on brightness component values of all pixel points in the color space image;
Step S2, carrying out edge detection on an initial iris region to obtain an initial edge line, obtaining boundary pixel points on the initial edge line according to the difference of pixel point gray values in a preset neighborhood range taking each pixel point as a center on the initial edge line, dividing the initial edge line into different edge curve segments by using the boundary pixel points, and obtaining the real edge probability of each edge curve segment according to the gradient direction change and gradient values of all pixel points on each edge curve segment;
and step S3, performing circle fitting on each real circular arc edge line to obtain a fitting circle center and a fitting radius of each real circular arc edge line, and respectively adjusting the fitting circle center and the fitting radius based on the real edge probability to obtain a complete iris region.
The detailed description of the step S1 to the step S3 in the embodiment of the eye image enhancement method for strabismus detection is given above, and will not be repeated here.
The method has the advantages that the real iris area of the eyes of the person to be detected cannot be obtained in the prior art, the eye image is firstly converted into the gray level image and the color space image, the brightness difference of the iris, the sclera and the eyelid is considered to be large, the initial iris area in the gray level image can be initially extracted based on the brightness component value of each pixel point in the color space image, the real edge of the initial iris area can be screened out according to the fact that the initial iris area is incomplete due to the shielding of the eyelid, the fake edge formed by shielding of the eyelid exists on the edge line of the initial iris area, and the real edge formed between the iris and the sclera is needed to be extracted, the boundary point between the fake edge and the real edge is considered to be positioned at the boundary position of the iris, the sclera and the eyelid with large local gray level difference, the initial edge line can be divided into a plurality of edge curve sections through the boundary pixel points, the shape of the iris is considered to be approximately round, the gradient change of the pixel points on the real edge is uniform, the gray level difference of the two sides is large, and the real edge of the initial iris area can be screened out according to the real edge possibility, namely the real edge line fitting is accurate, and the complete iris area is obtained by adjusting the circle center and circle center radius fitting.
It should be noted that the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (9)
1. A method of ocular image enhancement for strabismus detection, the method comprising:
Acquiring an eye image of a person to be detected, performing color space conversion on the eye image to acquire a gray level image and a color space image, and extracting an initial iris region in the gray level image based on brightness component values of all pixel points in the color space image;
obtaining a boundary pixel point on an initial iris region by edge detection, obtaining an initial edge line according to the difference of pixel point gray values in a preset neighborhood range taking each pixel point as a center on the initial edge line, dividing the initial edge line into different edge curve segments by using the boundary pixel point, and obtaining the real edge probability of each edge curve segment according to the gradient direction change and gradient values of all pixel points on each edge curve segment;
The method comprises the steps of carrying out circle fitting on each real circular arc edge line to obtain a fitting circle center and a fitting radius of each real circular arc edge line, respectively carrying out weighted summation on the fitting circle center and the fitting radius based on the real edge probability to obtain a complete iris region, carrying out weighted summation on the fitting circle center ordinate of each real circular arc edge line to obtain a real radius, carrying out weighted summation on the fitting circle center abscissa of each real circular arc edge line based on the weight coefficient to obtain a real circle center ordinate of each real circular arc edge line, carrying out weighted summation on the fitting circle center ordinate of each real circular arc edge line based on the weight coefficient to obtain a real circle center ordinate, carrying out weighted summation on the fitting radius of each real circular arc edge line based on the weight coefficient to obtain a real radius, taking the positions determined by the real circle center abscissa and the real circle center ordinate as a real circle center, and taking the circular region determined by the real circle center and the real radius as the complete iris region;
and enhancing the gray value of the pixel point in the complete iris region based on the gray value of the pixel point in the initial iris region to obtain a real iris region of the person to be detected.
2. The method for enhancing an eye image for strabismus detection according to claim 1, wherein the obtaining boundary pixel points on the initial edge line according to the difference of gray values of the pixel points in a preset neighborhood range centered on each pixel point on the initial edge line comprises:
Clustering all pixel points in a preset neighborhood range based on the gray values of the pixel points to obtain first clustering clusters, wherein the number of the first clustering clusters is 3;
Taking the absolute value of the difference value between the maximum value and the next largest value of the cluster centers in all the first clusters as a first difference value, and taking the absolute value of the difference value between the minimum value and the next largest value of the cluster centers in all the first clusters as a second difference value;
normalizing the minimum value in the first difference value and the second difference value to obtain the possibility of the demarcation point of each pixel point on the initial edge line;
and screening out boundary pixel points from all pixel points of the initial edge line according to the boundary point possibility.
3. The method according to claim 2, wherein the screening out boundary pixels from all pixels of the initial edge line according to the boundary point likelihood comprises:
Clustering all pixel points on the initial edge line based on the possibility of the demarcation point to obtain second clustering clusters, wherein the number of the second clustering clusters is 2;
And taking the pixel point in the second cluster corresponding to the maximum value of the cluster center in the second cluster as a demarcation pixel point.
4. The method for enhancing an eye image for strabismus detection according to claim 1, wherein the obtaining the true edge likelihood of each edge curve segment according to the gradient direction change and the gradient value of all pixel points on each edge curve segment comprises:
Taking the absolute value of the difference value of the gradient directions of any two adjacent pixel points on each edge curve section as the gradient direction variation quantity of the adjacent pixel points;
carrying out negative correlation normalization on variances of gradient direction variation amounts of all adjacent pixel points to obtain the degree of quasi-circles of each edge curve segment;
Normalizing the gradient value of each pixel point on each edge curve segment to obtain a standard gradient value of each pixel point; taking the average value of the standard gradient values of all pixel points on each edge curve segment as the integral gradient of each edge curve segment;
and taking the product value of the degree of the quasi-circle and the integral gradient as the real edge probability of each edge curve segment.
5. The method of claim 1, wherein the screening out true circular edge lines of the initial iris region from all edge curve segments based on the true edge likelihood comprises:
And taking the edge curve section with the real edge probability larger than a preset probability threshold as a real circular arc edge line of the initial iris region.
6. The method for enhancing an eye image for strabismus detection according to claim 1, wherein the performing a circle fit on each of the real circular arc edge lines to obtain a fitting center and a fitting radius of each of the real circular arc edge lines comprises:
In the gray level image, a connecting line between two end points of each real arc edge line is used as a chord line of the corresponding real arc edge line, and the distance between the midpoint of each real arc edge line and the midpoint of the corresponding chord line is used as the camber of the corresponding real arc edge line;
inputting the length of the chord line and the camber into a preset radius calculation equation and solving to obtain a fitting radius corresponding to the edge line of the real arc;
And (3) making a ray pointing to the middle point of the corresponding chord line from the middle point of each real circular arc edge line, and taking the position, which is on the ray and is away from the middle point of the real circular arc edge line and is equal to the fitting radius, as the fitting circle center of the corresponding real circular arc edge line.
7. The method for enhancing an eye image for strabismus detection according to claim 1, wherein the step of enhancing the gray value of the pixel point in the complete iris region based on the gray value of the pixel point in the initial iris region, and obtaining the real iris region of the person to be detected comprises:
obtaining filling gray values according to gray values of all pixel points in the initial iris region;
Taking the pixel points which are in the complete iris area and are not in the initial iris area as missing pixel points of the complete iris area;
and replacing the gray value of the missing pixel point with the filling gray value, and taking the replaced complete iris area as the real iris area of the person to be detected.
8. The method according to claim 7, wherein the obtaining the filling gray value according to the gray values of all the pixels in the initial iris region comprises:
And taking the average value of the gray values of all pixel points in the initial iris area as a filling gray value.
9. The method according to claim 1, wherein the extracting the initial iris region in the gray-scale image based on the luminance component values of the pixels in the color space image comprises:
Marking the pixel points of which the brightness component values are smaller than a preset brightness threshold value in the color space image, and marking the pixel points of corresponding positions in the gray level image;
and performing a closing operation on the region formed by the marked pixel points in the gray level image to obtain an initial iris region.
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