The present invention relates to a method by which the edge (disc)
the papilla is detected in fundus recordings and its form is mathematical
(Approximated) can be replicated.
Entry point of the optic nerve into the fundus is called the papilla
denotes, appears as a light, slightly elliptical
Area and causes in the visual field the "blind spot".
This entry point of the optic nerve is a polygonal, flattened
Bulging of the conjunctiva, in the center of a fine vascular tree
which is susceptible to allergic inflammation
Information of the shape of the papilla forms the basis for
an ophthalmological examination, especially of the green
Stars (glaucoma). At the glaucoma examination represents the bulge
the papilla is a well-studied, valid indicator of
the onset and progression of the disease. Glaucoma
represents irreversible damage and at the same time the
most common diseases of the optic nerve.
Green Star is known as a disorder of vision.
In the study of the green star, the observation applies
of the blind spot of the patient on the basis of a picture of the patient
Ocular fundus (fundus) as crucial. In recent years
It has become possible, as a result of the blind spot
Progress in the technology of recording evaluation quantitatively
to measure and evaluate.
Glaucoma is often diagnosed very late,
because in the early stage of the disease the peripheral vision
decreases and thus is not perceived by the patient. With progressive
Disease course these visual field failures occur in
Area of the macula.
the macula is an approx. 5 mm large area at the posterior pole
the retina, below the papilla, the largest
Visual acuity and also referred to as "yellow spot"
by virtue of
the slow course of the disease in glaucoma is of the greatest
Meaning, this disease by means of screening method in an early
Recognize disease stage.
The prior art discloses various methods
with which the papilla detected in Fundusaufnahmen and their shape
is modeled using a mathematical model.
So will in the DE 697 31 167 T2
a method and a device for the evaluation of stereo images of the ocular fundus described, with which a blind spot on the basis of stereo images of the fundus can be detected and evaluated. With the help of stereo recordings different height points (lowest and highest points) are determined from the three-dimensional data of the fundus and from this an outer peripheral line of the blind spot is determined. The papilla margin is characterized by a manual marking of several bases. A segmentation with an ellipse is not provided. A disadvantage of this solution has the effect that stereo recordings are required for the determination of an outer circumferential line of the blind spot, since otherwise no height points can be determined.
in  of Xu and others described algorithm is also based
on active contours, however, is for the intended
Use too expensive, since the papilla with any
Contour is approximated. A segmentation of the papilla
with an ellipse is sufficient for a glaucoma examination. One
Another disadvantage of the algorithm described by Xu and others
is to be seen in the specification of an initial contour. This should be
as close to the actual papilla margin
lie and must be given manually or by another algorithm
By contrast, Tang and others in  make an algorithm
described, with which the papilla also approximated with an ellipse
can be. By the mathematical methods used is
the mathematical effort, however, significantly higher, which
leads to higher computation times, so the procedure
hardly suitable for a screening-capable fundus camera
is. Moreover, this is analogous to the solution
also assumed an initial contour according to .
The most significant disadvantage of the known in the prior art solutions for glaucoma detection is the fact that the disc and / or cup area of the papilla is manually segmented by adaptable mathematical forms, such as circles and ellipses preferably by the physician. On the one hand this is time consuming and on the other hand the accuracy of the segmentation depends on the experiences and the skill of the doctor.
Another disadvantage is the fact that different methods
based on stereo image data, as these are not always or only with
considerable additional equipment are available.
The present invention is based on the object, a method
for a screening-capable fundus camera for recording
develop digital fundus images, with which an automated
Measurement of the papilla in these fundus images possible
is. The required computational effort should be so
be kept as low as possible.
According to the invention
the object by the features of the independent claims
solved. Preferred developments and refinements
are the subject of the dependent claims.
In the method according to the invention for the automated detection and segmentation of the papilla in fundus images, based on fundus recordings in suitable spectral ranges, for example in the green channel of a color photograph, with fully imaged papilla, after the papilla region is located and the starting point M n with n = 0 is selected four Sectors for determining the gray scale gradients in suitable spectral ranges, for example in the red channel of a color image, the gradient curves calculated from the gray scale gradients of the four sectors and from the global minima in each sector of the center of the resulting contour as the starting point M n + 1 for the process step b ) certainly. The method is completed when the deviation between the starting point M n + 1 and the starting point M n is within a specified tolerance.
Prerequisite for this is an algorithm for the detection of
To develop papilla and segmentation of the disc area
and to try it out.
Basis of an Overview of Algorithms for
Contour tracking and segmentation required for optic disc identification
and segmentation should be a procedure
for the detection of the papilla as well as for the segmentation of the disc area
be developed or compiled.
Investigation of the efficiency of the process
by means of a prototype in terms of sensitivity and specificity
respectively. The procedure is intended for use in the fundus imaging system
Carl Zeiss Meditec AG as a software library.
the inventive method for automated
Detection and segmentation of the papilla in fundus recordings in particular
for use in screening-capable fundus cameras
is intended, it can be used in principle for all fundus cameras,
that have a digital image capture,
A so-called fundus imaging system offers the possibility of electronic image acquisition and thus a direct diagnosis and examination documentation. Such a system is available with the VISUCAM PRO NM © and VISUPAC © from Carl Zeiss Meditec AG. In addition to the management of patient information, different findings can be performed and documented using the Graphical Findings Editor (GFE). With the nonmydriatic fundus camera VISUCAM PRO NM © , images can be taken without an extended (mydrialized) patient pupil with a resolution of 2196 × 1958 pixels at a viewing angle of 45 ° and images with 1620 × 1444 pixels at 30 °. The reproduction scale is 0.006 mm / pixel and is identical for both recording formats. The fundus images are stored as lossy, JPEG-compressed RGB color images with a color depth of 8-bits per color channel in a database and can serve as a basis for the inventive method for automated detection and segmentation of the papilla.
The procedure works extremely reliably, ie with a high success rate if the papilla in the Fundus images is completely displayed, the fundus images have a sufficiently high contrast and as possible no papilla-like artifacts or exudates (in the sense of: size, shape and color) included.
Invention will be described below with reference to an embodiment
described in more detail. Show this
1 : the procedure in the form of a flowchart,
2 a fundus image with the localized center M o and the fixed sectors 1 to 4,
3a : the statically determined gray values of the gray value curve of sector 1 and
3b : the gray values determined dynamically from sector 1
The inventive method for automated detection and segmentation of the papilla in fundus images, based on colored fundus images with fully imaged papilla and can be divided into the following process steps:
- a) The area of the papillae is located and the starting point M n with n = 0 is selected.
- b) Definition of four sectors to determine the gray value gradients.
- c) Calculation of the gradients from the gray scale gradients of the four sectors.
- d) determining the global minimum of an energy function over the set of all possible combinations of local minima of the gradient curves.
- e) determining the center point of the resulting contour as the starting point M n + 1 for the renewed method step b).
- f) The process is ended when the deviation between the starting point M n + 1 and the starting point M n is within a specified tolerance.
The inventive method is in 1 as a flowchart, starting from the localization of the pupil area and the selection of the starting point M n , up to the comparison of the coordinates of the last two starting points M n + 1 and M n and the completion of the segmentation shown.
Step a: Localization of the papilla area and definition of the starting point M n with n = 0.
In method step a), the area of the papilla is located and the starting point M o is selected. This is done by converting the colored fundus image into a binary image, detecting the binary objects and defining the center of gravity of the largest binary object as the starting point M o . This shows 2 a fundus image with the localized center M o and the specified sectors 1 to 4.
Gray value conversion can be done with different methods
respectively. Due to the pictures available in the RGB color space
the fundus camera can use a color channel directly as a grayscale image
become. In addition, it is possible the intensity channel
I or the brightness channel L of the color space to use.
While the intensity channel is formed from the arithmetic mean of the three color channels of the RGB color space,
The brightness channel represents the average of the minimum values and the maximum values of the three color channels of the RGB color space.
The arithmetic mean of these two values then forms the gray value, where
the intensities of the considered pixel f (x; y) and max (f RGB
(x; y)) represent their maximum and min (f RGB
(x; y)) their minimum.
After this gray value conversion or binarization of the color image is carried out under consideration of a Threshold classifies each pixel, separating the background and object pixels.
Following this, the individual object pixels become binary objects
joined together, the neighborhood relationships of the
individual object pixels are taken into account among each other.
For a localization of the papilla, which is usually the
largest coherent object in the fundus image
accordingly, becomes the largest binary object
The center of gravity of all object pixels in the largest binary object, which corresponds to the papilla, is evaluated as the papilla point and thus defined as the starting point M o .
An advantageous embodiment can be seen in that in step a) detected binary objects are compared with a typical papilla binary object and the center of gravity of the largest binary object is only defined as the starting point M o when the largest binary object corresponds to a typical papilla binary object.
Another advantageous embodiment results from the fact that a
normative database with average papilla parameters different
ethnic groups can easily be involved in the process.
Step b: Definition of four
Sectors for determining gray scale gradients.
Again 2 can be seen, the area around the starting point M o is divided into four sectors, the sectors 1 and 3 in horizontal and the sectors 2 and 4 in the vertical direction. Starting from the starting point M o , the gray-scale gradients f (u) are created on the search beams and formed by the mean value of the gray values on the associated circular arcs. It can be minimized by a large number of gray levels on the corresponding arc of the disturbing influence of the blood vessels.
The radii of the circular arcs can be defined statically or dynamically. While at the in 3a The radial radii of the circular arcs are constant and are determined by means of the statistical papilla parameters, the radii of the circular arcs at the in 3b shown dynamic variant of the distance from the current starting point M o .
Step c: Calculation of Gradient Gradients
from the gray value gradients of the four sectors.
Process step c), the calculation of the gradient curves takes place
from the gray value gradients of the four sectors.
the papilla has higher gray values than the fundus,
has the gradient at the edge of the papilla a minimum.
By disturbances inside and outside the papilla
There are several local minima per sector. That's why
the determined local minima in each search direction the papilla margin
or also represent variations within the papilla.
a minimum used in the further procedure, which is not the papilla margin
represents, it can lead to faulty segmentation results
come. For this reason, any combination of local minima
each search direction considered as a possible solution
Process step d: Determination of the global
Minimums of energy function over the set of all possible combinations
local minima of the gradients.
Process step d), the determination of the energy function
from the determined amounts of the gradients, the axial lengths
and the ratio of the axial lengths of the resulting
Contour for all combinations of minima of all search directions.
to active contours was used for the evaluation of each
Combinations defined an energy function. By inclusion
The determined statistical papilla parameter becomes the combination
local minima of the gradients in each sector
the lowest energy determined. Combinations with axial lengths
or axis length ratios outside
The statistical results are discarded and not considered further.
for the definition of a static parameter set,
that all terms of the energy function describe the same amount of energy.
To achieve this, each term is above all combinations
interpreted as a vector and normalized to the range [0 ... 1] (normalized
Energy function). The positions of the minima of the combination with
The lowest normalized total energy is the four vertices
the resulting contour.
Method step e: Determining the center point of the resulting contour as the starting point M n + 1 for the renewed method step b).
In method step e), the determination of the center point of the resulting contour takes place as starting point M n + 1 for the renewed method step b).
the combination of minima determined in process step d)
the lowest total energy the four vertices of the resulting
Forms the contour, the center of the contour opens up
to determine a simple way.
This center also represents the starting point M n + 1 for the next iteration.
Method step f: The method is ended when the deviation between the starting point M n + 1 and the starting point M n is within a specified tolerance.
In this method step, the just-determined new starting point M n + 1 is compared with the starting point M n . If the distance of the starting points lies within a specified tolerance, the method is ended. Otherwise, the process starts again with method step b). In the method, a deviation of less than 3 pixels is considered suitable. However, the accuracy of the method can be significantly increased if the deviation is limited to a maximum of 1 pixel.
Active circular arc model
a particularly advantageous embodiment of the invention
Method is used as the resulting contour an ellipse.
Evaluations had shown that the contour of a papilla only in
limited extent can be approximated with a circle. Essential
more precisely, the segmentation is possible with an ellipse.
For this reason, the method according to the invention
developed on the basis of an active circular arc model. This
Method is based on the iterative fitting of the four vertices
an ellipse to the edge area of the papilla.
Papilla edge can thus by simple mathematical means using
approximated to an ellipse, d. H. be segmented.
the inventive method for automated
Detection and segmentation of the papilla in fundus images, based
on colored fundus images with fully mapped
Papilla, a solution is provided
with an automated measurement of the papilla in fundus images
is possible without the required
Computing effort is disproportionately large.
particular advantage of the proposed method is to be seen in
that the segmented papilla as a reference point for a
Fundus coordinate system can be used.
Another advantage is that the process takes place on stereo image data
based on colored fundus images, with much higher
Probability are available as stereo image data.
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