JP2863937B2 - Perimeter measurement device - Google Patents

Description

Description: BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a visual field measuring device for measuring a range of a visual field of a human eye, and more particularly to an abnormal pattern analogy section of a visual field using a multilayer neural network. Field of the Invention The present invention relates to a visual field measurement device that is provided and can estimate an abnormal pattern of a visual field of a subject.

"Prior art" Perimetry is considered to be extremely important in ophthalmic clinical examinations, and is used for diagnosis of glaucoma, retinal detachment, brain disorder, hysteresis, and the like.

The field of view is "the area where you can look at one point and see with one eye"
However, since the sensitivity of the retina is not constant even within the range of the visual field, the visual field is accurately referred to as “visual field sensitivity distribution”. The perimeter used for this clinical examination includes a dynamic quantitative perimeter and a static quantitative perimeter.

A line connecting the points of equal sensitivity in each part of the retina is called an isosensitivity line isopter, and optotypes of various luminances and sizes are moved from the periphery to the center, and the first time the optotype is confirmed (or disappeared) The isopter can be obtained by obtaining a figure in which the points of (h) are connected. By appropriately selecting this isopter, what obtains a visual field table is a dynamic quantitative visual field.For example, a target is presented at various brightness on a semi-circular screen, and the subject is displayed at each position. The Goldman perimeter shows the brightness (threshold) that starts to be felt and displays this in a diagram or the like. By using this figure, the measurer judged an abnormality in the visual field of the subject (a site with low sensitivity).

Static quantitative perimetry measures the sensitivity of the retina as the reciprocal of the luminance when the target is recognized for the first time after fixing the position of the visual field and increasing the luminance.

These perimeters include two types: manual and automatic.
There was kind.

The manual measurement is a method in which a measurer presents an optotype and confirms a response to determine a subsequent measurement point.

On the other hand, the automatic measurement is a method in which the order of presenting the optotypes is determined in advance by a program or the like, and the presentation is executed according to the order. This automatic perimeter can also easily perform static quantitative measurement by suprathreshold stimulation. The static quantitative measurement by the suprathreshold stimulus is a method of presenting a stimulus light brighter than the threshold and confirming that it is visible.

[Problems to be Solved by the Invention] However, in both the manual type perimeter and the automatic perimeter, in order to accurately judge the abnormalities of the visual field, a pattern of various visual field abnormalities is required for a measurer. Therefore, considerable experience and attention are required, such as a sufficient understanding of the problem, and there is a problem that the burden on the measurer is extremely large. Especially for manual measurement,
If you examine 360 degrees every 30 degrees, you have to move the target repeatedly in 12 directions, and if you find an abnormal part, you must inspect the vicinity in more detail, There was a problem that required time and effort.

Therefore, there has been a strong demand for the appearance of a visual field measuring device that can reduce the burden on the subject and the subject by assisting the diagnosis of the subject by estimating the pattern of the visual field abnormality of the subject.

"Means for solving the problem" The present invention has been devised in view of the above problems, and a target presenting unit that presents a measurement target, and whether or not the subject has recognized the presented target. A multi-layer having a responding part for responding, and an input layer, a hidden layer, and an output layer, and having a nerve weight determined based on a typical response and a normal response when the subject's visual field is abnormal in advance. A neural network, and an analogization unit configured to input a response from the response unit to an input layer of the multilayer neural network and analogize an abnormal pattern of a visual field of the subject from an output of the output layer at that time. .

Further, the present invention provides an optotype presenting section for presenting a predetermined basic measurement optotype, a response section for responding whether or not the subject has recognized the presented optotype, and inputting the number of basic measurement optotypes. It has an input layer consisting of cells, a hidden layer, and an output layer consisting of output cells of the number of divisions, and has a nerve weight determined in advance based on a typical response when the subject's visual field is abnormal and a normal response. The analog neural network that performs the analog input of the multi-layer neural network and the response from the response section as input data to the input layer of the multilayer neural network, and analogizes the abnormal pattern of the visual field of the subject from the output of the output layer at that time. Part, an additional presentation unit for presenting an additional measurement target other than the basic measurement target to the target presentation unit, and when there is a response of the additional measurement target, the response of the basic measurement target in the vicinity thereof Determines the input data of the input layer of the multilayer neural network in relation to It is composed of a force data determination unit.

And a typical response when the field of view of the present invention is abnormal is:
Not only a single abnormal pattern but also a plurality of abnormal patterns can be formed by overlapping responses.

Furthermore, the typical response of the present invention when the visual field is abnormal can be formed by responses having different degrees of progression of the abnormal pattern.

[Operation] In the present invention configured as described above, the index presenting unit presents the measurement target, and the responding unit responds as to whether or not the subject has recognized the measurement index. A multi-layer neural network having an input layer, a hidden layer, and an output layer is determined based on a response between a case where the visual field is normal and a case where the visual field is abnormal, and derives a nerve weight. Is output to the input layer, and the output from the output layer is sent to the analogization unit, so that the analogization unit can analogize the abnormal pattern of the visual field of the subject.

Further, in the present invention, the optotype presenting unit presents a predetermined basic measurement index, and the responding unit responds as to whether or not the subject has recognized the measurement index. A multi-layer neural network with input, hidden and output layers of the number of basic measurement targets now leads to a nerve weight determined based on the responses of normal and abnormal visual fields. By inputting a response from the response unit to the input layer and sending an output from the output layer to the analogization unit, the analogization unit can analogize an abnormal pattern in the visual field of the subject. Further, the additional optotype unit presents an additional measurement optotype other than the basic measurement optotype to the optotype presenting unit, and when there is a response to this additional index, the input data unit is located near the additional measurement optotype. In relation to the response of the basic measurement target,
Input data for an input layer of the multilayer neural network is determined.

And a typical response when the field of view of the present invention is abnormal is:
Not only a single abnormal pattern, but also a response in which a plurality of abnormal patterns are overlapped can be used.

Furthermore, the typical response of the present invention when the visual field is abnormal may be a response having a different degree of progress of the abnormal pattern.

"Example" An example of the present invention will be described with reference to the drawings. 1 and 2 are views for explaining the mechanical configuration of the visual field measuring device 1, and the visual field measuring device 1 includes a visual field measuring device main body 100 and a gantry 200. The visual field measuring device main body 100 includes a housing 110, and the housing 110 is provided with a substantially hemispherical dome 120, and the inner surface of the dome 120 is a projection screen 130. A panel 140 is provided on the front part of the dome 120, and the panel 140 has a face receiving hole.
The face receiving hole 150 is provided with a face receiving member 160 for fixing the face. The forehead rest 170 and the chin rest 180 are attached to the face receiving member 160. The eye to be measured is a projection screen 130.
Is located near the substantially spherical center including the spherical center O.

As shown in FIG. 2, the housing 110 is provided with a projection optical system 300 on the rear side of the dome 120. The projection optical system 300 includes an illumination light source 310, a condenser lens 320, and an index 330.
, A collimator lens 340, a projection lens 350, and reflection mirrors 360, 360. Where the illumination light source
The 310, the condenser lens 320, the target 330, and the collimator lens 340 constitute a first optical system for emitting the target light as a parallel light beam. The projection lens 350 and the reflection mirror 360
Together with a rotating mirror 400 to be described later, a second optical system is configured to guide the target projection light to the target presenting point I of the projection screen 130 through the vicinity of the center of the sphere except for the center of the sphere O. The dome 120 has a guide hole 121 formed therein. The target projection light reflected by the reflecting mirror 360 is guided to the rotating mirror 400 through the guide hole 121.

The rotating mirror 400 is provided in the vicinity of the spherical center excluding the spherical center O. When the rotating mirror 400 is rotated, the optotype presenting location I is changed.

Next, an electrical configuration of the visual field measuring device 1 of the present embodiment will be described with reference to FIG. The electric configuration of the visual field measuring device 1 includes a visual field projection control unit 2, a response switch 3, a BP (back propagation) execution unit 4, an optotype projection program storage unit 5, a response storage unit 6, It comprises a unit 7, a display unit 8, and a CPU 9. The field-of-view projection control unit 2
Target drive mirror drive motor 21 and visual field focus drive motor
22 and for controlling the index projection light source 23,
This corresponds to the optotype presenting unit. The optotype projection rotating mirror drive motor 21 drives the rotary mirror 400, and the optotype presenting location I can be changed by rotating the rotary mirror 400. The optotype focusing drive motor 22 is for moving the imaging lens 350, and is a distance L from the rotation center of the rotating mirror 400 to the optotype presenting position I.
Can be changed. That is, when the rotating mirror 400 is rotated by driving the target mirror rotating mirror drive motor 21, the distance L from the rotation center of the rotating mirror 400 to the target presenting position I changes. In order to correct the change in the distance L, the target focusing drive motor 22 is driven, and the imaging lens is driven.
It is necessary to move 350 in the optical axis direction. As a result, regardless of the change of the target presenting position I, an image of the target whose size and luminance hardly change can be displayed on the projection screen 130. The optotype projection light source 23 controls the lighting of the illumination light source 310, and can change the brightness (intensity) of the light source by adjusting the light source voltage as well as switching between lighting and extinguishing. Then, by adjusting the intensity of the illumination light source 310, the luminance of the target on the projection screen 130 can be changed.

The response switch 3 corresponds to a response unit, and inputs whether or not the subject has recognized the target. It is desirable that the response switch 3 be operated by the person to be measured. However, when the person to be measured is a child or an elderly person, the measurer receives recognition from the person to be measured and is operated by the person to be measured. Good.

The BP execution unit 4 is for executing a determination by back propagation using a predetermined nerve weight based on a typical response when the visual field is abnormal and a normal response. Therefore, a typical response when the visual field is abnormal and the response of the subject are matched by the neural network, and an abnormal pattern of the visual field of the subject can be inferred.

The nerve weight used in the BP execution unit 4 is the following BP
Using the same neural network as the execution unit 4, it is predetermined based on a typical response when the visual field is abnormal and a normal response.

The optotype projection program storage section 5 stores a program for determining optotype presenting conditions for visual field measurement, and controls the luminance of the optotype, the position of the index, the lighting time, and the like to desired values. be able to. As the target projection program, for example, a screening measurement program, a meridional program, a glaucoma program, or the like can be adopted.

The response storage unit 6 is for storing whether or not the subject has recognized the optotype. That is, this is for storing whether or not the response switch 3 has been activated for the presented target. The operation unit 7 is for performing the entire operation of the visual field measurement device 1, and includes various control switches, a light pen, and the like. The display unit 8 includes a TV monitor, a printer, and the like.
It is possible to display the type and index distribution of the target projected on 0, a command input from a control switch or the like, and further, it is possible to display the result of the analogy of the abnormal pattern of the visual field of the measured person. .

The CPU 9 controls various arithmetic operations of the visual field measuring device 1.

In this embodiment configured as described above, when a measurement start command is input through the operation unit 7, the CPU 9 reads the control program from the visual field projection program storage unit 5 and executes it. CPU9
Controls the target projection control unit 2 according to the control program to drive the index projection light source 23. Further, the position of the target is determined by controlling the target mirror rotating mirror drive motor 21 and rotating the rotary mirror 400. The CPU 9 drives the target focusing motor 22 according to the rotation of the rotating mirror 400,
Adjust so that the luminance and size of the target do not change. The response storage unit 6 stores the response of the subject. Then, the CPU 9 performs matching with the typical abnormal pattern and the normal pattern of the visual field, and estimates the abnormal pattern of the visual field. Then, the estimated abnormal pattern of the visual field and the like are displayed on the display unit 8.

Next, the configuration of the multilayer neural network constituting the neural network will be described in detail with reference to FIG.

A neural network is composed of a plurality of nerve cells (neurons). One neuron consists of a cell body, dendrites (signal input part), and axons (signal output part).
It is composed of The axon (signal output part) is synapse-coupled with dendrites of other neurons, forming a network.

The learning method applied to this neural network is called a back propagation method. The structure of the neural network is composed of an input layer A, an intermediate layer B, and an output layer C as shown in FIG. It has a multilayer structure. Although there is a connection between layers, there is no connection between units in the layer.

Since a neuron can be regarded as a multi-input / single-output nonlinear element, in other words, it can be regarded as an element having a “threshold action”. That is, the output pulse is turned on when the total amount of input signals is higher than the threshold, and the output is turned off when the total amount is less than the threshold.

Thus, the input signal _{S 1, S 2, S 3} , the output signal net against · · · S _n, the weighted sum of products It is described as follows. That is, the structure of the network can be changed by changing the weight (W).
The weight (W) takes positive, negative, and zero values, where zero indicates that there is no connection. The input / output characteristic function is sigmoi
d function is applied. This sigmoid function is a differentiable pseudo-linear function, for example, Can be adopted. The value range of this function is 0 to 1, becoming 1 as the input value increases, and becoming 0 as the input value decreases. And when the input value is 0, it is 0.
It is set to 5.

Next, the algorithm of the back propagation learning rule will be described. The intermediate layer B may be any number of layers, and assumes a network without feedback coupling (interlayer coupling). Here, the intermediate layer B may be called a (hidden layer), that is, a hidden layer.

(A) First, an input signal such as an image pattern is input to an input layer.

(B) Next, from the input layer A to the output layer C, the state change of each neuron accompanying the signal transmission process is sequentially calculated.

(C) The output of the j-th neuron of the output layer C obtained in (b) is _{defined as Opj} , the desired output (teacher signal) of the neuron with respect to the input signal is _{defined as Tpj,} and the square error of the following equation is evaluated. Define and operate as a function. The given image pattern is defined as p.

(D) changing the synaptic connections or weights of the network such that the cost function is at a local minimum (preferably a minimum) (ie, so that the actual output is as close as possible to the desired output).

That is, the strength of all the couplings may be changed so as to reduce the output error. Here, the change amount of the weight _Wji when the image pattern p is given is Is determined. If further _{modified, Δ p W ji = ηδ pj} O pi ··· (5) In addition, O _pi is the input value from unit i to unit j, [delta] _pj the unit j is either the output unit or the intermediate unit In the case of an output unit, δ _pj = (t _pj −O _pj ) f ′ _j (net _pj ) (6) Equation (7) is a recursive function.

The above is the basic algorithm of the back propagation method. Learning of each synaptic connection (correction of the weight) proceeds from the output layer to the input layer in a direction opposite to signal propagation. This is why it is called back propagation. In this back propagation method, the calculation of ΔW is started from the output layer and proceeds to the unit of the intermediate layer. In the intermediate unit, the calculation cannot be performed unless the preceding stage ΔW is determined. Therefore (because it is recursive), calculations cannot be made without going back to the last input layer. Therefore, in the back propagation method, learning data is input, and the result is output (forward). Next, the strength of the bond is changed (backward) to reduce the resulting error.
Then, the learning data is input again. By repeating these, ΔW is determined so as to minimize the error.

Here, if indicated general formula _{ΔW, ΔW ji (n + 1} ) = ηδ pj O pj + αΔW ji (n) ··· (8) n is the number of times of learning, the first term on the right side [Delta] W, the second term Is
This is an additional term for preventing error oscillation and accelerating convergence.

Next, a back propagation method employed for learning a multilayer neural network will be described with reference to FIG.

FIG. 5 illustrates the back propagation execution subroutine (SUBROUTINE BP). First, in S51, an initial value of the nerve weight is set by a random number. Then, in S52, the calculation is repeatedly performed for the number of learning data. Then S53
Calculate the output of each cell. In this embodiment, the above equation (2) is used for calculating the output. And
In S54, an evaluation function is calculated by the above equation (3).
Next, in order to minimize the evaluation function value, a nerve weight correction amount is calculated in S55 according to the above equation (8). Note that the learning point number η and the stability constant α are empirically determined and are, for example, η = 0.4 and α = 0.6. Then, in S56, the nerve weight is corrected based on the nerve weight correction amount, and the process returns to S52 to repeatedly calculate. Therefore, in this embodiment, the nerve weight is corrected for each learning data. Further, in this embodiment, S5
At 7, the sum of the evaluation functions of the respective data is calculated, and at S58, it is determined whether the sum of the evaluation functions is smaller than a predetermined threshold value. That is, S5
In step 8, it is determined whether the sum of the errors is equal to or less than a predetermined value. If the sum of the evaluation functions is not equal to or less than the threshold value, the calculation is performed again using the nerve weight at this time. Then, when the sum of the evaluation functions becomes equal to or less than the predetermined value, the final nerve weight is output in S59. As a result, (SUBROUTINE B
P) ends.

Next, an analogy of an abnormal visual field pattern using the above-described multilayer neural network and learning by the back propagation method will be described in detail.

First, a typical pattern when the visual field is abnormal will be described with reference to FIG. FIG. 6 shows the dynamic amount visual field of the right eye. In addition, since it is a right eye, the right outside is wide.

FIG. 6A shows a typical visual field pattern in the case of a central dark spot. This is the case where there is a scotoma in the center of the visual field, in the case of disorders of the macular retina, such as macular degeneration and central retinitis, and diseases of the papillary macular line maintenance bundles, such as acute retrobulbar optic neuritis Is to be seen. FIG. 6 (b) shows a typical visual field pattern in the case of an annular dark spot. This is the case where the scotoma surrounds the center and assumes a ring shape, and is also seen in glaucoma when Seidel-Bjerrum scotoma (a phenomenon in which the Marriott scotoma extends and expands vertically) and retinitis pigmentosa is there. FIG. 6 (c) shows a typical visual field pattern in the case of visual field narrowing. In the case of afferent stenosis, the visual field in each direction becomes substantially the same from the periphery to the center. For example, it is found at the end of glaucoma, retinitis pigmentosa and the like. In some cases, stenosis is partially observed (subsidence), and in retinal detachment, stenosis in a portion corresponding to the detached portion is recognized. FIG. 6 (d) shows a typical visual field pattern in the case of semi-blindness. The semi-blindness is a state in which the visual field of the fixation point is cut off from the vertical line and the half-side visual field is lost due to an obstacle in the visual conduction path. . FIG. 6E shows a typical visual field pattern in the case of a Bierm scotoma. The Bierm scotoma is a scotoma that is found in a region (Bierm region) in the middle from the top and bottom of the Marriott blind spot and is characteristic of the glaucoma visual field. It is caused by damage to nerve fibers in the vertical direction of the optic disc. FIG. 6 (f) is an arcuate scotoma. Bierm scotoma usually appears in one of the up and down directions, and an extreme case of this is an arcuate scotoma. It occurs in the case of glaucoma. FIG. 6 (g) is an intensity dark spot. This intensity scotoma is a shape in which the arcuate scotoma extends in either the upper or lower direction to form a donut horizontally divided into two parts, and reaches a horizontal line opposite to the macula. The field of view at this time is also called the Renne stairs. FIG. 6 (h) shows the visual field in the case of normal. There is an oblong dark spot, which is a Marriott blind spot. Marriott blind spots occur because there is no photoreceptor cell in the optic disc and the corresponding field of vision becomes a scotoma.

Therefore, learning can be performed by using the above-described typical abnormal pattern of the visual field and the teacher signal. Here, the teacher signal is composed of bits corresponding to the number of abnormal patterns in the visual field. , 0, 0 ".

Next, an example in the case where an actual measurement is performed will be described with reference to FIG. First, FIG. 7 (a) shows the average value and the standard deviation of the subject having normal vision. Based on this, 4
Set two levels. The level 5 dB brighter than the normal average is set to LEVEL 0 (normal), the level 3 dB brighter than this is LEVEL 1, and the level 3 dB brighter is LEVE.
Let L2 be 3 dB brighter and LELVL3, and let the maximum brightness be LEVEL4. First, the target is presented at the luminance of LEVEL0,
If you don't see this, increase LEVLEL1, LEVEL2 and brightness. Then, the mark of the level at which the subject is seen is plotted, and this plotting is performed for each target. Seventh
FIG. 2B shows an example in which semi-blindness and visual field stenosis appear.

When using actual measurement results instead of typical abnormal patterns, the teacher signal may be, for example, "0.1, 0.0,
0.4, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, and so on.

Next, the operation of the visual field measuring device 1 according to the present embodiment will be described with reference to the flowcharts shown in FIGS.

(Measurement by a Measurer) First, a measurement in the case of a measurer will be described with reference to FIG. When the measurer selects a necessary measurement program (for example, quick screening of 71 points) by the operation unit 7 in S1, the CPU 9 reads the corresponding program from the optotype projection program 5 and executes the program. As a result, visual field measurement is performed at a plurality of points, and response data of the subject is stored in the response storage unit 6. Next, in S2, it is determined whether the response data is sufficient. If the response data is sufficient, proceed to S3, start the BP execution unit 4, and
Execute UTINE decision BP. The SUBRUTINE decision BP will be described later in detail. In step S4, it is determined that the visual field is abnormal based on the output cell response value obtained in step S3. Then, in S5, it is determined whether or not to continue the additional visual field measurement, and if it is necessary to proceed, the process proceeds to S6. If no additional visual field measurement is required, the determination in S4 is maintained, and the resulting abnormal visual field pattern is displayed on the display unit 8.

In S6, the measurer sets an additional measurement point. This setting method uses, for example, a light pen of the operation unit 7 or the like.
Additional measurement points can be plotted and set on the TV monitor. At this time, the operation unit 7 functions as an addition instruction unit. Then, in S7, the data of the additional measurement point is added to the previous data and used as input data. If the data is new, the data is used as it is.

If the response data is insufficient in S2, proceed to S6,
Additional measurement points are set.

Here, the subroutine judgment BP will be described in detail with reference to FIG. First, in step S1, the visual field measurement value is assigned to the extended input cell group corresponding to the position. Unlike the conventional back propagation configuration, this extended input cell group has a radial network configuration locally (small area around the measurement point of basic measurement) in the input layer. . Therefore, cells other than the cells corresponding to the basic measurement will be referred to as expanded input cell groups.

Next, in S2, the weight of the horizontal network is determined. Further, in S3, the output of the input cell corresponding to the additional input index is received, and the basic measurement cell input value is reset. Next, in S4, cells are prepared for the number of input cells, the number of intermediate cells, and the number of output cells used during learning. Then, in step S5, the neural weights connecting the cells, which are calculated in advance by the learning back propagation system, are read from the file, and the neural network is set. Further, in S6, each cell of the neural network configured in S5 outputs a response according to the input / output characteristic function. In S7, the output value of each cell in S6 and the corresponding abnormal pattern name in the visual field (judgment)
And exit.

Next, the handling of the response of the additional measurement point will be described in detail with reference to FIG. It is necessary to devise the response of the additional measurement point so as to give it to the response of the basic measurement target (measurement index before addition) near the additional measurement point. Therefore, an additional measurement target No. 1 as shown in FIG. 10 (a) can be set. Basic Measurement optotypes NO.1 around this additional measurement optotype NO.1 in the radius r _m
0, NO.11 and NO.12 are set so as to exist. And
Additional measurement target No.1 to basic measurement target No.10, NO.11, NO.1
If the distance to 2 is r ₁ , r ₂ , r ₃ respectively, then r ₁ ≦ r ₂ and r
And it has a ₁ ≦ r _3.

Then, the response of the additional measurement target and the response and the response of the basic measurement target are connected by a radial network. The weight of the network is determined according to the distance. As an example, a method of determining the weight in inverse proportion to the distance will be described. That is,
The weight is determined based on the inverse ratio of the distance with the shortest distance as the minimum unit.

For example, in the case where the response of the basic measurement target No. 10 is 2, the response of NO.11 is 2, the response of NO.12 is 3, and the response of the additional measurement target No. 1 is 4, .10 input data, And the input data of NO.11 is And the input data of NO.12 is Can be determined.

That is, if the input data is represented by a general formula, Note that, as shown in FIG. 10 (b), areas centered on the basic measurement indices are provided slightly overlapping. The response of the additional measurement target is devised so as to affect the response of the basic measurement target of the rear air containing it. That is, when a plurality of additional measurement targets exist in the area of a certain basic measurement target,
The response of the basic measurement target is corrected according to the inverse ratio of the distance with the shortest distance as the unit distance. The basic general formula is the same as the ninth formula.

The area is not limited to a circle, but may be a rectangle or the like. Although the size of the area can be determined as appropriate, it is necessary to precisely inspect the vicinity of the center.

Next, a modified example of the present embodiment will be described. First, the fixed target type visual field measuring apparatus 1 will be described with reference to FIG. Although the visual field measuring device 1 of the above-described embodiment and the rotating mirror 400 are rotated to move the optotype presenting position I, the LE is displayed on the screen.
A light source like D can be embedded as a target. Eleventh
FIG. 1A shows the appearance of an LED-type visual field measuring device 1. The visual field measuring device 1 is provided with a housing 500 and a panel provided with a circular hole 510 for receiving the face of a person to be measured and attached to the front side of the housing 500. 520, a cognitive switch 530, and a measured eye fixing unit 540 for fixing the subject's eye to be inspected at a predetermined position.
And an operation display device 550 formed on a side wall of the housing 500.
And a plurality of LEDs 560, 560,... Formed in the housing 500.
・ It is composed of a hemispherical dome with attached.

The operation display 550 includes a TV monitor 551 and a light pen 552.
And a printer 553, a control switch 554, and the like.

Next, the configuration of the electric system will be described with reference to FIG. The I / O interface 600 includes a light pen 552 operated by a measurer, an output signal from a control switch 554, a recognition signal from a recognition switch 530 for inputting whether or not the subject has recognized the target, and an initial signal. An initial measurement program selection signal from the measurement program selection unit 610 is input, the input signal is converted into a signal suitable for processing of an internal device, and the measurement result is converted into a signal that can be easily printed by the printer 553. Also, a response storage unit 645 for storing a response based on the recognition signal from the recognition switch 530 is provided.

The CPU 620 performs main control and the like of the visual field measuring device 1 of the present modification.

The LED matrix interface 630 is an interface for lighting the corresponding LED 560 under a predetermined condition in accordance with the optotype presenting condition read by the CPU 620, and forms a matrix with at least two transistor arrays.

The presentation condition storage unit 640 stores a program for realizing a target presentation condition (combination of lighting intensity, position, lighting time, and the like) that determines how the LED 560 of the hemispherical dome 570 is lit. For example, a screening measurement program and the like are stored.

GDC (Graphics Display Controller)
Inputs the LED array signal, the selected measurement program, the signal indicating the position of the LED being lit, and the response signal, and forms an image signal for displaying the information on the TV monitor 551. Then, this is output to the video memory 660. The timing control circuit 670 includes the clock oscillator 6
An appropriate timing signal is formed from the clock signal output from 80 and output to the GDC 650, the CPU 620, and the video memory 660.

The P / S converter 690 converts a parallel digital signal from the video memory 660 from parallel to serial to form a video signal, and outputs the video signal to the TV monitor 551.

An abnormal pattern analogy function of the visual field using the multilayer neural network of the present embodiment can be added to the LED-type visual field measuring apparatus 1 configured as described above.

Next, a modified example related to learning of a multilayer neural network will be described with reference to FIG.

The above-described back propagation subroutine calculates an evaluation function for each learning data, corrects the nerve weight based on the evaluation function, and repeatedly performs the calculation. On the other hand, in the present embodiment, the nerve weight correction amount is calculated and stored in S55. Further, in S57, the sum of the evaluation function of each data is obtained, and it is determined in S58 whether the sum (Error) is smaller than a predetermined threshold value. If the sum is larger than the threshold value, each nerve weight correction is performed in S581. As the sum of the quantities, the average value of each nerve weight correction amount is calculated. In S56, each nerve weight is corrected by the correction amount calculated in S581, and the calculation is repeated again using the nerve weight. Therefore, in this embodiment, after the nerve weight correction amount of all the learning data is calculated, it is corrected collectively. If it is determined in S58 that the sum of the evaluation functions has become smaller than the threshold value, the process proceeds to S59. The other steps are the same as those in the subroutine of FIG.

(Effect) The present invention configured as described above, a target presenting unit that presents a measurement target, a response unit that responds whether or not the subject has recognized the presented target, an input layer, A multi-layer neural network having a hidden layer and an output layer, and having a nerve weight determined based on a typical response and a normal response when the subject's visual field is abnormal in advance, and Is input to the input layer of the above-mentioned multilayer neural network, and an analogization unit that analogizes the abnormal pattern of the visual field of the subject from the output of the output layer at that time. There is an excellent effect that the pattern can be inferred and the measurer can assist the determination of the abnormal pattern in the visual field. Since the labor and time of the measurer are also reduced, the burden on the measurer and the person to be measured can be significantly reduced.

The present invention also provides an additional instructing unit for presenting an additional measurement target other than the basic measurement target to the target presenting unit, and when there is a response from the additional measurement target, a basic measurement target in the vicinity thereof. And an input data determining unit for determining input data of the input layer of the multilayer neural network in relation to the response of the multi-layer neural network. There is an effect that a measurement target to be measured can be added. Therefore, there is an excellent effect that the measurer can judge an appropriate visual field abnormal pattern.

[Brief description of the drawings]

FIG. 1 shows a visual field measuring apparatus according to one embodiment of the present invention. FIG. 1 is a diagram showing an external configuration of the present embodiment. FIG. 3 is a diagram for explaining an electrical configuration of the present embodiment, FIG. 4 is a diagram for explaining a multilayer neural network, FIG. 5 is a diagram for explaining a back propagation method, and FIG. FIG. 7 is a diagram showing a typical example, FIG. 7 is a diagram for explaining a specific measurement result, FIG. 8 is a diagram for explaining the operation of this embodiment, FIG. 9 is a diagram for explaining a subroutine of judgment BP, FIG. Is a diagram illustrating an additional measurement target,
FIG. 11 is a diagram illustrating a modification using LEDs for the screen, and FIG. 12 is a diagram illustrating a modification of the back propagation method. DESCRIPTION OF SYMBOLS 1 ... Visual field measuring device 2 ... Target projection control part 3 ... Response switch 4 ... BP execution part 5 ... Target target projection program storage part 6 ... Response storage part 7 ... Operation part 8 ... Display part 9 CPU 130 Shooting screen 300 Imaging optical system 330 Target 400 Rotating mirror

Claims (4)

(57) [Claims] 1. A target presenting section for presenting a measurement target, a responding section for responding whether or not the subject has recognized the presented target, an input layer, a hidden layer, and an output layer. A multi-layer neural network having a neural weight determined based on a typical response when the subject's visual field is abnormal and a normal response;
A visual field measuring device, comprising: an analog input unit for inputting a response from the response unit to an input layer of the multilayer neural network, and analogizing an abnormal pattern of a visual field of the subject from an output of the output layer at that time. . 2. A target presenting unit for presenting a predetermined basic measurement target, a response unit responding whether or not the subject has recognized the presented target, and inputting the number of basic measurement targets. It has an input layer consisting of cells, a hidden layer, and an output layer consisting of output cells of the number of divisions, and has a nerve weight determined in advance based on a typical response when the subject's visual field is abnormal and a normal response. The analog neural network that performs the analog input of the multi-layer neural network and the response from the response section as input data to the input layer of the multilayer neural network, and analogizes the abnormal pattern of the visual field of the subject from the output of the output layer at that time. Unit, an additional instruction unit for presenting an additional measurement target other than the basic measurement target to the target presentation unit, and when there is a response of the additional measurement target, the response of the basic measurement target in the vicinity thereof Input that determines the input data of the input layer of the multilayer neural network in relation to Perimetry apparatus; and a data decision section. 3. The visual field measuring apparatus according to claim 1, wherein a typical response when the visual field is abnormal is formed not only by a single abnormal pattern but also by a response in which a plurality of abnormal patterns are overlapped. 4. A visual field measuring apparatus according to claim 1, wherein the typical response when the visual field is abnormal is formed by responses having different degrees of progress of the abnormal pattern.