KR101905716B1 - Video-oculography nystagmus analytics apparatus using infrared camera and method thereof - Google Patents

Video-oculography nystagmus analytics apparatus using infrared camera and method thereof Download PDF

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KR101905716B1
KR101905716B1 KR1020170028235A KR20170028235A KR101905716B1 KR 101905716 B1 KR101905716 B1 KR 101905716B1 KR 1020170028235 A KR1020170028235 A KR 1020170028235A KR 20170028235 A KR20170028235 A KR 20170028235A KR 101905716 B1 KR101905716 B1 KR 101905716B1
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image
pupil
outputting
unit
infrared
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KR20180101816A (en
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남윤영
공영선
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순천향대학교 산학협력단
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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications
    • A61B5/4863Measuring or inducing nystagmus
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/10Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
    • A61B3/113Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for determining or recording eye movement
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00597Acquiring or recognising eyes, e.g. iris verification
    • G06K9/0061Preprocessing; Feature extraction

Abstract

More particularly, the present invention relates to a video nystagmus test apparatus and method, and more particularly, to a video nystagmus test apparatus and method for analyzing an image of an infrared pupil photographed by an infrared camera based on rotation conditions of a patient, And more particularly, to an apparatus and method for testing a video nystagmus using an infrared camera for performing a nystagmus test on a patient.

Description

Technical Field [0001] The present invention relates to a video-oculography nystagmus analytical apparatus using an infrared camera,

More particularly, the present invention relates to a video nystagmus test apparatus and method, and more particularly, to a video nystagmus test apparatus and method for analyzing an image of an infrared pupil photographed by an infrared camera based on rotation conditions of a patient, And more particularly, to an apparatus and method for testing a video nystagmus using an infrared camera for performing a nystagmus test on a patient.

Dizziness is a common symptom of having one in five adults experience at least once a year.

Dizziness usually occurs when an abnormality occurs in the ear (peripheral), brain (central), heart, or eye.

One of the abilities of a person is the ability to change his or her visual acuity according to the agitation of the head when the head shakes. In our body, it causes Vestibular-Ocular Reflex (VOR) to prevent changes in vision depending on the shaking of the head.

Vestibular reflex helps to fix the eye by moving the eye in the opposite direction of the rotation of the head.

Compensatory Eye Movement is induced by sensing the angular acceleration in the vestibular vestibular system and it is called the nystagmus.

Vestibular dysfunction, or nystagmus, occurs when the target comes out of the center, causing blurred vision, which is caused by abnormal vestibular reflexes.

Electro-nystagmography (ENG) and Sclera Search Coil System (SSCS) have been used to detect eye movement signals for diagnosing nystagmus.

The electric nystagmus recording method is a method in which electrodes are attached to the skin around the eyes, and depolarization occurring during eye movement is amplified by an electrical signal to graphize the amplitude of eye movement over time.

However, since the electric nystagmus recording method is not a method of capturing a signal directly from the external eye muscles, it can not be said that it provides accurate information about the eye muscles position. When measuring the vertical eye muscles, There was a problem that the electric signals from the rectus muscle were overlapped and the two eye movements could not be distinguished.

In addition, the electric nystagmus recording method is sensitive to changes in electrode attachment state due to movement of a patient and changes in skin resistance due to sweat, so that a device needs to be frequently calibrated. Since a plurality of electrodes need to be attached to a face, There is a problem in that the examination is difficult for the patient who does not perform well.

And the scleral search coil system is a method of three - dimensionally graphically changing the intensity of the magnetic field of the muscles generated during eye movement after contacting the sclera with a contact lens - type device with wire inside.

The scleral search coil system is able to receive a more accurate signal in comparison to the electric nystagmus recording method and has the merit that it can capture minute eye movements.

However, since the scleral search coil system uses a contact lens, its use is limited in children who are more open and cooperative.

Open Patent No. 10-2004-0100677 (December 23, 2004)

Accordingly, an object of the present invention is to provide an infrared camera for performing a nystagmus test on a patient by analyzing an infrared pupil image taken by an infrared camera based on a rotation condition of the patient, And to provide a video nystagmus test apparatus and method using the same.

According to another aspect of the present invention, there is provided an apparatus for testing a video nystagmus using an infrared camera, the apparatus comprising: a rotating chair rotatable in a left-right direction, An infrared ray photographing unit for infrared ray photographing an eye region including at least one of the two eyes of the patient and outputting an infrared ray eye image; And controlling the rotation of the rotating chair according to preset rotation control information for the nystagmus test, detecting the pupil by binarizing the infrared eye image, detecting the pupil motion characteristic, detecting based on the rotation control information And a nystagmus test unit for analyzing the pupil movement characteristics to perform a nystagmus test and outputting the result.

The nystagmus testing unit may include a rotation control unit for controlling rotation of the rotating chair according to predetermined rotation control information for nystagmus testing; An infrared image acquiring unit for acquiring and outputting the infrared eye image through the infrared image radiographing unit after controlling and driving the infrared image radiographing unit; An image processor for detecting a pupil after binarizing the obtained infrared eye image and outputting image data of the detected pupil; A pupil motion detection unit for receiving and analyzing the image data and outputting pupil motion characteristic information on pupil motion; And a nystagmus judgment unit for analyzing the pupil movement characteristic information detected based on the rotation control information to perform a nystagmus test and determine whether or not the nystagmus is present.

Wherein the image processing unit comprises: a region of interest designation unit for outputting an infrared front eye image including only an anterior region from the infrared eye image; An image converting unit for binarizing the infrared front eye image to output a binocular front eye image; An outline detection unit detecting an outline of the anterior segment from the binarized anterior segment image and outputting an anterior segment outline image; And a circular detection unit for detecting a pupil from the foreground contour image, detecting a center point of the pupil, and outputting pupil image data corresponding to the detected center point.

Wherein the image converting unit comprises: a first image converting unit for converting the infrared frontal image into a gray frontal image of a gray channel and outputting the gray frontal image; And a second image conversion unit for binarizing the gray anterior segment image based on a preset reference value to output the binarized anterior segment image.

The image processing unit may further include a filtering unit for performing morphological operations on the binarized anterior segment image output from the image transformation unit and applying erosion and expansion to remove noises.

Wherein the circle detection unit receives an anterior segment contour image for at least two binarized anterior segment images converted based on a plurality of different reference values to be changed from the contour detection unit and detects a circle from each anterior contour image, An optimal circle detecting unit for selecting and outputting an anterior contour image matching the pupil of the circle; And calculating a center point of the circle for the selected foreground contour image, estimating a distance and an angle of the actual pupil moving in consideration of the three-dimensional structure and the curved line of the pupil, and outputting the pupil image data And an image calibration unit.

Wherein the pupil motion detection unit comprises: a first interpolation unit that receives the image data and applies a linear spline algorithm to interpolate a missing signal due to eye blinking to output a first interpolated video signal; A differentiator for obtaining a slope from the first interpolated image signal and outputting a differential image signal obtained by squaring the slope; A fast phase removal unit for applying the differential image signal to the first interpolated image signal to output a slow phase signal from which the fast phase signal is removed from the first interpolated image signal; A second interpolator interpolating and outputting the slow phase signal using a linear spline algorithm; A filtering unit for filtering and outputting the interpolated slow phase signal; And a characteristic extraction unit for extracting gain, asymmetry, and phase characteristics from the slow phase signal and outputting the pupil motion characteristic information.

According to another aspect of the present invention, there is provided a video nystagmus test method using an infrared camera, comprising: a rotation control step of controlling rotation of a rotating chair according to predetermined rotation control information for nystagmus test; An infrared ray image acquiring step of acquiring and outputting an infrared ray image through the infrared ray radiographing unit after driving the infrared ray radiographing unit; An image processing step of binarizing the acquired infrared eye image, detecting a pupil, and outputting image data of the detected pupil; A pupil motion detection step of receiving and analyzing the image data and outputting pupil motion characteristic information on pupil motion; And a nystagmus judging step of analyzing pupil motion characteristic information detected based on the rotation control information to perform a nystagmus test, judging whether or not the nystagmus is present, and outputting the result.

Wherein the image processing step includes: an interest region designation step of outputting an infrared front eye image including only an anterior region from the infrared eye image; An image converting step of binarizing the infrared front eye image to output a binocular front eye image; An outline detection step of detecting an outline of the anterior segment from the binarized anterior segment image and outputting an anterior segment outline image; And a circle detection step of detecting a pupil from the foreground contour image, detecting a center point of the pupil, and outputting pupil image data corresponding to the detected center point.

Wherein the image conversion step comprises: a first image conversion step of converting the infrared frontal image into a gray frontal image of a gray channel and outputting the gray frontal image; And a second image conversion step of binarizing the gray anterior segment image based on a preset reference value to output the binarized anterior segment image.

The image processing step may further include a filtering step of performing morphological operations on the binarized anterior segment image output from the image transformation unit to apply erosion and expansion to remove noises.

Wherein the circle detection step receives the input from the foreground part outline image outline detection part for at least two or more binarized anterior segment images transformed based on the changed plurality of reference values and detects a circle from each anterior part outline image, An optimal circle detection step of selecting and outputting an anterior contour image matching the pupil of the circle; And calculating a center point of the circle for the selected foreground contour image, estimating a distance and an angle of the actual pupil moving in consideration of the three-dimensional structure and the curved line of the pupil, and outputting the pupil image data And an image calibration step.

The pupil motion detection step may include a first interpolation step of receiving the image data and applying a linear spline algorithm to interpolate a missing signal due to blinking and outputting a first interpolated video signal; A differentiating step of obtaining a slope from the first interpolated image signal and outputting a differential image signal obtained by squaring the slope; A fast phase removing step of applying the differential image signal to the first interpolated image signal to output a slow phase signal from which a fast phase signal is removed from the first interpolated image signal; A second interpolation step of interpolating and outputting the slow phase signal using a linear spline algorithm; A filtering step of filtering and interpolating the interpolated slow phase signal; And extracting gain, asymmetry and phase characteristics from the slow phase signal and outputting the pupil motion characteristic information.

In the present invention, the pupil motion is photographed by irradiating infrared rays, the pupil region is separated using the difference in contrast between the pupil and the iris in the captured image frame, and the center coordinates of the pupil are set by applying the boundary detection and the ellipsoidal approximation of the pupil It is possible to record three-dimensional motion (horizontal, vertical, and line) of the eye in real time in each time zone.

Further, since the eye movement can be recorded in detail in real time in a time slot, the present invention has the effect of performing more accurate nystagmus test.

1 is a block diagram of a video nystagmus test apparatus using an infrared camera according to the present invention.
2 is a detailed block diagram of a nystagmus test part of a video nystagmus test apparatus according to the present invention.
FIG. 3 is a detailed block diagram of the image processing unit of the nystagmus test unit according to the present invention.
FIG. 4 is a diagram illustrating a stepwise eye image processed in the image processing unit according to the present invention.
FIG. 5 is a diagram showing a binarized image and a circle detection image according to different reference values at the time of image binarization applied according to the present invention.
6 is a diagram showing a detailed configuration of a pupil motion detecting unit of the nystagmus testing unit according to the present invention.
7 is a diagram illustrating a horizontal pupil signal for horizontal pupil data according to an embodiment of the present invention.
8 is a diagram illustrating signal waveforms in the image processing step in the pupil motion detecting unit according to the present invention.
9 is a diagram for explaining a signal processing process of a slow phase signal in the pupil motion detecting unit according to the present invention.
10 is a graph showing a gain curve according to the rotating magnetic pole frequency of the present invention.
11 is a view showing a phase curve according to the rotating magnetic pole frequency of the present invention.
12 is a flowchart illustrating a video nystagmus test method using an infrared camera according to the present invention.
13 is a flowchart illustrating an image processing method in a video nystagmus test method according to the present invention.
FIG. 14 is a flowchart illustrating a pupil motion characteristic detection method in a video nystagmus test method according to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a configuration and operation of a nystagmus test apparatus using an infrared camera according to the present invention will be described with reference to the accompanying drawings, and a nystagmus test method in the apparatus will be described.

1 is a block diagram of a video nystagmus test apparatus using an infrared camera according to the present invention.

Referring to FIG. 1, a video nystagmus testing apparatus using an infrared camera according to the present invention includes a display unit 100, an infrared image capturing unit 200, a rotating chair 300, and a nystagmus checking unit 400, And may further include an input unit 500.

The display unit 100 displays information generated during the nystagmus test progress, nystagmus test results, etc. under the control of the nystagmus test unit 400 as text, graphics, still images, moving images, and the like.

The infrared ray image capturing unit 200 photographs at least one eye area of two eyes of a patient who is a subject of nystagmus test under infrared ray irradiation through an infrared LED (not shown) for irradiating infrared rays, And an infrared camera (not shown) for output.

The infrared imaging unit 200 may be configured in the form of a camera to be placed in front of the patient's face or in the form of virtual reality (VR) glasses combined with the display unit 100 to be worn on the patient's head .

Further, the infrared imaging unit 200 may further include an acceleration sensor (not shown) for measuring and outputting an acceleration of at least one of the patient and the head of the patient when the infrared imaging unit 200 is configured to be worn on the head It might be.

The rotating chair 300 is configured in the form of a chair in which the patient can sit and controls the direction and speed according to the control of the nystagmus testing unit 400 to rotate the chair.

The nystagmus checking unit 400 controls the rotation of the rotating chair 300 according to preset rotation control information for nystagmus examination and detects the pupil by binarizing the infrared eye image input from the infrared imaging unit 200 , Detects the motion characteristics of the detected pupil, analyzes the detected pupil movement characteristics based on the rotation control information, performs a nystagmus test, and outputs the result.

The input unit 500 may include at least one key, a button, or a switch for controlling the operation of the nystagmus test apparatus. The input unit 500 outputs an input signal corresponding to the operated key, button, or switch to the nystagmus test unit 400.

The input unit 500 may be a keyboard, a mouse, a touch pad, or the like, and allows a user to directly input or select a threshold value for binarizing an image from an administrator (or a "practitioner") according to the present invention.

2 is a detailed block diagram of a nystagmus test part of a video nystagmus test apparatus according to the present invention.

2, the nystagmus test unit 400 includes a rotation control unit 410, an infrared image acquisition unit 420, an image processing unit 430, a pupil motion detection unit 440, and a nystagmus determination unit 450.

The rotation control unit 410 controls the rotation direction and the rotation speed of the rotary chair 300 according to preset rotation control information for nystagmus examination.

The rotation control unit 410 sequentially or randomly selects one of the frequencies of 0.01, 0.02, 0.04, 0.08, 0.16, 0.32 and 0.64 Hz or a frequency of the frequencies of 0.01, 0.02, You will be able to control it.

In addition, the rotation control unit 410 provides the nystagmus judgment unit 450 with information on the direction and speed currently being controlled.

The infrared ray image acquiring unit 420 drives the infrared ray radiographing unit 200 to acquire an infrared ray image through the infrared ray radiographing unit 200 and output the obtained infrared ray eye image to the nystagmus checking unit 400.

The image processor 430 detects the pupil after binarizing the infrared eye image obtained through the infrared image acquiring unit 420 and outputs the detected pupil image data to the pupil motion detector 440.

The pupil motion detection unit 440 receives the image data, analyzes the pupil movement characteristic information, and outputs the pupil movement characteristic information on the motion of the pupil to the nystagmus determination unit 450.

The nystagmus judging unit 450 receives the rotation control information from the rotation control unit 410 and analyzes the detected pupil movement characteristic information based on the rotation control information to perform a nystagmus test to determine whether or not the nystagmus is present And displays it on the display unit 100.

FIG. 3 is a diagram illustrating a detailed configuration of an image processing unit of the nystagmus test unit according to the present invention. FIG. 4 is a diagram illustrating eye images processed in the image processing unit according to the present invention, And a binarization image and a circle detection image according to different reference values at the time of binarization. The detailed configuration and operation of the image processing unit 430 will be described below with reference to FIGS. 3 to 5. FIG.

The image processing unit 430 includes a region of interest specifying unit 431, an image converting unit 432, a filtering unit 435, an outline detecting unit 436, and a circle detecting unit 437.

The ROI specifying unit 431 outputs the infrared front eye image 502 obtained by extracting only the front eye part from the infrared eye image 501 of FIG. 4 input from the infrared image obtaining unit 420 to the image converting unit 432 . The front portion may be selected by an administrator through the input unit 500 or may be automatically detected by eye pattern detection.

The image converting unit 432 binarizes the infrared front eye image obtained through the ROI specifying unit 431 and outputs the binarizing front image 503.

The image converting unit 432 includes a first image converting unit 433 for converting the infrared front eye image into a gray front image of a gray channel and outputting the gray front image, And a second image converting unit 434 for outputting the binarized anterior segment image 503.

The binarization is performed by converting a pixel value of a gray anterior segment image converted to an exceeded gray channel based on a predetermined reference value to 255, and converting the following value to 0. The second image conversion unit 434 performs binarization corresponding to each reference value, and outputs the binarized anterior segment image 504 (see FIG. 4) to each reference value, ).

The filtering unit 435 transmits the binarized anterior segment image 504 obtained by removing the noise contained in the binarized anterior segment image 503 input from the second image conversion unit 434 to the outline detection unit 436. [

The filtering unit 435 removes noise by applying erosion and dilation in a morphology (or "morph") operation. The erosion is to replace the minimum pixel value among the neighboring pixels with the current pixel value, and to make the object of small noise (small chunk) disappear. The expansion is to replace the maximum pixel value among the neighboring pixels with the current pixel value, and to play the role of making the small holes in the object disappear. In other words, it removes noise through erosion operation, removes empty space in object through expansion operation, and restores size of reduced object.

The outline detection unit 436 detects the outline of the anterior segment from the binarized anterior segment image 504 filtered by the filtering unit 435 and transmits the anterior segment outline image 505 to the circular detection unit 437.

The circle detection unit 437 detects a circle corresponding to the pupil from the voltage unit contour image 505 input from the contour detection unit 436 and outputs the detected image data of the pupil corresponding to the circle, that is, the motion of the pupil.

When the binarization anterior segment image 504 for a plurality of reference values is output from the second image transformation unit 434, the circle detection unit 437 detects at least two binarization transformed based on a plurality of different reference values, An outer contour image 505 for the anterior segment image 503 is input from the contour detection unit 436 and a circle 506 is detected from each anterior contour image 505 and the detected circle 506 is inserted into the pupil An optimal circle detecting unit 438 for selecting and outputting a matching frontal contour image 505 and calculating a center point of the circle for the selected frontal contour image and considering the calculated three-dimensional structure and curve of the pupil And an image calibrating unit 439 for estimating the distance and angle at which the actual pupil moves, in units of pixels, and outputting the image data of the pupil.

That is, the optimal circle detection unit 438 obtains binarized frontal contour images 505-1, 505-2, 505-3, and 505-4 by applying different reference values as shown in FIG. 5, When a circle 506 is detected from the eye contour images, a circle will be detected as shown in 511-1 to 511-4 in Fig. 5, reference numeral 505-1 denotes a case where 262 is applied as a reference value, 505-2 denotes a case where 270 is applied as a reference value, 505-3 denotes a case where 275 is applied as a reference value, and 505-4 denotes a case where 285 is applied as a reference value .

Therefore, the optimal circle detecting unit 438 may select and output the frontal region contour image 505-3 for 511-3 in which the pupil and the detected circle 506 coincide with each other in 511-1 through 511-4.

Since the image calibrator 439 requires an angle per pixel value to obtain the velocity of the horizontal eye movement, the eye motion unit should be converted into an angle. The angle would preferably be 0.49 당 per pixel.

FIG. 6 is a diagram illustrating a detailed configuration of a pupil motion detection unit of the nystagmus test unit according to the present invention. FIG. 7 is a diagram illustrating a horizontal pupil signal for horizontal pupil data according to an embodiment of the present invention. FIG. 9 is a diagram for explaining a signal processing process of a slow phase signal in the pupil motion detecting unit according to the present invention, and FIG. 10 is a view for explaining a signal processing process in the pupil motion detecting unit according to the rotation FIG. 11 is a graph showing a phase curve according to the rotation stimulus frequency of the present invention. FIG. This will be described below with reference to Figs. 6 to 11. Fig.

The pupil motion detection unit 440 includes a first interpolation unit 441, a differentiator 442, a fast phase removal unit 443, a second interpolation unit 444, a filtering unit 445, a slow phase signal detection unit 446, And a characteristic extracting unit 447.

The first interpolator 441 receives the image data from the image processor 430 and applies a linear spline algorithm to interpolate the missing signal due to eye blinking to output the first interpolated video signal.

For example, in FIG. 7, in a horizontal pupil movement signal for horizontal pupil movement data, a frame in which a pupil can not be detected due to eye blinking is set to a negative value.

The first interpolator 441 applies a linear spline algorithm to compensate for frames set to negative values.

In addition, the first interpolator 441 converts the irregular sampling rate (27-29 frames / sec) into a signal of 30 Hz by applying a cubic spline algorithm, which is an interpolation method.

The first interpolator 441 interpolates the pupil movement signal including the horizontal pupil signal 701 shown in FIG. 7 corresponding to the image data to a pupil movement signal including the interpolated horizontal pupil signal 702 shown in FIG. And outputs it as video data.

The differentiator 442 obtains a slope by differentiating the interpolated first interpolated video signal as shown in (801) of FIG. 8 to output a signal as shown in (802) in FIG. 8, And outputs a differential image signal such as a differential image signal.

The fast phase removing unit 443 applies the differential image signal to the first interpolated image signal to generate a fast phase signal corresponding to a dotted rectangle as indicated by 804 in FIG. 8 in the first interpolated image signal 801 shown in FIG. And outputs a slow phase signal as shown in 901 of Fig.

The second interpolator 444 interpolates and interpolates the slow phase signal using a linear spline algorithm and outputs a slow phase signal as shown in 902 of FIG.

The filtering unit 445 filters the interpolated slow phase signal and outputs a slow phase signal such as 903 in FIG. Preferably, the filtering unit 445 is applied with a low-pass filter, and the cutoff frequency of the low-pass filter is preferably set by the following equation (1).

[Equation 1]

Figure 112017022146041-pat00001

Where k is a constant and n is the number of cycles.

The characteristic extraction unit 446 extracts the gain, asymmetry, and phase characteristics from the slow phase signal and outputs the pupil motion characteristic information.

Gain is the ratio of the amplitude of the eye movement to the amplitude of the head stimulus. This is calculated by dividing the maximum velocity of the slow phase eye movement by the maximum velocity of the head (chair). This can be expressed by the following equation (2).

&Quot; (2) "

Figure 112017022146041-pat00002

For example, patients with weak bilateral vestibles will have reduced gain at low frequency rotations.

As shown in FIG. 10, the gain can be expressed by a theoretical logarithmic gain curve according to the rotating stimulus frequency. The gain increases linearly with increasing frequency at a rotation at less than 0.05 Hz, is constant from about 1 to 100 Hz, It can be seen that at a higher rotation frequency, it decreases as the frequency increases. Reductions in gain can occur in all frequency stimuli, but only in certain stimulant frequencies. In the case of partial vestibular dysfunction, the gain is normally reduced abnormally at low frequencies (<0.1 Hz) and normally at high frequencies (> 0.1 Hz) And decreases with normal or high rate stimulation.

Symmetry / Asymmetry is a comparison of equivalent slow phase elements on the left and right when rotating left and right for each frequency. The asymmetry may be calculated by the following equation (3).

&Quot; (3) &quot;

Figure 112017022146041-pat00003

Here, b1 and b2 represent the maximum velocity of slow phase eye movement when rotating left and right, respectively.

In general, the normal symmetry range is 15 to 20%.

Phase is the time relationship between the onset of head movement and the reflex ocular response. If the head and eye move in the opposite direction at exactly the same speed, the phase is considered 180.. A phase lead appears when the movement of the head causes an eye reaction reflex, and a phase lag appears when the correction eye movement proceeds. Unusually long leads and short leads can be considered as peripheral lesions and cerebellar lesions, respectively.

The phase angle? Is calculated by the following equation (4).

&Quot; (4) &quot;

Figure 112017022146041-pat00004

Where f represents the frequency of the eye movement signal, and [Delta] t represents the time difference between the maximum velocity of the slow phase eye movement and the head (chair) motion.

The phase can be represented by a phase curve according to the rotating stimulus frequency as shown in Fig. When perfect antireflection occurs, the phase is 0..

12 is a flowchart illustrating a video nystagmus test method using an infrared camera according to the present invention.

Referring to FIG. 12, when the nystagmus test start event occurs, the nystagmus checker 400 controls the rotation direction and speed of the rotating chair 300 according to the preset rotation control information (S111). The nystagmus test start event may be generated when an input signal corresponding to the nystagmus test start command is input through the input unit 500. [ The rotation control information may include information such as direction, speed, and time for rotating the rotating chair 300.

When the control of the rotating chair 300 is performed, the nystagmus checking unit 400 drives the infrared ray photographing unit 200 to photograph an eye of the patient and start acquiring an infrared eye image (S113).

When the infrared eye image starts to be acquired, the nystagmus checker 400 performs image processing for binarizing the input infrared eye image to detect the pupil, and generates image data including motion information of the pupil (S115).

When the image data is generated, the nystagmus checker 400 estimates the pupil motion information from the image data (S117), and analyzes the pupil motion information based on the rotation control information to determine the nystagmus (S119).

13 is a flowchart illustrating an image processing method in a video nystagmus test method according to the present invention.

Referring to FIG. 13, the image processing unit 430 detects only the anterior region of interest, which is a region of interest, from the infrared eye image, and outputs an infrared front eye image including only the detected anterior region (S211 ).

The image processor 430 converts the infrared front eye image into a gray front image, which is a gray channel image (S213).

When the image is converted into a gray anterior segment image, the image processing unit 430 compares the gray anterior portion image with a preset reference value, converts a pixel value larger than the reference value to 255, and converts a pixel value smaller than the reference value to 0 And outputs the binarized anterior segment image (S215).

When the binarized anterior segment image is generated, the image processing unit 430 filters and outputs the binarized anterior segment image (S217). An outline of the anterior segment is detected from the filtered binarized anterior segment image to output an anterior segment outline image (S219).

When an anterior segment is detected, the image processor 430 detects a circle corresponding to the pupil from the anterior segment contour image and detects a center point of the circle (S221), generates image data of the pupil corresponding to the detected center point, (S223).

FIG. 14 is a flowchart illustrating a pupil motion characteristic detection method in a video nystagmus test method according to the present invention.

Referring to FIG. 14, the pupil motion detector 440 receives the image data output from the image processor 430 and interpolates a missing signal due to eye blinking by applying a linear spline algorithm to obtain a first interpolated image signal (Step S311).

When the first interpolation is performed, the pupil motion detector 440 obtains a slope from the first interpolated image signal, and outputs a differential image signal obtained by squaring the slope (S313).

When the differential image signal is generated, the pupil motion detector 440 applies the differential image signal to the first interpolated image signal to remove a fast phase signal from the first interpolated image signal, And outputs a slow phase signal which is a video signal (S315).

When the interpolated slow phase signal is input, the pupil motion detection unit 440 interpolates the second phase by applying a linear spline algorithm and outputs the interpolated second phase signal (S317).

The second interpolated slow phase signal is filtered by a low pass filter (S319), motion characteristic information including gain, asymmetry and phase information is extracted from the low pass filtered slow phase signal and output to the nystagmus judgment unit 450 do.

While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. It will be easily understood. It is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, it is intended to cover various modifications within the scope of the appended claims.

100: display unit 200:
300: rotating chair 400: nystagmus checker
410: rotation control unit 420: infrared image acquisition unit
430: image processing unit 431:
432: image converting unit 433: first image converting unit
434: second image conversion unit 435:
436: Outline detection unit 437:
438: Optimum circle detection unit 439: Image calibration unit
440: pupil motion detection unit 441: first interpolation unit
442: differentiator 443: fast phase control unit
444: Second interpolator 445: Filtering unit
446:
450: Nystagmus judgment unit

Claims (13)

  1. A rotating chair that is configured in the form of a chair in which the patient sits and is rotated in a lateral direction;
    An infrared ray photographing unit for infrared ray photographing an eye region including at least one of the two eyes of the patient and outputting an infrared ray eye image; And
    A controller for controlling the rotation of the rotary chair according to preset rotation control information for nystagmus testing, detecting the pupil by binarizing the infrared eye image, detecting the motion characteristic of the pupil, And a nystagmus test section for analyzing pupil movement characteristics to perform a nystagmus test and outputting the result,
    The nystagmus-
    A rotation control unit for controlling the rotation of the rotating chair according to preset rotation control information for nystagmus examination;
    An infrared image acquiring unit for acquiring and outputting the infrared eye image through the infrared image radiographing unit after controlling and driving the infrared image radiographing unit;
    An image processor for detecting a pupil after binarizing the obtained infrared eye image and outputting image data of the detected pupil;
    A pupil motion detection unit for receiving and analyzing the image data and outputting pupil motion characteristic information on pupil motion; And
    And a nystagmus judgment unit for analyzing the pupil motion characteristic information detected based on the rotation control information to perform a nystagmus test and determine whether or not the nystagmus is present,
    Wherein the pupil motion detection unit comprises:
    A first interpolator receiving the image data and applying a linear spline algorithm to interpolate a missing signal due to blinking and outputting a first interpolated video signal;
    A differentiator for obtaining a slope from the first interpolated image signal and outputting a differential image signal obtained by squaring the slope;
    A fast phase removal unit for applying the differential image signal to the first interpolated image signal to output a slow phase signal from which the fast phase signal is removed from the first interpolated image signal;
    A second interpolator interpolating and outputting the slow phase signal using a linear spline algorithm;
    A filtering unit for filtering and outputting the interpolated slow phase signal; And
    And a characteristic extracting unit for extracting gain, asymmetry, and phase characteristics from the slow phase signal and outputting the pupil motion characteristic information.
  2. A rotating chair that is configured in the form of a chair in which the patient sits and is rotated in a lateral direction;
    An infrared ray photographing unit for infrared ray photographing an eye region including at least one of the two eyes of the patient and outputting an infrared ray eye image; And
    A controller for controlling the rotation of the rotary chair according to preset rotation control information for nystagmus testing, detecting the pupil by binarizing the infrared eye image, detecting the motion characteristic of the pupil, And a nystagmus test section for analyzing pupil movement characteristics to perform a nystagmus test and outputting the result,
    The nystagmus-
    A rotation control unit for controlling the rotation of the rotating chair according to preset rotation control information for nystagmus examination;
    An infrared image acquiring unit for acquiring and outputting the infrared eye image through the infrared image radiographing unit after controlling and driving the infrared image radiographing unit;
    An image processor for detecting a pupil after binarizing the obtained infrared eye image and outputting image data of the detected pupil;
    A pupil motion detection unit for receiving and analyzing the image data and outputting pupil motion characteristic information on pupil motion; And
    And a nystagmus judgment unit for analyzing the pupil motion characteristic information detected based on the rotation control information to perform a nystagmus test and determine whether or not the nystagmus is present,
    Wherein the image processing unit comprises:
    An interest region specifying unit for outputting an infrared anterior segment image including only an anterior region from the infrared eye image;
    An image converting unit for binarizing the infrared front eye image to output a binocular front eye image;
    An outline detection unit detecting an outline of the anterior segment from the binarized anterior segment image and outputting an anterior segment outline image; And
    A pupil detection unit for detecting a pupil from the anterior segment contour image, detecting a center point of the pupil, and outputting pupil image data corresponding to the detected center point,
    The circular detection unit
    The method includes receiving an anterior segment contour image for at least two binarized anterior segment images transformed based on a plurality of different reference values to be changed from the contour detection unit, detecting a circle from each anterior contour image, An optimal circle detection unit for selecting and outputting an image of a frontal contour line to output; And
    Calculating a center point of the circle for the selected foreground contour image and estimating a distance and an angle of the actual pupil moving in a pixel unit considering the three-dimensional structure and the curved line of the pupil, And a calibration unit.
  3. delete
  4. 3. The method of claim 2,
    The image converter may include:
    A first image converter for converting the infrared front eye image into a gray front image of a gray channel and outputting the gray front image; And
    And a second image converter for binarizing the gray anterior segment image based on a preset reference value and outputting the binarized anterior segment image.
  5. 3. The method of claim 2,
    Wherein the image processing unit comprises:
    Further comprising a filtering unit for performing a morphological operation on the binarized anterior segment image output from the image transform unit and applying erosion and expansion to remove the noisy image.
  6. delete
  7. delete
  8. A rotation control step of controlling the rotation of the rotating chair according to preset rotation control information for nystagmus testing;
    An infrared ray image acquiring step of acquiring and outputting an infrared ray image through the infrared ray radiographing unit after driving the infrared ray radiographing unit;
    An image processing step of binarizing the acquired infrared eye image, detecting a pupil, and outputting image data of the detected pupil;
    A pupil motion detection step of receiving and analyzing the image data and outputting pupil motion characteristic information on pupil motion; And
    And a nystagmus judgment step of analyzing pupil motion characteristic information detected based on the rotation control information to perform a nystagmus test and judging whether or not nystagmus is present,
    The pupil movement detecting step may include:
    A first interpolation step of receiving the image data and applying a linear spline algorithm to interpolate a missing signal due to blinking and outputting a first interpolated image signal;
    A differentiating step of obtaining a slope from the first interpolated image signal and outputting a differential image signal obtained by squaring the slope;
    A fast phase removing step of applying the differential image signal to the first interpolated image signal to output a slow phase signal from which a fast phase signal is removed from the first interpolated image signal;
    A second interpolation step of interpolating and outputting the slow phase signal using a linear spline algorithm;
    A filtering step of filtering and interpolating the interpolated slow phase signal; And
    And extracting gain, asymmetry, and phase characteristics from the slow phase signal and outputting the pupil motion characteristic information.
  9. A rotation control step of controlling the rotation of the rotating chair according to preset rotation control information for nystagmus testing;
    An infrared ray image acquiring step of acquiring and outputting an infrared ray image through the infrared ray radiographing unit after driving the infrared ray radiographing unit;
    An image processing step of binarizing the acquired infrared eye image, detecting a pupil, and outputting image data of the detected pupil;
    A pupil motion detection step of receiving and analyzing the image data and outputting pupil motion characteristic information on pupil motion; And
    And a nystagmus judgment step of analyzing pupil motion characteristic information detected based on the rotation control information to perform a nystagmus test and judging whether or not nystagmus is present,
    Wherein the image processing step comprises:
    An interest region designation step of outputting an infrared anterior segment image including only an anterior region from the infrared eye image;
    An image converting step of binarizing the infrared front eye image to output a binocular front eye image;
    An outline detection step of detecting an outline of the anterior segment from the binarized anterior segment image and outputting an anterior segment outline image; And
    Detecting a pupil from the anterior segment contour image, detecting a center point of the pupil, and outputting image data of a pupil corresponding to the detected center point,
    Wherein,
    The method comprising: receiving input from at least two or more binarized anterior segment images transformed based on a plurality of different reference values to be changed; detecting a circle from each anterior segment contour image; An optimal circle detection step of selecting and outputting an anterior contour image; And
    Calculating a center point of the circle for the selected foreground contour image and estimating a distance and an angle of the actual pupil moving in a pixel unit considering the three-dimensional structure and the curved line of the pupil, And calibrating the video camera using the infrared camera.
  10. 10. The method of claim 9,
    Wherein the image conversion step comprises:
    A first image conversion step of converting the infrared front eye image into a gray front image of a gray channel and outputting the gray front image; And
    And outputting the binarized anterior segment image by binarizing the gray anterior segment image with reference to a preset reference value, and outputting the binarized anterior segment image.
  11. 10. The method of claim 9,
    Wherein the image processing step comprises:
    Further comprising a filtering step of performing a morphological operation on the binarized anterior segment image output from the image transformation unit to remove noises by applying erosion and expansion.
  12. delete
  13. delete
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CN201880009670.6A CN110325104A (en) 2017-03-06 2018-01-03 It is quivered check device and method using the image nystagmus of thermal camera
PCT/KR2018/000114 WO2018164361A1 (en) 2017-03-06 2018-01-03 Nystagmus video test device and method using infrared camera

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US20100036289A1 (en) 2007-01-26 2010-02-11 University Of Florida Research Foundation, Inc. Apparatus and Methods for Assessment of Vestibulo-Ocular Reflexes
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JP2015515289A (en) 2012-02-28 2015-05-28 マリア ソサ,アナ Method, apparatus and system for diagnosing and treating mood disorders

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KR100336176B1 (en) * 1999-08-21 2002-05-10 김윤종 Apparatus of check and stimulation to vestibular dysfunction
KR20040100677A (en) 2003-05-23 2004-12-02 두산중공업 주식회사 Steam pipe cleaning method in steam turbin
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US20100036289A1 (en) 2007-01-26 2010-02-11 University Of Florida Research Foundation, Inc. Apparatus and Methods for Assessment of Vestibulo-Ocular Reflexes
JP2015515289A (en) 2012-02-28 2015-05-28 マリア ソサ,アナ Method, apparatus and system for diagnosing and treating mood disorders
KR101501165B1 (en) * 2013-12-02 2015-03-11 한국산업기술대학교산학협력단 Eye-mouse for general paralyzed patient with eye-tracking

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