U.S. patent application number 15/821626 was filed with the patent office on 2018-05-24 for method and system for classifying optic nerve head.
The applicant listed for this patent is DELPHINIUM CLINIC LTD.. Invention is credited to Kate Coleman.
Application Number | 20180140180 15/821626 |
Document ID | / |
Family ID | 60450660 |
Filed Date | 2018-05-24 |
United States Patent
Application |
20180140180 |
Kind Code |
A1 |
Coleman; Kate |
May 24, 2018 |
METHOD AND SYSTEM FOR CLASSIFYING OPTIC NERVE HEAD
Abstract
Provided are a method and system for identifying and classifying
the owner, age and health of an optic nerve head and its
vasculature based on analysis of vector relationships of blood
vessels and the neuroretinal rim within an image of the optic nerve
head to each other.
Inventors: |
Coleman; Kate; (County
Dublin, IE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
DELPHINIUM CLINIC LTD. |
Co. Dublin |
|
IE |
|
|
Family ID: |
60450660 |
Appl. No.: |
15/821626 |
Filed: |
November 22, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 2207/20084
20130101; G06T 7/10 20170101; G06T 2207/20081 20130101; A61B 3/0025
20130101; G06T 7/0014 20130101; A61B 3/1233 20130101; G06T
2207/10101 20130101; G06T 2207/30101 20130101; G06T 2207/30041
20130101; G06K 9/00617 20130101; G06K 9/0061 20130101; A61B 3/12
20130101; A61B 3/14 20130101 |
International
Class: |
A61B 3/00 20060101
A61B003/00; A61B 3/14 20060101 A61B003/14; A61B 3/12 20060101
A61B003/12; G06T 7/00 20060101 G06T007/00; G06T 7/10 20060101
G06T007/10; G06K 9/00 20060101 G06K009/00 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 22, 2016 |
IE |
S2016/0260 |
Claims
1. A computer-implemented method of classifying the optic nerve
head, the method comprising operating one or more processors to:
segment an image of an optic nerve head from a photographic image
of an eye; segment the image of the optic nerve head into multiple
segments each containing blood vessels and neuroretinal rim fibres;
extract features from the segmented images, the features describing
relationships between the blood vessels themselves and between the
blood vessels and the neuroretinal rim fibres in each of the
segmented images; identify characteristics of the optic nerve head
based on the extracted features; and classify the image of the
optic nerve head based on the identified characteristics.
2. The method of claim 1, comprising segmenting an image of a
non-dilated or dilated eye of a human or any other eye bearing
species with an optic nerve to obtain an optic nerve head
image.
3. The method of claim 1, wherein the segmenting of the image of an
optic nerve head from a photographic image of an eye is performed
with a deep neural network architecture using a fully convolutional
network.
4. The method of claim 3, wherein the optic nerve head is located
by classifying each pixel in the image.
5. The method of claim 1, wherein the segmenting of an image of an
optic nerve head from a photographic image of an eye comprises
rendering a geometric shape around the optic nerve head and
cropping the image accordingly.
6. The method of claim 1, wherein the segmenting the image of the
optic nerve head into multiple segments comprises using at least
one of machine learning, deep neural networks, and a trained
algorithm to automatically identify the blood vessels and
neuroretinal rim fibres.
7. The method of claim 1, wherein the relationships between the
vessels themselves and between the blood vessels and the
neuroretinal rim comprise vectors mapped between points on the
blood vessels and the neuroretinal rim in each of the segmented
images.
8. The method of claim 7, wherein the identifying characteristics
of the optic nerve head comprises generating training sets for
identifying the relationships between the vessels themselves and
between the blood vessels and the neuroretinal rim.
9. The method of claim 1, comprising, for each segment:
superimposing multiple concentric circles on the segment;
determining intersection points of the circles with blood vessels
and branches thereof and intersection points between the blood
vessels and branches thereof and the neuroretinal rim fibres;
mapping vectors between the intersection points; determining
distances of the vectors; determining ratios of the vector
distances; combining sequences and/or permutations of the ratios
into an image representation; searching a lookup table for the
closest representation to the image representation; and classifying
the optic nerve head according to the closest representation
found.
10. The method of claim 9, comprising returning an identity of the
optic nerve head according to the closest representation found.
11. The method of claim 1, comprising using at least one of machine
learning, deep neural networks, and a trained algorithm to
automatically identify the blood vessels and optic nerve head
neuroretinal rim as belonging to the individual eye image at that
moment in time.
12. The method of claim 1, comprising classifying the optic nerve
head image as being likely to be glaucomatous or healthy.
13. The method of claim 1, comprising classifying the optic nerve
head image as being likely to belong to an adult or a child.
14. The method of claim 1, comprising identifying when the optic
nerve head image changes.
15. The method of claim 14, comprising identifying changes to
relationships within the optic nerve head image.
16. A computing system configured for classifying the optic nerve
head, the computing system comprising: a memory; and one or more
processors configured to: segment an image of an optic nerve head
from a photographic image of an eye; segment the image of the optic
nerve head into multiple segments each containing blood vessels and
neuroretinal rim fibres; extract features from the segmented
images, the features describing relationships between the blood
vessels themselves and between the blood vessels and the
neuroretinal rim fibres in each of the segmented images; identify
characteristics of the optic nerve head based on the extracted
features; and classify the image of the optic nerve head based on
the identified characteristics.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims priority to Irish Application No.
S2016/0260, filed on Nov. 22, 2016, which is incorporated herein by
reference.
FIELD OF THE INVENTION
[0002] This invention relates generally to image recognition
techniques for the eyes of humans or animals, and more specifically
to a method and system for detecting characteristics of the optic
nerve head of humans or animals and any changes in these
characteristics, either due to age, pathology or disease, for the
purposes of diagnostics, identification, age assessment, encryption
or related analytic applications.
BACKGROUND OF THE INVENTION
[0003] The most common forms of preventable and avoidable blindness
globally are glaucoma, macular degeneration and diabetic
retinopathy. All present with physical changes to the
shapes/colours of the normal structures, the nerve and
nerve/vascular layer known as the retina, at the back of the eye.
Diagnosis of disease is made by direct observation of changes to
the normal appearance of these three locations: the circular optic
disc, the plain macula and the plain retina and vessel pattern.
[0004] Until recently, only highly skilled ophthalmologists and
opticians could safely examine the back of the eye using dilating
eye drops and complex medical equipment such as ophthalmoscopes and
special lenses. In the last decade, major advances in imaging have
led to the development of non-mydriatic cameras whereby anyone can
take a digital photograph of the back of their eye, simply by
placing their head on a chin rest and looking in to the camera.
More recently, the ubiquitous mobile phone has camera accessories
and fixtures to take the same images. Ongoing research is
continuing to image the retinal layers in increasingly fine detail,
suggesting the possibility of pre-damage disease detection.
[0005] Glaucoma is a condition where the optic nerve is excessively
vulnerable and starts to thin out, losing ability to transmit
images from the retina to the brain. Early thinning can be detected
by observing the changes in the appearance of the optic disc (the
head of the nerve where it leaves the eyeball), as illustrated in
FIG. 1, as described below. Early detection can mean early
treatment and prevention of irreversible sight loss.
[0006] In the last decade, new advances in medicine have introduced
skilled procedures, such as selective laser trabeculoplasty (SLT)
and micro drain implants, which can control previously uncontrolled
glaucoma. The preceding decade introduced drugs called
prostaglandin inhibitors which transformed the medical management
of the majority of previously blinding cases of glaucoma. The fact
is that in the developed world, an estimated 50% of patients with
glaucoma, "the silent thief", cannot access these new sight saving
remedies and glaucoma remain undetected. Once nerve damage has
occurred, vision is irretrievably lost.
[0007] Photographic examination of the optic nerve head fibres (the
optic disc) as they enter the eyeball through the cribriform plate
from the brain has only been accessible to specialists until
recently. FIG. 1a illustrates a normal optic nerve head and blood
vessels within a fundus photograph. FIG. 1b is an image of advanced
glaucoma showing large pale `cup` and thin neuroretinal rim
(right). The large paler area (sometimes called the cup) represents
the area free of axons where the nerve has been `cored out`. The
blood vessels branch from the centre (the central retinal artery)
which can be seen to be displaced between 12 o clock and 11 o clock
on the right of the rim beside the arrow, before it `bends` up
around the rim. In the last decade, major advances in imaging have
led to the refinement of non-mydriatic cameras whereby anyone can
take a digital photograph of the optic nerve head at the back of
their eye simply by placing their head on a chin rest and looking
at the camera. More recently, the ubiquitous mobile phone has
camera accessories and fixtures using adaptive optics to take
equivalent 2D images of the undilated eye. FIG. 2a is a
photographic image of an optic disc from a PEEK mobile phone fundus
camera attachment. FIG. 2b illustrates an example of a D-Eye phone
ophthalmoscope/camera attachment.
[0008] Ongoing research is continuing to image the retinal layers
in increasingly fine detail, suggesting the possibility of accurate
identification, recognition and early nerve fibre disease
detection, especially glaucoma. Most advanced clinical imaging of
the optic nerve head uses SD-Optical Coherence tomography (OCT), a
three-dimensional scanning ophthalmic camera. The latter is too
complex for general use although is increasingly applicable for
specialized screening.
[0009] Almost all studies heretofore have analysed the optic nerve
head for glaucoma disease. Furthermore, these studies have focused
on what is called the cup-disc ratio, using segmentation of the
disc rim minus the inner cup, as a glaucoma index. However, a
cup-disc ratio does not definitively indicate axonal optic nerve
fibre loss. Furthermore, the ratio is a summary of the measurement
of a specific radius of a disc, which is rarely a perfect circle.
This is illustrated in FIG. 3, a schematic representation of the
optic nerve head photograph images of FIG. 1. Referring to FIG. 3,
AB is the center to the rim, and AC is the center to the retina.
The cup/disc ratio is the proportion AB to AC. It is also well
accepted amongst ophthalmologists that although an increased optic
cup-disc ratio suggests a risk of glaucoma, there is a high chance
of over fitting with a labeled data set from patients already
diagnosed, with an unacceptable chance that glaucoma can progress
with loss of axons without affecting the cup/disc ratio.
[0010] The use of imaging the retinal blood vessels as a biometric
marker has been around for many years, yet it still remains a
challenge to develop a safe robust biometric to address
shortcomings with current biometrics, such as retinal scans,
fingerprints and iris scans. Table 1 summarises relevant research
on retinal biometrics. Ahmed et al applied a method using
semicircular discs around the optic nerve head with only 84.2 and
89.2% accuracy. Kose et al employ vessel segmentation of similarity
(length) measurements with circular sampling.
TABLE-US-00001 TABLE 1 Summary of retinal biometric studies Jiu et
al. 2016 Pre-processing Retinal vessel Random Clinical data sets
with feature vector analysis of bifurcation points only, small. 93%
extraction bifurcation points. for circle chosen. accuracy
Traditional Vector along Retina analysed machine learning length of
vessel Kose et al 2011 Retinal vessel segmentation with circular
sampling and vessel length vector Drozd 2012 Retinal vessel Poor
optic disc Retina analysed 8% best result bifurcation localisation
analysed Bevilacqua et al Bifurcation points Cloud of points Retina
analysed 2008 on retina Ahmed et al Optic nerve head Semicircular
Optic nerve head 2012 segment section examined
[0011] The unique image of the optic nerve head is from a fixed
environment without variation in lighting conditions, which hamper
pupil size for retinal scans or pupil light change for iris scans.
The disc image is inaccessible without full-directed gaze and
compliance from the individual, unlike the iris images, which can
be captured remotely and reproduced illegally. The optic disc is
approximately 1-2 mm in diameter, close to the back of the eye and
with unique features making it significantly more accessible, more
accurate and easier to image than full retinal blood vessels
scans.
[0012] It has been suggested that there is a decrease in
cup-to-disk ratio and neuroretinal rim area as age increased in
studies based on Asian populations. FIG. 4 is a diagrammatic
illustration of what happens to the position of the blood vessels
in the optic nerve head when thinning of the neuroretinal rim
occurs over time. FIG. 5 is a photographic image of the optic nerve
head of a patient with progressive glaucoma over ten years,
demonstrating enlargement of the central pale area (cup) as the rim
thins, with displacement of their blood vessels.
[0013] In view of the above, there is a need for an improved method
and system for detecting and analysing changes in the optic nerve
head.
SUMMARY OF THE INVENTION
[0014] The present disclosure provides a computer-implemented
method as detailed in claim 1 and a system according to claim 16.
Advantageous features are provided in dependent claims.
[0015] The present disclosure provides a computer-implemented
method and system for analysing, categorising and/or classifying
characteristics of the optic nerve head, including morphometric and
volume characteristics.
[0016] The optic nerve is an outpouching of the brain, and its
axons (nerve fibres) carry impulses back from the lining of the eye
(the retina) to the visual cortex in the brain for vision. The
nerve fibres are fed by a central retinal artery and vein, which
enter the nerve behind the optic nerve head and branch within the
papilla of the optic nerve head to immediately travel over the
neuroretinal rim, across Elschnigs line, to the superior and
inferior parts of the retina lining the eyeball.
[0017] The arrangement of the blood vessels within the optic nerve
itself is completely original to every individual eyeball. This
arrangement will change as the eyeball grows. In this regard, the
relationship of the size and position of the blood vessels and the
nerve axons will alter as the nerve and vessels grow at different
rates until adulthood. The characteristics of the axon fibres may
change if they are lost due to conditions such as glaucoma, or
indeed swollen with inflammation or other less common conditions.
The characteristics of the blood vessels, like all arteries and
veins, may be altered if the pressure of the blood therein
increases, causing them to dilate, or harden and constrict, or
should diseases such as diabetes or coagulation disorders affect
their permeability.
[0018] The position of the blood vessels themselves, in relation to
their main trunk, and in relation to the axons which they pass
through and over in the optic nerve head, will also change when
their support/floor of axons changes its position. Loss of axons,
such as with glaucoma, will cause a shift in the adjacent vessels
and the distance between the centre of the vessel and the other
vessel/or neighbouring axons will change. Loss of axons will also
change the appearance of the neuroretinal rim.
[0019] The present disclosure comprises a computer-implemented
method for automatic recognition and identification of the optic
nerve head image at the time of image capture. The process uses a
deep neural network to segment the optic nerve head from a
photographic image of the eye and automatic feature extraction/and
or a second deep neural network to train an algorithm to describe
the image of the optic disc blood vessels in terms of their
proportionality and relationship to each other, based on the angles
of the superior and inferior vascular arcade and their branches
within the optic nerve head space. The angles of the vessels to the
concentric circles change as the positions of the vessels move,
causing the length of the vectors from point to point to change as
well as training an algorithm to identify the optic nerve axon
fibres pattern visible in the 2D optic disc image.
[0020] The present disclosure also comprises a process which
develops a training algorithm to segment an optic nerve head axon
fibres pattern and classify it as glaucomatous or not.
[0021] Furthermore, the present disclosure comprises a training
algorithm which detects a point where the image of optic nerve head
axon fibres and blood vessel proportionality vectors change to
indicate either nerve fibre disease and/or blood vessel disease, an
example of such specifically being glaucoma progress or acute
hypertension or intracranial hypertension.
[0022] Furthermore, the present disclosure trains an algorithm to
identify the likelihood of the optic nerve head proportionality
vectors being that of an adult versus a child, with the probability
of determining the age of the optic nerve head and vessels being
examined, as will be described later.
[0023] The computer-implemented method comprises computer vision
algorithms using methods such as filtering, thresholding, edge
detection, clustering, circle detection, template matching,
transformation, functional analysis, morphology, etc., and machine
learning (classification/regression, including neural networks and
deep learning) to extract features from the image and classify or
analyse them for the purposes described herein. Such analysis shall
apply to various methods of imaging the optic nerve head as far as
the cribriform plate, including fundus imagery, optical coherence
tomography and/or any future medical or commercial imaging
technologies, including the use of refractive and colour filters
and different wavelengths of light (infrared, near-infrared,
ultraviolet, etc.).
[0024] The present disclosure will allow for the detection of
actual spatial changes due to loss of axons in the neuroretinal rim
per se and changes in vessel proportionality independent of cup
disc ratio and thus can monitor progressive changes, becoming
increasingly sensitive with repeated multiple imaging (as with a
self-owned smart phone camera) of the same optic nerve head axons
and vessels.
[0025] The present disclosure uses a hybrid approach to feature
extraction. Deep neural networks are used to segment salient areas
of the optic nerve head axons and vessels and further machine
learning algorithms are used to extract features to identify and
classify the vector relationships of optic nerve head vessels and
axons to each other, in order to output: [0026] Identification of
optic nerve head ownership [0027] Age of optic nerve head [0028]
Health/disease or disease progression status of the optic nerve
head
[0029] The machine learning algorithm can be used to train a deep
neural network, or in itself identify and classify an optic nerve
head. The methodology of the present disclosure may be used as a
biometric, as a detector of glaucoma, as a detector of disease
progression and as a determinant of age of the optic disc. The
methodology of the present disclosure may be used with all types of
fundus cameras, with OCT angiography (OCT-A), with non-mydriatic
fundus photography or smartphone fundus imaging for automatic
identification and classification of the optic disc and/or with
photographs of the optic disc. The methodology of the present
disclosure may be used separately or simultaneously on photographic
images of right and left optic discs from the same
animal/human/species.
BRIEF DESCRIPTIONS OF DRAWINGS
[0030] The present disclosure will be more clearly understood by
the following description of some embodiments thereof, given by way
of example only, with reference to the accompanying drawings, in
which:
[0031] FIG. 1a illustrates a fundus photographic image of a normal
optic nerve head and blood vessels and the surrounding retina;
[0032] FIG. 1b is an image of advanced glaucoma showing large pale
`cup` and thin neuroretinal rim;
[0033] FIG. 2a is a photographic image of an optic disc from a PEEK
mobile phone fundus camera attachment;
[0034] FIG. 2b illustrates an example of a D-Eye phone
ophthalmoscope/camera attachment;
[0035] FIG. 3 is a graphic example of the cup/disc ratio (CDR);
[0036] FIG. 4 is a diagrammatic illustration of what happens to the
position of the blood vessels in the optic nerve head when thinning
of the neuroretinal rim occurs over time;
[0037] FIG. 5 is a photographic image of the optic nerve head of a
patient with progressive glaucoma over ten years, demonstrating
enlargement of the central pale area (cup) as the rim thins, with
displacement of their blood vessels;
[0038] FIG. 6 illustrates OCT angiography (OCT-A) photographic
images of a healthy optic nerve head vasculature (on the left) and
on the right, a dark gap (between the white arrows) showing loss of
vasculature of early glaucoma in a patient with no loss of visual
fields;
[0039] FIG. 7a is an image of the optic nerve head divided into
segments;
[0040] FIG. 7b illustrates a graph showing loss of neuroretinal rim
according to age;
[0041] FIG. 8a is a process flow illustrating how an image of the
optic nerve head is classified as healthy or at-risk of glaucoma by
a dual neural network architecture, according to an embodiment of
the present disclosure;
[0042] FIG. 8b is a process flow illustrating an image of the optic
nerve head being cropped with feature extraction prior to
classification, according to an embodiment of the present
disclosure;
[0043] FIG. 9 is a flowchart illustrating an image classification
process for biometric identification, according to an embodiment of
the present disclosure;
[0044] FIG. 10a shows one circle of a set of concentric circles
intersecting with the optic nerve head vasculature;
[0045] FIG. 10b is an image of concentric circles in a 200
pixel.sup.2 segmented image intersecting with blood vessels and
vector lines;
[0046] FIG. 11 is a concatenation of all blood vessel intersections
for a given set of concentric circles--this is a feature set;
[0047] FIG. 12 illustrates an example of feature extraction with a
circle at a radius of 80 pixels, according to an embodiment of the
present disclosure;
[0048] FIG. 13 illustrates an example of a segmented image of optic
nerve head vessels before and after a 4 degree twist with 100%
recognition;
[0049] FIG. 14 illustrates a table of a sample feature set of
resulting cut-off points in pixels at the intersection of the
vessels with the concentric circles;
[0050] FIGS. 15a to 15c illustrate a summary of optic nerve head
classification processes according to embodiments of the present
disclosure;
[0051] FIG. 16 is a flowchart illustrating a computer-implemented
method of classifying the optic nerve head, according to an
embodiment of the present disclosure; and
[0052] FIG. 17 is a block diagram illustrating a configuration of a
computing device which includes various hardware and software
components that function to perform the imaging and classification
processes according to the present disclosure.
DETAILED DESCRIPTIONS OF THE DRAWINGS
[0053] The present disclosure provides a computer implemented
method and system for analysing, categorising and/or classifying
relationships of characteristics of the optic nerve head axons and
its blood vessels therein.
[0054] Machine learning and deep learning are ideally suited for
training artificial intelligence to screen large populations for
visually detectable diseases. Deep learning has recently achieved
success on diagnosis of skin cancer and more relevant, on detection
of diabetic retinopathy in large populations using 2D fundus
photographs of the retina. Several studies have previously used
machine learning to process spectral-domain optical coherence
tomography (SD-OCT) images of the retina. Some studies have used
machine learning to analyse 2D images of the optic nerve head for
glaucoma, including reports of some success with deep learning.
Other indicators of glaucoma which have been analysed with machine
learning include visual fields, detection of disc haemorrhages and
OCT angiography of vasculature of the optic nerve head rim
[0055] The present disclosure uses convoluted neural networks and
machine learning to map the vectors between the vessels and their
branches and between the vessels and the neuroretinal rim. These
vectors are constant and unique for each optic nerve head and
unique for an individual depending on their age. FIGS. 5 and 6
demonstrate results of change in the neuroretinal rim with age by
analyzing change in each segment of the rim. As the optic nerve
head grows, the position of the blood vessels and their angles to
each other changes, and thus their relationship vectors will change
as the relationships to the blood vessels and to the axons change.
The artificial intelligence is also trained with an algorithm to
detect changes in the relationship of the vectors to each other,
and to the neuroretinal rim, so that with that loss of axons, such
as with glaucoma, change will be detected as a change in the
vectors and an indicator of disease progression.
[0056] The computer-implemented method may comprise computer vision
algorithms, using methods such as filtering, thresholding, edge
detection, clustering, circle detection, template matching,
transformation, functional analysis, morphology, etc., and machine
learning (classification/regression, including neural networks and
deep learning) to extract features from the images and classify or
analyse the features for the purposes described herein.
[0057] The algorithms may be configured to clearly identify the
optic disc/nerve head as being most likely to belong to a specific
individual to the highest degree of certainty as a means of
identification of the specific individual for the purposes of
access control, identification, authentication, forensics,
cryptography, security or anti-theft. The method may use features
or characteristics extracted from optic disc/nerve images for
cryptographic purposes, including the generation of encryption
keys. This includes the use of a combination of both optic
discs/nerves of an individual.
[0058] The algorithms may be used to extract features or
characteristics from the optic disc/nerve image for the purposes of
determining the age of a human or animal with the highest degree of
certainty for the purposes of security, forensics, law enforcement,
human-computer interaction or identity certification.
[0059] The algorithms may be designed to analyse changes in the
appearance of the optic nerve disc head/volume attributable to
distortion due to inherent refractive errors in the eyeball under
analysis. The algorithm may be configured to cross reference
inherent changes in size, for example, bigger disc diameter than
normal database, smaller disc diameters than normal database,
tilted disc head.
[0060] The algorithms may include calculation and analyses of ratio
of different diameters/volume slices at different multiple testing
points to each other within the same optic nerve head, and
observing the results in relation to inherent astigmatism and
refractive changes within the eyeball of the specific optic nerve.
Refractive changes can be due to shape of the eyeball, curvature
and power of the intraocular lens and/or curve and power of the
cornea of the examined eyeball.
[0061] The algorithm may include the detection of a change of
artery/vein dimensions as compared with former images of the same
optic nerve head vessels and/or reference images of healthy optic
nerve head blood vessels.
[0062] The algorithm may be used for the purposes of diagnosing
changes in artery or vein width to reflect changes in blood
pressure in the vessels and/or hardening of the vessels.
[0063] The algorithms may be applied to the optic nerve head of
humans, of animals including cows, horses, dogs, cats, sheep,
goats; including uses in agriculture and zoology.
[0064] The algorithms may be used to implement a complete software
system used for the diagnosis and/or management of glaucoma or for
the storage of and encrypted access to private medical records or
related files in medical facilities, or for public, private or
personal use.
[0065] The algorithms may be configured to correlate with changes
in visual evoked potential (VEP) and visual evoked response (VER)
as elicited by stimulation of the optic nerve head before, after or
during imaging of the optic nerve head.
[0066] The algorithms may also model changes in the response of the
retinal receptors to elicit a visual field response/pattern of the
fibres of the optic nerve head within a 10 degree radius of the
macula including the disc head space.
[0067] The algorithms may be adapted to analyse the following:
[0068] 1) Appearance/surface area/pattern/volume of the average
optic disc/nerve head/vasculature for different population groups
and subsets/racial groups, including each group subset with
different size and shaped eyes, including
myopic/hypermetropic/astigmatic/tilted disc sub groups, different
pigment distributions, different artery/vein and branch
distributions, metabolic products/exudates/congenital changes (such
as disc drusen/coloboma/diabetic and hypertensive
exudates/haemorrhages. [0069] 2) Differences in appearance/surface
area/pattern/volume of the optic disc/nerve head/vasculature when
compared to the average in the population. [0070] 3) Differences in
appearance/surface area/pattern/volume of the optic disc/nerve
head/vasculature when compared to previous images/information from
the same patient in the population. [0071] 4) Appearance/surface
area/pattern/volume of the optic nerve head/vasculature anterior
and including the cribriform plate for different population groups
and subsets/racial groups, including each group subset with
different size and shaped eyes, including
myopic/hypermetropic/astigmatic/tilted disc sub groups, including
different pigment distributionism, including different artery/vein
and branch distributions, including metabolic
products/exudates/congenital changes (such as disc
drusen/coloboma/diabetic and hypertensive exudates/haemorrhages.
[0072] 5) Differences in appearance/surface area/pattern/volume of
the optic nerve head/vasculature anterior and including the
cribriform plate for different population groups and subsets/racial
groups, including each group subset with different size and shaped
eyes, including myopic/hypermetropic/astigmatic/tilted disc sub
groups, including different pigment distributions, including
different artery/vein and branch distributions, including metabolic
products/exudates/congenital changes (such as disc
drusen/coloboma/diabetic and hypertensive exudates/haemorrhages
when compared to the average in the population. [0073] 6)
Differences in appearance/surface area/pattern/volume of the optic
nerve head/vasculature anterior and including the cribriform plate
for every different population groups and subsets/racial groups,
including each group subset with different size and shaped eyes,
including myopic/hypermetropic/astigmatic/tilted disc sub groups,
including different pigment distributions, including different
artery/vein and branch distributions, including metabolic
products/exudates/congenital changes (such as disc
drusen/coloboma/diabetic and hypertensive exudates/haemorrhages
when compared to previous images/information from the same patient
in the population. [0074] 7) Classifying the remaining optic nerve
head and associated vasculature and the ten millimetres deep to the
surface, as being normal/abnormal; as being at a high probability
of representing a damaged nerve head, as being a volume which is
abnormal in relation to the position of other factors at the
posterior pole of the fundus, factors/patterns such as distance of
the optic nerve head and/or vasculature and rim to the macula;
distance to the nasal arcade of arteries and veins, distance to the
temporal arcade of veins and arteries. [0075] 8) Describing the
patterns representing the likelihood of the relationship of the
optic nerve outer rim/inner rim/cup/rim pigment/peripapillary
atrophy to the fundus vessels/macula as being abnormal; as having
changed when compared to an image of the same fundus taken at an
earlier time or later time. [0076] 9) Attributing the likelihood of
the measured volume of optic disc/nerve/vasculature visible to the
examiner's eye/camera lens or as measured by OCT/OCT-Angiography as
being diagnostic of glaucoma/at risk for glaucoma (all sub groups
of glaucoma) and all group of progressive optic nerve
disorders/degenerative optic nerve disorders including
neuritis/disseminated sclerosis/; as being evidence of being a
lower or higher nerve head volume when compared to earlier or later
volume or surface area measurements of the same optic nerve head,
or being compared to a database/databases of normal, diseased or
damaged optic nerve head, in every population subset and racial
distribution, particularly Caucasian, Asian, south Pacific and all
African races/descendents. [0077] 10) Attributing the likelihood of
the measured volume/area of optic disc/nerve/vasculature visible to
the examiner's eye/camera lens or as measured by OCT/computer
vision technology, as being evidence of being a lower or higher
nerve head volume when compared to earlier or later volume or
surface area measurements of the same optic nerve head, or being
compared to a database/databases of normal, diseased or damaged
optic nerve head, in every population subset and racial
distribution, particularly Caucasian, Asian, south Pacific and all
African races/descendents, for all age related changes to the optic
nerve/central nervous system, in particular, Alzheimer's disease
and diabetic neuropathy and infective nerve disorders such as
syphilis/malaria/zika viruses. [0078] 11) Clearly identify the
optic disc/nerve head and vasculature as being most likely to
belong to a specific individual to the highest degree of certainty.
[0079] 12) Clearly identify the optic disc/nerve head and
vasculature as being most likely to belong to a specific individual
to the highest degree of certainty as a means of identification of
the specific individual for secure access to any location, virtual
or special/geographic. For example, [0080] a) to replace
fingerprint access to electronic/technology innovations, as in
mobile phones/computers; to replace password/fingerprint/face
photography for secure identification of individuals accessing
banking records/financial online data/services. [0081] b) to
replace fingerprint access to electronic/technology innovations, as
in mobile phones/computers; to replace password/fingerprint/face
photography for secure identification of individuals accessing
Interpol/international/national security systems [0082] c) to
replace fingerprint access to electronic/technology innovations, as
in mobile phones/computers; to replace password/fingerprint/face
photography for secure identification of individuals accessing
health records/information data storage/analysis.
[0083] The present disclosure provides a computer-implemented
method of classifying the optic nerve head, the method comprising
operating one or more processors to: segment an image of an optic
nerve head from a photographic image of an eye; segment the image
of the optic nerve head into multiple segments each containing
blood vessels and neuroretinal rim fibres; extract features from
the segmented images, the features describing relationships between
the blood vessels themselves and between the blood vessels and the
neuroretinal rim fibres in each of the segmented images; identify
characteristics of the optic nerve head based on the extracted
features; and classify the image of the optic nerve head based on
the identified characteristics.
[0084] It will be understood in the context of the present
disclosure that for the purposes of classifying the optic nerve
head, the optic nerve head includes the optic nerve head (optic
disc) itself and the associated vasculature including blood vessels
emanating from the optic nerve head. The optic nerve head also
includes neuroretinal rim fibres located in the neuroretinal rim.
It will also be understood that image segmentation is the process
of dividing or partitioning a digital image into multiple segments
each containing sets of pixels. The goal of segmentation is to
simplify and/or change the representation of an image into
something that is more meaningful and easier to analyse.
[0085] The method involves identification of the region of
interest, that is the optic nerve head and its vasculature. A deep
neural network may be used to segment the image of the optic nerve
head and associated blood vessels. The method uses a Deep Neural
Network for segmentation of the image. As a non-limiting example,
Tensorflow.RTM. from Google Python.RTM. library was used as
follows. Results on a small sample training set had a Sorensen-Dice
coefficient of 75-80%.
[0086] The method includes automatic high-level feature extraction
and classification of the image, for any of the purposes described
herein (identification, age determination, diagnosis of optic nerve
head vessels and/or axonal fibre loss and/or changes) or a second
deep neural network trained to use artificial intelligence to
identify/classify the image, for any of the purposes described
herein (identification, age determination, diagnosis of optic nerve
head vessels and/or axonal fibre loss and/or changes).
[0087] Once the image of the optic nerve head and its vasculature
is segmented from the image of the eye, the optic nerve head image
is further segmented according to the blood vessels within and the
optic nerve head neuroretinal rim fibres. Segmentation of the optic
nerve head image is illustrated in FIG. 7a. Features are extracted
from the segmented images, the features comprising relationships
between the vessels themselves and between the blood vessels and
the neuroretinal rim. The segmenting the image of the optic nerve
head into multiple segments comprises using at least one of machine
learning, deep neural networks, and a trained algorithm to
automatically identify at least one of i) blood vessel patterns and
ii) optic nerve head neuroretinal rim patterns. The relationships
between the vessels themselves and between the blood vessels and
the neuroretinal rim are described using vectors mapped between
points on the blood vessels and the neuroretinal rim in each of the
segmented images.
[0088] At least one of machine learning, deep neural networks and a
trained algorithm may be used to automatically identify the image
of at least one of the i) blood vessel patterns and ii) optic nerve
head neuroretinal rim patterns as specifically belonging to an
individual eye image at that moment in time. The optic nerve head
image may be classified as being likely to be glaucomatous or
healthy. The optic nerve head image may be classified as being
likely to belong to an adult or a child. It may be identified when
the said image changes i.e. develops changes to blood vessel
relationship and/or optic nerve fibre head, or has changed from an
earlier image of the same optic nerve head, such as with disease
progression and/or ageing.
[0089] The method of the present disclosure can map the vessel
relationships and predict the most likely age category of the optic
nerve head being examined based on the set of ratios of vessels and
vessel to rim and the algorithms form the deep learning data base
processing. The neuroretinal rim thickness decreases with age while
the position of the vessels will and vector rim distances will
drift. FIG. 7b illustrates a graph showing loss of neuroretinal rim
according to age. Children's optic nerve heads have a different set
of vector values compared to adults.
[0090] In more detail, the method may comprise, for each segment:
superimposing multiple concentric circles on the segment;
determining intersection points of the circles with blood vessels
and branches thereof and intersection points between the blood
vessels and branches thereof and the neuroretinal rim; mapping
vectors between the intersection points; determining distances of
the vectors; determining ratios of the vector distances; combining
sequences/permutations of the ratios into an image representation;
searching a lookup table for the closest representation to the
image representation; and classifying the optic nerve head
according to the closest representation found.
[0091] Several embodiments of the system are detailed as follows.
In a first embodiment, as illustrated in FIG. 8a, the image is
classified as healthy or at-risk of glaucoma by a dual neural
network architecture.
[0092] 1. A 2D photographic image of an eye may be obtained using a
45 degree fundus camera, a general fundus camera, an assimilated
video image, or a simple smartphone camera attachment, or a printed
processed or screen image of the optic nerve head, or an image or a
photograph of an OCT-A image of an optic nerve head, from either a
non-dilated or dilated eye of a human or any other eye bearing
species with an optic nerve. A first fully convolutional network
may locate the optic nerve head by classifying each pixel in the
image of the eye.
[0093] 2. The fully convolutional network then renders a small
geometric shape (e.g. circle) around the optic nerve head and crops
the image accordingly.
[0094] 3. This resulting image can be fed to a trained second
convolutional neural network, or have manual feature extraction,
which makes a high-level classification of the optic nerve head as
healthy or at risk of glaucoma.
[0095] In a second embodiment as illustrated in FIG. 8b:
[0096] 1. A first fully convolutional network identifies a fixed
area around the vessel branch patterns.
[0097] 2. The image is then cropped accordingly and a variety of
features are extracted from the resulting image including the
vessel to vessel and vessel to nerve fibre ratios.
[0098] 3. The image is classified as adult or child, and/or
including the ability to detect changes with age on the same image
in subsequent tests and therefore identify the age of the optic
nerve head being segmented using artificial intelligence and/or
manual feature extraction.
[0099] FIG. 9 is a flowchart illustrating an image classification
process for biometric identification, according to an embodiment of
the present disclosure. Referring to FIG. 9, the image
classification process according to the present embodiment includes
using an imaging device to capture an image of the eye 110,
segmenting an image of the optic nerve head and its vasculature
from the eye image 120, using feature extraction to segment the
blood vessels 130, superimposing concentric circles on each of the
segmented images 140, for each circle, determining intersection
points of the circle with the blood vessels and neuroretinal rim
150, determining distances between the intersection points 160,
determining proportions of the distances 170, combining
sequences/permutations of the proportions into an image
representation 180, and searching a database or lookup table for
the closest representation as an identity of the optic nerve head
190 and returning the identify of the optic nerve head 200.
[0100] As an experimental non-limiting working example of image
classification, the methodology of the present disclosure is
further described by reference to the following description and the
corresponding results. A data set consisted of 93 optic nerve head
images taken at 45 degrees with a fundus camera (Topcon Medical
Corporation) with uniform lighting conditions. Images were labelled
by ophthalmologists as being healthy or glaucomatous based on
neuroretinal rim assessment. Criteria for labelling were based on
RetinaScreen. Glaucoma was defined as a disc >0.8 mm in diameter
and/or difference in cup-disc ratio of 0.3, followed by
ophthalmologist examination and confirmation. The technique was
first proofed for 92% concordance with full clinical diagnosis of
glaucoma being visual field loss and/or raised intraocular pressure
measurements.
[0101] The first step, pre-processing, involves a fully
convolutional network cropping the image of the eye to a fixed size
around the optic nerve head at the outer neuroretinal rim
(Elschnig's circle). The blood vessels are manually segmented (see
FIG. 7a) into individual blood vessels and branches thereof.
Multiple concentric circles are superimposed on each of the
segmented images and the intersection of a circle with a specific
point on the centre of a blood vessel is extracted, as illustrated
in FIG. 10a and FIG. 10b. FIG. 10a shows one circle of a set of
concentric circles intersecting with the optic nerve head
vasculature. Note the angle between the axes and the vectors
reflects changes in direction of the vessel position, as with
change in neuroretinal rim volume which causes vessels to shift.
FIG. 10b is an image of concentric circles in a 200 pixel.sup.2
segmented image intersecting with blood vessels and vector lines.
FIG. 11 is a concatenation of all blood vessel intersections for a
given set of concentric circles--this is the feature set. This
image is used to match against other feature set images in a
database. The Levenstein distance is used to do the similarity
match. The image with the lowest Levenstein distance is deemed to
be the closest match. A sample feature set is shown in FIG. 12 and
the table in FIG. 14. A summary of intersection points is generated
from the extracted concentric circles from the center of the optic
nerve head in the image of FIG. 12. The white area represents the
blood vessels. For each circle 100 points may be extracted, which
correspond to an area that belongs to a blood vessel (white), and
black relates to intervascular space along the circles. The top
border of the picture corresponds to the circle of radius=1 pixel;
the lower border corresponds to the circle of radius=100 pixels.
FIG. 14 illustrates a table of a sample feature set of resulting
cut-off points in pixels at the intersection of the vessels with
the concentric circles.
[0102] In one example, seven concentric circles may be superimposed
on the segmented image from the centre of the optic nerve head with
respective ratios of 50, 55, 60, 65, 70, 80 and 90 pixels. The
intersection of the circles with the blood vessels is mapped, as
illustrated in the flow diagram of FIG. 9, and summarised as shown
in FIG. 10. The proportions are calculated using machine learning
to classify the extracted sequences and/or permutations of
proportions to 1-nearest neighbour (k-NN). k-NN also known as
K-Nearest Neighbours is a machine learning algorithm that can be
used for clustering, regression and classification. It is based on
an area known as similarity learning. This type of learning maps
objects into high dimensional feature spaces. The similarity is
assessed by determining similarity in these feature spaces (we use
the Levenstein distance. The Levenstein distance is typically used
to measure the similarity between two strings (e.g. gene sequences
comparing AATC to AGTC would have a Levenstein distance of 1). It
is called the edit distance because it refers to the number of
edits that are required to turn one string into another.
[0103] The sequences/permutations of proportions is used as the
sequence of original features for the optic disc image.
[0104] Example of vector of distances=[A,B,C,D,E,F]
[0105] Example of vector of proportions [A/B, B/C, C/D, E/F,
F/A].
[0106] For each picture, the set of nine vectors of proportions
represents its feature set. FIGS. 9 and 11.
[0107] Adversarialism was challenged with a 4 degree twist as
illustrated in FIG. 13. Adversarialism is the result of a small
visually undetectable change in pixels in the image being examined,
which in 50% of cases causes convoluted neuronal network algorithms
to classify the image as a different one (e.g. a missed diagnosis
in a diseased eye). Despite the twist to alter the pixels, the
result was still 100% accurate because the change maintained the
correct vector relationships which establish the unique identity of
the optic nerve fibre head and therefore the reliability of the
invention. Leveinstein distance is used to compare the sequences of
proportions, where the atomic cost of swapping two proportions is
the square value of the difference of the logarithms of the
proportions:
[0108] Atomic cost=(log(a)-log(b)) 2 (the cost of swapping two
proportions of different value) Each insertion of deletion has a
cost of one unit.
[0109] The results are illustrated in FIG. 13. The k-NN algorithm
was trained with all 93 pictures. The algorithm was then used to
identify an image from the set as being the particular labelled
image. 100% of images selected were accurately identified. The
images from the training set were then twisted 4 degrees, to
introduce images separate to the training set. The algorithm was
then challenged to correctly identify the twisted images and
accuracy per labelled image was 100%. Taking the correct and
incorrect classification as a binomial distribution and using the
Clopper-Pearson exact method, it was calculated that with 95%
confidence the accuracy of the system is between 96% and 100%.
[0110] The Clopper-Pearsons exact method uses the following
formula:
( 1 + n - x + 1 xF ( 1 - .alpha. / 2 ; 2 x , 2 ( n - x + 1 ) ) ) -
1 < p < ( 1 + n - x ( x + 1 ) F ( .alpha. / 2 ; 2 ( x + 1 ) ,
2 ( n - x ) ) ) - 1 ##EQU00001##
[0111] where x is the number of successes, n is the number of
trials, and F(c; d1, d2) is the 1-c quantile from an F-distribution
with d1 and d2 degrees of freedom.
[0112] Note, the first part of the equation is the lower range for
the interval and the second then highest, which in this case is
100%.
[0113] Table 2 below summarises research with traditional machine
learning and deep learning in the region of the optic nerve head
and the surrounding retina, emphasizing their differences with the
methodology of the present disclosure. None of the research
identified the relationships within the optic nerve head of the
vessels and axons to each other, nor has any used the relationships
for biometric identification or optic disc age assessment. Some
studies are performed with three dimensional frequency domain
optical coherence tomography (FD-OCT) imaging, which only has
achieved 62% sensitivity in screening tests for glaucoma and 92% in
clinical sets. Others, such as the present disclosure, use 2D
fundus photographs of the retina and optic nerve head. The present
disclosure provides the ability to uniquely identify the optic
nerve head and its vasculature in order to be able to screen for
changes to the optic nerve head and blood vessels with a minimum of
95% specificity and a sensitivity greater than 85% to avoid missing
a blinding preventable condition such as glaucoma. Almost all work
with traditional machine learning and recent deep learning makes a
diagnosis of glaucoma based on a small clinical set commenting only
on the vertical cup disc ratio and in a few, textural analysis.
Data sets have excluded the general population with all the ensuing
morphological and refractive variations, precluding any sensitivity
for screening the general population. As mentioned, none has the
power to 100% identify the optic nerve head, as with the present
disclosure. Identification means the power to state `not the same`
as previous disc identification, i.e., to say the optic nerve head
has changed. Almost all studies prior to the present disclosure
have analysed the optic nerve head for glaucoma disease and not
basic optic nerve head vessels to neuroretinal rim relationship.
Furthermore, they have focused on what is called the cup-disc
ratio, as illustrated in FIG. 3, using segmentation of the disc
outer rim minus the inner cup, as a glaucoma index. However, a
cup-disc ratio is not definitively due to axonal optic nerve fibre
loss and furthermore, the ratio is a summary of the measurement of
a specific radius of a disc which is rarely a perfect circle. It is
also well accepted amongst ophthalmologists that although an
increased optic cup-disc ratio suggests a risk of glaucoma, there
is a high chance of over fitting with a labelled data set from
patients already diagnosed, with an unacceptable chance that
glaucoma can progress with loss of axons without affecting the
cup/disc ratio.
TABLE-US-00002 TABLE 2 Summary of machine learning to detect
glaucoma Jiang liu, 2014 US Patent U.S. Pat. No. Glaucoma Automatic
Small data set 8,705,826 B2 diagnosis Machine No relationship to
ARGALI. learning vessels or unique Cup Disc Ratio identification.
detection(CDR) Huang et al, US Patent Pub. Glaucoma Automatic No
relationship/vessel 2010 20100277691 A1 diagnosis Machine pattern.
3 parts of eye: learning FD-OCT. CDR, macula, CDR peripapillae Zhou
Zhang US Patent Pub. Glaucoma Traditional Disc Haemorrhage only.
2009 201020157820A1 Disc detection Machine No vessel with learning
relationship/pattern/optic Vessel `kink` for nerve head analysis
inner rim Chen X et al, Glaucoma Deep learning No vessel 2015 Outer
disc relationship/pattern margin only segmented Claro et al, Optic
disc Automatic 93% accuracy. Small 2016 segmentation machine data
set, no comment and Textural learning on position of feature
vasculature/relationship extraction to rim. Juan Xu, 2010 US Patent
Glaucoma Automatic Disc margin only, no U.S. Pat. No. 7,992,999B2
SD-OCT learning comment on vasculature. SD-OCT Salam A et al
Feature Hybrid Small data set, extraction and structural restricted
to glaucoma CDR changes and diagnosis only; No combination machine
comment on optic disc learning vasculature Haleem et al, RIFM
Unsupervised Double disc diameter 2016 Fundus machine (retina and
optic nerve photograph learning head Vessel segmented Scanning
Laser plus pixel textural ophthalmoscopy analysis) (SLO) 94%
accuracy, CDR CDR glaucoma used data set Sedai S et al, Glaucoma
Deep learning Small data set (50) 2016 CDR CDR, clinical dx
Fuente-arriaga Glaucoma Machine 93% sensitivity et al 2014 Vascular
learning Only three segment `bundles` analysis of vascular `bundle`
movement Muhammadd Hybrid deep Glaucoma Hybrid using 87.3% best
accuracy for H et al learning (HDLM) diagnosis. CNN on OCT OCT.
2017 on OCT OCT results HDLM 93% on retinal nerve fibre. No
reference to optic nerve head/vessels. Annan et al Deep learning
for Combination of Deep learning effective 2016 glaucoma CDR and
local using CNN features Kanti Roy et al Right vs left eye Deep
learning Small data set, no 2017 analysis of disc Long et al
Segmented Machine No classification made. vessels branch learning
pattern analysed Gulshan et al Diabetic Deep learning No comment on
optic 2017 Retinopathy nerve head/vasculature Niemejjer et al US
Patent Pub. Blood vessel OCT 2012213423 segmentation Solanki et al
US Patent Retinal features Machine Not optic nerve head. U.S. Pat.
No. 9,008,391 learning Not optic nerve head vasculature
relationship and ratios to rim. present disclosure Identification
Deep and 100% identification Age machine specific vessel pattern
determination learning and relationships within Glaucoma the optic
nerve head progression
[0114] There are a number of possible applications of the methods
described herein as follows. One application is to clearly identify
the optic nerve head and its vasculature as being most likely to
belong to a specific individual to the highest degree of certainty.
Here, the second stage of the method is a convolutional neural
network trained on a large dataset of fundus images (cropped by a
fully convolutional network at the first stage to a fixed geometric
shape around the optic nerve head or, in an alternative
configuration, cropped to a fixed area around the optic nerve head
vessel branch patterns) labeled with identities (with multiple
images for each identity) to produce a feature vector describing
high-level features on which optic nerve heads can be compared for
similarity in order to determine identity. The method may use
features or characteristics extracted from optic nerve head images
for cryptographic purposes, including the generation of encryption
keys. This includes the use of a combination of both optic
discs/nerves/vessels of an individual, or as a means of
identification of the specific individual for the purposes of use
as a biometric, use online to allow access to secure online
databases, use with any device to access the device, use with any
device to access another device (for example a car). This may be
done as a means of identification of the specific individual for
secure access to any location, either in cyberspace or through a
local hardware device receiving the image of the individual's optic
nerve head directly. For example, to replace or be used in
combination with other biometric devices, such as
fingerprint/retina scan/iris scan in order to access electronic
devices such as mobile phones or computers.
[0115] Another application can be to determine the age of a human
or animal with the highest degree of certainty for the purposes of
security, forensics, law enforcement, human-computer interaction or
identity certification. Here, the second stage of the method is a
convolutional neural network trained on a large dataset of fundus
images (cropped by a fully convolutional network at the first stage
to a fixed geometric shape around the optic nerve head or, in an
alternative configuration, cropped to a fixed area around the optic
nerve head vessel branch patterns) labelled for age which can take
a new fundus image and classify the age of the individual.
[0116] In addition to humans, the algorithms may be applied to the
optic nerve head of animals/species including cows, horses, dogs,
cats, sheep, goats; including uses in agriculture and zoology. The
algorithms may be used to implement a complete software system used
for the diagnosis and/or management of glaucoma or for the storage
of and encrypted access to private medical records or related files
in medical facilities, or for public, private or personal use.
[0117] The methodology of the present disclosure may be used to
detect changes as the neuroretinal rim area reduces with age. This
will have an important role in cybersecurity and the prevention of
cyber-crimes relating to impersonation and/or inappropriate access
to the internet to/by children.
[0118] FIGS. 15a to 15c illustrate a summary of optic nerve head
classification processes according to embodiments of the present
disclosure. Referring to FIG. 15a, a first process includes
capturing an image of the optic nerve head using an imaging device
810a, determining or authenticating the user 820a, classifying the
optic nerve head using a two-stage algorithm as described above
830a, and classifying the optic nerve head as healthy or at-risk
840a. Referring to FIG. 15b, a second process includes capturing an
image of the optic nerve head of a user using an imaging device
810b, extracting a region of interest using a two-stage algorithm
as described above 820b and, and estimating the age of the user
830b. Referring to FIG. 15c, a third process includes capturing an
image of the optic nerve head of a user using an imaging device
810c, extracting a region of interest using a two-stage algorithm
as described above 820c and, and granting or denying the user
access to a system 830c.
[0119] FIG. 16 is a flowchart illustrating a computer-implemented
method 1000 of classifying the optic nerve head, according to an
embodiment of the present disclosure. Referring to FIG. 16, the
method comprises operating one or more processors to: segment an
image of an optic nerve head from a photographic image of an eye
1010; segment the image of the optic nerve head into multiple
segments each containing blood vessels and neuroretinal rim fibres
1020; extract features from the segmented images, the features
describing relationships between the blood vessels themselves and
between the blood vessels and the neuroretinal rim fibres in each
of the segmented images 1030; identify characteristics of the optic
nerve head based on the extracted features 1040; and classify the
image of the optic nerve head based on the identified
characteristics 1050.
[0120] FIG. 17 is a block diagram illustrating a configuration of a
computing device 900 which includes various hardware and software
components that function to perform the imaging and classification
processes according to the present disclosure. Referring to FIG.
16, the computing device 900 comprises a user interface 910, a
processor 920 in communication with a memory 950, and a
communication interface 930. The processor 920 functions to execute
software instructions that can be loaded and stored in the memory
950. The processor 920 may include a number of processors, a
multi-processor core, or some other type of processor, depending on
the particular implementation. The memory 950 may be accessible by
the processor 920, thereby enabling the processor 920 to receive
and execute instructions stored on the memory 950. The memory 950
may be, for example, a random access memory (RAM) or any other
suitable volatile or non-volatile computer readable storage medium.
In addition, the memory 950 may be fixed or removable and may
contain one or more components or devices such as a hard drive, a
flash memory, a rewritable optical disk, a rewritable magnetic
tape, or some combination of the above.
[0121] One or more software modules 960 may be encoded in the
memory 950. The software modules 960 may comprise one or more
software programs or applications having computer program code or a
set of instructions configured to be executed by the processor 920.
Such computer program code or instructions for carrying out
operations for aspects of the systems and methods disclosed herein
may be written in any combination of one or more programming
languages.
[0122] The software modules 960 may include at least a first
application 961 and a second application 962 configured to be
executed by the processor 920. During execution of the software
modules 960, the processor 920 configures the computing device 900
to perform various operations relating to the embodiments of the
present disclosure, as has been described above.
[0123] Other information and/or data relevant to the operation of
the present systems and methods, such as a database 970, may also
be stored on the memory 950. The database 970 may contain and/or
maintain various data items and elements that are utilized
throughout the various operations of the system described above. It
should be noted that although the database 970 is depicted as being
configured locally to the computing device 900, in certain
implementations the database 970 and/or various other data elements
stored therein may be located remotely. Such elements may be
located on a remote device or server--not shown, and connected to
the computing device 900 through a network in a manner known to
those skilled in the art, in order to be loaded into a processor
and executed.
[0124] Further, the program code of the software modules 960 and
one or more computer readable storage devices (such as the memory
950) form a computer program product that may be manufactured
and/or distributed in accordance with the present disclosure, as is
known to those of skill in the art.
[0125] The communication interface 940 is also operatively
connected to the processor 920 and may be any interface that
enables communication between the computing device 900 and other
devices, machines and/or elements. The communication interface 940
is configured for transmitting and/or receiving data. For example,
the communication interface 940 may include but is not limited to a
Bluetooth, or cellular transceiver, a satellite communication
transmitter/receiver, an optical port and/or any other such,
interfaces for wirelessly connecting the computing device 900 to
the other devices.
[0126] The user interface 910 is also operatively connected to the
processor 920. The user interface may comprise one or more input
device(s) such as switch(es), button(s), key(s), and a
touchscreen.
[0127] The user interface 910 functions to facilitate the capture
of commands from the user such as an on-off commands or settings
related to operation of the system described above. The user
interface 910 may function to issue remote instantaneous
instructions on images received via a non-local image capture
mechanism.
[0128] A display 912 may also be operatively connected to the
processor 920. The display 912 may include a screen or any other
such presentation device that enables the user to view various
options, parameters, and results. The display 912 may be a digital
display such as an LED display. The user interface 910 and the
display 912 may be integrated into a touch screen display.
[0129] The operation of the computing device 900 and the various
elements and components described above will be understood by those
skilled in the art with reference to the method and system
according to the present disclosure.
[0130] The words comprises/comprising when used in this
specification are to specify the presence of stated features,
integers, steps or components but does not preclude the presence or
addition of one or more other features, integers, steps, components
or groups thereof.
* * * * *