Intelligent Detection of Glaucoma Using Ballistic Optical Imaging

Advanced Engineering Informatics 40 (2019) 107-129
© 2019 The Authors. Published by Elsevier Ltd. 107
Intelligent Detection of Glaucoma Using Ballistic
Optical Imaging
P.S.Jagadeesh Kumar1
, Xianpei Li2
Thomas Binford3
, Yanmin Yuan4
Wenli Hu5
, Yang Yung6
, Mingmin Pan7
, J.Ruby8
1, 2, 3
School of Engineering,
Stanford University, California, United States.
4
Department of Bioengineering,
Harvard University, Cambridge, United States.
5, 6, 7
Biomedical Engineering Research Centre,
Nanyang Technological University, Singapore.
8
Division of Medical Sciences,
University of Oxford, Oxford, United Kingdom.
Abstract:
Immense imaging strategies has been adept in sprouting investigative supports
for glaucoma discovery. Ballistic optical imaging such as optical coherence
tomography have been inspected for estimating the key constraints for
glaucoma estimation such as disc area, cup area, cup volume, disc and rim area,
cup to disc ratio and thickness of the retinal nerve fiber layer. This paper
presents a scheme for glaucoma detection by ballistic optical imaging and
machine intelligence. Indubitably, support vector machine constructed on
adaptive learning algorithm propping correlation clustering has appropriate
practicality in distinguishing glaucoma. Support vector clustering exemplifies
to be analogous in inspecting glaucoma using the cluster connotation through
kernel density estimation of the likelihood distribution. The projected
procedure has a success rate of 97.25% over the dataset restrained on 600 and
300 optical coherence tomography images of glaucomatous and non-
glaucomatous retina correspondingly. Therefore, the efficacy of glaucoma
detection grounded on machine intelligence and ballistic optical imaging has
extensive recitation in glaucoma medication.
Keywords:
ballistic optical imaging, optical coherence tomography, machine intelligence,
support vector clustering, glaucoma
Reference to this paper should be made as follows: Kumar, P.S.J., Li, X.,
Binford, T., Yuan, Y., Hu, W., Yung, Y., Pan, M., and Ruby, J. (2019)
“Intelligent Detection of Glaucoma Using Ballistic Optical Imaging”,
Advanced Engineering Informatics 40 (2019) 107-129.

PSJ Kumar et al.
Advanced Engineering Informatics 40 (2019) 107-129 108
Biographical notes: P.S.Jagadeesh Kumar is working for the Department of
Computer Science, School of Engineering, Stanford University, California,
United States. He received his BE in EEE from the University of Madras in
1999. He obtained his MBA in HR from the University of Strathclyde,
Glasgow, and the UK in 2002. He obtained his ME in 2004 with specialisation
in CSE from the Annamalai University, Chidambaram, India. He achieved his
MS in Computer Engineering from the New Jersey Institute of Technology,
Newark, and the USA in 2006 and his first Doctorate from the University of
Cambridge, the United Kingdom in 2013. He received his second doctorate
from Harvard University, Cambridge, United States in April 2019 for his
contribution in the project titled "Deep Robotics and Human Brain Simulation
using Neural Networks".
Xianpei Li received her doctorate, master and bachelor‟s degree from Stanford
University, California, United States. From July 2005 onwards, she serves
Stanford University at various portfolio. Currently, she works at the Institute
for Computational and Mathematical Engineering, Stanford University. Her
current project includes "An Introduction to Crooklet Transforms for Medical
Image Processing".
Thomas Binford received a Ph.D. in 1965 from the University of Wisconsin
Madison, under the supervision of Myron L. Good; his thesis was entitled
"Angular Distribution and Polarization of Neutral Hyperons Produced in
Association with Neutral Kaons". He was a Fulbright Scholar at the Tata
Institute of Fundamental Research in Mumbai, India from 1965 to 1966, and a
research scientist at the MIT Artificial Intelligence Laboratory from 1966 to
1970. From 1970 to 2000 he was a professor of computer science at Stanford
University; in 2000 he retired to become an emeritus professor. While at
Stanford, Professor Binford supervised more than 40 PhD theses while leading
research in computer vision, artificial intelligence, medical image processing,
radar image understanding, and robotics.
Yanmin Yaun is working as Professor in the Department of Bioengineering,
Harvard University, and Cambridge, United States from 2016. She completed
her Ph.D. from Tokyo University in the year 2009 and served Nanyang
Technological University, Singapore from 2011 to 2015. She is at present
betrothed with the “Deep Robotics and Human Brain” project funded by
Harvard University, Cambridge, United States. Her major research interest
includes Bioengineering, Biomedical Engineering, Medical Engineering,
Robotics and Computational Neuroscience. She is one of the renowned
scientists in the field of Biomedical Engineering and Bioengineering.
Wenli Hu is Professor in Biomedical Engineering Research Centre, Nanyang
Technological University, Singapore. He has 15 plus years of experience in
academics and research. He received his doctoral degree from Beijing
University, China in 2012. He is associated with Nanyang Technological
University since 2012 in different cadre. He has published more than 100
publications in reputed journals and conferences. His foremost research
interest includes Biomedical Engineering, Medical Engineering, Neuroscience
and Ophthalmology. He received the young scientist award from Perth
University, Australia for his impact in the field of medicine.

Intelligent Detection of Glaucoma Using Ballistic Optical Imaging
109
Yang Yung is currently working as a Professor in Biomedical Engineering
Research Centre (BMERC), Nanyang Technological University, Singapore. He
has 20 plus years of experience in research and development. He completed his
Bachelor‟s degree in Biomedical Engineering from the University of Malaya,
Malaysia in 1989. He obtained his Master‟s in Biomedical Engineering from
Monash University Australia in 1996. He attained his first Doctorate from
Monash University Malaysia in Biomedical Engineering in 2006.
Mingmin Pan is currently working as Associate Professor in Biomedical
Engineering Research Centre (BMERC), Nanyang Technological University,
Singapore. She received her Bachelor‟s degree in Biomedical Engineering from
the University of Malaya, Malaysia in 2003. She obtained her Master‟s in
Biomedical Engineering from University of Malaya, Malaysia in 2007. In
2015, she achieved her Doctorate from University of Malaya, Malaysia in
Biomedical Engineering.
J.Ruby is a Medical and Surgical Robot researcher at the University of Oxford,
United Kingdom. She completed her post-graduation in Medical-Surgical and
Health Care from the University of Cambridge, United Kingdom. She
completed her undergraduate in Nursing Education and Health Sciences from
the University of Oxford, United Kingdom.
1 Introduction
Retinal photography is recycled for recovering credentials, monitoring and refining the
patients on the eye conditions. Digital retinal photography offers an enduring record of
the eye strength and to denote in future. Retinal photography aids the patients to visualize
and better comprehend the eye disorder. Certain eye situations that may necessitate
retinal photography consist of diabetes, high blood pressure, age-related macular
disintegration, glaucoma, optic nerve complaint, retinal deteriorations, retinal lacerations
and retinal impartialities (Qi Wang, Sinisa D et al., 2011). To apprehend these images,
the modern digital retinal imaging expertise were exploited. The retinal imagining
scheme such as ballistic optical imaging affords enhanced image quality, as the
paraphernalia customs an avantgarde 3-D eye tracking arrangement. Preferably, ballistic
imaging is grounded on unscattered or individually backscattered ballistic photons.
Nevertheless, more-scattered quasiballistic photons are frequently restrained as well to
intensify the signal strength. For concision, successive practice of the term ballistic
photons also denotes to quasiballistic photons. Ballistic imaging delivers high spatial
tenacity but struggles from restricted imaging perspicacity. The purpose of ballistic
imaging is to discard nonballistic photons and to recollect ballistic photons.
Communicated ballistic photons take littler paths and reach at the detector earlier than
nonballistic photons do, which in turn termed as Time of Flight. Time-gated imaging and
coherence-gated holographic imaging are constructed on this transformation
(P.S.Jagadeesh Kumar, J.Ruby, J.Lepika, J.Tisa, J.Nedumaan, 2014). Communicated
ballistic light has better-quality collimation (lesser deviation) than nonballistic light.
Spatial-frequency filtered imaging in addition to optical heterodyne imaging are
constructed on this variance. Ballistic light recollects the incident polarization in a
nonbirefringent sprinkling better than nonballistic light does. Polarization difference

PSJ Kumar et al.
imaging is grounded on this difference. Ballistic light embraces a well-defined wavefront
than nonballistic light and hereafter can have improved attentive. These possessions of
ballistic imaging organization permit the optometric subordinates to speedily capture
vivacious retinal photographs on almost all patient. Retinal photography is surrounded
once a year if the optometrist considers it therapeutically essential for the citations or
monitoring of an eye disorder. Additional retinal photography is obligatory, or if one
wishes to enhance retinal photography to the monotonous eye test for improved
realization of the eye fitness (Apeksha R. Padaria, Bhai lal Limbasiya, 2015). A dilated
retinal exam is a test, the optometrist may accomplish to examine the eyes for indications
of eye disease and additional vision problems. This innocuous, effortless test consents the
optometrist to better inspect the structures of the eye together with the lens, retina, optic
nerve and blood vessels (Imran Qureshi, 2015). It might be accomplished on patients of
any age on a systematic basis. This test is done by placing eye drops to each eye, which
relax the iris muscle instigating it to upsurge or widen in size. The suppositories used also
reduces the focusing muscle in the eye which may momentarily blur the vision and make
lighter sensitive for about 4-6 hours. A dilated retinal exam is constantly achieved in the
case of shortened vision or abrupt vicissitudes in the vision. It is also accomplished
annually on patients with diabetes, glaucoma, hormonal macular deterioration, retinal
disintegrations such as high shortsightedness or previous retinal tears and detachments,
cataracts, patients with high blood pressure. A dilated retinal exam may also be achieved
if the optometrist adverts any vision difficulties that necessitate further inspection. It is
frequently accomplished in concoction with digital retinal photography as shown in Fig.
1 and optical coherence tomography (OCT) retinal imaging to support in the analysis and
nursing of eye conditions.
Fig. 1 Retinal Photography.
Support vector machines (SVM) are supervised learning facsimile with related
learning algorithms that examine information employed for taxonomy and scrutiny.
Provided a set of training instances, each apparent for fitting into one of two groups, an
SVM training algorithm fabricates a representation that consigns novel exemplars into
one group or the further, constructing a binary linear classifier (P.S.Jagadeesh Kumar,
2016). SVM is a depiction of the paradigm as summits in space, plotted so that the
demonstration of the disconnect groups are alienated by an obvious fissure that is as
spacious as probable. Novel instances are then recorded into that identical space and

111
envisaged to fit into a group pedestal on which plane of the fissure they descend on. In
adding up to accomplishing linear taxonomy, SVM can shrewdly realize a non-linear
taxonomy making use of what is portrayed as kernel trick, unreservedly recording their
inputs into multi-dimensional characteristic spaces (J. Liu, D.W.K. Wong, J.H. Lim et al.,
2008). When statistics are not recognized, supervised learning is not feasible, and an
unsupervised learning methodology is obligatory, which endeavours to locate innate
clustering of the information to groups, and then plot novel information to these
fashioned groups (P.S.Jagadeesh Kumar, J.Ruby, Yang Yung, Mingmin Pan, Wenli Hu,
2018). The clustering algorithm which affords an enhancement to the SVM is called
support vector clustering and is frequently employed in engineering relevance when
information is not familiar or when only certain information is recognized as a pre-
processing for a taxonomy pass. This article represents the superior adaptation of the
taxonomy scheme for sustaining glaucoma identification. Glaucoma is an anthology of
retinal anarchy which produces damage to the optic nerve and results in sightlessness
(Apeksha Avinash, K. Magesh, C. Vinoth Kumar, 2016). The dialect "glaucoma" is from
Greek glaukos which refers sapphire, emerald, or hoary.
2 Glaucoma
Glaucoma is a collection of eye ailment which effect in harm to the optic nerve and loss
of vision. The widespread kind is open-angle glaucoma through fewer common kinds as
well as closed-angle glaucoma and normal-tension glaucoma. Open-angle glaucoma
extends gradually with time and there is no twinge (Archana Nandibewoor et al., 2013).
Side vision may embark on to diminish pursued by innermost vision ensuing in loss of
vision if not diagnosed appropriately. Closed-angle glaucoma can present slowly or
abruptly. The rapid staging might engage rigorous eye twinge, imprecise vision, mid-
dilated pupil, reddishness of the nausea. Loss of vision due to glaucoma, once has
emerged, is everlasting. Risk factor for glaucoma embraces augmented pressure in the
eye, a family evidence of the fact, migraines, blood pressure, and corpulence. In support
of eye pressure, a significance of larger than 21 mmHg is frequently employed, with
constrained pressure leading to a superior risk. However, a small number might comprise
high eye pressure for long time and by no means makes bigger harm. On the other hand,
optic nerve injury might arise with normal pressure too, accredited as normal-tension
glaucoma (Qi Wang, Sinisa D, 2011). The open-angle glaucoma is supposed to be
sluggish egress of aqueous humor from side to side the trabecular meshwork whereas in
closed-angle glaucoma the iris obstructs the trabecular meshwork. Exploration is by a
long-drawn-out eye assessment. Often the optic nerve demonstrates an attribute
recognized as cupping. If treated near the beginning it is likely to premeditated or put a
stop to the evolution of ailment with medicine, laser healing, or surgical procedure
(Apeksha R. Padaria, Bhai lal Limbasiya, 2015). The objective of these healings is to
reduce eye pressure. A large variety of dissimilar modules of glaucoma medication are
accessible. Laser healing may be effectual in both open-angle and closed-angle
glaucoma. Huge categories of glaucoma surgeries might be employed in people who do
not retort passably to further procedures. Ruling of closed-angle glaucoma is a
therapeutic crisis. Concerning 69 to 122 million populaces have glaucoma worldwide.
The disease influences regarding 30 million citizens in India. It emerges more frequently

PSJ Kumar et al.
among elder citizens. Closed-angle glaucoma is further rampant in women (Archana
Nandibewoor et al., 2013).
Glaucoma is called the "quiet burglar of vision" since the loss of vision typically
happens gradually above an extended epoch of time. Globally, glaucoma is the second-
leading reason for the loss of vision following cataracts. Examining for glaucoma is
typically carried out as fraction of a customary eye screening executed by optometrists
and ophthalmologists. Checking for glaucoma ought to comprise dimensions of the
tonometry, gonioscopy, and assessment of the optic nerve to seem for any perceptible
harm to it, or modify in the cup-to-disc ratio and as well rim facade and vascular
alteration. A ceremonial visual field test is supposed to be attained (Imran Qureshi,
2015). The retinal nerve fiber coating can be appraised with imaging methods such as
optical coherence tomography, polarimetry, and ophthalmoscopy. Due to the compassion
of all techniques of tonometry to corneal width, schemes such as Goldmann tonometry
must be amplified with pachymetry to evaluate Central Corneal Thickness (CCT). A
thicker-than-average cornea can affect in a pressure interpretation upper than the factual
pressure, while a thinner-than-average cornea can create a pressure reading inferior than
the factual pressure. Since pressure computing error can be originated by additional than
CCT i.e., corneal hydration, elastic properties, etc., it is impractical to regulate pressure
computation supported only on CCT assessments (P.S.Jagadeesh Kumar, J.Ruby,
J.Lepika, J.Tisa, J.Nedumaan, 2014). The frequency doubling illusion can also be
exercised to categorize glaucoma with the exploit of a frequency doubling expertise.
Inspection for glaucoma may also be appraised with further consideration specified to
gender, pursuit, and narration of drug employed, refraction, bequest and ancestors‟
healthiness record. Absolute glaucoma is the final arena of all category of glaucoma. The
eye has rebuff to visualization, nonexistence of pupillary light reflex and pupillary retort,
and has a pebbly manifestation. Brutal throbbing is subsisting in the eye (J. Liu, D.W.K.
Wong, J.H. Lim et al., 2008). The therapeutic of absolute glaucoma is a weary practice
like cyclocryoapplication, cyclophotocoagulation, before immunization of 99% alcohol.
Glaucoma is a parasol expression for eye circumstances which ruin the optic nerve, and
be capable of escorting to blindness. The foremost reason of injury to the optic nerve is
Intraocular Pressure (IOP), disproportionate fluid pressure inside the eye, which might be
owing to a variety of rationale together with obstruction of drainage canals, and tapering
or finality of the slant amid the iris and cornea. The principal dissection in classifying
dissimilar kinds of glaucoma is open-angle and closed angle glaucoma (Dimitrios Bizios,
Anders Heij et al., 2010). The open angle relates to the angle where the iris congregates
the cornea which likely to be as broad and unbolt as it is supposed to be, permitting the
fluid from within the eye to deplete, thus mitigating the interior pressure as illustrated in
Fig. 2. When this angle is lessened or clogged, pressure can upsurge, and ultimately harm
the optic nerve causing loss of vision. Chronic glaucoma relates to deliberate bottleneck
of the drainage canals ensuing in augmented eye pressure which reasons optic nerve
break (Ganesh Babu T. R, R. Sathish Kumar, Rengaraj Venkatesh, 2014). This apparent
as a plodding loss of the visual field, preliminary with a loss of peripheral vision, but
eventually the complete vision will be mislaid if not diagnosed. This is the very frequent
kind of glaucoma, related for 70% of cases in India. Inception is sluggish and simple, and
loss of vision is steady and irreparable.

113
Fig. 2 Normal Eye and Glaucoma Eye.
Narrow angle glaucoma the iris distorts onward, tapering the angle that saps the eye,
rising pressure inside the eye. If not diagnosed, it can guide to the medical disaster of
angle closure glaucoma. In angle closure glaucoma, the iris distorts onward and grounds
corporeal contact among the iris and trabecular meshwork, which in order wedges
depletion of the aqueous humor from inside the eye (P.S.Jagadeesh Kumar, J.Ruby,
J.Lepika, J.Tisa, J.Nedumaan, 2014). This contact might intermittently impair the
strenuous function of the meshwork in anticipation of its short fall to maintain rapidity
with aqueous construction, and the intraocular pressure increases. Beginning of warning
sign is impulsive, and roots throbbing and further indications that are obvious, and is
delighted as a medical crisis. Nothing like open-angle glaucoma, angle-closure glaucoma
is an outcome of the angle among the iris and cornea closing (Ganesh Babu T. R, R.
Sathish Kumar, Rengaraj Venkatesh, 2014). These have a propensity to happen in the far-
sighted, which have slighter than usual frontal cavity, building the corporal contact
further probable. Normal Tension Glaucoma (NTG) is a situation where the optic nerve is
injured though Intraocular Pressure (IOP) is in standard choice (12-22mm Hg). At
privileged jeopardy are those with ancestor‟s record of NTG, those of Indian origin, and
those with account of heart disease. The reason of NTG is anonymous. Secondary
glaucoma relates to some crate in which an added syndrome, distress, medicine or
practice grounds augmented eye pressure, following in optic nerve harm and vision loss,
and might be placid or brutal. It can be owing to eye damage, exasperation, a
protuberance, or sophisticated cases of diabetes. It can also be grounded by convinced
drugs like steroids. Healing is likely to be open-angle otherwise angle-closure glaucoma
(Syed SR. Abidi, Paul Het al., 2007). In pseudoexfoliation glaucoma (PEX) the pressure
is appropriate to the accretion of infinitesimal grainy protein filaments, which can chunk
usual drainage of the aqueous humor. PEX is customary in those exceeding 70, and
further in women. Pigmentary glaucoma is rooted by pigment cells bogging off from the
flipside of the iris and hovering roughly in the aqueous humor. Eventually, these pigment
cells know how to hoard in the frontal cavity in such a means that it can commence to
obstruct the trabecular meshwork (Ganesh Babu T. R, R. Sathish Kumar, Rengaraj
Venkatesh, 2014). An unusual circumstance, it transpires typically between Caucasians,
often males in their middle 19s to 37s, mainly shortsighted. Primary juvenile glaucoma is
a neonate or infantile aberration where optical hypertension is perceptible at confinement

PSJ Kumar et al.
or soon after that and is originated by anomalies in the frontal cavity angle expansion that
wedge depletion of the aqueous humor (K.Narasimhan, K.Vijayarekha, 2011). Uveitic
Glaucoma is owed to uveitis, the engorgement and irritation of the uvea, the core stratum
of the eye. The uvea offers most of the blood contribution to the retina. Amplified eye
pressure in uveitis can affect from the soreness by itself or commencing from the steroids
exercised to cure it.
3 Ballistic Optical Imaging
Ballistic photons are the light photons that are portable over a sprinkling medium in a
straight line. They are also identified as ballistic light. If laser throbs are directed through
a turbid medium such as mist or body tissue, utmost photons are either arbitrarily
sprinkled or engrossed. Though, athwart short distances, a few photons pass through the
sprinkling medium in straight lines (Apeksha Avinash, K. Magesh, C. Vinoth Kumar,
2016). These coherent photons are denoted as ballistic photons. Photons that are slightly
scattered, recollecting some grade of coherence, are denoted as snake photons. If
proficiently perceived, there are numerous applications for ballistic photons particularly
in coherent high resolution medical imaging systems (Dimitrios Bizios, Anders Heij et
al., 2010). Ballistic scanners by means of ultrafast time gates and optical coherence
tomography (OCT) consuming the interferometry principle are two of the prevalent
imaging systems that bank on ballistic photon detection to generate diffraction-limited
images (N.B.Prakash, D.Selvathi, 2017). Benefits over other prevailing imaging
modalities such as ultrasounds and magnetic-resonance imaging is that ballistic imaging
can accomplish a sophisticated resolution in the order of 1 to 10 micro-meters, though it
agonizes from limited imaging depth. Additionally, more dispersed 'quasi-ballistic'
photons are frequently restrained as well to upsurge the signal strength i.e., signal-to-
noise ratio. Owed to the exponential decrease with reverence to distance of ballistic
photons in a sprinkling medium, frequently image processing methods are functional to
the raw captured ballistic images, to rebuild high quality ones (Zhang Z, Khow CK, Liu
J, Cheung YLC, Aung T, et al. (2012). The aim of ballistic imaging modalities is to
throwaway non-ballistic photons and to recollect ballistic photons carrying suitable data.
OCT is a non-invasive trial that permits the optometrist to yield high resolution scans
of retina, optic nerve, cornea, and iris (N.B.Prakash, D.Selvathi, 2017). Fig. 3 shows the
determination of the retinal layers through OCT. With a cross section of a retina being
not more than 0.5mm thickness, OCT might scan down to 4 microns or 0.004mm. This
high resolution scan affords the optometrist with an exhaustive reading of the eye
structures, and better understanding of the eye health. Fig. 4 demonstrates the thickness
measurement of Retinal Nerve Fiber Head (RNFL) over OCT. Though the images bent
by an OCT look like x-rays, they are not. Fig. 5 signifies the retinal rim thickness
measurement using OCT. Optical coherence tomography imaging is innocuous and can
be regularly done on patients of all age group (Malaya Kumar Nath, Samarendra
Dandapat, 2012). The OCT images are fashioned by means of eye safe infrared light,
which sources no harm to the imaged tissue. OCT imaging is a crucial advance in the eye
care industry. OCT delivers superior intuition into numerous eye diseases such as:
glaucoma, age-related macular degeneration, retinal disorders, diabetic retinopathy, and
numerous other eye conditions (Zhang Z, Khow CK, Liu J, Cheung YLC et al., (2012).

115
Fig. 6 shows the comparison of right and left eye for glaucoma progression employing
OCT. This tool is the gold standard for nursing the retinal health of patients on
suppositories such as Plaquenil (Hydroxychloroquine), a general medication employed to
treat malaria and arthritis. For patients with a family history of glaucoma, OCT imaging
is used to identify early signs or delicate changes in optic nerve health. This results in
earlier treatment options or peace of mind knowing things are stable (Malaya Kumar
Nath, Samarendra Dandapat, 2012). Fig. 7 displays Optic Nerve Head (ONH)/Ganglion
Cell Complex (GCC) summary report with OCT. Visual field testing permits the
optometrists to study the optical trail between the eyes and the visual cortex of the brain.
This modest but effective eye test can pick up delicate vicissitudes in peripheral field loss
that may otherwise go unnoticed. Visual field testing is enormously clinically appreciated
as it consents the optometrist to better analysis, document, and monitor the eye conditions
(Syed SR. Abidi, Paul Het al., 2007). Certain eye conditions that require visual field
testing include glaucoma, optic nerve disease, retinal degenerations, retinal lesions,
retinal detachments, unexplained vision loss, thyroid eye disease, tumors, and strokes
secondary to heart disease (K.Narasimhan, K.Vijayarekha, 2011). This inspection is often
combined with OCT retinal imaging and retinal photography to get a comprehensive
summary of the health of the optic nerve.
Fig. 3 Retinal layers through OCT.

PSJ Kumar et al.
Fig. 4 Thickness measurement of RNFL over OCT.
Fig. 5 Neuro-retinal Rim thickness measurement using OCT.

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Fig. 6 Comparing right and left eye for glaucoma progression employing OCT.
Fig. 7 Optic Nerve Head (ONH)/Ganglion Cell Complex (GCC) summary report with OCT.

PSJ Kumar et al.
4 Support Vector Clustering Procedure
A support vector depiction of a data set is applied as the source of the Support Vector
Clustering (SVC) procedure. Consider { } ⊆ be a data set of N summits, by means of
⊆ I d, the data space (P.S.Jagadeesh Kumar, J.Ruby, 2016). By a nonlinear
transformation Φ from to various high dimensional characteristic spaces, the nominal
surrounding sphere of radius is looked for. This is portrayed by the constrictions:
‖ ( ) ‖ (1)
where || · || is the Euclidean standard and is the middle of the sphere. Soft constrictions
are integrated by totaling relaxed variables : (K. Stapor, 2006)
‖ ( ) ‖ (2)
through ≥ 0. To explain this predicament, Lagrangian is established as
∑ ‖ ( ) ‖ ∑ + C∑ (3)
where ≥ 0 and ≥ 0 be Lagrange multipliers, C is an invariable, and C is a
consequence term. Placing to zero the plagiaristic of L with deference to , and , in
that order (G. Yang, Y. Ren et al., 2010), guides to
∑ (4)
∑ ( ) (5)
(6)
The complementarily situations of (S. Wang, Z. Li et al., 2012) effects in
(7)
‖ ( ) ‖ (8)
It trails from Equation (3) that the image of a point with > 0 and > 0 deceits
external to the feature-space sphere. Equation (4) affirms that such a position has = 0,
hence it can be concluded from Equation (6) that = C. This will be termed as a
Bounded Support Vector or BSV (K. Stapor, 2006). A point with = 0 is plotted
within or to the facade of the characteristic space sphere. If its 0 < < C in that case,
Equation (8) entails that its representation ( ) pretenses on the peripheral of the
characteristic space sphere. Such a position will be related to as a support vector or SV
(S. Lahmiri, C. S. Gargour et al., 2012). SVs recline on cluster restrictions, BSVs lounge
exterior the precincts, and all further positions stretch out within them. Note that however
C ≥ 1 no BSVs subsist because of the constrictions (P.S.Jagadeesh Kumar, Krishna

119
Moorthy, 2013). Through these relations the variables , and may be eliminated,
turning the Lagrangian into the Wolfe dual structure that is a utility of the variables :
∑ ( ) ∑ ( ) (9)
because the variables do not materialize in the Lagrangian they may be reinstated with
the constrictions:
(10)
The SV technique is pursued and symbolizes the dot products ( ) by a
pertinent Mercer kernel ( ). All the way through this manuscript, Gaussian kernel
is employed.
( ) ‖ ‖
(11)
Through width parameter q, as noted in (G. Yang, Y. Ren et al., 2010), polynomial
kernels do not yield taut form pictures of a cluster. The Lagrangian is at present given
by:
∑ ( ) ∑ ( ) (12)
At every position is defined as the aloofness of its depiction in characteristic space
from the middle of the sphere:
‖ ‖ (13)
In observation of Equation (12) and the implication of the kernel is rewritten as:
∑ ( ) ∑ ( ) (14)
The radius of the sphere is:
{ | } (15)
The outlines that enfold the positions in data space are definite by the set
{ | } (16)
They are construed as forming cluster restrictions. In sight of Equation (14), SVs recline
on cluster restrictions, BSVs are exterior, and every further summit lounge within the
clusters (P.S.Jagadeesh Kumar, J.Ruby, J.Nedumaan, J.Tisa, J.Lepika, 2017).

PSJ Kumar et al.
5 Cluster Overlapping System
Cluster overlapping system might be functional in cases where clusters strappingly
overlap; on the other hand, a diverse elucidation of the corollary is compulsory
(Srinivasan C, Suneel Dubey, Ganesh Babu T.R, 2015). In this manuscript is anticipated
to exercise in such a case, a high BSV system, and understand the sphere in characteristic
space as in lieu of cluster interiors, moderate than the shroud of all data. Note that
equation (14) for the manifestation of the sphere in data space can be articulated as;
{ | ∑ ( ) } (17)
where ρ is strong-minded by the worth of this summation on the support vectors (Trevor
Hastie, Saharon Rosset, Robert, Ji Zhu., 2004). The set of summits together with this
form is given by:
{ | ∑ ( ) } (18)
In the excessive case when approximately all data positions are BSVs (p → 1), the
computation in this term (Abhishek Dey et al., 2016),
∑ ( ) (19)
is generally equivalent to
∑ ( ) (20)
This preceding term is familiar as Parzen window estimation of the density gathering up
to an optimization aspect, if the kernel is not suitably optimized. In this high BSV system,
the form in data space is probable to include a little quantity of summits which recline
close to the paramount of the Parzen estimated density (S. Wang, Z. Li et al., 2012). In
further vocabulary, the form identifies the middle of the likelihood distribution. SVC is
exploited as a “discordant” clustering algorithm, preliminary from a minute assessment of
q and escalating it. The preliminary assessment of q might be preferred as
‖ ‖
(21)
At this level, all sets of positions fabricate a considerable kernel assessment, ensuing in a
particular cluster. At this rate no outliers are desired, therefore C = 1 is chosen. Since q is
augmented, it is anticipated to discover divergence of clusters. Albeit seeming as
hierarchical huddling, counterexamples are initiated when exercising BSVs. Therefore,
stern hierarchy is not assured, except the algorithm is functional discretely to every
cluster that is moderate than the whole dataset. This option is not accomplished at this
time, in turn to demonstrate how the cluster composition is tattered as q is augmented.
Initiating with p = 1/N, or C = 1, any outliers is not permitted (P.S.Jagadeesh Kumar,
J.Ruby, J.Nedumaan, J.Tisa, J.Lepika, 2017). Proviso, since q is being augmented,
clusters of particular or certain positions break off, otherwise cluster restrictions befall to
be uneven, p ought to be augmented in turn to scrutinize what ensues when BSVs are
permitted. In common, a superior standard appears to be based on the quantity of SVs: a
least number ensures smooth precincts. As q enhances this quantity augments, the same
as in Fig. 8. If the extent of SVs is intense, p ought to be augmented, whereby numerous
SVs might be bowed into BSVs, and smooth cluster restrictions materialize (Srinivasan

121
C, Suneel Dubey, Ganesh Babu T.R, 2015). On the other hand, it is projected to
analytically enhance q and p beside the direction that assures a negligible quantity of
SVs. A subsequent standard for superior cluster resolution is the constancy of cluster
obligation over some assortment of the two strictures. An imperative concern in the
discordant loom is the pronouncement when to stop isolating the clusters. A lot of
advances to this predicament subsist in (S. Lahmiri, C. S. Gargour et al., 2012).
Nevertheless, it is alleged that in SV background it is usual to exercise the quantity of
support vectors as a suggestion of a consequential resolution. Therefore, it is believed to
impede SVC when the portion of SVs surpasses certain threshold (P.S.Jagadeesh Kumar,
Yamin Yuan, Yang Yung, Mingmin Pan and Wenli Hu, 2018).
Fig. 8 Overlap between clusters underlying likelihood distribution
6 Adaptive Learning Algorithm
The structural SVM algorithm offers a broad scaffold for erudition with versatile
controlled yield spaces (Trevor Hastie, Saharon Rosset, Robert, Ji Zhu., 2004). The
adaptive learning algorithm exercised in this manuscript is SVM supervised clustering.
The SVM algorithm resolves this quadratic series:
‖ ‖ ∑ (22)
( ) ( ) ( ) (23)
At this point, Equation (22) restrains the distinguishing SVM quadratic principle and
flaccid limits. Disparity in Equation (23) articulates the pair of restraints that permits to
discover the preferred supposition. This meticulous QP is described the SVM ∆1 m
program, that is, flaccid norm is 1, and trouncing proceeds as the periphery. Added QPs
are depicted but SVM ∆1 m is utilized because it is further compatible with the
correlation clustering algorithm. ( ) signifies a factual esteemed trouncing among a
factual cluster and an expected cluster . ( ) if = , and ∆ ascends as the two
clusters befall further disparate (S. Zou, Y. Huang et al., 2008). In examination segment,
two trouncing functions ∆ are betrothed: a loss pedestal on the precision and recall gain

PSJ Kumar et al.
and a “setwise” loss that reckons the magnitude of setwise cluster association with the
clusters diverge on. The ( ) function precedes a mutual attribute depiction of an
effort and production . In the erudition for correlation clustering (Abhishek Dey et
al., 2016),
( ) | | ∑ ∑ (24)
Because ( ) is the correlation clustering determined for respective instruction
example ( ), and each probable mistaken clustering , SVM clustering algorithm
stumble on the vector to construct the importance of the intention for the accurate
grouping be superior than the significance of the goal for this erroneous grouping by no
less than a fringe of supervised clustering with SVM the loss between and . The
advance in the SVM algorithm is to establish with no constrictions, and iteratively
discover the major desecrated constriction.
This algorithm for SVM ∆1 m is:
1: Input: (x1, y1), . . . ,(xn, yn), C,
2: Si ← for all i = 1, . . . , n
3: repeat
4: for i = 1, . . . , n do
5: H(y) ≡ ∆(yi, y)+wTΨ(xi, y)−wTΨ(xi, yi)
6: compute Y = argmaxy Y H(y)
7: compute εi = max{0,maxyεi H(y)}
8: if H(y) > εi + then
9: Si ← Si ∪ {Y}
10: w ← optimize primal over S = Si
11: end if
12: end for
13: Awaiting no alteration in Si through iteration
By means of elucidating argmaxy H(y), the adaptive learning algorithm discovers the
clustering linked with the largest part of the desecrated constrictions for ( ). Given
that H is the lowest amount of required flaccid for below the existing , if H(Y) > +,
the constriction is desecrated by further, so the self-possession and re-optimize is
introduced. The algorithm reiterates these progressions in anticipation of no innovative
constrictions were established.
7 Implementation and Analysis
The support vector clustering implemented on the retinal data set which is a distinctive
yardstick in pattern recognition prose. The iris data set includes 900 factual images of
gritty and glaucoma retina. Individual cluster is linearly discrete from the further by an
apparent contravene in the likelihood distribution. The residual clusters have considerable
overlap, and were estranged at q = 2 p = 0.43. Nevertheless, at these standards of the
constraints, the auxiliary cluster divides into two. When these two clusters are well

123
thought-out jointly, it comprehended in one miss-classification. Accumulating the third
principal component, accomplished the three clusters at q = 5 p = 0.60, with three miss-
classifications for non-glaucomatous OCT images. Through the fourth principal
component the significance of miss-classifications augmented to six for non-
glaucomatous OCT images, employing q = 9 p = 0.34. Additionally, the quantity of
support vectors augmented with ever-increasing dimensionality. The enhanced recital in
2D or 3D might be recognized to the noise attenuation of principal component analysis.
The domino effect is comparably constructive with existing clustering algorithms.
Intended for high dimensional datasets, the predicament was to get hold of a support
vector description: the quantity of support vectors shoot from one cluster to all data
points in a separate cluster. The quadratic programming quandary of Equation (18) can be
resolved by the cluster overlapping algorithm which was projected as an adept
contrivance for clusters that strappingly overlap. A heuristic is employed to subordinate
this estimate: the complete adjacency matrix is not premeditated, but only the adjoining
with support vectors. The memory necessities of the cluster overlapping algorithm are
squat: it can be realized by means of O (1) memory at the expenditure of a decline in
competence. This formulates SVC practical even for awfully outsized datasets. Though
while sprint on the NP-coreference predicament, following that learning algorithm
congregate in relation to 1121 precincts, which be conventional into an SVM QP
reoptimized 50 times. The transparency of clustering with these diminutive sets is small
comparative to the time expend in reoptimizing the QP; by means of Gaussian kernel,
merely one percent of the time depleted in reoptimizing the QPs was exhausted for
clustering. Of all the accounted testing, SVM learning algorithm continually took less
time to congregate with below a more distinctive instance.
Table I demonstrates the percentage detection of glaucoma using SVM as the
intelligent paradigm. 600 glaucomatous OCT images along with 300 non-glaucomatous
OCT images were fed as the dataset for adaptive learning algorithm. The key features
like disc area, cup area, cup volume, disc and rim area, cup to disc ratio and thickness of
the retinal nerve fiber layer were measured for classifying the glaucomatous iris. The
incidence of glaucoma based on the OCT observation through the proposed scheme is
shown in Fig. 9. The graph clearly shows that the major cause for glaucoma is due to the
increased RFNL thickness and the next major cause is due to increased cup-to-disc ratio.
The proposed algorithm displayed an overall 97.25% success rate as shown in Fig. 10.
Out of 600 glaucomatous images, 579 were precisely classified and 21 were falsely
classified contributing to 96.5% of success rate. Out of 300 non-glaucomatous images,
294 were precisely classified and 6 were falsely classified contributing to 98% of success
rate. Fig. 11 displays that the falsely classified is very negotiable compared to the
precisely classified in both glaucomatous and non-glaucomatous images classification.
The mean values of the glaucoma key features like disc area, cup area, cup volume, disc
and rim area, cup to disc ratio and thickness of the retinal nerve fiber layer were
estimated based on standard deviation of mean age group between 10 to 80 years.

PSJ Kumar et al.
8 Conclusion
A new-fangled retinal detection for glaucoma constructed over SVM technique using
ballistic optical imaging is projected. This method has no unequivocal presumption of
both the quantity, or the silhouette of support vector clusters. It has two constraints,
allowing it to attain assorted clustering responds. The constraint q of the Gaussian kernel
establishes the extent at which the information is explored, and as it is augmented,
clusters instigate to segregate the customary and retina with glaucoma. The other
constraint, p, is the malleable periphery invariable that pedals the quantity of outliers.
This constraint facilitates scrutinizing glaucoma exaggerated eye and untying among
overlapping clusters. This is in distinction with a large amount of clustering algorithms
ascertained in the prose that have rebuff coordination for dealing with outliers.
Nevertheless, it might be outstanding for clustering glaucoma instances with strappingly
overlapping clusters; SVC might demarcate only comparatively diminutive cluster
nucleus. A surrogate for overlapping clusters is to make use of a support vector
description for all clusters. An exceptional benefit of this algorithm is that it can engender
cluster restrictions of subjective contour, while existing algorithms that utilize the
geometric expression are the largest part habitually restricted to hyper-ellipsoids. In this
deference, SVC is redolent of highly categorized neurons discrete in a multi-dimensional
attribute space.
The anticipated algorithm has substantial perfection; being pedestal on kernel
technique it evades unambiguous computations in the multi-dimensional attribute space,
and so is further competent. In the high p system, support vector clustering turns out to be
analogous to the scale space technique that explores the cluster group using kernel
density estimation of the likelihood distribution, where cluster midpoints are distinctive
by the neighborhood maxima of the concreteness. The projected system has 97.25%
attainment rate on data set restraining a mixture of 900 realistic images of persistent and
glaucoma retina; hence the computational advantage of contingent on the support vector
machine based adaptive learning algorithm have reticent determination in accomplishing
glaucoma dexter. In forthcoming days, to achieve more precise and accurate
classification of glaucoma, the standard deviation (SD) of age groups for mean estimation
of glaucoma key features like disc area, cup area, cup volume, disc and rim area, cup to
disc ratio and thickness of the retinal nerve fiber layer can be categorised into four groups
based on age; Child (less than 15 years), Middle (15–40 years), Adult (40-65 years), and
Elder (more than 65 years). SVM based machine intelligence grounded on quadratic
normalization has reserved inclusive command in understanding glaucoma therapeutic
using ballistic optical imaging.
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127
TABLE I. PERCENTAGE DETECTION OF GLAUCOMA
Type of
Retina
Total
Number
of Eyes
Inspected
Medical
Optical
Imaging Type
Medical
Optical
Imaging
Sub Type
Cause for
Glaucoma
Precisely
Classified
Falsely
Classified
Success
Rate
Glaucomatous 600
Ballistic
Optical
Imaging
Optical
Coherence
Tomography
Disc Area* (52)
Cup Area* (46)
Cup Volume* (47)
Disc Rim Area* (92)
Cup to Disc Ratio* (114)
Thickness of the RNFL* (228)
579 21 96.5
Non-
Glaucomatous
300
Ballistic
Optical
Imaging
Optical
Coherence
Tomography
Nil 294 6 98.0
Overall Percentage of Success Rate 97.25
*Disc Area is the area of the optic disc.
[The mean disc area is 1.61 ± 0.43 mm2
. (± Standard Deviation)]
*Cup Area is the area of the cup portion of the optic.
[The mean disc area is 0.52 ± 0.37 mm2
*Cup Volume is the volume of the cup portion of the optic.
[The mean cup volume is 0.31 ± 0.22 mm3
*Disc Rim Area is the area between the cup rim margin and the disc edge.
[The mean disc rim area is 1.11 ± 0.42 mm2
*Cup to Disc Ratio is the ratio of the diameter of the cup portion of the optic disc to the total diameter of the optic disc.
[The normal cup-to-disc ratio is 0.3.]
*Thickness of the RNFL is the thickness of the retinal nerve fiber layer.
[The mean thickness of RNFL is 100.2 ± 11.9 μm. (± Standard Deviation)]
*Mean values are calculated for age groups between 10 to 80 years.

PSJ Kumar et al.
Fig. 9. Incidence of glaucoma
Fig. 10. Success rate of glaucoma detection

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Fig. 11. Precisely classified versus falsely classified.

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