Diagnostics of Optic Nerve Head Pathologies using Structural

Analysis of Eye Ultrasound B-scan Images

A. Kriščiukaitis

1,3

, V. Valuckis

, A. Kybartaitė-Žilienė

and L. Kriaučiūnienė

Neuroscience Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania

Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania

Department of Physics, Mathematics and Biophysics, Lithuanian University of Health Sciences, Kaunas, Lithuania

Department of Ophthalmology of Hospital of Lithuanian University of Health Sciences, Kaunas, Lithuania

Keywords: Eye B-scan Image, Structural Image Analysis, Optic Nerve Head, Drusen.

Abstract: Optic nerve head drusen are congenital and developmental anomalies in a form of calcific degeneration in

some of axons of the optic nerve head. Diagnostic difficulties may be encountered when drusen are buried

deep within the nerve tissue in the optic nerve head, as they can resemble optic disc swelling or other

pathologies. Diagnosing optical disc drusen correctly is important to avoid unnecessary work-up and to

avoid overlooking potential serious conditions such as true papilledema. We propose the method based on

structural analysis of the eye B-scan images combined with mathematical morphology to reveal valuable

estimates reflecting pathogenic changes in the optic nerve and surrounding structures for improvement of

diagnostic quality.

1 INTRODUCTION

Optic nerve head (or optic disk) drusen are

congenital and developmental anomalies in a form

of calcific degeneration in some of axons of the

optic nerve head (Davis and Walter, 2003). Due to

that visual acuity is usually not affected but visual

fields of patients can be abnormal and deteriorate

over time (Davis and Walter, 2003). Drusen of the

optic disc may be easily diagnosed when glowing

yellow hyaline bodies are visible during

ophthalmoscopy. However, diagnostic difficulties

may be encountered when drusen are buried deep

within the nerve tissue in the optic nerve head, as

they can resemble optic disc swelling based on the

ophthalmoscopic appearance alone (Kurz-Levin and

Landau, 1999). Optic disk swelling may be

associated with raised intracranial pressure that is

transmitted to subarachnoid space surrounding an

optic nerve, thereby interrupting metabolic processes

of the nerve and consequently leading to edema and

eventual visual impairment or loss (Passi et al.,

2013). Differentiation of optic disc edema caused by

papilledema or other optic neuropathy from optic

nerve head drusen is very important clinically.

However, using for that B-scan ultrasonography, and

even fluorescein angiography or computed

tomography (CT) remains problematic (Johnson et

al., 2009) Misleading diagnostic conclusions could

be made in differentiation of optic nerve edema,

drusen covering the optic nerve head and combined

optic nerve edema and drusen cases. Diagnosing

optical disc drusen correctly is important to avoid

unnecessary work-up and to avoid overlooking

potential serious conditions such as true

papilledema. Kurz-Levin and Landau (1999)

reviewed retrospectively the clinical records of 142

patients (261 eyes) with suspected drusen of the

optic disc and stated that drusen of the optic nerve

head are diagnosed most reliably using B-scan

echography compared with both pre-injection

control photography and CT scans.

Structural analysis of the eye B-scan images

combined with mathematical morphology methods

can reveal valuable estimates reflecting pathogenic

changes in the optic nerve and surrounding

structures. The idea of this study was to elaborate a

method for computer-assisted evaluation of eye B-

scan ultrasonography images providing optimal

objectivized estimates for optic nerve head

diagnostics.

The proposed estimates should allow

differentiation of following cases: i) optic nerve

Kriš

ciukaitis, A., Valuckis, V., Kybartait

e-Žilien

e, A. and Kriau

ci ¯unien

e, L.

Diagnostics of Optic Nerve Head Pathologies using Structural Analysis of Eye Ultrasound B-scan Images.

DOI: 10.5220/0005762301010104

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 2: BIOIMAGING, pages 101-104

ISBN: 978-989-758-170-0

101

edema; ii) drusen covering the optic nerve head; iii)

combined optic nerve edema and drusen.

2 METHODS

2.1 Image Data Sample

B-scan ultrasound eye images were registered in

Department of Ophthalmology of Lithuanian

University of Health Sciences Hospital by means of

OTI-Scan 1000 ultrasound scanner (Optos, USA), at

10 MHz frequency. An expert-ophthalmologist

selected 11 typical images representing drusen at the

optic nerve head cases, 3 typical images as

representing optic nerve edema cases, and 5 images

– combined drusen and edema cases.

2.2 Structural Analysis of the Images

The structural analysis algorithm of the eye B-scan

images is presented on the left of Figure 1. The

whole image firstly is filtered using mathematical

morphology operations “opening” and “closing”

(Najman and Talbot, 2010) discarding registration

noise and filling the gaps in area represented by

white pixels beyond the retinal surface. Count of

white pixels in every row of the image-representing

matrix forms array, which has local minima at the

level of optic nerve (see the graph on the right of

Figure 1). Boundaries of this hollow are considered

as preliminary boundaries of the optic nerve zone.

We search for the pixels representing retinal surface

in every row of image-representing matrix. They are

found as the first maximal contrast points to the right

from the center of an image. Knowing a priori that

the zone of the optic nerve can be elevated in regard

to retinal surface due to edema, we exclude rows of

preliminary detected optical nerve zone from this

procedure. A retinal surface is expected to be round

shaped, so we best fit part of the ellipse to detected

retinal surface representing pixels. The final

estimation of a width of the optic nerve is performed

at 3 mm depth from the fitted ellipse according to

maximal gradient of pixel values at this depth

(shown by two white dots on B-scan image in Figure

1). The final detection of the optic nerve zone is

done using these points. Pixels from rows of image

array from this zone are used for further analysis.

2.3 Diagnostic Feature Estimation and

Analysis

Elevation of the optic nerve head in regard to the

retinal surface is the first feature. It is estimated as

the area between fitted ellipse, representing retinal

surface and the detected real optic nerve surface

(bold white dots on B-scan image in Figure 1). The

optic nerve drusen are represented by aggregates of

white pixels, when certain elevation is present.

However heuristic approach to construct decision

rules for identification of analyzed pathological

structures became sophisticated and we found it as

not reliable during preliminary tests. Therefore we

performed statistical analysis of the pixel values

from the zone of the optic nerve. Following features

were selected and estimated for further analysis:

normalized counts of histogram at standardized 21

bins covering the most expected range of pixel

values together with the main descriptive statistic

parameters of the pixels - mean; variance; skewness;

Figure 1: The algorithm of structural analysis of B-scan eye images (left), illustration of its result (center). Count of white

pixels in every row of the image, used for preliminary detection of the optic nerve zone is shown on the right graph.

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Table 1: Eigenvalues of canonical functions.

Function Eigenvalue % of Variance Cumulative % Canonical Correlation

1 134,070 93,2 93,2 0,996

2 9,820 6,8 100,0 0,953

Table 2: Wilks' Lambda criterion for canonical functions.

Test of Function(s) Wilks' Lambda Chi-square df Sig.

1 through 2 0,001 61,941 24 0,000

2 0,092 20,242 11 0,042

kurtosis and entropy (26 parameters in total for

every analyzed picture). We normalized values of all

features dividing them by estimate of their spread

(difference between the maximum and minimum).

Discriminant analysis was used to construct

canonical discriminant functions best classifying

data reflecting analyzed cases (“drusen”, “drusen

and edema”, “edema”). The canonical function

involves several initial features optimizing their

weight for the best separation of groups. The typical

canonical function is:

 l

 l

... l

 C

(1)

where X are the features and l are their weights.

Quality of classification using canonical functions

was determined by Wilk’s Lambda statistics. For

details about discriminant analysis see (Klecka,

1980). All calculations were performed using IBM

SPSS Statistics 22 package.

3 RESULTS

Typical examples of eye B-scan images with

determined main eye structures in case of “drusen”,

“drusen and edema” and “edema” are shown in

Figure 2. Twelve of 26 initial features were involved

constructing two canonical functions for data

classification according Fisher statistics and Wilk’s

Lambda criterion. Percentage of variation covered

by every canonical function is reflected by

corresponding eigenvalue of co-variation matrix

presented in Table 1.

Quality of classification determined by Wilk’s

Lambda criterion is presented in Table 2. As we see,

values of Wilk’s Lambda criterion are below 0.1 and

significance values are below 0.05 for both

canonical functions, what means that both of them

could be used for classification. However, according

to eigenvalues and percentage of variation covered

(93.2% vs. 6.8%), we can recommend the 1

canonical function for classification of the cases.

Standardized canonical discriminant function

coefficients are presented in Table 3.

Table 3: Standardized Coefficients of Canonical

Discriminant Function.

Variables

Functions

1 2

VAR3 19,976 -3,643

VAR4 3,554 6,430

VAR5 11,114 4,164

VAR6 6,823 -5,266

VAR7 12,205 -3,969

VAR8 -1,619 2,627

VAR9 -6,323 8,439

VAR10 24,528 -13,592

VAR11 17,923 10,234

VAR12 -13,304 -0,878

VAR13 25,300 -9,504

VAR15 2.835 6.142

It is interesting that only certain histogram

counts were selected to include into canonical

functions. Maximal values of coefficients indicate

the most important features for classification of the

cases. As we see, the 3

, the 10

and the 13

histogram counts were found as the most significant.

All 100% of original grouped cases were correctly

classified. However, only 58,8% of cross-validated

grouped cases were correctly classified. Stepwise

selection of features for classification according to

Fisher statistics, using minimum partial F to enter

3.84 and maximum partial F to remove 2.71,

selected variance of pixel values as the only feature

useful for classification. In this case 76.5% of

original grouped cases were correctly classified.

Involvement of neither optic nerve head elevation

estimate nor optic nerve diameter at 3 mm depth into

features set did not improve the classification

results.

Diagnostics of Optic Nerve Head Pathologies using Structural Analysis of Eye Ultrasound B-scan Images

103

Figure 2: Examples of eye B-scan images with determined main eye structures in cases of A: “drusen”, B: “edema” and C:

“drusen and edema”.

4 DISCUSSION

The elaborated algorithm for data preprocessing and

structural analysis of ultrasound eye B-scan images

extracts subset of pixels, representing the zone of

interest – the area of the optic nerve close to the

retinal surface. Estimated statistical parameters of

pixels were shown as informative features for

differentiation between the three clinical cases.

Constructed canonical functions correctly

classifying all three clinical cases revealed the main

features to be considered when analyzing ultrasound

eye B-scan images. Classification significance was

acceptable. However very limited number of cases

has been analyzed so far and it is the weakest point

of this study. Nevertheless elaborated set of data

preprocessing and structural analysis algorithms

together with proposed methods of statistical

analysis forms a basis for future investigations

including more representative sample of images

covering a wide range of clinical cases.

The elaborated structural analysis algorithm

determines key-structures in the eye B-scan images.

We expect that it would be useful for feature

estimation in semi-automated or automated

diagnostics of other pathologies in the eye.

5 CONCLUSIONS

The elaborated method of computer-assisted

evaluation of eye B-scan images provides the

optimal set of features for diagnostics of pathologies

in the optic nerve head. We demonstrated its

suitability on limited number of pathologies, but it

could be used in many other cases, where

diagnostics is based on analysis of eye B-scan

images.

REFERENCES

Davis P, and Walter J. Optic Nerve Head Drusen.

Seminars in Ophthalmology 2003;18(4): 222–242.

Johnson L; Diehl M; Hamm C; Sommerville D; Petroski

G; Differentiating Optic Disc Edema From Optic

Nerve Head Drusen on Optical Coherence

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Klecka W.R. (1980), Discriminant Analysis, Sage

University Paper Series on Quantitative Applications

in the Social Sciences, 07-019. Beverly Hills, CA:

Sage Publications.

Kurz-Levin M; Landau K. A Comparison of Imaging

Techniques for Diagnosing Drusen of the Optic Nerve

Head. Arch Ophthalmol. 1999;117(8):1045-1049.

Najman L, and Talbot H. (Eds). Mathematical

morphology: from theory to applications, ISTE-Wiley.

ISBN 978-1-84821-215-2. (520 pp.) June 2010.

Passi, N., Degnan A. J., and Levy L. M. MR Imaging of

Papilledema and Visual Pathways: Effects of

Increased Intracranial Pressure and Pathophysiologic

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