28-06-2014, 09:50 AM
Novel Fractal Feature-Based Multiclass Glaucoma Detection and Progression Prediction
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Abstract
We investigate the use of fractal analysis (FA) as the
basis of a system for multiclass prediction of the progression of
glaucoma. FA is applied to pseudo 2-D images converted from 1-D
retinal nerve fiber layer data obtained from the eyes of normal
subjects, and from subjects with progressive and nonprogressive
glaucoma. FA features are obtained using a box-counting method
and a multifractional Brownian motion method that incorporates
texture and multiresolution analyses. Both features are used for
Gaussian kernel-based multiclass classification. Sensitivity, specificity,
and area under receiver operating characteristic curve
(AUROC) are computed for the FA features and for metrics obtained
usingwavelet-Fourier analysis(WFA) and fast-Fourier analysis
(FFA). The AUROCs that predict progressors from nonprogressors
based on classifiers trained using a dataset comprised
of nonprogressors and ocular normal subjects are 0.70, 0.71, and
0.82 for WFA, FFA, and FA, respectively. The correct multiclass
classification rates among progressors, nonprogressors, and ocular
normal subjects are 0.82, 0.86, and 0.88 for WFA, FFA, and
FA, respectively. Simultaneous multiclass classification among progressors,
nonprogressors, and ocular normal subjects has not been
previously described. The novel FA-based features achieve better
performance with fewer features and less computational complexity
thanWFA and FFA.
NTRODUCTION
LAUCOMA is a progressive optic neuropathy that results
in structural damage to the retina and loss of vision [1].
The retina has about a million retinal ganglion cells, the axons
of which constitute the retinal nerve fiber layer (RNFL) [2]. At
a cellular level, glaucoma is characterized by a progressive and
rapid death of the ganglion cells and their axons in the RNFL
due to the process of apoptosis [2].
Progressive glaucomatous damage to the RNFL is irreversible
and worsens vision loss. Consequently, detecting glaucomatous
progression is critical in the management of the disease. Visual
field (VF) tests using standard automated perimetry are widely
used in diagnosing progression; however, structural changes in
the RNFL often precede functional changes in VF data [3]. In
addition, inherent variability in VF test results often interferes
with clinical determinations of progression. Improved methods
for detecting and/or predicting progressive changes in the RNFL
structural data are needed
BACKGROUND REVIEW
One key to glaucoma management is the evaluation of
whether a subject’s visual status has improved, worsened (progressed),
or remained stable (nonprogressed). Our goal is to
predict which glaucoma eyes are likely to progress.
B. SLP
SLP utilizes a polarized light to scan the retina and estimate
the thickness of the peripapillary RNFL as a function of angle
around the retina [6], [7]. Because of the presence of the cornea
and lens, the thickness measurement is subject specific
METHODS AND DATASETS
flowchart of the implementation of our methods is shown
in Fig. 1. For preprocessing, the RNFL data are normalized to
zero mean and transformed to the pseudo 2-D image [10]. Normalization
is performed at different stages because our methods
involve multiple analyses in which the data need to be brought
on similar scale for further processing. WFA and FA featurebased
techniques are applied on the pseudoimages to obtain
representative features for subsequent classification.
Principal component analysis (PCA) and the Gaussian kernel
method are utilized for feature selection. PCA is used because
our data are correlated and PCA is more effective in handling
correlated data over other equivalent methods. Note we do not
use multiple discriminant analysis (MDA) because MDA maximizes
the difference between values of the dependent variables,
whereas PCA maximizes the variance in all the variables
CONCLUSION AND FUTURE WORK
The efficacy of novel FA feature-based techniques for detecting
and predicting glaucomatous progression is demonstrated.
The performance of our method is compared with those of conventional
feature-based methods (WFA and FFA). Our method
yields an AUROC of 0.82, compared with those of 0.70 and
0.71 forWFA and FFA, respectively. The effectiveness of multiclassification
of progressors, nonprogressors, and ocular normal
subjects is also shown using Gaussian kernel-based multiclass
SVM (see Table VI). Our FA feature-based multiclass SVM
method achieves a correct rate of 0.88 using FA, compared with
those of 0.82 and 0.86 usingWFA and FFA, respectively. In addition,
it is also shown that multiclass SVM can simultaneously
discriminate the different stages of progression, which is the first
attempt in the literature. Our FA feature-based techniques also
perform with fewer features and less computational complexity
than those works reported in the literature [8], [9].
Glaucomatous progression is highly patient-dependent. To
achieve a clinically important progression predictor, further algorithmic
improvements are needed. A potential area for developing
these improvements involves fusing structural and functional
data. Fusion of both data may better reflect glaucomatous
damages since clinically discernible changes are shown both in
structure and function. For such an application, it is expected
that investigation of correlation among different features and
appropriate multiclass classification will be necessary.