Ensemble Learning Optimization for Diabetic Retinopathy Image

Analysis

Hanan S. Alghamdi, Lilian Tang and Yaochu Jin

Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, U.K.

ywords: Ensemble Learning, Ant Colony Optimization, Diabetic Retinopathy, Ensemble Diversity.

Abstract: Ensemble Learning has been proved to be an effective solution to learning problems. Its success is mainly

dependent on diversity. However, diversity is rarely evaluated and explicitly used to enhance the ensemble

performance. Diabetic Retinopathy (DR) automatic detection is one of the important applications to support

the health care services. In this research, some existing statistical diversity measures were utilized to

optimize ensembles used to detect DR related signs. Ant Colony Optimization (ACO) algorithm is adopted

to select the ensemble base models using various criteria. This paper evaluates several optimized and non-

optimized ensemble structures used for vessel segmentation. The results demonstrate the necessity of

adopting the ensemble learning and the advantage of ensemble optimization to support the DR related signs

detection.

1 INTRODUCTION

Diabetic retinopathy (DR) is one of the common eye

diseases associated with diabetes. As the rising

prevalence of diabetes worldwide, DR will become a

more important problem. Early detection of DR is

very essential for effective treatment. Given the

increasing number of DR patients worldwide and the

need of regular eye examination, the development of

automated screening of DR has received a lot of

attention from many research communities. The

main goals are to reduce the ophthalmologists’

workload and to improve the health care services.

DR computer-aided-diagnosis (DR-CAD) systems

are designed to distinguish between normal and

abnormal retinal images, in which DR symptoms

appear. Figure 1 shows two samples of retinal

fundus images; normal and image with DR lesion

called haemorrhages. Vessel segmentation is a vital

component of DR-CAD systems. Retinal blood

vessels must be excluded to reduce the false

positives in the detection of pathological lesions

such as haemorrhages shown in Figure 1-b. The

automatic detection of vessels can also be useful in

in locating other anatomical structures such as optic

disc and fovea. Furthermore, vessel segmentation is

an important diagnosis key for several retinal

pathologies leading to vascular anomalies. Many

vessel segmentation techniques and algorithms have

Figure 1: Retinal Fundus Images Samples.

been developed in the literature. However, the

presence of noise, the variability of image

acquisition equipment, and the presence of some

pathological lesion make the vessel segmentation

process more and more challenging. This

emphasizes the need for developing more accurate

automated techniques (Preethi and Vanithamani,

2012) (Khan, Shaikh, and Mansuri, 2011) (Fraz et

al., 2012). Vessel segmentation studies can be

classified into two main groups, rule-based

approaches and machine learning approaches

(Marin, Aquino, Gegundez-Arias, and Bravo, 2011).

Adaptive thresholding (Xu and Luo, 2010) (Jiang,

Society, and Mojon, 2003), vessel tracking (Liu and

Sun, 1993) (Vlachos and Dermatas, 2010)

(Delibasis, Kechriniotis, Tsonos, and Assimakis,

2010), mathematical morphology (Zana and Klein,

1999) (Zana and Klein, 2001), and matched filtering

a) Normal Retina

b) Retina with DR Lesions

(haemorrhages)

471

S. Alghamdi H., Tang L. and Jin Y..

Ensemble Learning Optimization for Diabetic Retinopathy Image Analysis.

DOI: 10.5220/0005296604710477

In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-2015), pages 471-477

ISBN: 978-989-758-089-5

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

(Zhang, Zhang, Zhang, and Karray, 2010)

(Chaudhuri, Chatterjee, Katz, Nelson, and

Goldbaum, 1989), are all examples of the rule based

approach. On the other hand, machine learning

approach which can be supervised or unsupervised

learning, is used to classify all pixels in an image

into vessel or non-vessel classes. Supervised

learning, such as work presented in (Becker and

Rigamonti, 2013) and (Ricci and Perfetti, 2007) is

based on training a classifier on a set of manually

labelled reference images known as gold standard or

ground truth data. By contrast, unsupervised

learning performs the vessel segmentation without

any prior labelling information. Examples of such

approaches were suggested in (Ricci and Perfetti,

2007) and (Tolias and Panas, 1998). In this research

we adopt heterogeneous ensemble learning for pixel

classification.

The use of ensemble models or ensemble

classifiers has proved to provide better result than a

single complex algorithm in many problems as

ensembles are able to better resolve the bias-

variance trade-off. However, it is well known in the

literature that in order for the ensemble models to

have better performance, diversity should be

maintained. This diversity manifests itself as

disagreement or ambiguity among the ensemble

members and the performance of any ensemble

models is largely dependent on diversity. Diversity

is usually supposed to be enforced by the designing

process such as using different training samples.

Despite of its importance, how the diversity

could be utilized to enhance the performance of

ensemble is rarely studied, which calls for further

investigations.

In this paper, we adopt several ensemble

structures used for retinal vessel segmentation. By

developing ensemble models we are aiming to

obtain much more accurate and robust learning

model that outperforms any individual base model.

An ant colony optimization algorithm is used to

optimize the ensemble structures based on several

criteria including the diversity and the ensemble

members’ performances. The results confirm the

effectiveness of the ensemble learning and the

advantage of ensemble optimization.

The rest of the paper is organized as follows:

Section 2 presents some existing vessel detection

techniques that we have used as the base models in

the ensembles. Section 3 discusses some important

aspects of ensemble learning and motivates the

importance of ensemble optimization. Section 4

describes our ensemble optimization approach.

Section 5 presents and discusses the results of the

optimized ensembles. Section 6 concludes the paper

and suggests further works.

2 VESSEL SEGMNATION

METHODS

The large numbers of vessel segmentation

approaches available in the literature makes the

process of choosing base models to construct

ensemble systems more complex and challenging. In

spite of this fact, we evaluated several methods

available from the literature found to achieve good

performance. Moreover, we developed a new fast

and efficient classifier model. All these methods

were used in constructing different ensemble

structures. In the following, we describe each type of

the base models used in constructing ensembles used

in this paper for vessel segmentation.

In (Soares, Leandro, Cesar Júnior, Jelinek, and

Cree, 2006) Bayesian classifier is applied with class-

conditional probability density distribution (PDF)

defined as a linear combination of Gaussian models.

The feature set consists of two-dimensional Gabor

wavelet transform responses taken at different

scales, augmented with pixels intensity. The method

is called Gaussian Mixture Model (GMM). The

number of Gaussian models k varies in each

experiment. The approach was trained and tested on

DRIVE and STARE publically available datasets.

The training phase was performed on both datasets.

The resulted classifiers were also tested on both

datasets.

We also developed a simple and fast vessel

segmentation algorithm based on pixel

classification. Linear discriminant classifier (LDC)

(Richard O. Duda , Peter E. Hart, 2000) is used with

Principle Component Analysis (PCA) a classical

dimension reduction method to select different

number of features. The feature set consists of the

output of Gaussian filters and its derivatives up to

order 2 taken at multiple variances and the green

channel intensity of the original image. Features

were normalized to zero mean and unit variance.

DRIVE training set was used for training the

classifier. This approach is similar to the one used in

(Niemeijer, 2006) in which kNN (k=30) is used to

obtain the vessel non-vessel probability map and

then thresholding is performed to get the binary

segmentation. However, LDC shows extremely fast

and efficient training and testing phases compared to

the kNN classifier. This efficiency is crucial in

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constructing ensembles as many models should be

trained and used for classification.

However, when using LDC for pixel classification,

we have noticed that the boundary between the field

of view (FOV) and the black background result in

many false positives (FP) on the boundary. Thus, we

enhanced the result of the LDC classifier by

adopting the diffusion approach used in (Huynh,

2013). The process starts by relabeling five pixels on

the boundary as background then diffuse the colour

in FOV through the background by using the heat

equation. After the diffusion, the features are

extracted and tested using the LDC classifier in a

space reduced by PCA.

We also tested an algorithm called Multi Scale

Line Tracking Algorithm (MSLTA) (Vlachos and

Dermatas, 2010). The algorithm starts by extracting

a set of pixels, called seeds from the image

histogram. The histogram is divided into three

sections by two threshold values T_low and T_high.

The threshold T_low is estimated by the percentage

of pixels belonging to background, while the

threshold T_high is estimated by the percentage of

pixels belonging to bright structure. Pixels with

intensity between these two values are expected to

be blood vessel and extracted as seeds for line

tracking algorithm. All seed pixels are evaluated

using five different scales to construct a confidence

matrix used to get the initial candidate blood vessels.

Then a 3×3 median filter is applied to remove FP

pixels and to fill gaps in some blood vessels lines.

Finally, morphological directional filtering and

morphological reconstruction are applied to further

enhance the result.

In (Goh, 2011), adaptive thresholding is used on

the contrast-enhanced image to get candidate blood

vessel objects. Then two kinds of detectors are used

to make the final decision. In the first detector, the

linear properties of the detected objects are

evaluated to filter out non-blood vessel objects. In

the second detector, features of the obtained objects

are extracted and used by the classifier ensemble.

The ensemble consists of an optimized set of neural

networks trained using different training sets or

different subsets of features. The base models are

generated using different neural networks, different

initial weights and different number of hidden units.

This results in 270 base models. These base models

are optimized by using Genetic Algorithm. The

probability of each base model to be selected is

proportional to its accuracy compared to the sum of

all ensemble models accuracies.

3 ENSEMBLE LEARNING

We constructed five non-optimized ensemble

models using the methods described previously.

Ensemble A is constructed based on GMM methods

trained and tested on DRIVE using different number

of Gaussian models (k=1, 5, 10, 15, 20). As all of

these methods are similar except in one parameter,

the diversity is not expected to be high. Ensemble B

is also constructed using GMM methods however,

STARE dataset is used to train some of the

classifiers which are then tested on DRIVE. Thus,

we expect the diversity of ensemble B is higher than

that of ensemble A, as different training sets are

used.

Ensemble C was constructed by using different

LDC-PCA methods with different number of

features. Ensemble D was formed by all GMM and

LDC-PCA methods. MSLTA, the adaptive

thresholding approach, kNN classifier and all of the

GMM and LDC-PCA methods were combined

together to form ensemble E. Therefore, as different

training datasets, features, methods and parameters

are used in this case. Thus the diversity of this

ensemble is expected to be the highest.

We evaluated the performance of all models on

DRIVE testing dataset. The evaluation is performed

quantitatively by comparing classification

performance in terms of accuracy, sensitivity and

specificity. The results are shown in Table 1.

We also estimate the diversity of the five

ensembles by calculating the diversity disagreement

measure (Schapire, 2003) and relate this to the

improved ensembles’ performances. The greater the

disagreement measure, the higher the ensemble

diversity. Figure 2 shows an example of the visual

results of the five non-optimized ensembles

compared to the gold standard and to an expert

human observer.

The results in Table 1 highlight some interesting

aspects of ensemble learning. First, we can see that

as the diversity increases, the improvement of the

ensemble performance becomes more evident. For

instance, the least diverse ensemble is C which

consists of the same models trained on the same

features and only the number of features is different

in each model. Ensemble C manifests the lowest

improvement in accuracy and no improvement in

specificity. On the other hand, the greatest diversity

is manifested in ensemble E where different

algorithms, different classifier parameters, different

features and different training sets where used. This

indeed accompanies with the greatest improvement

in ensemble accuracy and specificity which is indeed

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473

Table 1: Performance evaluation of non-optimized ensembles.

Ensemble

Disagreement

Diversity Measure

Accuracy Specificity Sensitivity

Base Model

Average

Ensemble

Base Model

Average

Ensemble

Base Model

Average

Ensemble

Human Observer - 94.72 97.25 77.60

A 0.0234 94.00 94.43 97.08 96.89 73.13 77.86

B 0.0260 93.75 94.52 97.66 98.34 67.13 68.57

C 0.0094 91.33 91.44 98.50 98.21 41.69 44.55

D 0.0348 92.94 94.46 97.94 98.68 58.65 65.68

E 0.0385 92.58 94.22 97.97 99.09 55.53 60.89

Gold Standard Human Observer

A B

C D

Figure 2: Example of Vessel Segmentation Results of non-

optimized ensembles.

higher than the specificity achieved by the expert

human observer.

Consequently, we assume that this relationship

between the diversity and the improvement of the

ensemble performance can be utilized to construct a

better performance ensemble.

However, ensemble E achieved highest specificity

but has lowest sensitivity compared to other

ensembles. Thus, we assume that when increasing

the number of base models, the overall performance

may not be improved and the sensitivity in particular

may be decreased significantly. Thus it is more

desirable to construct ensemble with a small number

of models not only to reduce the complexity but also

to obtain an ensemble that outperforms its best

models. At the same time to achieve better

specificity we should maximize this number as much

as possible.

4 ENSEMBLE LEARNING

OPTIMIZATION

A direct approach of selecting a subset of base

models to construct an ensemble is to select models

which have the highest performance. This approach

has several weaknesses, including over-fitting,

sensitivity to the noise and possible selection of

identical base models.

Since the success of ensemble learning is largely

related to the base models performance as well as

the diversity among these models, these aspects

should all incorporated into the selection of base

models process. Moreover, the performance of any

base model should be evaluated based on its

individual performance as well as its performance

within the ensemble. In other words, selecting high

performance base models is not sufficient to ensure

the constructing of better ensemble. The base

models should be diverse and each base model

should contribute to enhance the overall ensemble

performance.

Motivated by the above reasons, in this research

we propose an ACO-Based algorithm to search and

select the base models in an attempt to construct

better performance ensemble to support DR

automatic detection.

By employing the ACO algorithm in optimizing

ensemble learning we aim to select the base models

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which perform well as individual and contribute well

to the overall ensemble performance. The main

advantages of this approach are to alleviate over-

fitting by reducing the number of models, thus

reducing the model complexity. The standard ACO

algorithm is used in this research to optimize the 20

base classifier models presented in Table 1 based on

different heuristics.

The problem of optimizing classifier ensemble

can be described as an ACO problem. The base

classifiers can be represented as vertices in a graph

with edges representing the next classifier to be

selected to construct the ensemble. Heuristic

desirability and pheromone trail intensity are

associated with each classifier. Each ant will select

randomly the first classifier to construct its

ensemble. Several measures were used to evaluate

the constructed solutions as ensemble accuracy,

sensitivity and specificity. Updating pheromones

phase is achieved by decreasing all the pheromone

values associated with all classifiers through

pheromone evaporation and by increasing the

pheromone values associated with best so far

ensemble. For example if sensitivity is employed

then that means the more sensitive the ensemble is,

the more pheromone quantity will be added to

classifiers used in that ensemble. The ACO-

ensemble optimization algorithm proposed in this

study is illustrated in

5 RESULTS AND DISCUSSIONS

Several experiments were conducted to optimize the

ensemble. First, accuracy, sensitivity and specificity

of the base models are used in each ensemble as

heuristics to guide the ACO search. These measures

are also used to evaluate the ants’ constructed

solutions. This results in three different optimized

ensembles shown in Table 3. The results show that

optimizing ensembles based on accuracy or

sensitivity results in much higher sensitivity, higher

accuracy and comparable specificity compared with

ensembles optimized by specificity. Thus, in the

subsequent experiments, accuracy and sensitivity are

used to evaluate the ant’s candidate solution.

Diversity is incorporated into the optimization

process of ensembles. ACO search is guided by

seven diversity measures available from the

literature (Brown, Wyatt, Harris, and Yao, 2005).

Results are shown in

Table 4 and Table 5.

Table 4 and Table 5, show that using

disagreement diversity measures to select the base

models during the ACO optimization, lead to the

best accuracy compared to all other diversity

measures.

The specificity and sensitivity achieved by

employing disagreement and Q-static however, are

Table 2: ACO proposed algorithm for ensemble

optimization.

Input: classifiers oracle outputs for some training data

samples

O = ( o

, o

, …, o

)

Output: base model classifiers for ensemble

1. Initialize ACO Parameters

2. Initialize Pheromone;

3. Determine the population of ants (m);

4. For each ant k do

Repeat

Choose in probability the classifier

to include;

Use the heuristic to adjust the

probability selection;

Append the partial solution with the

candidate classifier

Until ant k has chosen p classifiers

Evaluate the constructed ensemble ܧ

௞

;

Use ensemble performance to

evaluate the constructed ensemble

If (termination condition not met) do

For each classifier ܿ

௜

used in ensemble ܧ

௞

Update Pheromone ݌

௜

ased on the solution quality,

ࡸࢋ࢔ࢍ࢚ࢎ

௞

End for

For each classifier ܿ

௝

Evaporate Pheromone ݌

௝

End for

Else terminate;

Table 3: Vessel segmentation optimized ensembles guided

by ensemble accuracy, specificity and sensitivity.

Ensemble

Accuracy Specificity Sensitivity

# ACO Heuristic

1 Accuracy 94.72 97.75 74.10

2 Specificity 92.46 98.57 50.52

3 Sensitivity 94.27 96.46 79.48

Table 4: Vessel segmentation optimized ensembles guided

by diversity and ensemble accuracy.

Experiment Settings

Accuracy Specificity Sensitivity

# Diversity Measure

Q-statistic

94.73 97.85 73.59

Disagreement

94.75 97.93 73.17

Double-fault

94.60 97.56 74.50

Kappa-statistic

94.66 98.00 71.91

Entropy

94.69 97.61 74.86

Generalized diversity

94.70 97.71 74.24

Coincident failure

diversity

94.66 97.83 73.15

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475

Table 5: Vessel segmentation optimized ensembles guided

by diversity and ensemble sensitivity.

Experiment Settings

Accuracy Specificity Sensitivity

# Diversity Measure

Q-statistic

94.12 96.50

77.88

Disagreement

94.66 97.33 76.52

Double-fault

94.15 96.97 74.94

Kappa-statistic

94.61 97.22 76.88

Entropy

94.56 97.26 76.29

Generalized diversity

94.56 97.10 77.36

Coincident failure

diversity

94.62 97.22 76.96

comparable to the other measures. In Table 6, the

best sensitivity achieved is due to incorporating the

Q-statistic diversity measure into the ACO

optimization. Although in this case Q-statistic results

in low specificity, when looking at the visual

segmentation result, we assume that these errors can

be reduced by excluding the OD and FOV

boundaries from the image before the segmentation.

In order to compare the performance of non-

optimized and optimized ensembles, their

performance is graphically illustrated in Figure 3.

The result of Q-statistic is used in this figure for

the Accuracy-Diversity and Sensitivity-Diversity

optimized ensembles. The reason of this is that Q-

statistic gives the highest performance ensembles

compared to other diversity measures. As shown in

the figure, ensembles optimized by sensitivity

resulting in highest sensitivity and very comparable

results in specificity and accuracy compared to the

best ensemble. Based on these findings, we propose

to adopt the sensitivity optimized ensembles for

blood vessel segmentation in the DR diagnosis

system.

6 CONCLUSIONS AND FUTURE

WORKS

In this paper several optimized and non-optimized

ensemble structures used for vessel segmentation

have been evaluated. All of the developed ensembles

were evaluated with multiple performance

indicators, i.e., accuracy, sensitivity and specificity

as well as the diversity aspect of these ensembles.

The diversity was evaluated using different

statistical diversity measures. The relationship

between the performance measurers and the

diversity measures were analysed.

Based on the findings in this work, we aim to

further study the development and evaluation of a

fully optimized ensemble learning model to support

the DR related signs detection. This model is

expected to enhance several DR system components

such as blood vessel detector, optic disc detector, red

lesion detector and bright lesion detector. Moreover,

as the model is of a generic form, it should also be

applicable to other complex pattern recognition

problems.

The optimization should be performed to learn

the parameters of the base model, select the base

models; optimize the number of the base models and

to learn the features used for each model. Moreover,

the optimization should also employ the diversity

measures and the performance of base models.

Figure 3: Comparisons of different ensembles structures performance.

100

Accuracy Specificity Sensitivity

Ensemble of all methods

Accuracy -Accuracy

Optimized Ensemble

Specificity -Specificity

Optimized Ensemble

Sensitivity -Sensitivity

Optimized Ensemble

Accuracy -Diversity

Optimized Ensemble

Sensitivity -Diversity

Optimized Ensemble

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