Using Phase Congruency Model for Microaneurysms Detection in

Fundus Image

Zhitao Xiao

, Fang Zhang

, Lei Geng

, Jun Wu

, Xinpeng Zhang

, Long Su

and Chunyan Shan

School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China

Tianjin Medical University Eye Hospital, Tianjin 300384, China

Tianjin Medical University Metabolic Disease Hospital, Tianjin 300070, China

Keywords: Microaneurysms, Phase Congruency, Directional Cross-Section Profiles, Diabetic Retinopathy.

Abstract: This paper addresses an automatic detection method of microaneurysms in color fundus images, which

plays a key role in computer assisted diagnosis of diabetic retinopathy, a serious and frequent eye disease.

The main concentration of this paper is to detect microaneurysms with phase congruency. The first step

consists in image normalization and green channel extraction. The second step aims at obtaining

microaneurysms candidate regions, which is achieved using phase congruency. Then the irrelevant

information, such as the vessel fragments, is removed by constructing directional cross-section profiles.

Through testing on 50 fundus images provided by ROC website, the experimental results show that this

method can accurately get microaneurysms in color fundus images.

1 INTRODUCTION

Diabetic retinopathy (DR) is a complication of

diabetes that results from damage to the blood

vessels of the light-sensitive tissue at the back of the

eye (retina). It is a sight-threatening disease that

possibly leads to vision impairment and blindness. It

has been shown that an automated DR screening

system would be a great assist in the processes of

diagnosing and progression tracking.

Microaneurysm (MA) is the swelling of the capillary

caused by a weakening of the vessel wall, which

appears as the tiny and reddish isolated dot. MAs are

amongst the first clinical signs of the presence of

DR. Hence, the automatic detection of MAs in color

fundus images is critical for diagnosing the process

of Diabetes and plays an important role on mass DR

screening.

In section 2 we will briefly review the available

methods for fundus images MAs detection, after

which in section 3 phase congruency theory is

introduced. The proposed method is described in

details in section 4. Finally, the experiment results

and conclusion of our method are presented in

section 5 and section 6.

2 STATE-OF-THE-ART MA

DETECTORS

MAs are characterized by their diameter which is

always smaller than 125μm. They have typically low

contrast and may be hard to distinguish from noise

or pigmentation variations. In addition, color fundus

images often suffer from non-uniform illumination,

poor contrast and noise. Therefore, achieving

efficient detection of MAs becomes a complex and

challenging issue. Existing MAs detection

algorithms can be divided into three categories,

mathematical morphology methods (Spencer, 1996;

Hipwell, 2000; Fleming, 2006; Walter, 2007),

supervised learning based methods (Sinthanayothin,

2002; Niemeijer, 2005; Zhang, 2010), and filter

based methods (Quellec, 2008; Hatanaka, 2012).

Spencer et al. (Spencer, 1996) described a

mathematical morphology based detection method

for MAs. Using shade-correction and top-hat

transformation, they gave satisfactory results.

Whereas it is in fluorescein angiographies images

and not appropriate for color fundus images. To

detect the MA candidates in color fundus

photographs, Niemeijer et al. (Niemeijer, 2005)

presented a hybrid scheme that uses both the top-hat

transformation and the supervised pixel

158

Xiao Z., Zhang F., Geng L., Wu J., Zhang X., Su L. and Shan C..

Using Phase Congruency Model for Microaneurysms Detection in Fundus Image.

DOI: 10.5220/0005186801580163

In Proceedings of the International Conference on Pattern Recognition Applications and Methods (ICPRAM-2015), pages 158-163

ISBN: 978-989-758-077-2

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

classification based method. Zhang et al. (Zhang,

2010) proposed a method based on Sparse

Representation Classifier (SRC). Hatanaka et al.

(Hatanaka, 2012) achieved automatic detection in

non-dilated fundus images using the double-ring

filter and the artificial neural network (ANN).

Although these methods described above have

achieved automatic detection of MAs in different

kinds of fundus images, there are still some

problems, such as high false detection rate, high

missing detection rate, and complex operation. The

fundamental reason is that these approaches mainly

use gradient information to describe image. Gradient

based detection methods are sensitive to the contrast

of image and noise. Thus such algorithms require

initial images with high quality. If the fundus images

are degraded due to the non-uniform illumination or

low contrast, these methods will have difficulties to

detect the real MAs and exclude a large number of

non-MA objects. And the system will be more

complex in further classification based on the region

feature. On the whole, gradient-based approaches

are difficult to achieve good detection results for

fundus images with complex background.

Phase information is consistent with human

visual system perception characteristics. It has many

advantages in image description, such as invariance

to contrast and brightness, high noise immunity.

Therefore this article describes a novel MAs

detection method based on phase information.

Because phase information is invariant to contrast

and brightness, it needs no enhancement

preprocessing. Moreover, the proposed method

detects MAs directly, which avoids the complex

process of feature training and object classification.

As a result, it greatly reduces the complexity and

running time. The performance of the proposed

method is demonstrated through experiments.

3 PHASE CONGRUENCY

MODEL

Phase Congruency model (PC) is an image feature

detector (Morrone, 1987; Xiao, 2004). It assumes

the most accordant point of phase in Fourier

component as the feature points, which can be used

to detect step feature, line feature and roof feature.

PC has been successfully applied to texture

segmentation, edge detection, image denoising and

other fields with satisfactory results. Using PC for

marking feature has significant advantages over

gradient-based methods. It is invariant to image

brightness and contrast. Hence it provides an

absolute measurement of the significance of feature

points, which accords to human visual perception

characteristics. These excellent features make it

ideal for medical images with various characteristics.

4 METHOD OF MA DETECTION

IN FUNDUS IMAGE BASED ON

PHASE CONGRUENCY

The proposed method is divided into three

processes: preprocessing, getting MA candidates,

and screening MAs. The flowchart of this method is

illustrated in Figure 1.

Figure 1: Flowchart of MAs detection in fundus image

based on PC.

4.1 Preprocessing

Firstly, for setting the parameters conveniently, the

images are resized to horizontal resolution 768

pixels using bicubic interpolation. Compared with

the red and blue channels, the objects such as blood

vessels and MAs in the retinal layer are best

represented (see Figure 2) in the green channel.

Therefore, the green channel of the color fundus

image is chosen for the subsequent processing. In

the green channel of color fundus image, MAs

appear as dark patterns, small, isolated and circular

shape, as shown in Figure 3.

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(a) original image; (b) green channel.

Figure 2: Original image and its green channel image.

Figure 3: MA details in green channel.

4.2 MA Candidates Detection

4.2.1 Objects Detection based on PC

Here, we use the PC calculating method provided by

Kovesi (Kovesi, 1999), which extended the 1-D PC

to allow the calculation of 2-D PC of image by

applying 1-D analysis over several orientations and

scales. After PC calculation, a pattern mask is

chosen to eliminate the boundary of the image. The

PC detection result is shown in Figure 4 (Because

the image has low gray value after PC processing, in

order to demonstrate the result of PC clearly, the

image is shown with gray enhancement). The

structures which have large local energy (including

MAs) are preserved, while blood vessels and other

large lesions have been filtered out mostly.

Figure 4: Detection result from PC.

4.2.2 MA Candidates Detection

After feature detection by PC, points with high gray-

scale correspond to the targets with salient features

in green channel fundus image. Thus, MA

candidates can be extracted by thresholding. Here,

one-tenth of the maximum gray value in the PC

detection result is selected as the threshold to

achieve binarization for Figure 4. Then the area

filtering is executed, through which large structures

(blood vessels and other large lesions) and small

structures (noise) are removed, and the MA

candidates (including some false MAs and the real

MAs) are left. Figure 5 shows the filtered results

superimposed on the green channel image. We can

see that most of the MA candidates occur on the

blood vessels. Some corners and junctions of blood

vessels, which have similar brightness and shape

with MAs, lead to error detection. With the

knowledge of pathology, MAs are located on

capillaries. And as these capillaries are not visible in

color fundus images, MAs should appear as isolated

patterns. Based on these characteristics, false MAs

can be wiped away (shown in next Section).

Figure 5: MA candidates superimposed on the green

channel image.

4.3 Cross-Section Construction

According to the shape feature of MA candidates,

non-MA targets can be excluded. For each MA

candidate point (x,y), a W×W neighborhood window

centered on (x,y) is taken at the original green

channel image (W=2L-1. L is considered according

to the image resolution. Here, L=8 is selected

corresponding to the image horizontal resolution of

768 pixels). In the selected neighborhood, eight

scanning lines with different angles passing through

the center point are tested (here, they are 0°, 22°,

45°, 66°, 90°, 111°, 135°, 156°). The records of the

pixel gray values along the eight scanning lines

constitute a set of one dimensional intensity profiles,

in other words, form a set of cross-section profiles

(Lazar, 2013). Vessel segments, background regions

and MAs have different characteristics, as shown in

Figure 6. It is found that MAs show significant

Gaussian-like peaks for all directions. While in the

case of the blood vessels, only the profiles of

scanning lines cross the vessel show clear peaks. As

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the directions of the scanning lines approximate to

the direction of the vessel segment, the peaks of the

profiles become more and more unclear, until almost

completely disappears.

(a)vessel segment; (b)background region; (c)MA.

Figure 6: Cross-section profiles of a vessel segment,

background region and an MA.

Peak detection is applied on each profile. Several

parameters for the peaks, including size, height, and

shape are calculated subsequently. Once a peak is

detected at the center of the window, the slope

between adjacent pixels is calculated. Thereby the

four special points of the peak are determined.

Specifically, the values of inc

and inc

correspond

to the start and the end indices of the increasing

ramp. Similarly, dec

and dec

denote the boundaries

of the decreasing ramp, respectively. Figure 7 shows

a graphical interpretation of the ramps. Then the

following five properties of each peak are calculated

using these four points.

(1) The peak width is the difference between the

start and the end indices of the peak:

() () ()

peak e s

wideciinci

(1)

(2) The top width is the size of the gap between

the increasing and the decreasing ramp:

() () ()

top s e

w i deci inci

(2)

Figure 7: Four special points of peak detection.

(3) The average ramp height:





() () () 2

inc dec

Rheights i h i h i

(3)

where

( ) [ ( )] [ ( )]

inc e s

hi Pinci Pinci





(4)

stands for the increasing ramp height, and

()[()][()]

dec s e

hi Pdeci Pdeci





(5)

is the decreasing ramp height, P[·] denotes the gray

value of a given pixel;

(4) The average ramp slope:





() () () 2

inc dec

Rslopes i S i S i

(6)

where

() () () ()

inc inc e s

S i h i inc i inc i

(7)

is the increasing ramp slope, and

() () () ()

dec dec e s

S i h i dec i dec i

(8)

is the decreasing ramp slope;

(5) The peak height is computed as the difference

between the intensity of the center pixel and a

baseline that connects the start and the end of the

profile:

)]([)]([)(

)])([)]([(][

)(

iincPiincLiw

iincPidecPLP

sspeak

peak







(9)

where i=0,1,2,...,7, represents the eight scanning

directions. After obtaining the five properties, Score

is calculated by the following equation, which has

considered the shape, symmetry, sharpness and

contrast of the candidates.

peak

top peak

peak

hRslopes

w w Rslopes Rheights h

Score



  





  

(10)

where

peak

is the minimum of h

peak

Rslopes



is the

mean of Rslopes,

peak



is the standard deviation of

peak

. Similarly,

top



Rheights



Rslopes



and

peak



are the standard deviation of w

top

, Rheights, Rslopes

and h

peak

, respectively.

By calculating the Score of a large of real MAs,

we get the Score value range as [20, 30] for the real

MAs. Accordingly, when the candidate’s Score

value falls into this range, it can be considered as the

real MA. Otherwise, it will be treated as the false

target and be removed away.

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5 RESULTS AND DISCUSSION

5.1 Materials and Results

To examine the performance of the proposed

method, we utilize the fundus images provided by

Retinopathy Online Challenge (ROC) website

(Retinopathy Online Challenge, 2008). The ROC

provides 50 training cases and 50 test cases, in

which “gold standard” locations of MAs are

provided for the training cases, and the MAs in the

test ones are not labeled. In the experiment, the

computerized scheme is developed using the training

set, and the performance of the proposed method is

validated with “gold standard”. Figure 8 shows the

MAs detection results of three images from the

training set, where (a1), (a2) and (a3) are the “gold

standard” given by ROC, (b1), (b2) and (b3) are the

final MAs detection results of our method. There are

uneven illumination and low contrast problems in

(a1) and (a2). Although the image quality of the

three selected fundus pictures are different,

experimental result shows that the proposed method

can detect the MAs accurately in color fundus

images without any enhancement processing.

5.2 Quantitative Evaluation and

Comparison

In the image level, the detection sensitivity,

specificity and accuracy are selected as the

algorithm stability criteria, which are defined as the

following formulas (Gao, 2012):

sensitivity

TP FN





specificity

TN FP





TP TN

accuracy

TP TN FP FN







where TP means true positive, FP is false positive,

TN stands for true negative, and FN notes false

negative.

In this paper, 50 fundus images in training set are

detected. To the image level, the method achieved

sensitivity of 94%, specificity of 100%, and

accuracy of 96%, respectively. Experimental data

show that this method can give good performance in

detecting MAs in color fundus images, which also

obtain satisfactory results for distinguishing between

normal and diseased fundus images.

Table 1 shows the sensitivity of different

methods at 1.0 FP per image (that is, the sensitivity

when the number of False Positive is 1.0).

Sensitivity is the number of true MAs correctly

detected, while false positive is the number of non-

MAs detected as MAs. Table 1 demonstrates the

performance of the proposed method and some

existed methods. From Table 1, one can see that our

method can precisely locate MAs with high

sensitivity at low false positive rate.

(a1) (b1)

(a2) (b2)

(a3) (b3)

(a1), (a2), (a3) are MAs labeled by ROC (MAs are circled); (b1),

(b2), (b3) are the MAs detection results of the proposed method

(white dots in squares are detected MAs).

Figure 8: MAs detection results of the proposed method.

Table 1: Comparison of the sensitivity at 1.0 FP per image.

Sensitivity at 1.0 FP

Niemeijer (Niemeijer, 2005) 0.018

Math Morph (Spencer, 1996) 0.072

SRC (Zhang, 2010) 0.13

double-ring (Hatanaka, 2012) 0.15

Proposed Method 0.23

6 CONCLUSIONS

This paper proposes a novel MAs detection method

in color fundus image based on phase information,

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which including three processes, i.e. preprocessing,

getting MA candidates, and screening MAs. The PC

model is used to get MAs candidates. The obtained

MAs candidates are very near to the true MAs,

which give the good basis for next processing. Then,

the irrelevant information, such as the vessel

fragments, is removed by constructing directional

cross-section profiles. This approach is invariant to

image contrast and brightness, which needs no

enhancement processing. The experiments results on

50 images provided by ROC website show that this

method can accurately detect microaneurysms in

color fundus images.

ACKNOWLEDGEMENTS

This work was supported by the National Nature

Science Foundation of China (NSFC) under grant

No. 61102150 and the Tianjin Science and

Technology Supporting Projection under grant No.

13ZCZDGX02100.

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