Design and Implementation of Thresholding Algorithm based on MFR for Retinal Fundus Images

IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 6, Ver. V (Nov – Dec. 2015), PP 11-14
www.iosrjournals.org
DOI: 10.9790/0661-17651114 www.iosrjournals.org 11 | Page
Design and Implementation of Thresholding Algorithm based on
MFR for Retinal Fundus Images
S. A. Jameel1
, Dr. A. R. Mohamed Shanavas2
1
(Assistant Professor, P.G and Research Department of Computer Science, Jamal Mohamed College
(Autonomous), Tiruchirappalli-620 020, Tamil Nadu, India
2
(Associate Professor, P.G and Research Department of Computer Science, Jamal Mohamed College
(Autonomous), Tiruchirappalli-620 020, Tamil Nadu, India
Abstract: In this paper, the entropy of maximum filter response (MFR) is applied followed by normalization
and thresholding for retinal fundus image is used. The performance of our proposed method has been assessed
on 23 images representing the publicly available dataset; High-Resolution Fundus (HRF) Image Database.
Keywords: Entropy, Fundus Image, Maximum filter response(MFR), Thresholding
I. Introduction
The Vessel extraction is significant in the survey of [4] digital fundus images since it helps in
diagnosing retinal diseases, particularly in evaluating the extremity of the disease in borderline cases. The
medical motivation concerning the segmentation of blood vessels of retinal images is to curb the background
and accentuate the small vessels so that characteristics such as irregular branching, tortuosity, entropy,
neovascularization [5] become more visually prominent. These medical markers assist ophthalmologists in
detecting various retinopathies especially diabetic retinopathy (DR), which is a main problem of diabetes. In the
view of World Health Organization (WHO), [6] more than 220 million people all over the world have diabetes
and deaths because of diabetes are estimated to double between 2005 and 2030. Also, the WHO states that
testing for diabetic retinopathy is an expenditure conserving involvement which will help in reducing the
difficulty of diabetes (World Health Organization Media Centre, 2015). The main problem faced in vessel
extraction of low-resolution images is the effective extraction of the smaller vessels. This is because, during
conventional pre-processing methods like smoothing and regular histogram equalization, the shorter vessels get
averaged out with the background. This ends in the combining of these vessels with the setting, making it hard
to segment them out because of the low variation with the background. Therefore, small vessel enhancement
algorithm is applied to the retinal images for better accurate outcomes. However the small vessels are improved,
the accuracy of the results would also depend on the effectiveness of the vessel segmentation algorithm. Other
structures like the optic disc, fovea centralis, etc. should be removed and only the retinal vessels should be
produced, as false detections affect the accuracy of the result. [1] The blood vessels in the fundus images are
enhanced by applying Gaussian 2nd order derivative filter successively by varying sigma. In our paper in order
to obtain the enhanced blood vessels, the maximum frequency response is chosen from the responses of applied
Gaussian filters. As the blood vessels are enhanced very sharply by the Gaussian 2nd order derivative filter, a
simple intensity-based thresholding approach is applied to extract the blood vessels.
II. Related Works
Jaeger, Stefan, et al presented their approach using the maximum filter response across all scales.[9] In
their method for doing a soft classification the maximum filter response for true red lesions is used. [10] M.
Varma et al presented that in Maximum Response (MR) sets, the MR8 filter bank consists of 38 filters but only
8 filters response. The filter bank consists of filters at many positions but their outputs are “collapsed" by
recording only the maximum filter response across all orientations. [11] An efficient, mask-size independent
algorithm for the maximum filter was proposed by Gil et al. Given the maximum filter response in each pixel,
the Non-Maximum Suppression (NMS) reduced to a supplementary differentiation of every pixel value with its
maximum neighbor was presented.[12] M. Varma et al compared the performance of these three models
proposed by Cula and Dana, Leung and Malik, Schmid and a rotation-invariant model based on the maximum
filter response across all positions for every filter type. [13] M. Varma et al used the VZ-MR8 (maximum filter
response independent of orientation) descriptor and SVM (Support Vector Machine) with 2 kernel for
classification. [14]

Design and Implementation of Thresholding Algorithm based on MFR for Retinal Fundus Images
III. Proposed Algorithm
3.1 Maximum Filter Response(MFR) by Entropy-based thresholding
In order to segment out blood vessel from retinal image, MFR image is processed by entropy-based
thresholding scheme. This takes into account the spatial distribution of gray intensities, is applied as image pixel
intensities are not independent of each other. Specifically, a local entropy thresholding technique is described in
[2] is implemented, which can well preserve the spatial structures in the binarized/thresholded image. Two
images with similar histograms but dissimilar spatial distribution will result in different entropy (also dissimilar
threshold values).
The co-occurrence matrix of the image F is a QP dimensional matrix QPijtT  ][ that gives an
idea of the change of intensities between neighboring pixels, indicating spatial structural information of an
image. Depending on the methods in which the gray level i pursues gray level j, different definitions of co-
occurrence matrix are possible. So, we made the co-occurrence matrix asymmetric by allowing the horizontally
right and vertically lower transitions.[7] So, ijt is termed as follows:
  

P
l
Q
k
ijt
1 1

(1)
where
otherwise
jklfandiklf
or
jklfandiklf
if
0
),1(),(
)1,(),(
1











otherwise
jklfandiklf
or
jklfandiklf
if
0
),1(),(
)1,(),(
1











The probability of co-occurrence ijp of gray levels i and j can, therefore, be written as
 

i j
ij
ij
ij
t
t
p
(2)
If
,10,  Lss is a threshold. Then s can partition the co-occurrence matrix into 4 quadrants,
into A, B, C and D. We can define the following quantities:
 

s
i
s
j
ijA pP
0 0
(3)
 





1
1
1
1
L
si
L
sj
ijC pP
(4)
Normalizing the probabilities within each separate quadrant, as the sum of the probabilities of every
quadrant equals one, [8] we get the following cell probabilities for different quadrants:
   








 






s
i
s
j
L
oi
L
j
ijij
L
i
ijij
A
ijA
ij
tt
tt
P
p
P
0 0
1 1
0
1
0
/
/

 
 s
i
s
j
ij
ij
t
t
0 0
(5)
for
sjsi  0,0
Similarly,
 





1
1
1
1
L
si
L
sj
ij
ij
C
ijC
ij
t
t
P
p
P
(6)
for 11,11  LjsLis
The second order entropy of the object can be defined as
A
ij
s
i
s
j
A
ijA PPsH 2
0 0
)2(
log
2
1
)(   
 (7)
Likewise, the second-order entropy of the background can be written as
C
ij
L
si
L
sj
G
ijC PPsH 2
1
1
1
1
)2(
log
2
1
)(  





(8)
Hence, the total second-order local entropy of the object and the background can be written as
)()()( )2()2()2(
sHsHsH CAT 
(9)
The gray level corresponding to the maximum of
2
TH gives the optimal threshold for object-
background classification
3.2 Algorithm
Step 1: Select maximum response of Gaussian filter
D = max (D(x, x), D(x, y), D(y, y))
Step 2: BVE (D)  Blood vessel extraction
D = Norm (D)
Step 3: Apply entropy thresholding to obtain blood vessel extracted image
BV = Enthresh (D)
IV. Experimental Results
The proposed technique is tested using the publicly available dataset High-Resolution Fundus (HRF)
Image Database. [3] At first the input image is preprocessed and derivative filters are applied. Then maximum
filter response is applied followed by normalization and thresholding.
4.1 Dataset
The public database contains at the present fifteen images of healthy patients, fifteen images of patients
with diabetic retinopathy. The proposed method uses fifteen images of healthy patients, eight images of patients
with diabetic retinopathy out of 15 images. [3]
4.2 Discussion
The proposed method is used to extract the blood vessels; in our experiments, a set of 23 colour retinal
images from the publicly available datasets were used. This gives a good opportunity to test the algorithm on
images with different features; normal, abnormal, different sizes. The algorithm is implemented using Matlab.
The proposed technique is tested using the publicly available dataset High-Resolution Fundus (HRF) Image
Database. The work is extended to implement to calculate the tortuosity of retinal blood vessels.
V. Conclusion
In this paper, a new algorithm for vessel extraction for thresholding based on the entropy of maximum
filter response is proposed. This is achieved by means of preprocessing followed by derivative filters. The

proposed algorithm is tested using the publicly available database. This work can be extended to calculate the
tortuosity of retinal vessels combining with another method.
References
[1]. Ramlugun, G. S., et al. Small retinal vessels extraction towards proliferative diabetic retinopathy screening. Expert Systems with
Applications (2011), doi:10.1016/j.eswa.2011.07.115.
[2]. N. R. Pal and S.K. Pal, “Entropy: A New Defination and Its Applications”, IEEE Transactions on systems, Man and Cybernetics,
vol.21, No. 5, September 1991.
[3]. H. Yu, S. Barriga, C. Agurto, G. Zamora, W. Bauman, and P. Soliz, “Fast vessel segmentation in retinal images using multiscale
enhancement and second-order local entropy,” Proc. SPIE, vol. 8315, pp. 83151B-1–83151B-12, Feb. 2012.
[4]. Carnimeo, Leonarda, and Rosamaria Nitti. "On Classifying Diabetic Patients’ with Proliferative Retinopathies via a Radial Basis
Probabilistic Neural Network." Advanced Intelligent Computing Theories and Applications. Springer International Publishing,
2015. 115-126.
[5]. Zalev, Jason, and Michael C. Kolios. "Detecting abnormal vasculature from photoacoustic signals using wavelet-packet features."
SPIE BiOS. International Society for Optics and Photonics, 2011.
[6]. Setacci, C., et al. "Diabetic patients: epidemiology and global impact." Journal of Cardiovascular Surgery 50.3 (2009): 263.
[7]. C. Chang, K. Chen, J. Wang, and M. Althouse, “A relative entropy-based approach to image thresholding,” Pattern recognition,
vol. 27, no. 9, pp. 1275–1289, 1994.
[8]. Chanwimaluang, Ihitiporn, and Guoliang Fan. "An efficient algorithm for extraction of anatomical structures in retinal images."
Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on. Vol. 1. IEEE, 2003.
[9]. Jaeger, Stefan, et al. "Automatic tuberculosis screening using chest radiographs." Medical Imaging, IEEE Transactions on 33.2
(2014): 233-245.
[10]. Niemeijer, Meindert, et al. "Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus
photographs." Medical Imaging, IEEE Transactions on 29.1 (2010): 185-195.
[11]. Varma, Manik, and Andrew Zisserman. "A statistical approach to texture classification from single images." International Journal
of Computer Vision62.1-2 (2005): 61-81.
[12]. Neubeck, Alexander, and Luc Van Gool. "Efficient non-maximum suppression." Pattern Recognition, 2006. ICPR 2006. 18th
International Conference on. Vol. 3. IEEE, 2006.
[13]. Varma, Manik, and Andrew Zisserman. "Classifying images of materials: Achieving viewpoint and illumination
independence." Computer Vision—ECCV 2002. Springer Berlin Heidelberg, 2002. 255-271.
[14]. Zhang, J., et al. Local features and kernels for classifcation of texture and object categories: An indepth study. Technical Report
RR-5737, INRIA Rhône-Alpes, 2005.
Fig 1. Flow chart of the proposed algorithm
Fig 2. GUI for Thresholding based on Entropy of Maximum Filter Response

Design and Implementation of Thresholding Algorithm based on MFR for Retinal Fundus Images

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Design and Implementation of Thresholding Algorithm based on MFR for Retinal Fundus Images