2012 7th International Conference on Electrical and Computer Engineering 20-22 December, 2012, Dhaka, Bangladesh
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Optimized MFR & Automated Local Entropy Thresholding for Retinal Blood Vessel Extraction Saumitra Kumar Kuri, Sanika.S Patankar, and Jayant V.Kulkarni, Member, IEEE Department of Instrumentation and Control Engineering Vishwakarma Institute of Technology, Bibwewadi, Pune-411037, India. Email:
[email protected],
[email protected],
[email protected] Abstract-Retinal blood vessel extraction plays important role in diagnosis of many diseases such as diabetic retinopathy (DR), hypertension, glaucoma and arteriosclerosis. In this paper optimized matched filter response is used to enhance the blood vessel followed by local entropy thresholding used to segment the vessels automatically. First optimized matched filter are applied to the retinal images to enhance vessels then we used their corresponding co-occurrence matrix & automatic to find local entropy thresholding that used for segmented the blood vessel in retinal image. The results shows that automated local entropy thresholding more successful compare to other methods in our proposed matched have 95.86 % average accuracy.
In our literature are mainly based on MFR kernels [5], a Gaussian shaped curve models the cross section of a vessel and detects vessels using MFR kernels. The MFR kernels used to enhance the blood vessel. Automated local entropy thresholding can well keep the spatial structure of vessel extraction.
Index Terms- Diabetic retinopathy, Matched filter, Local Entropy Threshold, Retinal image, Vessel extraction.
III. PROPOSED VESSEL SEGMENTATION METHODS
I. INTRODUCTION Extraction of blood vessel in retinal images plays an important role in the diagnosis and treatment diseases, such as diabetic retinopathy (DR), hypertension, glaucoma and arteriosclerosis. Diabetic retinopathy is the leading ophthalmic pathological cause of blindness among people of working age in developed country. DR is not a curable disease, laser photocoagulation can prevent major vision loss if detected early stages [1]. However, DR patients perceive no symptoms until visual loss develops, usually in the later disease stages, when the treatment is less effective. So, to ensure the treatment is received in time, diabetic patients need annual eyefundus examination [2]. The employment of digital images for eye diseases diagnosis could be exploited for computerized early detection of DR. A system that could be used by non experts to filtrate cases of patients not affected by the disease, would reduce the specialists’ workload, and increase the effectiveness of preventive protocols and early therapeutic treatments [3]. 978-1-4673-1436-7/12/$31.00 ©2012 IEEE
II. MATERIALS We use DRIVE [6] database in TIFF format, this database has been widely used by other researchers to test vessel segmentation. Every image was capture at 584× 565 pixels, 8 bits per color plane.
In this paper we are not using preprocessing for original image. First we use modified matched filter to enhance the vessels, second we using automatic local thresholding for segmentation, finally we used median & length filter. A. Proposed Optimized Matched Filter The use of a 2-D matched filter for retinal vessel extraction was first proposed in [5]. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in fundus images. Blood vessels usually have poor local contrast compare to background. The two dimensional matched filter kernel is design to convolve with the original images to enhance the blood vessel. Mathematically, the Matched filter kernel can be described as ି௫ మ
K(x, y) = - exp (ଶఙమ ) , for |x| 3ı, |y| L / 2
(1)
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Optimized Matched filter kernel ି௫ మ
K(x, y) = - exp (
ଶఙ మ
) , for |x| 6, |y| 1
(2)
Where ı defines the spread of the intensity profile, L is used as kernel size of priori information about vessel length. In implementation this kernel needs to be rotated for all possible angles. We used 12 kernels which produced from 150 degree of rotation. The new coordinate calculated with multiplication of kernel by the transpose of rotation matrix Rotation matrix define ܿ ߠݏെߠ݊݅ݏ r =ቂ ቃ (3) ߠݏܿ ߠ݊݅ݏ Pi = [u v] = K(x, y) * rT Ki(x, y) = - exp (
ି௨మ ଶఙ మ
) Pi ߳ N,
Normalising the probability within individual quadrants, such that the sum of probabilities of each quadrant equals to one, we get the following cell probability (8) PijA = ݐ /ሺσ௦ୀ σ௦ୀ ݐ ) PijB = ݐ /ሺσ௦ୀ σିଵ ୀ௦ାଵ ݐ ) ିଵ PijC = ݐ /ሺσିଵ ୀ௦ାଵ σୀ௦ାଵ ݐ )
(10)
௦ PijD = ݐ /ሺσିଵ ୀ௦ାଵ σୀ ݐ )
(11)
The second order local entropy of the object can be defined as ଵ
(4) (5)
A neighborhood N is define such that N= {(u, v)| |u| 6, |v| 1 } and mi = σே ܭሺݔǡ ݕሻȀ ܣis used for to normalization the mean value of the filter. A denotes the number of points in N. The convolution kernel, K'i(x, y) = Ki(x, y) - mi . (6) The matched kernels, K'i (12 rotational kernels) are convolved with the green channel of retinal image.The output image is the maximum response from the 12 resultant images.
(9)
HA(2)(s) = െ σ௦ୀ σ௦ୀ PijA log2 PijA ଶ
(12)
Similarly the background written as ଵ
C C ିଵ HC(2)(s) = െ σିଵ ୀ௦ାଵ σୀ௦ାଵ Pij log2 Pij
ଶ
(13)
The gray level corresponding to the maximum of HT(2)(s) = HA(2)(s) + HC(2)(s)
(14)
The gray level corresponding to the maximum of HT(2)(s) gives the optimal threshold for objectbackground classification. This Threshold find it automatically form the Entropy-Threshold Curve. C. Median & Length Filter
B. Automated Local Entropy Thresholding Local entropy thresholding technique described in [7] .The co-occurrence matrix of the image F is an Q ൈ R dimensional matrix T = |tij|QxR that gives an idea about the transition of intensity between adjacent pixels, indicating spatial structural information of image. Depending upon the ways in which the gray level i follows gray level j, different definition of cooccurrence matrix are possible. Here, we made the co-occurrence matrix asymmetric by considering the horizontally right and vertically lower transitions. Let tij be the (i, j)th entry of the co-occurrence matrix. Then the probability of co-occurrence Pij of gray levels i and j is (7) Pij= ݐ /ሺσ σ ݐ ) Obviously 0 Pij 1 If s, 0 s L- 1, is a threshold, then s partitions the co-occurrence matrix into four quadrants, namely A, B, C and D.
We used (3ൈ ͵ሻsize of median filter [8] to reduce the salt & pepper noise & length filter is used to remove isolated pixels by using concept of connectivity. IV. EXPERIMENTAL AND SIMULATION RESULTS In this paper, we evaluated accuracy(Acc). Acc is a global measure providing the ratio of total wellclassified pixels. The computational time of whole process of our method takes approximate (8 sec) for each fundus image. We use twenty set of test DRIVE [6] database in TIFF format. our method average accuracy is 95.86% that is higher to other methods.
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TABLB I VESSEL CLASSIFICATION
Vessel detected Vessel not detected
Acc =
Vessel present True Positive (TP) False Negative (FN)
Vessel absent False Positive (FP) True Negative (TN)
REFERENCES [1]
்ା்ே
(15)
்ା்ேାிାிே
TABLE II PERFORMANCE RESULT ON DRIVE DATABASE IMAGES Acc
Image 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Average
accuracy in the case of normal retinal images and images with obscure blood vessel appearance. The smaller blood vessel could not segmented by threshold probing technique in our method performs very well for this type of case.
[3]
0.9817 0.9837 0.9830 0.9557 0.9853 0.9674 0.9467 0.9534 0.9456 0.9394 0.9353 0.9753 0.9625 0.9510 0.9409 0.9352 0.9253 0.9681 0.9816 0.9564 0.9586
[4]
[5]
[6]
[7]
[8] [9]
TABLB III PERFORMANCE RESULTS COMPARISON TO OTHER METHODS ON THE DRIVE DATABSE
Method Niemeijer et al.[9] Chaudhuri et al.[5] Jiang and Mojon [10] Mendonca et al. [11] Martinez-Perez et al. [12] Chinsdikici and Aydin [13] Staal et al.[14] Our Method
[2]
(Acc) 0.9417 0.8773 0.8911 0.9463 0.9344 0.9293 0.9441 0.9586
[10]
[11]
[12]
[13]
V. CONCLUSIONS In this paper, we have introduced an efficient algorithm for fully automated blood vessel segmentation in retinal images using the local entropy thresholding. This method computational time is very fast (8 Sec) compare to others methods, can achieve accurate segmentation results with good
[14]
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Fig. 1 Original Image Extraction Channel
F
Fig. 4 Segmented (Threshold =134) & After Median Filter Result
Fig. 2 Green channel Image & MFR result
Fig. 3 Entropy-Threshold curve (Threshold =134)
Fig. 5 Gold standard (Hand-labeled ground truth) & Final Result
Fig. 6 Compared result of Gold standard & Final result. True Positive (TP) =White pixels, False Positive (FP) = Red pixels, False Negative (FN) = Blue Pixels, True Negative (TN) = Black pixels.
