International Journal of Computer Engineering and ...

International Journal of Computer Engineering and Applications, Volume XI, Issue IX, September 17, www.ijcea.com ISSN 2321-3469

A SURVEY PAPER ON METHODS USED FOR THE EXTRACTION OF RETINAL VESSELS FOR DIABETIC RETINOPATHY Sandhya Soman1,Vani Chakraborty2 1Department of Computer Science, UG, Kristu Jayanti College, Bangalore,India 2Department of Computer Science,UG,Kristu Jayanti College, Bangalore, India

ABSTRACT: Due to the sedentary lifestyles these days, majority of the population in the developing and developed countries suffer from diabetes-a common disease due to body’s inability to maintain its glucose level. A good count of them, have poor vision due to diabetic retinopathy. This disease, if diagnosed at its non-proliferative stage, can enable and make it easy for the ophthalmologists to find effective cure for it. Thus a lot of research has been done in order to automate imaging systems which can identify diseased retinal images from healthy ones. A very vital step in that is the segmentation of the blood vessels of retina from the other micro developments in the retina due to the diseased condition. Hence, in this paper, we have conducted a study of the various techniques available in the literature for the identification of retinal blood vessels.

Keywords: Segmentation, curvelet transform, thresholding, line tracking, bottom hat transformation, diabetic retinopathy.

[1] INTRODUCTION The mammalian eye provides a three dimensional coloured image of the object through conscious light perception. The eye is composed of several layers, each of which plays a significant role in the formation of image of the focused object on the last layer of the eye i.e. the retina. With age or due to other degenerative conditions, the retinal ability is affected which may result in asymptotic loss of vision in the affected patients. One common cause of degeneration is Diabetic Retinopathy, which is medical condition resulting in damage of the retinal structure either due to 1 Sandhya Soman, Vani Chakraborty

A SURVEY PAPER ON METHODS USED FOR THE EXTRACTION OF RETINAL VESSELS FOR DIABETIC RETINOPATHY

neovascularization, formation of cotton wool spots, micro-aneurysm resulting in blurred vision, difficulty in night vision and complete blindness in extreme cases. Computer aided diagnostic systems can help in the effective diagnosis and treatment of DR. The success of such systems depends on a great extent on the efficient extraction of blood vessels from retinal images. In this paper, an attempt is made to enlist techniques for the identification and segmentation of retinal blood vessels in the images so that they can be distinguished from other micro vascular structures formed during diseased conditions and thus would enable the ophthalmologists to carry out effective diagnosis.

[2] DIABETIC RETINOPATHY Most of the people suffering from diabetes also have diabetic retinopathy which is because of the damage caused to the retinal blood vessels. This [25] is when high blood sugar levels causes blood vessels in the retina to swell(micro-aneurysm) and leak or may stop blood from passing through them. It may also result in abnormal new blood vessels to grow on the retina (neo-vascularization). which may result in poor vision or complete blindness if left treated .

[3] DRIVE AND OTHER DATABASES A number of automatic detection tools for DR are proposed in the literature and the success of these tools depends upon their ability of efficient detection of blood vessels. Several public databases are available which can be used by researchers to verify the effectiveness and efficiency of their proposed algorithms, like DIARETDB1, DIARETDB2, and DRIVE etc. The following section discusses about these databases.

1. DRIVE: Digital Retinal Images for Vessel Extraction [1] The DRIVE [1] database enables comparative studies on segmentation of blood vessels in retinal images. The data was obtained from a screening program in Netherlands which included retinal images from around 400 diabetic subjects aged between 25 and 90 years .The images are JPEG compressed.

2. The STARE (STructured Analysis of the Retina) Project [2] The project was conceived and initiated in 1975 by Michael Goldbaum, at the University of California, San Diego. The database has around 400 raw retinal images.

3. DIARETDB0 and DIARETDB1 [3] These are two standard diabetic retinopathy databases with calibration 0 and 1 DIARETDB0 contains 130 color images of which 20 are normal and 110 DR affected DIARETDB1 contains 89 color images of which 5 are normal and 84 contain microaneurysms.

2 Sandhya Soman, Vani Chakraborty


[3] RETINAL BLOOD VESSEL DETECTION TECHNIQUES Automatic and quick extraction of blood vessels from retinal images has always extracted interest of researchers. The early detection of disease depends on a great extent on the ability of the given technique to separate these blood vessels from other micro developments on the retinal surface. A number of methods for blood vessel detection have been proposed in the literature by various authors. Some of the techniques are:

3.1 Bottom – hat transformation In digital image processing, bottom hat transform [22] is an operation that helps to highlight minute dark spots in a given image. It inverts high - frequency regions. Bottom- hat transforms are used for various image processing tasks, such as feature extraction, background equalization, image enhancement, and others. It can be used for images composed of brightest points and darkest regions. It preserves the sharp bottoms of an image by shaving [22] off the sharp valleys (erosion), reconstructing without sharp valleys(dilation) and then subtracting from the original image

3.2 Thresholding Thresholding [23] is a binarization technique used to split an image into smaller segments, or junks, using at least one colour or grayscale value to define their boundary. The advantage of obtaining first a binary image is that it reduces the complexity of the data and simplifies the process of recognition and classification.

3.3 Curvelet transform Curvelet transform [21] is a multi-scale transformation technique which aims to deal with curved edges in a 2D image requiring fewer coefficients for representation than a ridgelet. It most suitable for object with curves and for low contrast uniformly illuminated images. 3.4 Tracking Line tracking method [24] is used to trace a line on the image with a certain angular orientation and diameter. By utilizing the image histogram, the pixel area boundaries will be determined to be tracked by the threshold value corresponding to the frequency of the intensity image. The 4-neighbours and 8-neighbours of a pixel are important elements to be considered in any line tracking algorithm.

We have made an attempt in this paper, to enlist the image segmentation and enhancement techniques used in the literature for vessel extraction, the database used by authors to test their proposition and the accuracy attained in their work. The following table is a result of one such attempt.



RETINAL BLOOD VESSEL DETECTION TECHNIQUES CURVELET TRANSFORM

S.No.

AUTHOR(s) SUPPORTING TECHNIQUES

EXTRACTS

1.

Sudeshna Al [4] (2014)

et.  CT and Matched filtering: to intensify blood vessel response  Optimal thresholding: to extract different vessel silhouettes from background Saleh  PCA and CT: highlight Shahbeig [5] edges (2013)  Morphological function with multi-directional structure: extract vessels  Adaptive filter: refine frills Esmaeili et. Al  CT and match [6] filtering: contrast enhancement and intensifying blood vessels and edge extraction  length filtering: remove misclassified pixels Selvathi [7]  CT: to represent (2012) edges  Support Vector Machine: to classify each pixel as vessel/nonvessel

Thin, medium and DRIVE thick retinal vessels

0.9552

frills with the size DRIVE of smaller than arterioles in images

0.9458

exudates and DRIVE micro- aneurysm

ROC space*: 0.9631

Detection of DRIVE abnormal structures at a non-proliferative stage

0.94

2.

3.

4.

1.

D. Devaraj et.  Al [8] (2014) 

2.

Mithun et. Al (2014) [9]

 

3.

Manjiri et. Al (2013) [10]

 

THRESHOLDING Adaptive median smoothed vessel thresholding: removes image unwanted pixels subtraction, binarising, erosion, and complement: to smoothen vessel image Binary thresholding: Optic disc and blood vessels edge detection Morphological operation Histogram equalization: Extracts for image enahancement bifurcation points 2D median filter:

DATAB ASE

DRIVE

ACCUR ACY

0.95

STARE and DRIVE Diaretdb0 Diaretdb1 DRIVE

0.95 0.96 0.98 4

Sandhya Soman, Vani Chakraborty


4.

Lili Xu et. Al (2010) [11]

highlighting vessels  Adaptive local complete vascular DRIVE threshold: to get good tree structure contrast of large blood vessels  SVM: to classify vessel segments

0.932

BOTTOM HAT TRANSFORMATION [18]

1.

A. Halter et al (2015) [12]

lighter  Median and Gaussian Vessels than darker filter: noise removal  Unsharp Masking: to background sharpen the image

0.93

2.

Zhu et. Al (2017) [13]

 Discriminative feature Exhibits vectors after applying speed bottom hat robustness transformation are fed to ELM(Extreme Learning Machine) classifer

0.9607

RANDO M RETINA L IMAGES high DRIVE and

TRACKING [18]

1.

2.

3.

Yi Yin et al  Bayesian method: for [15] detection of vessel edge point  Gaussian model: local vessel’s sectional intensity profile Helen et. Al  Star networked pixel [16] tracking algorithm: determines processed pixel is part of vessel or not  adaptive histogram equalization : to enhance image  Morphological operations: enhance vessel contrast Marios Vlachos  Multi-scale tracking: et. Al  Median filtering: to [17] restore disconnected  vessels and eliminate noise  Morphological reconstruction: remove errorneous attributes

vessel bifurcation STARE and crossing

0.9290

Vessels in color DRIVE image of retina

0.9583

Extracts network

vessel DRIVE



[4] CONCLUSION AND FUTURE WORK Extraction of blood vessels through curvelet transform is especially beneficial for low contrast uniformly illuminated images. Using curvelet transform along with other techniques like Principal Component Analysis/ length filtering/ SVM (support vector machine), authors were able to distinguish vessels on the basis of their thickness as thick, thin and medium vessels. Hence this technique can be used to detect the swelling in the retinal blood vessels (micro-aneurysm symptom). This method provides accuracy in the range of 94-96% as supported by literature but has considerable computational overheads which is around 10-20 times more than that of FFT(Fast fourier transform) having a dependence of n2logn for a n X n image. Thresholding benefits the images having contrast variations and is capable of detecting the complete vascular tree structure. Majority of the authors have supported using a local thresholding scheme than a global one as although global Thresholding are faster than the local ones but they miss on a lot of details in the vascular tree. Local Thresholding on the other hand are more exact but are slower. Authors have reported accuracy in the range of 93-98% using Thresholding techniques coupled with other techniques like image subtraction, and complementation and have also claimed to extract special bifurcation points in the vessel structure using this technique. Bottom hat transformation can extract small braches of blood vessels as they have lower reflectance from other retinal surfaces with an accuracy rate of 93-96%. Authors have reported it to be a relatively faster technique and are preferred in case of images which are composed of bright and

dark areas because identifying threshold values in such cases is difficult. Tracking methods are capable of identifying the complete vessel network with an average accuracy of 92-95% and were preferred by authors because of their ability to track the path of blood vessels on the basis of their angular orientation though they were reported to be computationally slower. The future work will include investigation of an approach which uses machine classifiers along with bottom hat transformation technique to delve deeper into the ocular manifestations.

REFERENCES [1] http://www.isi.uu.nl/Research/Databases/DRIVE/ [2] http://cecas.clemson.edu/~ahoover/stare/ [3] http://www.it.lut.fi/project/imageret/diaretdb0/ [4] Sudeshna Sil Kar, Santi P. Maity, Claude Delpha, “On retinal blood vessel extraction using curvelet transform and differential evolution based maximum fuzzy entropy”, In: ICIP (2014). [5] Shahbeig, Saleh. "Automatic and quick blood vessels extraction algorithm in retinal images. In: Image Processing, IET 7.4 (2013): 392-400. [6] Esmaeili, Mahdad, et al. "Extraction of retinal blood vessels by curvelet transform." Image Processing (ICIP),2009 16th IEEE International Conference on. IEEE, (2009).


International Journal of Computer Engineering and Applications, Volume XI, Issue IX, September 17, www.ijcea.com ISSN 2321-3469 [7] Selvathi, D., and Neethi Balagopal. "Detection of retinal blood vessels using curvelet transform." In: Devices,Circuits and Systems (ICDCS), 2012 International Conference on. IEEE, (2012). [8] Deepashree Devaraj, Prasanna Kumar S.C., “Blood Vessels Segmentation with GUI in Digital Fundus Images for Automated detection of Diabetic Retinopathy”, In: International Conference on Contemporary Computing and Informatics (IC3I), pp. 915-920, (2014). [9] Mithun, Niluthpol Chowdhury, Sourav Das, and Shaikh Anowarul Fattah. "Automated detection of optic disc and blood vessel in retinal image using morphological, edge detection and feature extraction technique." In: Computer and Information Technology (ICCIT), 2013 16th International Conference on. IEEE, (2014). [10] Patwari, Manjiri B., et al. "Extraction of the Retinal Blood Vessels and Detection of the Bifurcation Points." In:International Journal of Computer Applications 77.2 (2013). [11] Xu, Lili, and Shuqian Luo. "A novel method for blood vessel detection from retinal images." In: Biomedical engineering online 9.1 (2010) [12] Halder Amiya, Bhattacharya Pritam, 2015. An application of Bottom Hat Transformation to Extract Blood Vessel from Retinal Images.IEEE ICCSP 2015 Conference. 1791–1795.(2015) [13] Zhua Chengzhang, Zoua Beiji, Zhao Rongchang, Cui Jinkai, Duan Xuanchu, Chen Zailiang, Liang Yixiong, 2017. Retinal vessel segmentation in colour fundus images using ExtremeLearning Machine (2017) [14] Shah Syed, Tang Tong. “Blood Vessel Segmentation in Color Fundus Images Based on Regional and Hessian Features”, May 2017, DOI: 10.1007/s00417-017-3677-y (2017) [15] Yin, Yi, Mouloud Adel, and Salah Bourennane. "An automatic tracking method for retinal vascular tree extraction." In: Acoustics, Speech and Signal Processing(ICASSP), 2012 IEEE International Conference on. IEEE, (2012). [16] Ocbagabir, Helen, et al. "A novel vessel segmentation algorithm in color images of the retina." In: Systems, Applications and Technology Conference (LISAT), 2013 IEEE Long Island. IEEE, (2013.) [17] Pohankar Neha, Wankhade N. R. “Different methods used for extraction of blood vessels from retinal images”. In: WCFTR’16, (2016) [18] Solkar Sonam, Das Lekha. “Survey on Retinal blood vessel segmentation techniquees for detection of diabetic retinopathy”. In: IJEECS. ISSN 2348-117X, June 2017 (2017) [19] Peter Bankhead, C.Normam Scholfield,J.Graham McGeown,Tim M.Curtis, “Fast Retinal vessel detection and measurement using wavelets and edge location refinement”, In: PLOS One Journal, March 12,(2012). [20] “Detection of vessels in eye retina using line tracking algorithm with matlab code”, https://matlabfreecode.wordpress.com. [21] M Deepa, “Wavelet and Curvelet based thresholding techniques for image denoising”. In: International

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A SURVEY PAPER ON METHODS USED FOR THE EXTRACTION OF RETINAL VESSELS FOR DIABETIC RETINOPATHY [22] Mohan Anaswara, Das Seena, “Medical Image Enhancement Techniques by Bottom Hat and median filtering”, In: International Journal of Electronics Communication and Computer Engineering, Vol 5, Issue(4) July, Technovision-2014, ISSN 2449-071x (2014) [23] Verma Jaiprakash, Desai Khushali, “Image to Sound Conversion”, In: International Journal of Advance Research in Computer Science and management studies, Vol1, Issue6, Nov (2013) [24] Detection Of Vessels In Eye Retina Using Line Tracking Algorithm With Matlab Code https://matlabfreecode.wordpress.com/tag/image-segmentation/ [25] https://www.aao.org/eye-health/diseases/what-is-diabetic-retinopathy
