- Research
- Open Access
Retina identification based on the pattern of blood vessels using fuzzy logic
- Wafa Barkhoda1Email author,
- Fardin Akhlaqian1,
- Mehran Deljavan Amiri1 and
- Mohammad Sadeq Nouroozzadeh2
https://doi.org/10.1186/1687-6180-2011-113
© Barkhoda et al; licensee Springer. 2011
- Received: 2 July 2011
- Accepted: 23 November 2011
- Published: 23 November 2011
Abstract
This article proposed a novel human identification method based on retinal images. The proposed system composed of two main parts, feature extraction component and decision-making component. In feature extraction component, first blood vessels extracted and then they have been thinned by a morphological algorithm. Then, two feature vectors are constructed for each image, by utilizing angular and radial partitioning. In previous studies, Manhattan distance has been used as similarity measure between images. In this article, a fuzzy system with Manhattan distances of two feature vectors as input and similarity measure as output has been added to decision-making component. Simulations show that this system is about 99.75% accurate which make it superior to a great extent versus previous studies. In addition to high accuracy rate, rotation invariance and low computational overhead are other advantages of the proposed systems that make it ideal for real-time systems.
Keywords
- retina images
- blood vessels' pattern
- angular partitioning
- radial partitioning
- fuzzy logic
1. Introduction
Biometric is composed of two Greek roots, Bios is meaning life and Metron is meaning measure. Biometrics refers to human identification methods which based on physical or behavioral characteristics. Finger prints, palm vein, face, iris, retina, voice, DNA and so on are some examples of these characteristics. In biometric, usually we use body organs that have simpler and healthier usage. Each method has its own advantages and disadvantages and we could combine them with other security methods to resolve their drawbacks. These systems have been designed so that they use people natural characteristics instead of using keys or ciphers, these characteristics never been lost, robbed, or forgotten, they are available anytime and anywhere and coping them or forging them are so difficult [1, 2].
Characteristics which could be used in biometric system must have two important uniqueness and repeatability properties. This means that the characteristic must be so that it could recognize all people from each other and also it must infinitely be measurable for all peoples.
Humans are familiar with biometric for a long time but it become popular in the last two centuries. In 1870, a French researcher first introduced human identification system based on measurement of body skeleton parts. This system was used in United States until 1920. Also, in 1880, fingerprint and face were proposed for human identification. Another usage of biometric goes back to World War II, when Germans record people's fingerprint on their ID. Also retina vessels first have been used in 1980. Iris image is another biometric that has been used so far. Although use of them has been suggested in 1936 but due to technological limitations they have not been used until 1993.
Biometric features are divided into physical, behavioral, and chemical categories based on their essence. Using physical characteristics is one of the oldest identification methods which get more diverse by technological advancements. Fingerprint, face, iris, and retina are examples of the most popular physical biometrics. The most important advantages of this category are their high uniqueness and their stability over time.
Behavioral techniques evaluate doing of some task by the user. Signature modes, walking style, or expression style of some statement are examples of these features. Moreover, typing or writing style or voice could be classified as behavioral characteristics too. Lack of stability over time is a great drawback of these features because people's habits and behaviors are being changed over time and therefore these characteristics will be changed accordingly. For resolving this problem, database of human features must be updated frequently.
Chemical techniques measure chemical properties of the user's body like body smell or blood glucose, these features are not stable in all conditions and situations therefore they are not dependable so much.
Blood vessel's pattern of retina is unique among people and forms a good differentiation between peoples. Owing to this property retina images could be one of the best choices for biometric systems. In this article, a novel human identification system based on retinal images has been proposed. The proposed system has two main phases like other pattern recognition system; these phases are feature extraction and decision-making phases. In feature extraction phase, we extracts feature vectors for all images of our database by utilizing angular and radial partitioning. Then, in decision-making phase, we compute Manhattan distance of all images with each other and make final decision using fuzzy system. It is noted that we have used 1D Fourier transform for rotation invariance.
The rest of the article is organized as follows: in Section 2, we investigate retina and corresponding technologies. In Section 3, we described the proposed algorithms with its details. Simulation results and comparison of them with previous studies have been represented in Section 4 and finally, Section 5 is the conclusion and suggestion for some future studies.
2. Overview of retinal technology
Retina is one of the most dependable biometric features because of its natural characteristics and low possibility of fraud because pattern of human's retinas rarely changes during their life and also it is stable and could not be manipulated. Retina-based identification and recognition systems have uniqueness and stability properties because pattern of retina's vessels is unique and stable. Despite of these appropriate attributes, retina has not been used so much in recent decades because of technological limitations and its expensive corresponding devices [3–6]. Therefore, a few identification studies based on retina images have been performed until now [7–10]. Nowadays, because of various technological advancements and cheapen of retina scanners, these restrictions have been eliminated [6, 11]. EyeDentify Company has marketed the first commercial identification tool (EyeDentification 7.5) in 1976 [6].
Xu et al. [9] used the green grayscale retinal image and obtained vector curve of blood vessel skeleton. The major drawback of this algorithm is its computational cost, since a number of rigid motion parameters should be computed for all possible correspondences between the query and enrolled images in the database [12]. They have applied their algorithm on a database which consists of 200 different images and obtained zero false recognition against 38 false rejections.
Farzin et al. [12] have suggested another method based on wavelet transform. Their proposed system consists of blood vessel segmentation, feature generation, and feature matching parts. They have evaluated their system using 60 images of DRIVE [13] and STARE [14] databases and have reported 99% as the average success rate of their system in identification.
Ortega et al. [10] used a fuzzy circular Hough transform to localize the optical disk (OD) in the retinal image. Then, they defined feature vectors based on the ridge endings and bifurcations from vessels obtained from a crease model of the retinal vessels inside the OD. They have used a similar approach given in [9] for pattern matching. Although their algorithm is more efficient than that of [9], they have evaluated their system using a database which only includes 14 images.
2.1. Anatomy of the retina
Anatomy of the human eye [16].
Retina images from four different subjects [12].
Two studies are more complete and more impressive among the various studies that have been done about uniqueness of the people's blood vessels pattern [12]. In 1935, Simon and Goldstein [7] first introduced uniqueness of the pattern of vessels among peoples; they also have suggested using of retina images for identification in their subsequent articles. The next study has been done by Tower in 1950 which showed that pattern of retina's blood vessels is different even for twins [16, 17].
2.2. The strengths and weaknesses of retinal recognition
Pattern of retina's blood vessels rarely changes during people's lives. In addition, retina has not contact with environment unlike the other biometrics such as finger print; therefore, it is protected from external changes. Moreover, people have not access to their retina and hence they could not deceive identification systems. Small size of the feature vector is another advantage of retina to the other biometrics; this property leads to faster identification and authentication than other biometrics [18].
Despite of its advantages, use of retina has some disadvantages that limit application of it [12]. People may suffer from eye diseases like cataract or glaucoma, these diseases complicate identification task to a great extent. Also scanning process needs to a lot of cooperation from the user that could be unfavorable. In addition, retina images could reveal people diseases like blood pressure; this maybe unpleasant for people and it could be harmful for popularity of retina-based identification systems.
3. The proposed system
In thisarticle, we explain a new identification method based on retina images. In this section, we review the proposed algorithm and its details. We examine simulation results in the next section. These results are obtained using DRIVE standard database, as we could see later the proposed system has about 99.75% accuracy.
In addition to its high accuracy, the suggested system has two other advantages as well. First, it is computationally inexpensive so it is very favorable for using in real-time systems. Also the proposed algorithm is resistant to rotation of the images. Rotation invariance is very important for retina-based identification systems because people may turn their head slightly during scanning time. In the proposed algorithm, a suitable resistance to the rotation has been formed using 1D Fourier transform.
As we mentioned earlier, our system composed of two feature extraction and decision-making components. In feature extraction phase, two feature vectors are extracted by angular and radial partitioning. In decision-making phase, first two Manhattan distances are obtained for images and then individual is identified by utilizing the fuzzy system. We will explain angular and radial partitioning along with the proposed systems and its parts in the following sections.
3.1. Angular partitioning
Angular partitioning.
where R is the radius of the surrounding circle of the image.
Since fτ(i) = f(i - l) is true, we could conclude that the feature vector has been circularly shifted.
Based on the property |F(u)| = |F τ (u)|, the scale, translation, and rotation invariant image feature are chosen as Ψ = {|F(u)|} for u = 0, 1, 2, ..., K-1. The extracted features are robust against translation because of the aforementioned normalization process. Choosing a medium-size slice makes the extracted features more vigorous against local variations. This is based on the fact that the number of pixels in such slices varies slowly with local translations. The features are rotation invariant because of the Fourier transform applied [19].
3.2. Radial partitioning
Radial partitioning.
3.3. Feature extraction
Overview of the feature extraction component.
Steps of feature vector extraction in the proposed system. (a) Initial image of the retina. (b) Retina's image after preprocessing step. (c) Pattern of blood vessels extracted by the algorithm in [13]. (d) Thinned pattern of vessels using a morphological algorithm. (e) Angular portioning. (f) Radial partitioning.
In next step, we must extract patterns of blood vessels from the retina images. Until now, various algorithms and methods have been suggested for recognition of these patterns; in our system, we have used a method like in [13] (see Figure 6c). Also we have used a morphological algorithm [20] for thinning the extracted patterns. A sample output of the morphological algorithm has shown in Figure 6d. In fact we have used only thicker and more significant vessels for identification and have eliminated thinner ones.
In next step, we generate two separate feature vectors for each image using angular and radial partitioning simultaneously (see Figure 6e, f). The procedure is as follows: first we partition the image based on type of the partitioning and then we let number of sketch pixels within each section as feature value of that segment. After finishing this step, we have two feature vectors correspond to angular and radial partitioning which will be used on decision-making phase.
3.4. Decision-making phase
Pattern matching is a key point in all pattern-recognition algorithms. Searching and finding similar images to a requested image in database is one of the most important tasks in image-based identification systems. Feature vectors of the query image and images in the database are compared to each other and nearest image to the query image returned as a result. In suggested algorithms for pattern matching, various distance criterions have been used as similarity measure. Manhattan distance and Euclidian distance are two of the most important similarity measures used until now [21–23]. Also some systems have used weighted Manhattan and Euclidian distances as their similarity measures [24, 25].
Decision making component used in [27].
Membership function of input variables.
Membership function of output variable.
- 1.
If (AP is Low) and (RP is Low) then (Similarity is High)
- 2.
If (AP is Low) and (RP is Medium) then (Similarity is High)
- 3.
If (AP is Low) and (RP is High) then (Similarity is Medium)
- 4.
If (AP is Medium) and (RP is Low) then (Similarity is High)
- 5.
If (AP is Medium) and (RP is Medium) then (Similarity is Low)
- 6.
If (AP is Medium) and (RP is High) then (Similarity is Low)
- 7.
If (AP is High) and (RP is Low) then (Similarity is Medium)
- 8.
If (AP is High) and (RP is Medium) then (Similarity is Low)
- 9.
If (AP is High) and (RP is High) then (Similarity is Low)
It is noted that we have mapped Manhattan distances to the range of [0 1000]. The value of the output is in the range [0 1], when the value is close to 1 it means that two images are very similar. Finally, we consider closest image to the query image as result. Using this fuzzy system, we reached to 99.75 accuracy that is superior to previous studies.
4. Simulation results
Simulation results along with results of other studies
Method | Accuracy rate |
---|---|
Radial partitioning | 91.5 |
Angular partitioning | 98 |
Angular and radial partitioning | 98.75 |
Farzin et al. [12] | 99 |
The proposed method | 99.75 |
Proposed system's results after rotation
Rotation degree | 3 | 7 | 10 | 15 | 20 | 30 | 40 | 45 | 67 | 90 | 120 | 153 | 250 | Average |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Accuracy rate | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 99.45 | 100 | 100 | 98.82 | 100 | 99.87 |
Proposed system's results after size variation
Image size | 64 × 64 | 128 × 128 | 171 × 171 | 256 × 256 | 384 × 384 | 512 × 512 | Average |
---|---|---|---|---|---|---|---|
Accuracy rate | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
5. Conclusion and future works
We have proposed an identification system based on retina image in thisarticle. The suggested system uses angular and radial partitioning for feature extraction. After feature extraction step, Manhattan distances between the query image and database images are computed and final decision is made based on the proposed fuzzy system. Simulation results show high accuracy of our system in comparison with similar systems. More over rotation invariance and low computational overhead are other advantages of system that make it suitable for use in real-time systems.
As mentioned earlier, the best results obtained when we used 5-degree angle for angular partitioning. We could use other angles as well so we may have different feature vectors with different lengths for each image. Hence, we can generate various feature vectors for images and use them to train a neural network. Then, we can use the trained neural network for decision making. Use of neural network for improving results will be considered in future studies.
Declarations
Authors’ Affiliations
References
- Jain A, Bolle R, Pankanti S: Biometrics: Personal Identification in a Networked Society. Kluwer Academic Publishers, Dordrecht, The Netherlands; 1999.View ArticleGoogle Scholar
- Zhang D: Automated Biometrics: Technologies and Systems. Kluwer Academic Publishers, Dordrecht, Netherlands; 2000.View ArticleGoogle Scholar
- Hill RB: Rotating beam ocular identification apparatus and method. US Patent 4393366 1983.Google Scholar
- Hill RB: Fovea-centered eye fundus scanner. US Patent 4620318 1986.Google Scholar
- Johnson JC, Hill RB: Eye fundus optical scanner system and method. US Patent 5532771 1990.Google Scholar
- Hill RB: Biometrics: Personal Identification in Networked Society. Edited by: Jain A, Bolle R, Pankati S. Springer, Berlin; 1999:126.Google Scholar
- Simon C, Goldstein I: A new scientific method of identification. N Y J Med 1935,35(18):901-906.Google Scholar
- Tabatabaee H, Milani Fard A, Jafariani H: A novel human identifier system using retina image and fuzzy clustering approach. In Proceedings of the 2nd IEEE International Conference on Information and Communication Technologies (ICTTA '06). Damascus, Syria; 2006:1031-1036.Google Scholar
- Xu ZW, Guo XX, Hu XY, Cheng X: The blood vessel recognition of ocular fundus. In Proceedings of the 4th International Conference on Machine Learning and Cybernetics (ICMLC '05). Guangzhou, China; 2005:4493-4498.Google Scholar
- Ortega M, Marino C, Penedo MG, Blanco M, Gonzalez F: Biometric authentication using digital retinal images. In Proceedings of the 5th WSEAS International Conference on Applied Computer Science (ACOS '06). Hangzhou, China; 2006:422-427.Google Scholar
- [http://www.retica.com/index.html]
- Farzin H, Moghaddam HA, Moin MS: A novel retinal identification system. EURASIP J Adv Signal Process 2008.,2008(280635):View ArticleGoogle Scholar
- Staal J, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B: Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imag 2004,23(4):501-509. 10.1109/TMI.2004.825627View ArticleGoogle Scholar
- Hoover A, Kouznetsova V, Goldbaum M: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imag 2000,19(3):203-210. 10.1109/42.845178View ArticleGoogle Scholar
- Goh KG, Hsu W, Lee ML: Medical Data Mining and Knowledge Discovery. Springer, Berlin, Germany; 2000:181-210.Google Scholar
- Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imag 1989,8(3):263-269. 10.1109/42.34715View ArticleGoogle Scholar
- Tower P: The fundus oculi in monozygotic twins: report of six pairs of identical twins. Arch Ophthalmol 1955,54(2):225-239. 10.1001/archopht.1955.00930020231010View ArticleGoogle Scholar
- Chen WS, Chih KH, Shih SW, Hsieh CM: Personal identification technique based on human Iris recognition with wavelet transform. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '05). Volume 2. Philadelphia, PA, USA; 2005:949-952.Google Scholar
- Chalechale A: Content-based retrieval from image databases using sketched queries. PhD thesis, School of Electrical, Computer, and Telecommunication Engineering, University of Wollongong 2005.Google Scholar
- Gonzalez RC, Woods RE: Digital Image Processing. Addison-Wesley; 1992.Google Scholar
- Pass G, Zabih R: Histogram refinement for content-based image retrieval. Proceedings 3rd IEEE Workshop on Applications of Computer Vision 1996, 96-102.View ArticleGoogle Scholar
- Del Bimbo A: Visual Information Retrieval. Morgan Kaufmann Publishers; 1999.Google Scholar
- Jacobs CE, Finkelstein A, Salesin DH: Fast multiresolution image querying. Proceedings ACM Computer Graphics (IGGRAPH 95). USA 1995, 277-286.Google Scholar
- Bober M: MPEG-7 Visual shape description. IEEE Trans Circ Syst Video Technol 2001,11(6):716-719. 10.1109/76.927426View ArticleGoogle Scholar
- Won CS, Park DK, Park S: Efficient use of MPEG-7 edge histogram descriptor. Etri J 2002,24(1):23-30. 10.4218/etrij.02.0102.0103View ArticleGoogle Scholar
- Barkhoda W, Tab FA, Amiri MD: Rotation invariant retina identification based on the sketch of vessels using angular partitioning. In Proceedings International Multiconference on Computer Science and Information Technology (IMCSIT'09). Mragowo, Poland; 2009:3-6.Google Scholar
- Amiri MD, Tab FA, Barkhoda W: Retina identification based on the pattern of blood vessels using angular and radial partitioning. In Proceedings Advanced Concepts for Intelligent Vision Systems (ACIVS 2009). LNCS 5807, Bordeaux, France; 2009:732-739.View ArticleGoogle Scholar
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.