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FIREFLY OPTIMIZED ANFIS FOR SEGMENTATION OF BLOOD VESSELS AND OPTICAL DISC
1S.BEULA PRINCY AND 2DR. S.DURAISAMY
1Reserach Scholar ,Research & Development Center , Bharathiar University, Coimbatore
2Assistant professor, Department of Computer Science, Chikkanna Government Arts College, Tiruppur
Corresponding Author Email id: [email protected]
Abstract-- Retinal image analysis is becoming increasingly famous in the form of a nonintrusive diagnosis technique in modern ophthalmology. Automated analysis of retinal images is a process with challenges, whose aim is to offer automated techniques for helping in the primitive detection and diagnosis of several eye diseases like diabetic retinopathy and age-related macular degeneration (AMD). In order to diagnose these diseases, much significance has been given to the segmentation of the blood vessels. Earlier, the Markov Random field, neural networks, Adaptive Neuro Fuzzy Inference System (ANFIS) and Genetic optimization based ANFIS (G-ANFIS) have been evolved. But it has not rendered a sufficient result. For the purpose of solving this issue, the new system proposed a Firefly optimization based ANFIS with creditable regard to Mahalanobis Distance (F-ANFIS+MD).
At initial step Time Domain Constraint Estimator (TDCE) is proposed for image denoising and Adaptive Histogram Equalization (AHE) is used for image enhancement. Next feature selection is performed by Modified Particle Swarm Optimization (MPSO) exploiting the Harmonic Search (HS) algorithm. Vessel detection and optic Disc (OD) segmentation is performed by F-ANFIS-MD and Adaptive Markov Random Field (AMRF). Then Gray Level Co-Occurrence Matrix (GLCM) features are extracted to analysis the eye diseases like Cataracts, Glaucoma, Ocular Motor Nerve Palsies and Rubeosis Iridis. Finally, the evaluation results reveal the performance of the vessel detection of the newly introduced F- ANFIS-MD approach. At last, the results from the performance evaluation confirm the performance of the vessel detection of the novel F-ANFIS-MD approach.
Keywords--- Retinal Image, MRF, Firefly Algorithm (FA), Vessel Segmentation and Optical Disc Segmentation.
1. INTRODUCTION
Retinal image analysis is getting highly well-known to be a nonintrusive diagnosis technique in modern ophthalmology [1].
The morphology of the retinal blood vessel and the optic disk is a significant structural indicator for the assessment of the existence and criticality of retinal diseases like diabetic retinopathy, hypertension, glaucoma, hemorrhages, vein occlusion, and neovascularization.
Blood vessels in the retina form part of the retina, which serves in supplying blood and oxygen to the blood vessels of the retina [2] [3]. In case the blood and oxygen supplies are not going smooth, then this might indicate if there are any health problems (hypertension, cardiovascular, stroke or diabetes). In order to detect these defective veins, the segmentation of blood vessels in the retinal digital images is carried out. There exists several approaches developed to deal with the segmentation of blood vessels (supervised and un-supervised), but still do not satisfy the condition of standards [4]: (1) Branching blood
vessels, (2) Merging of adjacent blood vessels, (3) Loss of blood vessels are thin / small and (4) Error detection of blood vessels in the particular area.
The optic disc (OD) is treated to be one among the important features of a retinal fundus image. It is the location where the blood vessels and optic nerves enter the retina of human eyes. The OD is considered to be the brightest portion of the normal fundus images. Detecting the optic disc (OD) is regarded to be one among the important part of evaluation of digital color retinal images [5]. The location of the optical disc is regarded to be the landmark for analyzing and identifying the eye disease and blood vessels in retinal images.
Youssif et.al proposed an optic disc detection, which is carried out by making use of matched filter. The segmentation of the retinal vessels is done by employing a simple and standard 2-D Gaussian matched filter. As a result, a vessels direction map of the segmented retinal vessels is got by utilizing the same
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segmentation algorithm.Then the segmented vessels are thinned, and filtered exploiting local intensity, in order to finally indicate the OD-center candidates.
For having an accurate segmentation, multi-scale line detection technique is employed. It is an efficient technique for the automatic extraction of blood vessels from color retinal images. The method is designed on the basis of the fact that by varying the length of a fundamental line detector, line detectors at differing scales are accomplished. In order to keep up the strength and avoid the setbacks of every individual line detector, the line responses at changing scales are combined linearly in order to yield the final segmentation for every retinal image [6].
For achieving automatic segmentation, 2-D Gabor wavelet and supervised classification is brought into use. The technique generates segmentations by the classification of every image pixel to be vessel or non vessel, depending on the feature vector of the pixel. Feature vectors consist of the intensity of the pixel along with the two- dimensional Gabor wavelet transform responses obtained at various scales. The Gabor wavelet has the capability of tuning to particular frequencies, thereby permitting for noise filtering and vessel enhancement in one single step.
The system makes use of a Bayesian classifier having class-conditional probability density functions (likelihoods) defined as Gaussian mixtures, rendering a rapid classification, when being capable of modeling complicated decision surfaces [7].
2. RELATED WORKS
Mueen et al devised an algorithm comprising of the potential pre-processing methods (contrast enhancement) and automated thresholding for performing the automated segmentation of blood vessels. Usually, the contrast seen among the blood vessels and the retinal tissues are seen to be fuzzy in the fundus images.
Contrast-Limited Adaptive Histogram Equalization (CLAHE) was realized for contrast enhancement by restricting the maximum slope in the transformation function. Moreover, ISO Data method was utilized for automated thresholding [8].
Soares et.al introduced a technique for the detection of blood vessels. The
primary step comprises of the application of anisotropic diffusion filtering in the initial vessel network. In the second subsequent step, a multi-scale line- tracking procedure permits the detection of all vessels with same kind of dimensions at a selected scale [9].
Computation of the individual image maps needs various steps. At first, some number of points gets pre-selected making use of the Eigen values of the Hessian matrix. Then these points are supposed to be close to a vessel axis.
Thereafter, for every pre-selected point, the computation of the response map is done from the gradient information of the image at the present scale. At last, the multi-scale image map is obtained after merging the individual image maps at varying scales (sizes). Two openly available datasets have been utilized for testing the performance of the advised technique.
Chaudhuri et al. [10] proposed a framework for the segmentation of the blood vessels in the retina. The technique is dependent on the optical and spatial characteristics of the vessels, in which the approximation of the gray-level profile of the cross section of a blood vessel is done by a Gaussian shaped curve and a matched filter is utilized in order to describe the piecewise linear segments of the blood vessels. At last, twelve various templates are developed in order to look out for vessel segments along every probable direction.
Kawata et al [11] performed the analysis of the blood vessel structures and detection of the blood vessel diseases, with the help of cone beam CT images. X- ray digital angiograms are gathered employing rotational angiography. 3D image reconstruction is conducted by means of a short scan cone-beam filtered back projection algorithm on the basis of the shorter injection time of the contrast medium. At first, a graph description process does the extraction of the curvilinear centerline structures of the vessel tree making use of thresholding, removal of the small connected components, and 3D fusion procedures.
Thereafter, a 3D surface representation process does the extraction of the features of convex and concave shapes on blood vessel surface. The algorithm is executed over a set consisting of patient images having abdominal blood vessels, with two
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aneurysms and one stenosis, and then the results are revealed. Morphology indicates the study about object forms or shapes. Ana et al demonstrated an automated approach for segmenting the blood vessels in retinal images by taking the morphological properties into consideration.This system makes use of the intensity and morphological characteristics of vascular structures, like linearity, connectivity, and width [12]. Fraz et.al proposed a morphological processing that assists in the detection of the vessels.
This system defines two techniques for the identification of general vascular segments that are associated with two diverse mechanisms for the classification of every pixel to be belonging to vessel or not and then these approaches were utilized for vasculature segmentation application also. The techniques used are pixel processing based and tracking approach.
The pixel processing based technique employs two step approaches. In the first step, the process enhancement is carried out, and the chief aim of this process is the selection of an initial set of pixels that has to be validated to be vessels in the
next second step. The subsequent approach is the beginning of tracking by finding the vessel points utilized for the tracing of the vasculature, by the measurement of few image characteristics [13].
3. PROPOSED METHODOLOGY
In this work, with the aim of improving the classification of the retinal blood vessels to have more efficiency, a Firefly optimization based Adaptive Neuro Fuzzy Inference System is developed with regard to Mahalanobis Distance (F-ANFIS+MD).
At first, this scheme makes use of Time Domain Constraint Estimator for image denoising and AHE for the purpose of image enhancement. Then the features are selected for retinal vessel segmentation by Modified Particle Swarm Optimization (MPSO) making use of Harmony Search (HS) algorithm. Then, the vessel detection is conducted making use of the newly introduced F-ANFIS with MD. Thereafter the Optical Disc (OD) segmentation is performed making use of AMRF. Then GLCM feature are extracted to analyze the eye diseases. The overall processing of the newly introduced system
is shown in Fig 1.
Figure 1. Overall Process
3.1. Image Denoising using Time Domain Constraint Estimator (TDCE)
Image denoising has an important role to play in digital image processing. There are several schemes for the removal of noise
from images. Any good denoising strategy should be capable of retrieving as much possible image details though the image is hugely impacted by noise. In this new system, Time Domain Constraint
Segmented Results Retinal images Pre-processing
Image denoising by Time Domain Constraint Estimator (TDCE)
Image enhancement by Adaptive Histogram Equalization (AHE)
Vessel detection by F-ANFIS +MD
Optic disk segmentation by Adaptive Markov Random Field (AMRF)
Feature selection by MPSO -HS
GLCM Feature extraction
Diseases analyses
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Estimator (TDCE) is utilized for the denoising of fundus images [14]. The technique maintains the contrast of the images in addition to the image details while also eliminating the noise. Let ŝ = Hx ∈ 𝑅𝑚 refer to the linear estimator of the pure signal vector s where 𝐻 ∈ 𝑅𝑚 ∗𝑚 . H is got through the minimization of the residual signal r = ŝ - s∈R𝑅𝑚 . This is expressed by Equation 1.r= Hx-s=H(s+n)-s=(Hs-s)+Hn=𝑟𝑠+ 𝑟𝑁 (1) where the signal distortion is referred to by rS, and the noise residual is indicated by rN. Both the signal distortion and residual noise cannot be reduced simultaneously. TDCE is introduced to deal with signal distortion and noise reduction. TDCE helps in maintaining the signal distortion by reducing the residual noise since the residual noise energy with regard to Equation 2 is,
𝜀𝑁2 = | 𝑟𝑁 |22 =tr{𝑟𝑁𝑟𝑁𝑇} = tr {H((𝑛𝑛𝑇))𝐻𝑇}
=tr{𝐻𝑅𝑛𝐻𝑇} (2)
The energy of the residual noise is seen with a threshold corresponding to Equation 3.
𝜀𝑁2 𝑚𝑣2 where 𝑣2 refers to a positive constant (3)
Equation 4 is employed for minimizing the energy of the signal distortion.
εs2 = ||es||22= tr{esesT}= tr(H-Im)Rs(H − Im)T (4)
By merging Eq.3 and Eq.4, the time domain constraint estimation is got, 𝑚𝑖𝑛𝐻𝜀𝑁2 subject to 𝜀𝑁2 ≤ 𝑚𝑣2
The amount of noise is reduced by decreasing the threshold level 𝑣2 . By utilizing Tuhn-Tucker conditions to reduce the constraints, optimal distortion can be got. In case it meets the gradient equation of the Lagrangian, then H can be considered to be a feasible point, which is stationary. In a similar manner, making use of a data matrix, which has n realizations and X𝜖𝑅𝑚 ×𝑛, then ŝ =XW∈
𝑅𝑚 ×𝑛 where H𝜖𝑅𝑛×𝑛 using Eq.6, the residual matrix,E , is got.
E= ŝ-s=XH-s=s(H-𝐼𝑛)+NH=𝐸𝑠+ 𝐸𝑁 (5)
where, 𝐸𝑠𝑎𝑛𝑑𝐸𝑁 refer to the respective signal distortion and the residual noise matrix. The Lagrangian formulation is expressed in Eq.6.
L(H,𝜇) = 𝜀𝑠2 + 𝜇(𝜀𝑁2 − 𝑚𝑣2 ) (6) Where, 𝐿(𝐻, 𝜇) is the Lagrangian multiplier. With, an optimum H is got (denoised signal), as in Eq.7.
𝐻𝑇𝐷𝐶𝐸 = 𝑅𝑠(𝑅𝑠+ 𝜇𝑅𝑛)−1 (7) It was observed that the Time Domain Constraint Estimator (TDCE) revealed a higher performance in the PSNR enhancement of retinal fundus image.
3.2. Image Enhancement using AHE Image enhancement is a procedure of removing the unnecessary distortion owing to degradation in contrast, unnecessary noise, incorrect intensity saturation, blurring effect etc, and then decides about the information hidden in images. Adaptive Histogram Equalization (AHE) is mostly used in image processing methodologies and it’s useful in increasing the contrast present within the images. In the adaptive algorithms, every pixel is changed on the basis of the pixels, which are present in a region enclosing that pixel. This region is known as contextual region. The adaptive histogram equalizations are computation wise intense and for this cause, few techniques were developed in order to raise the speed of the original technique. In case the system contains an image of n x n pixels, along with k intensity levels and the size of contextual region is referred to by m x m, then the time necessary for computations is O(n2(m + k)). Better results are got if the system uses just four closest grid points rather than the histogram of neighborhood pixels from a mobile window. The change in every pixel is carried out by the interpolation of the mappings of the four closest points. It has been utilized for improving the contrast images and it is appropriate for the improvement of the local contrast with more detail along with over amplified noise. Fig 2 shows the results of analyzed images after denoising and enhancement.
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Input Image Denoised Image Enhanced Image Figure 2. Retinal Images After pre-Processing
3.3. Feature Selection for Retinal Vessel Segmentation using MPSO with Harmonic Search Algorithm
Particle Swarm Optimization (PSO) is basically a metaheuristic algorithm motivated by the social behavior of populations along with collaborative characteristics. The PSO mimics this collaboration of species and is extensively utilized in resolving mathematical optimization problems. PSO shows easier understanding, simpler operation, and fast searching. It has been applied with success in many fields. It is transformed to create a Modified PSO (MPSO) by making harmony search algorithm for having a rapid performance.
HSA is based on the concept of the behavior of the musician in the search for better harmonies. It attempts in finding the optimal solution based on an objective function. The harmony is associated with the optimization solution vector; the musicians are associated with decision variables, and the musical instrument’s pitch range bears analogy to the value range of the decision variable. Harmony at a particular time having analogy with the solution vector at particular iteration and agreements of the audience is associated with the objective function. When every variable chooses one value in the HSA, it will adopt one among the three rules:
1. Choosing a random value from the permitted range.
2. Choosing a value from the harmony memory.
3. Choosing an adjacent value of one among the values in the harmony memory.
These rules are identical to the musician’s behavior, exploited for producing a piece of music having a perfect harmony: Composing new or random notes
1. Playing any well-known tune with precision from his or her memory.
2. Playing something identical to the tune mentioned above.
First, the modified PSO-HS makes use of HS for searching, and thereafter it makes use of the position of the PSO update mode for accelerating the particles to the optimal solution convergence.
Simultaneously, the random elimination scheme of CS can escape local optima with success, thereby enhancing the performance of searching for the optimal solution.
In PSO algorithm, the velocity of the particle is updated by employing
𝑣𝑖𝑑 𝑘 + 1 = 𝜔𝑣𝑖𝑑 𝑘 + 𝜂1𝑟1 𝑃𝑖𝑑 − 𝑥𝑖𝑑 𝑘 + 𝜂2𝑟2 𝑃𝑔𝑑− 𝑥𝑖𝑑 𝑘 (8)
where 𝑣𝑖𝑑 𝑘 + 1 refers to the 𝑑th component of particle’s velocity after the 𝑘 + 1 th update is finished. 𝑝𝑖𝑑 is presently the particle’s best solution of 𝑑th component just after the 𝑘th update is completed; 𝑝𝑔𝑑 is presently the population’s best global solution of the 𝑑th component once after the 𝑘th update is completed; 𝜂1 and 𝜂2refer to the positive constant parameters known as acceleration coefficients, regulating the movement steps of particles; 𝜔 refers to the inertia weight, which controls the impact of earlier values of the particle’s velocity on next subsequent one. 𝑟1 and 𝑟2refer to random variables with a range [0, 1].
Particle position is updated by making use of
𝑥𝑖𝑑 𝑘 + 1 = 𝑣𝑖𝑑 𝑘 + 𝑣𝑖𝑑 𝑘 + 1 (9)
where 𝑥𝑖𝑑 𝑘 + 1 𝑏 refer to the 𝑑th component of particle’s position after the (𝑘+1)th update is completed and 𝑣𝑖𝑑 𝑘 + 1 stands for the 𝑑th component of the particle’s velocity after the (𝑘 + 1)th update is finished .
The HS algorithm [15], and is dependent on the natural musical performance processes which occur when a musician looks out for a better state of harmony, like that during the jazz improvisation. Jazz improvisation
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attempts in finding musically appealing harmony (a perfect state) as decided by an aesthetic standard, just like the optimization process tries finding a global solution (a perfect state) decided by an objective function. The pitch of every musical instrument decides the aesthetic quality, just like the objective function value is decided by the set of values allocated to every design variable.HS algorithm optimization process, referred below, comprises of five steps.
Step 1: Parameters Initialization The optimization problem, specified as below:
Minimize f(x) subject to
Xj= 1,2,…,N, (1)xj (10) where f(x) is an objective function, it computed by using the vessel detection accuracy; x the set of each decision variable xj; N the number of decision variables, Xj the set of the possible range of values for each decision variable, that is 𝑥𝑗𝑚𝑖𝑛 and 𝑥𝑗𝑚𝑎𝑥are the lower and upper boundaries of the jth decision parameter, respectively. HS algorithm parameters are also specified in this step, namely, the Harmony Memory Size (HMS), or the number of solution vectors in the harmony memory, Harmony Memory Considering Rate (HMCR), Pitch Adjusting Rate (PAR), Bandwidth Distance (BW), and the Number of Improvisations (NI), or stopping criterion, and the Harmony Memory (HM) where all the solution vectors are stored.
Step 2: Harmony Memory Initialization and Evaluation: A random initial population gets generated, which is:
𝑥𝑖,𝑗0= 𝑥𝑗𝑚𝑖𝑛+𝑟𝑗 (𝑥𝑖𝑚𝑎𝑥 − 𝑥𝑗𝑚𝑖𝑛) (11) where and ∈ [0,1] refers to a uniformly distributed random number that is newly generated for every value of j.
Theoretically, the pitch can be adjusted either linearly or nonlinearly, but practically, linear adjustment is used.
Therefore,
𝑥𝑛𝑒𝑤 = 𝑥𝑜𝑖𝑑 + 𝑏𝑟𝑎𝑛𝑔𝑒 * 𝜀V (12) where xold refers to the already available pitch or solution from the harmony memory, and xnew stands for the new pitch just after the action of pitch adjusting. This typically generates a new
solution around the already available quality solution by changing the pitch by a small random amount slightly [16]. Here ε refers to a random number generator seen in the range of [-1, 1]. Pitch adjustment is identical to the mutation operator in genetic algorithms. Solution vectors in HM are evaluated, and then their objective function values are computed.
Step 3: Improvisation In this step, a New Harmony vector is produced on the basis of three rules, such as, memory consideration, pitch adjustment and random selection. The value of a design variable could be chosen from the values that are stored in HM having a probability HMCR. It can be adjusted further by moving to a neighbor value of a chosen value from the HM along with a probability of pitch adjusting rate (PAR), or, it can be chosen in random from the set consisting of all candidate values without taking the stored values in HM into consideration, with the probability (1 - HMCR).
Step 4: Harmony Memory Update In case the New Harmony vector is better compared to the worst vector, depending on the objective value and/or constraint violation, then the worst one will be replaced by the new vector.
Step 5: Termination criterion check HS algorithm is stopped if the termination criterion (e.g. maximum number of improvisations) has been satisfied. Else, steps 3 and 4 are repeated.
Hybrid Algorithm
Step 1: The parameter is set, and then the initialization of the population is done.
Population is initialized in random that are inclusive of initialization position 𝑝 and velocity 𝑉 of individual.
Step 2: The initial fitness value of the population is computed making use of the objective function, and the fitness value and position of the global optimal individual are decided.
Step 3: Harmony search mode gets initiated. The new solution is then updated by applying equation (12)
The fitness values of the new and old individuals are then compared; the result that is better gets chosen to a new- generation individual. The objective function (accuracy) is computed. The significant retinal features are modeled.
Step 4: PSO search mode is initialized.
The position and velocity of the individual
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are updated, and thereafter a new individual is generated. The position is updated by making use of formula (9), and the velocity is updated by employing the formula (8). Prior to updating the velocity, the inertia weight coefficient requires to be updated by applying𝜔 = 𝜔𝑚𝑎𝑥 − 𝜔𝑚𝑎𝑥 − 𝜔𝑚𝑖𝑛 ∗𝑖𝑡𝑒𝑟
𝑟𝑢𝑛 (13) where iter and run refer to the current iteration times and maximum iteration times of the algorithm, correspondingly
𝜔𝑚𝑎𝑥 and 𝜔𝑚𝑖𝑛refer to the maximum and minimum inertia weights, correspondingly. When comparing the fitness values of new and old individuals, the one having the better result is chosen as a new individual, and the global optimal individual gets updated.
Step 5: An n-dimensional vector 𝑅𝑛 = [𝑟1, 𝑟2, … . 𝑟𝑛] is generated, and 𝑟1follows a uniform distribution with [0,
1]. When 𝑟𝑖 > 𝑃𝑎, a new individual is generated randomly. When the fitness values of the old and new nests are compared, the better one will be chosen to be a new generation of individuals in the population:
𝑇𝑒𝑚𝑝𝑖 = 𝐿𝑏 + 𝑈𝑏 − 𝐿𝑏 ∗ 𝑟𝑎𝑛𝑑 1, 𝑑 ∗ 𝑅𝑓 (14)
Step 6: The global and individual optimal values are updated for the images given. The optimal positions of every individuals and entire populations are updated.
Step 7: When the termination condition of the algorithm is satisfied, then the optimal position of the nest is generated as the output, and the algorithm is stopped; else, Step 3 is carried out. Figure 3 shows the selection of features in the retinal image.
Figure 3. After Feature Selection results - MPSO-HS algorithm 3.4. Vessel Detection Using Firefly
Optimized ANFIS Firefly Algorithm
At first Firefly Algorithm (FA) is performed based on the flashing behaviors of fireflies [17-18]. In short, FA makes use of the below three idealized rules:
1. Fireflies are unisex such that one firefly will get attracted to other fireflies with no regard to their sex.
2. The attractiveness is proportional to the brightness, and they both reduce with the increase in their distances. Therefore in the case of any two flashing fireflies, the one that is less bright will make a move towards the brighter one. In case, there exists no brighter one compared to a certain firefly, it will move in random.
3. The brightness of a firefly is decided by the landscape of the objective function.
Since the attractiveness of firefly is proportional to the light intensity as observed by the neighborhood fireflies, now the variation of attractiveness 𝛽 with the distance 𝑑 is defined by
𝛽 = 𝛽0𝑒−𝛾𝑑2 (15)
where β0refers to the attractiveness at d = 0. The movement of a firefly x that is attracted to another more attractive (brighter) firefly y is decided by,
ax t+1= atx + β0e −γdx ,y2 aty− atx + αtϵxt
(16) where the second term is because of the attraction. The third term refers to randomization with𝛼𝑡acting as the randomization parameter, and it is a vector consisting of random numbers obtained from a Gaussian distribution or uniform distribution at time 𝑡 if 𝛽0 = 0, it is a simple random walk alternately, if 𝛾 = 0, it decreases to a variant of particle swarm optimization.
Architecture of ANFIS
A fuzzy model is typically a knowledge- based system featured by a set of rules.
These rules carry out the modeling of the relationship between input and output.
They are saved in a fuzzy rule base and described by their antecedent and consequents. In order to have the explanations simplified, the fuzzy inference system that is considered is supposed to consist of n=2 inputs (x1 and
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x2) and one output (z). The ANFIS’sarchitecture [19-21] having two inputs and one output as indicated in Fig. 4.
Figure 4. Equivalent ANFIS Architecture In the design step, the system is seen
extended from the 2-input system towards the 4-input system. The first step in the application of FA for training the ANFIS’s parameters is to describe the solution space or range of variables that has to be optimized, a set of constraints (if it is present) along the fitness function. In this technical work, accuracy is utilized in the form of the fitness function for evaluating the quality of ANFIS. The second step is the solution encoding;
every ANFIS “i” is denoted by a firefly.
The set of fireflies stand for a population.
Every firefly consists of two parts: a set of antecedent parameters and set of consequent parameters. Four input variables can be expressed in the form of the following data vector (xt, xt-1, xt-2, xt-3).
The output is referred as xt+1. Let it be assumed that an input variable is indicated by three fuzzy sets (three Gaussian membership functions). The rules are generated as
If 𝑥1 𝑖𝑠 𝐹1𝑖 (𝜎1𝑖, 𝑐1𝑖)& 𝑥2 𝑖𝑠 𝐹2𝑗 𝜎2𝑗, 𝑐2𝑗 &
𝑥3 𝑖𝑠 𝐹3𝑙 𝜎3𝑙, 𝑐3𝑙 &𝑥4 𝑖𝑠 𝐹4 (𝜎4𝑚, 𝑐4𝑚)
then 𝑦𝑠= 𝑝𝑠𝑥1+ 𝑞𝑠𝑥2+ 𝑘𝑠𝑥3+ 𝑡𝑠𝑥4 +𝑟𝑠 (17) The parameters that are required to adjust ANFIS, are coded into the individual real number code chain Training Step of FA
Step 1: Initialization of the population of fireflies. Based on the scale of firefly
population, a set of fireflies will be generated in random. Every firefly is mapped onto ANFIS’s parameter set.
Step 2: Compute the fitness value accuracy of every firefly. The light intensity of every firefly is computed by making use of the fitness function:
Step 3: Based on the light intensity, attractiveness between fireflies is compared, and the fireflies having less brightness is moved to brighter one.
Step 4: Return to step 2 till the system fitness satisfies either the fitness threshold or if the iterative number is greater the maximum permissible iterative number, terminate the process. The position and the fitness value of the best firefly becomes the output.
Once the fitness value of the chosen features are found, an ANFIS classifier, which performs an assessment depending on the best fitness values of the features selected of their distance measure, the retina images gets classified. In the classification of the retina image, the feature vector is provided as an input to the classifier. As the pure vessels are computation wise very demanding to be used directly, the necessary information is chosen from the retina image. Figure 5 shows the detection of blood vessels. Fig 6 shows the detection of blood vessels.
𝐴
1𝐴
2𝐵
1𝐵
2II
II
II N
N
∑
𝑓𝑊2
𝑊1 𝑊1
𝑊2
𝑊2𝑓2 𝑊1𝑓1 Layer1
Layer2 Layer3
Layer4
Layer5 X Y
X Y
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Figure 5. Vessel Segmentation 3.5. OD Segmentation using
Adaptive Markov Random Fields (AMRFs)
The newly introduced system presented an Adaptive Markov Random Fields (AMRFs) [22] for the process of OD segmentation. MRF has since been identified to be an accurate model for representing the local statistical dependence of the image. It yields an easy and consistent means for the modeling of the spatial-contextual information that is integrated in the neighborhood of every pixel. The change detection issue under the MRF framework can be regarded to be a job of energy minimization. The energy function is provided by:
𝐸(𝑋𝑅) = 𝐸𝑑𝑎𝑡𝑎 𝑌𝑚𝑛, 𝑋𝑚,𝑛 + 𝜆𝐸𝑠𝑚 𝑌𝑚𝑛 (18)
where 𝐸𝑑𝑎𝑡𝑎 𝑌𝑚𝑛, 𝑋𝑚 ,𝑛 refers to the individual unary energy term, and 𝐸𝑠𝑚 𝑌𝑚𝑛 defines the inter-pixels class dependence. indicates the smooth weight, 𝐸𝑠𝑚 𝑌𝑚𝑛 , which is formulated in the following relationship
𝐸𝑠𝑚 𝑌𝑚𝑛 = ∑{ 𝑔, , 𝑚 ,𝑛 }∈𝑌𝛿𝑘(𝑌 𝑚, 𝑛 , 𝑌 𝑔, ) (19)
where {(g, h), (m, n)}∈N indicates the clique types present in the neighborhood.
𝛿𝑘(. ) stands for an indicator function.
Hence, the problem of change detection can therefore be resolved by getting the
least energy configuration of the MRF.
Generally, pixels that are located along the borders have more possibility to possess diverse labels and they have less influence from its neighboring pixels. On the contrary, the pixels, which are located far from the edges generally belong to the same cluster and contain a tight correlation among them. In order to model the spatial-contextual information that is included in the neighborhood of every pixel with more sense, an adaptive MRFs having a changeable order of neighborhood N and variable smooth weight λ is adopted . For the pixels that are positioned in the region tagged with index C, a small λ and the first-order neighborhood N1 is adopted; for the pixels in the region that is marked with index HC, a huge λ and a second-order neighborhood N2 are chosen; and the pixels located in region that are marked with index N are provided the label with no change directly with no further detection. In this manner, the adaptive MRF provides a more accurate model for representing the local statistical dependence of image. A last change map is got by reducing the energy function. Fig 6 shows the results of OD segmentation process.
Figure 6. OD Segmented Image 3.6. GLCM Feature Extraction based
Detection
Gray Level Co-Occurrence Matrix (GLCM) has emerged to be a well-known statistical technique for the extraction of textural feature from images. A GLCM is a matrix in which the number of rows and
columns is equivalent to the number of gray levels, G, present in the image.
Therefore, the usage of statistical features is one among the primitive techniques that are introduced in the image processing literature. Haralick [23]
demonstrated the usage of co-occurrence
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matrix or gray level co-occurrence matrix.It takes the association between two neighboring pixels into consideration, the first pixel is called as a reference and the second is referred to as a neighbor pixel.
The textural features like Angular Second Moment, Contrast, Inverse Difference Moment, Entropy, Correlation, Cluster Shade, Cluster Prominence, Haralick
Correlation get extracted in the newly introduced system for every visible band (R, G, B). Depending on the features, the diagnosis of diseases like Cataracts, Glaucoma, Ocular Motor Nerve Palsies and Rubeosis Iridis are done. A sample image containing Glaucoma is given as input and the processing system displays the result shown in fig 7.
Figure 7. Disease Detection 4. PERFORMANCE EVALUATION
For performing the performance evaluation of this technical work, in the vessel segmentation technique, openly available datasets, DRIVE having a total of 40 images is used. The performances of this technique are then tested against various alternate approaches. For facilitating the performance comparison between the Firefly optimized ANFIS with MD, GANFIS+MD Adaptive Neuro Fuzzy Inference System (ANFIS) with MD, Neural Network (NN) with the Contrast Limited
Adaptive Histogram Equalization (NN+CLAHE) and MRF retinal blood vessels segmentation techniques, parameters like accuracy, precision, recall and f-measure are obtained for measuring the performance of the segmentation.
Accuracy (ACC): Accuracy is the ratio with regard to the correctly performed classification or the tracking made of normal and abnormal scenes in traffic monitoring process
ACC = TP + TN
TP + FN + FP + TN ∗ 100 20
Figure 8. Accuracy Comparison Fig 8 shows the accuracy metric
comparison done between the existing and the new methodologies. The techniques include MRF, NN with CLAHE, ANFIS with MD, G-ANFIS+MD and F- ANFIS+MD, it provides average accuracy results of 49.73%, 53.575%, 62.72%, 70.718% and 76.844% respectively.
Precision: Precision indicates the correctness of the classification or the tracking performed in the traffic
monitoring video files.
Figure 9. Precision Comparison Fig 9 shows the precision metric comparison made between the already existing and new methods. The methods are inclusive of MRF, NN with CLAHE, ANFIS with MD, GANFIS + MD and
40 45 50 55 60 65 70 75 80 85 90
10 20 30 40
Accuracy(%)
No of images
MRF NN+CLAHE ANFIS+MD
G-ANFIS+MD F-ANFIS+MD
Precision = TN
TP + FP ∗ 100 21
11
FANFIS + MD. The techniques include MRF, NN with CLAHE, and ANFIS with MD, G-ANFIS + MD and F-ANFIS + MD, provides average precision results of 51.08%, 53.33%, 67.6533% , 72.92% and 77.951% respectively. It can be noticed from this graph that the newly introduced FANFIS+MD yields a higher precisionvalue in comparison with the other available system.
Recall: Recall is defined as the completeness of the classification or tracking performed in the traffic monitoring video files.
Figure 10. Recall Comparison Fig 10 shows the recall metric
comparison made between the already existing and the new methods. The methods are inclusive of MRF, NN with CLAHE; ANFIS with MD, GANFIS+MD and FANFIS+MD. The techniques include MRF, NN with CLAHE, ANFIS with MD, G- ANFIS + MD and F-ANFIS+MD, provides average recall results of 48.46%, 53.85%,
58.493% , 68.643% and 75.77%
respectively. It can be seen from this graph that the newly introduced and FANFIS+MD reveals a higher recall value in comparison with the other available system
F-measure: F-measure of the system is defined to be the weighted harmonic mean of its precision and recall.
Figure 11. F-Measure Comparison Fig 11 shows the f-measure metric
comparison made between the already existing and the new methods. The methods are inclusive of MRF, NN with CLAHE, ANFIS with MD, GANFIS+MD.
The techniques include MRF, NN with CLAHE, ANFIS with MD, G-ANFIS+MD, and F-ANFIS+MD, it provides average f – measure results of 49.7342%,53.575%,
62.728%,70.718% and 76.844%
respectively. It can be seen from this graph that the newly introduced and FANFIS+MD provides a higher f-measure value in comparison with the other available system.
5. CONCLUSION
The new system showed a novel scheme for the blood vessels
40 45 50 55 60 65 70 75 80 85 90
10 20 30 40
Recall(%)
No of images
MRF NN+CLAHE ANFIS+MD
G-ANFIS+MD F-ANFIS+MD
40 45 50 55 60 65 70 75 80 85 90
10 20 30 40
F-measure(%)
No of images
MRF NN+CLAHE
ANFIS+MD G-ANFIS+MD
Recall = TP + FN TN ∗ 100 (22)
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segmentation and optic disk in retinal image. In this work a new Firefly optimization based Adaptive Neuro Fuzzy Inference System with Mahalanobis Distance (F-ANFIS+MD) is proposed for the segmentation of blood vessels and optic disc in retinal image. The denoising of the images are carried out by making use of Time Domain Constraint Estimator (TDCE) , image enhancement is accomplished by making use of Adaptive Histogram Equalization (AHE), focused over improving the quality of images in a significant manner. Later, MPSO with HS algorithm for effective feature selection is carried out to assure the Retinal Vessel Segmentation. Then the detection of vessels is performed by using F- ANFIS+MD. At the same time Adaptive Markov Random Field (AMRFs), segmentation of an optic disc is carried out in this work. Finally the GLCM features are extracted, which, in turn, provides good information about the presence of diseases such as Cataracts, Glaucoma, Ocular Motor Nerve Palsies and Rubeosis Iridis. It demonstrates itself to be very efficient for retinal blood vessel segmentation. The proposed FANFIS with MD scheme is proposed for increasing classification results compared to the other techniques.REFERENCES
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