CAD SYSTEM FOR AUTOMATIC DETECTION OF BRAIN TUMOR THROUGH MRI BRAIN TUMOR DETECTION USING HPACO CHAPTER V BRAIN TUMOR DETECTION USING HPACO

Transcription

1 CHAPTER V BRAIN TUMOR DETECTION USING HPACO 145

2 CHAPTER 5 DETECTION OF BRAIN TUMOR REGION USING HYBRID PARALLEL ANT COLONY OPTIMIZATION (HPACO) WITH FCM (FUZZY C MEANS) 5.1 PREFACE The Segmentation of Brain Tumor from Magnetic Resonance Image is an important but time-consuming task performed by medical experts. The digital Image processing community has developed several segmentation methods. Four of the most common methods are: 1. Amplitude Thresholding 2. Texture Segmentation 3. Template Matching 4. Region-Growing Segmentation. Segmentation is the second stage where Optimization forms an important part of our day to day life. Many scientific, social, economic and engineering problems have parameter that can be adjusted to produce a more desirable outcome. Over the years, numerous techniques have been developed to solve such optimization. This study investigates the most effective optimization method, known as Hybrid Parallel Ant Colony Optimization (HPACO) is introduced in the field of Medical Image Processing. Hybrid Parallel Ant Colony Optimization (HPACO) algorithm is a recent populationbased approach inspired by the observation of real Ant s Colony and based upon their collective behaviour. 146

3 In HPACO, solutions of the problem are constructed within an iterative process, by adding solution components to partial solutions. Each individual ant constructs a part of the solution using an artificial pheromone, which reflects its experience accumulated while solving the problem, and heuristic information dependent on the problem FCM Algorithm is one of the popular Fuzzy Clustering algorithms which are classified as constrained Soft Clustering algorithm. A Soft Clustering Algorithm finds a soft partition of a given data set by which an element in the data set may partially belong to multiple clusters. The suspicious region is segmented using algorithm HPACO. A New CAD System is developed for verification and comparison of brain tumor detection algorithm. Hybrid Parallel Ant Colony Optimization determine the threshold value of given image to select the initial cluster point then the clustering algorithm Fuzzy C Means calculates the optimal threshold for the brain tumor segmentation. 5.2 FUZZY C MEANS Segmentation is one of the first and most important tasks in image analysis and computer vision. In the previous works, various methods have been proposed for object segmentation and feature extraction, described in [67, 40]. However, the design of robust and efficient segmentation algorithms is still a very challenging research topic, due to the variety and complexity of images. Image segmentation is defined as the partitioning of an image into nonoverlapped, consistent regions which are homogeneous in respect to some characteristics such as intensity, color, tone, texture, etc. 147

4 The image segmentation can be divided into four categories: (i) thresholding (ii) clustering (iii) edge detection and (iv) region extraction. In this paper, a clustering method for image segmentation will be considered. Clustering is a process for classifying objects or patterns in such a way that samples of the same cluster are more similar to one another than samples belonging to different clusters. There are two main clustering strategies: the hard clustering scheme and the fuzzy clustering scheme. The conventional hard clustering methods classify each point of the data set just to one cluster. As a consequence, the results are often very crisp, i.e., in image clustering each pixel of the image belongs just to one cluster. However, in many real situations, issues such as limited spatial resolution, poor contrast, overlapping intensities, noise and intensity in homogeneities reduce the effectiveness o hard (crisp) clustering methods. Fuzzy set theory [75] has introduced the idea of partial membership, described by a membership function. Fuzzy clustering, as a soft segmentation method, has been widely studied and successfully applied in image clustering and segmentation [53, 85, 90]. Among the fuzzy clustering methods, fuzzy c-means (FCM) algorithm [89,150,151] is the most popular method used in image segmentation because it has robust characteristics for ambiguity and can retain much more information than hard segmentation methods [105,152]. Although the conventional FCM algorithm works well on most noise-free images, it is very sensitive to noise and other imaging artifacts, since it does not consider any information about spatial context. 148

5 Smoothing step has been proposed in [124,130,146]. However, by using smoothing filters important image details can be lost, especially boundaries or edges. Moreover, there is no way to control the trade-off between smoothing and clustering. Thus, many researchers have incorporated local spatial information into the original FCM algorithm to improve the performance of image segmentation [109,136,153] Noordam et al proposed a geometrically guided FCM (GG-FCM) algorithm, a semi-supervised FCM technique, where a geometrical condition is used determined by taking into account the local neighborhood of each pixel [93]. Pham modified the FCM objective function by including a spatial penalty on the membership functions. The penalty term leads to an iterative algorithm, which is very similar to the original FCM and allows the estimation of spatially smooth membership functions [102,103,104]. Ahmed et al proposed FCM_S where the objective function of the classical FCM is modified in order to compensate the intensity in homogeneity and allow the labelling of a pixel to be influenced by the labels in its immediate neighborhood. One disadvantage of FCM_S is that the neighborhood labelling is computed in each iteration step, something that is very time-consuming [2, 3]. Chen and Zhang proposed FCM_S1 and FCM_S2, two variants of FCM_S algorithm in order to reduce the computational time. These two algorithms introduced the extra mean and median-filtered image, respectively, which can be computed in advance, to replace the neighborhood term of FCM_S. Thus, the execution times of both FCM_S1 and FCM_S2 are considerably reduced [19, 22]. 149

6 Szilagyi et al proposed the enhanced FCM (EnFCM) algorithm to accelerate the image segmentation process. The structure of the EnFCM is different from that of FCM_S and its variants. First, a linearly-weighted sum image is formed from both original image and each pixel s local neighborhood average gray level. Then clustering is performed on the basis of the gray level histogram instead of pixels of the summed image. Since, the number of gray levels in an image is generally much smaller than the number of its pixels, the computational time of EnFCM algorithm is reduced, while the quality of the segmented image is comparable to that of FCM_S [114,154]. More recently, Cai et al. Proposed the fast generalized FCM algorithm (FGFCM) which incorporates the spatial information, the intensity of the local pixel neighborhood and the number of gray levels in an image. This algorithm forms a nonlinearly-weighted sum image from both original image and its local spatial and gray level neighborhood. The computational time of FGFCM is very small, since clustering is performed on the basis of the gray level histogram. The quality of the segmented image is well enhanced [19]. Fuzzy C-Means (FCM) Algorithm The fuzzy c-means (FCM) clustering algorithm was first introduced by Dunn [40] and later extended by Bezdek [67]. The algorithm is an iterative clustering method that produces an optimal partition by minimizing the weighted within group sum of squared error objective function J m. =åå N c m 2 J m u jid i - i= 1 j= 1 ( x v )(1 ) where the i th pixel is the center of the local window and the j th j pixel represents the 150

7 set of the neighbors falling into the window around the i th pixel. ( p i - p j ) are the coordinates of pixel i and i x is its gray level value. l andl s g are two scale factors playing a role similar to factor a in FCM, and s i is defined N = ) =Ì m { x1, x2,... x N IR is the data set in the m -dimensional vector space, N- is the number of data items, c is the number of clusters with, 2 c< Nu is the ji degree of membership of xi in the th j cluster, m is the weighting exponent on each 2 fuzzy membership v i is the prototype of the center of cluster j, d ( x i - v j ) is a distance measure between object xiand cluster center v j A solution of the object function j m can be obtained through an iterative process, which is carried as follows 1) Set values for c, m ande. (0) 2) Initialize the fuzzy partition matrix U. 3) Set the loop counter b = 0. 4) Calculate the c cluster centers (b) v with U. (b) j v ( b) j N b å( u ji) i= 1 = N ( b) å( u ji ) i= 1 m x m i (2) ( b+1) 5) Calculate the membership matrixu. u ( b+ 1) j = c å k= 1 æ d ç è d 1 ji ki ö ø 2 / m-1 (3) ) ( 6) If max{ + 1) U ( b U b }< e then stop, otherwise, set b= b+ 1and go to step 4. Figure 5.1 Fuzzy C Means Algorithm 151

8 Start Initialize c, m ande (0) matrix U. max {U (b) U (b+1) } < ɛ Yes No v ( b) j N b å( u ji) i= 1 = N ( b) å( u ) ji i= 1 m x m i u ( b+ 1) j = c å k= 1 æ d ç è d 1 ji ki ö ø 2/ m-1 (3) Stop Figure 5.2 Flow Diagram for Fuzzy C Means Algorithm 152

9 5.3 PREVIOUS WORK CAD SYSTEM FOR AUTOMATIC DETECTION OF BRAIN TUMOR THROUGH MRI Corina et al. studied Active Contour Model for segment the brain MRI images successfully [27]. Mao et al. designed an automatic segmentation method using Fuzzy k- means, Ant colony optimization for process the optimal labeling of the image pixels [78]. Dana et al. designed a method on 3D Variation Segmentation for process due to the high diversity in appearance of tumor tissue from various patients [28,68]. Jayaram et al. represented Fuzzy Connectedness and Fuzzy sets used to develop the concept of fuzzy connectedness directly on the given image for facilitating the image segmentation [60,61]. Hideki et al. specified a technique for Partition the image space into meaningful regions [54]. Kabir et al. prescribed a method Markov random field model for segmenting stroke lesions on MR Multi sequences [64]. Leung et al. presented Contour Deformable Model for segmenting required region from MRI [76]. Marcel Prastawa said VALMET Segmentation validation tool is used to detect intensity outliers and dispersion of the normal brain tissue intensity clusters [79,80,81,82]. Tang et al. presented Multi resolution image segmentation. For segmenting the brain tissue structure from MRI [117]. Pierre et al. prescribed Atlas-based segmentation for Propagation of the labeled structures on to the MRI [94,106]. Jayaram et al described a method on Evaluating Image Segmentation Algorithm for segmenting objects from source image [60, 61]. Jaffrey et al designed a new method semi automatic segmentation method on volume tracking for estimate tumor volume with process [62,63]. 153

10 5.4 SEGMENTATION BY HYBRID PARALLEL ANT COLONY OPTIMIZATION ALGORITHM The MRI image is stored in a two-dimensional matrix and a kernel is extracted for each pixel. A unique label is assigned to the kernels having similar patterns. In the labeling process, a label matrix is initialized with zeros. The size of the label matrix is equal to the size of the MRI image. For each pixel in the image, the label value is stored in the label matrix at the location corresponding to its central pixel coordinates in the gray level image[26,36,37,38,39,46,146] MARKVOV RANDOM FIELD A pattern matrix is maintained to store the dissimilar patterns in the image. For each pixel, a kernel is extracted and the kernel is compared with the patterns available in the pattern matrix. Once it finds any matches the same label value is assigned to the currently extracted kernel..the labels are assigned integer values starting with one and incremented by one whenever a new pattern occurs [71]. Finally the pattern matrix contains all the dissimilar patterns in the image and the corresponding label values are also extracted from the label matrix. For each pattern in the pattern matrix, the posterior energy function value is calculated using the following formula. 9 U( x ) = { å ([(y 2 2 i -μ) / (2*σ )]+ log(σ))+v( x )} i=1 Where, y is the intensity value of pixels in the kernel, m is the mean value of the kernel, s is the standard deviation of the kernel, V is the potential function of the kernel and x is the label of the pixel. If x 1 is equal to x 2 in a kernel, then V (x) = b, otherwise 0, where b is visibility relative parameter (b 0). 154

11 The challenge of finding the Maximum A Posterior (MAP) estimate of the segmentation is to search for the optimum label which minimizes the posterior energy function U(x). In this section a new effective approach, HPACO is proposed for the minimization of MAP estimation. HPACO is applied to find the optimum label from the pattern matrix. Initially, the dissimilar patterns, the corresponding labels and the MAP values are stored in a solution matrix and the parameters such as number of iterations (NI), number of ants (NA), initial pheromone value (T 0 ) are assigned the values of 100, 20 and respectively. Also the solution matrix contains separate columns for pheromone and flag values of each ant. The flag value is used to indicate whether the kernel has been selected previously or not. Initially all the flag values are set to zero and the pheromone values are assigned T 0. At the initial step, all the ants are assigned random kernels and the pheromone values are updated. The posterior energy function value for all the selected kernels from each ant is extracted from the solution matrix. Compare the posterior energy function value for all the selected kernels from each ant, to select the minimum value from the set, which is known as Local Minimum (Lmin) or Iterations best solution. This local minimum value is again compared with the Global Minimum (Gmin). If the local minimum is less than the global minimum, then the local minimum is assigned with the current global minimum. Then the kernel that generates this local minimum value is selected and its pheromone is updated. 155

12 The pheromone value for the remaining kernels is updated. Thus the pheromone values are updated globally. This procedure is repeated for all the image pixels. At the final iteration, the Gmin has the optimum label of the image. The corresponding kernel is selected from the pattern matrix. The intensity value of the center pixel in the kernel is selected as optimum threshold value for segmentation. In the MRI image, the pixels having lower intensity values than the threshold value are changed to zero. The entire procedure is repeated for any number of times to obtain the more approximated value. Step 1: Read the brain image or the stored in a two dimensional matrix. Step 2: Divide the image to 3x3 labels (cells). Step 3: For each label in the image, calculate the posterior energy U (x) value. U(x)={Σ[(y-μ) 2 /(2*σ 2 )]+Σ log(σ)+σv(x)} Where y = intensity value of pixels in the kernel, μ = mean value of the kernel, σ = standard deviation of the kernel, V = potential function of the kernel, and x = center pixel of the label. If x1 is equal to x2 in a kernel, then V(x) = β, otherwise 0, where β is visibility relative parameter (β 0). Step 4: The posterior energy values of all the labels are stored in a separate matrix. Step 5: HPACO System is used to minimize the posterior energy function. The procedure is as follows: Step 6: Initialize the values of number of iterations (N), number of ants (K) for colonies, initialize number of colonies (M), initial pheromone value (T 0 ), a constant value for pheromone update (ρ).[here, we are using N=20, K=10, M= 10, T 0 =0.001 and ρ=0.9]. Step 7: initliasition for each colonies. { Step 8: slave colonies systems { colony 1, colony 2. Colony M-1 } 156

13 Algorithm of colony 1 M ij Original Image CAD SYSTEM FOR AUTOMATIC DETECTION OF BRAIN TUMOR THROUGH MRI for each pixel in M ij G kernel of the border pixel of size 3 3 from M U fitness value; the posterior energy U (x) is calculated. U (x) ={å[( y-m ) 2 / ( 2 * s 2 )] +å log(s) + å V(x)} end N 50; K 10; T ; r 0.9 S {U(x),T 0, flag} flag column mentions whether the pixels is selected by the ant or not. Store the energy function values in S. Initialize all the pheromone values with T 0 = repeat for N times for each pixel in M ij for each ant g i a random kernel for each ant, which is not selected previously. T new (1 r) * T old + r * T 0 for g i End Lmax max(u i (x)) if (Lmax < Gmax) then Gmax = Lmax g Select the ant, whose solution is equal to local maximum T new (1 a) * T old + a * DT old, only for g End, End Similarly this algorithm is used to M-1 slave ant colonies and also master ant colonies system } 157

14 Step 8: The final highest global value derived from slave ant colony system. Step 9: Master colony system Step 10: The master colony yield global optimum value Step 11: compare to 8 and 10, the highest global optimum value treated as a optimum threshold value. The Gmin has the optimum label which minimizes the posterior energy function. Step 12: The optimal value HPACO is used to select the initial cluster point. FCM- HPACO Algorithm is the following: Step 13: Calculate the cluster centers. C = (N/2)1/2 Step 14: Compute the Euclidean distances D ij = CC p Cn Step 15: Update the partition matrix 1 U ij = c 2/(m-1) (Repeat step 4) Until Max[ U ij (k+1)-u ij k ] < is æ d ö åç ij k=1è d ø kj satisfied Step 16: Calculate the average clustering points. c c n n 2 i å i åå ij ij i=1 i=1 j=1 C = J = U d Step 17: Compute the adaptive threshold Adaptive threshold =max (Adaptive threshold, c i ) i=1...n Figure 5.3 HPACO with FCM 158

15 In the MRI image, the pixels having lower intensity values than the adaptive threshold value are changed to zero. The entire procedure is repeated for in the MRI image, the pixels having lower intensity values than the adaptive threshold value are changed to zero. The entire procedure is repeated for any number of times to obtain the more approximated value. 5.5 IMPLEMENTATION OF HPACO WITH FCM After completing all the process the generated output is given to the FCM as input. The optimal value of HPACO through MRI Brain Image is given as an input for FCM. The aim of FCM is to find cluster centres (centroids) that minimize dissimilarity function. The membership matrix (U) is randomly initialized as c å i=1 U ij =1; Where i is the number of cluster j is the image data point The dissimilarity function can be calculated with this equation c c n n 2 i = å i = åå ij ij i= 1 i= 1 j= 1 C J U d Where U ij - is between 0 and 1 C i - is the centroid of cluster i d ij - is the Euclidean distance between i th and centriod (C i ) and j th data point M - is a weighting exponent. 159

16 To calculate Euclidean distance (d ij ) Euclidean distance (d ij ) = Cluster center pixels - current neuron d ij = CC p C n where CCp - is the Cluster center pixels C n - is the current neuron i.e. Number of clusters is computed as C = (N/2)1/2 where N= no. of pixels in image To find the Minimum dissimilarity function can be computed as U = ij c å 1 2/(m-1) æ d ö ij ç k=1è d kjø where d ij= x i -c j and d kj = x i c k x i - is the i th of d- dimensional data c j - is the d-dimensional center of the cluster x so these iteration will stop when the condition Max ij { U (k+1) ij -U k ij } < is satisfied where - is a termination criterion between 0 and 1 K - is the iteration step 160

17 The step of the FCM Algorithm has been listed Step 1: Initialize U = U ij matrix Step 2: At K step initialize centre vector C (k) = C j taken from HPACO Clustering Algorithm Step 3: Update U (k), U (k+1), then compute the dissimilarity function U = ij c å 1 2/(m-1) æ d ö ij ç k=1è d kjø If U (k+1) - U (k) < then stop. Otherwise return to step3. Figure 5.4 FCM Algorithm 5.6 EXPERIMENTS AND RESULTS The sliding window of 3 3, 5 5, 7 7, 9 9 and are analyzed. In that 3 3 window is based on the high contrast value than 5 5, 7 7, 9 9, and The following table shows the adaptive threshold value, no. of pixels in the tumor region, execution time and weight vector. The execution time in HPACO with FCM, the 3x3 is 14, 5x5 is 28, 7x7 is 25, 9x9 is 23 and 11x11 is 18,Adaptive threshold for HPACO with FC M is 3x3 is 185, 5x5 is 164, 7x7 is 160, 9x9 is 148 and 11x11 is 139, the number of segmented pixel in HPACO with FC M of 3x3 is 1000, 5x5 is 1995, 7x7 is 2285, 9x9 is 3445and 11x11 is 8881,Weight vector for HPACO with FCM is 3x3 is 14, 5x5 is 28, 7x7 is 25, 9x9 is 23 and 11x11 is 18 are shown in Fig

18 Table 5.1 Performance Evaluations of HPACO with FCM Value/ Neighborhood pixels 3x3 5x5 7x7 9x9 11x11 Adaptive Threshold Number of Segmented Execution Time Weight

19 Figure 5.5 Performance Evaluations of HPACO with FCM 163

20 Table 5.2 Comparative Analysis of Existing Approaches Sliding Author Types of Representation window Value of Weight Vector Total No. of Tumor value (pixel) Execution Time Existing Approach HSOM- FCM 3x Existing Approach GA- FCM 3x BLOCK BASED Proposed Approach 5x TECHNIQUE Proposed Approach HPACO- FCM 5x Proposed Approach PSO- FCM 5x

21 Figure 5.6 Comparative Analysis of Existing Approaches 165

22 The comparative analysis shows that proposed system has a much lower tumor value and lesser execution time when compared to existing approach. The following graph shows the performance analysis of HSOM, GA, HPACO with Fuzzy. It is clear from the graph that the tumor and the execution time are much better when compared to existing approach. 166

23 Figure 5.7 Select the HPACO with FCM Algorithm 167

24 Figure 5.8 Segmented output of HPACO with FCM Algorithm 168

25 Figure 5.9 Output Image saved to the output folder 169

26 Figure 5.10 Confirmation of HPACO with FCM Saved to Output Folder 170

27 E PERFORMANCE ANALYSIS 5.7 SUMMARY In this work, a novel approach was applied to MRI Brain Image segmentation based on the Hybrid Parallel Ant Colony Optimization (HPACO) with Fuzzy Algorithm have been used to find out the optimum label that minimizes the Maximizing a Posterior estimate to segment the image. The HPACO search is inspired by the foraging behaviour of real ants. Each ant constructs a solution using the pheromone information accumulated by the other ants. In each iteration, local minimum value is selected from the ants solution and the pheromones are updated locally. The pheromone of the ant that generates the global minimum is updated. At the final iteration global minimum returns the optimum label for image segmentation. In the above 3 3, 5 5, 7 7, 9 9, windows are analyzed the HPACO with Fuzzy of 3 3 window is chosen based on the high contrast than 5 5, 7 7, 9 9, and The detection of brain tumor region using Hybrid Parallel Ant Colony Optimization with Fuzzy C Means is investigated. A New CAD System is developed for verification and comparison of brain tumor detection algorithm. HPACO with FCM automatically determines the adaptive threshold for the brain tumor segmentation. 185

28 E PERFORMANCE ANALYSIS CHAPTER VI CLASSIFICATION 186