CHAPTER 5 OPTIMUM SPECTRUM MASK BASED MEDICAL IMAGE FUSION USING GRAY WOLF OPTIMIZATION

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1 CHAPTER 5 OPTIMUM SPECTRUM MASK BASED MEDICAL IMAGE FUSION USING GRAY WOLF OPTIZATION Multimodal medical image fusion is an efficient technology to combine clinical features in to a single frame called fused image. The visual quality and edge enhancement are real challenges in clinical diagnosis and computer guided surgery. To overcome the limitations of existing fusion technique, proposed a new medical image fusion technique called Optimum Spectrum Mask Fusion (OSMF). Unlike the conventional masking technique, in OSMF the scale values are selected dynamically using a recently introduced optimization algorithm called Gray Wolf Optimization (GWO). 5.1 OPTIMUM SPECTRUM MASK FUSION This section is divided into 2 subsections as follows, need of optimum value in spectrum mask and proposed optimum spectrum mask fusion Need of Optimum Scale Value in Spectrum Mask In conventional masking techniques, the static scale values are using irrespective of the varying multimodal input images.the scale values, which selected for one set of input images might be ineffective in other input images because of multimodal acquisition devices. The gain factor of masking based techniques are controlled by scale values, the optimum scale value can adaptively control the gain irrespective of input images. 67

2 5.1.2 Proposed Optimum Spectrum Mask Fusion In this section, proposed a new approach in multi modal medical image fusion called, Optimum Spectrum Mask Fusion (OSMF). The objective of OSMF is to obtain a contrast enhanced multimodal fusion using optimum masking approach. The general GWO algorithm is used to adjust the optimum scale values (s 1 opt, s 2 opt ) for mask creation. The block diagram of proposed OSMF is given in Figure 5.1. In which, the input images are transformed into Fourier space using the Equation (5.1). Where F (u,v) denotes the Fourier space of input image I(x, y). The obtained Fourier spectrum of the input images are optimally scaled using scale values [s 1 opt, s 2 opt ] are obtained using GWO algorithm. In formulated masks, the mask 1 provides the structural features and mask 2 contains the soft tissue details for MR-T2 cases and metabolic features in SPECT and PET cases. The respective formulated masks are called spectrum mask1 and spectrum mask 2. The obtained mask images are fused using pixel based averaging fusion rule, the formulated fused image is Fourier domain. The inverse transform is used for getting the spatial domain fused image; the mathematical expression is given in Equation (5.2). M 1 x=0 N 1 y=0 F (u,v) = I(x, y) e j2π(u x M +v y N ) (5.1) M 1 u=0 N 1 v=0 I F (x,y) = F(u, v) e j2π(u x M +v y N ) (5.2) In which, M and N represents the number of rows and columns of input medical image, the considered images with size of M = N =256, I F (x,y) is the resultant spatial domain fused image. 68

3 Figure 5.1 Block diagram of Optimum Spectrum Mask Fusion 5.2 GRAY WOLF OPTIZATION This section explains the application of conventional GWO algorithm in multimodal medical image fusion applications. Unlike conventional masking technique, instead of conventional scaling, OSMF have optimum scale values. The GWO is a swarm intelligence algorithm, based on the hunting mechanism of gray wolves family in nature. The steps of GWO are described below: Initialization of Gray Wolf Positions In OSMF application, the range of scale value [s 1, s 2 ] for GWO is considered in [0, 1]. The scale values are considered as search agents in GWO. In OSMF analysis, considered 50 iterations and 50 gray wolfs in each pack. The pack size and iterations are fixed using trial and error approach Fitness Function The GWO algorithm is applied for optimal selection of scale values in OSMF. Mutual Information () is a well-known performance measure for multi 69

4 modal fusion and it provides the amount of information retained in the fused image frame. In OSMF, is used as fitness function and it provides better fusion assessment. The mathematical expression for is given in Equation (5.3). (x, y) = P(x, y)log P(x,y) x y (5.3) P(x)P(y) In Equation (5.3), P(x, y) is probability distribution function, P(x) and P(y) are marginal probability functions of both the modalities respectively Social Hierarchy of Gray Wolf Family In nature, gray wolfs are known for social leadership and they are living in a pack. The average member of gray wolf pack consists of Hierarchical order of gray wolf pack is divided in four levels, alpha(α), beta (β), delta(δ) and omega(ω). The first level hierarchy, alpha is making decisions regarding hunting, sleeping place, time to wake, and so on. Beta is second level ranking in a gray wolf pack, the best subordinates to alpha in activates. Omega is the lowest level order in grey wolf pack, plays the role of scapegoat. In pack, omega wolves always have to submit to all the other wolves. In gray wolf pack, if a wolf is not an alpha, beta, or omega, which is called delta wolf and they have to submit to alphas and betas, but they dominate the omega in gray wolf social hierarchy Encircling Prey During their hunting, before the attack gray wolf encircles the prey. The mathematical expressions are given in Equations (5.4) - (5.7). D = C. X p (t) X(t) (5.4) X(t + 1) = X p (t) A. D (5.5) 70

5 A = 2a. r 1 a (5.6) C = 2. r 2 (5.7) In which, X p is the position vector of the prey, t indicates current iteration, X represents the position vector of gray wolf. Where A and C are coefficient vectors and D is the direction vector. In which, r 1, r 2 are random vectors in the range of [0, 1] and a linearly decreases from 2 to Hunting The gray wolves have the intelligence to find the location of its prey. In which, hunt is leaded under the guidance of alpha. The beta and delta might also help in hunting mechanism. The alpha wolfs have the best knowledge about prey position followed by beta and delta in a pack. In GWO, each iterations forwards three best search agents such as, alpha, beta and delta to the next level, all other agents in gray wolf family are treated as gamma. The mathematical expressions are given in Equations (5.8) - (5.10). D = C 1. X X, D β = C 2. X β X, D δ = C 3. X δ X (5.8) X 1 = X A 1. (D ),X 2 = X β A 2. (D β ),X 3 = X δ A 3. (D δ ) (5.9) X(t + 1) = X 1 +X 2 +X 3 3 (5.10) Attacking Prey During the hunting, when the prey stops the movement gray wolves finish the hunt by attacking the prey. During the process of approaching the prey (as per mathematically standard) the gray wolfs need to decrease the value of a, where a is updated from 2 to 0 during the course of iterations. 71

6 5.2.6 Search for Prey The GWO usually perform search based on the positions of the alpha, beta, and delta. The gray wolfs diverge from each other to search for prey and converge to attack the prey. The mathematical modeling of divergence, considered with random values larger than 1 or less than -1 to impel the search agent to diverge away from prey, in which, C vector is treated as the effect of obstacles to approach prey, which appear in the paths of wolves and literally inhibit them, by swiftly and calmly approaching prey and each search agent updates its distance from the prey. The pseudo code for GWO algorithm is given in Figure 5.2. Figure 5.2 Pseudo code of GWO 72

7 5.3 RESULTS AND DISCUSSION The OSMF is tested with 995 sets of medical images and the average results are given. The multi modal medical image fusions used in this section are MR- SPECT fusion, MR-PET fusion, MR-CT fusion and MR: T1- T2 fusion. The various measures used in quantitative analysis are Mutual Information (), Entropy, Standard Deviation (SD),Edge Strength (Q AB F), Structural Similarity Index (SSIM) and Peak Signal to Noise Ratio (PSNR).The mathematical expressions of above measures are given in chapter 3. The OSMF is compared with various states of art fusion techniques such as DCT, IHS, DFT and DWT. In GWO, optimum scale analysis is based on 50 iterations and evaluated the best scale values. The obtained optimum scale values as are given in Table 5.1.In which, the average of best scale values of all the clinical cases of each fusion application is given. The computational analysis is done using Matlab 2010a with Pentium dual core processor with speed 2.30 GHz. The size of the considered input images are 256x256 in JPEG format. Table 5.1 Implementation results of GWO Type of images s 1 best s 2 best MR-SPECT MR-PET MR-CT MR-T1-T

8 5.3.1 MR-SPECT Fusion The MR-SPECT fused modality provides the anatomical structure and metabolic features in a single frame. The OSMF technique is tested for 140 patients MR and SPECT images for performance analysis. The average values of quantitative results are given in Table 5.2. Examples of four MR-SPECT cases among the 140 sets are given in Figure 5.3. The quantitative results shows that the OSMF based fusion has grater performance than that of conventional transform techniques. The PSNR value of conventional FFT is whereas the proposed spectrum masking has The SSIM value of conventional FFT is while the proposed OSMF has Entropy value of FFT based fusion technique is given whereas the proposed technique has Edge quality measure for conventional FFT is while the proposed technique has The obtained results show that the proposed OSMF has greater amount of fusion in terms of structural mapping, edge quality, contrast enhancement and retained information than that of other existing fusion techniques for MR-SPECT multimodal medical image fusion approaches. Table 5.2: Results of MR-SPECT fusion Entropy Q AB F STD SSIM PSNR DCT DWT FFT IHS Proposed

9 MR SPECT MR-SPECT fused MR SPECT MR-SPECT fused MR SPECT MR-SPECT fused SPECT MR-SPECT fused MR Figure 5.3 Examples of MR-SPECT fusion using proposed technique 75

10 Entropy STD SSIM PSNR 10 0 DCT DWT FFT IHS Proposed Figure 5.4 Results of MR-SPECT fusion for Case 1 dataset Entropy STD SSIM PSNR 0 DCT DWT FFT IHS Proposed Figure 5.5 Results of MR-SPECT fusion for Case 2 dataset 76

11 5.3.2 MR-PET Fusion In MR-PET fused modality provides the structural and metabolic information in a single image. The proposed OSMF technique is tested for 375 patients MR and PET images. The average values of MR-PET fusion are compared with various existing techniques and are given in Table 5.3. Examples of four MR-PET fusion cases among the total 375 sets are given in Figure 5.3. The quantitative results shows that the OSMF based fusion has higher fusion than that of existing transform based techniques. The PSNR value of conventional FFT is whereas the OSMF has The SSIM value of conventional FFT is while the OSMF has Entropy value of FFT based fusion technique is given whereas the OSMF technique has Edge quality measure for conventional FFT is while the proposed technique has The obtained MR-PET fusion results show that the OSMF has greater amount of fusion in terms of structural mapping, edge quality, contrast enhancement and retained information than that of other existing fusion techniques. Table 5.3 Results of MR-PET fusion Entropy Q AB F STD SSIM PSNR DCT DWT FFT IHS Proposed

12 MR PET MR-PET fused MR PET MR-PET fused MR PET MR-PET fused MR PET MR-PET fused Figure 5.6 Examples of MR-PET fusion using proposed technique 78

13 Entropy STD SSIM PSNR 0 DCT DWT FFT IHS Proposed Figure 5.7 Results of MR-PET fusion for Case 1 dataset Entropy STD SSIM PSNR 0 DCT DWT FFT IHS Proposed Figure 5.8 Results of MR-PET fusion for Case 2 dataset 79

14 5.3.3 MR-CT Fusion In MR-CT fused modality provides the soft and hard tissues in a single image. The proposed OSMF technique is tested for 210 patients MR and CT images. The average values of quantitative fusion metrics for MR-CT in comparison with states of art fusion techniques are given in Table 5.4. Examples of two MR and CT cases among the 210 sets are given in Figure 5.4. The clinical details of both examples are given in chapter 3. The quantitative results shows that the OSMF based fusion has grater performance than that of conventional transform techniques. The PSNR value of conventional FFT is whereas the proposed spectrum masking has The SSIM value of conventional FFT is while the proposed OSMF has Entropy value of FFT based fusion technique is given whereas the proposed technique has Edge quality measure for conventional FFT is while the proposed technique has The obtained results show that the proposed OSMF has greater amount of fusion in terms of structural mapping, edge quality, contrast enhancement and retained information than that of other existing fusion techniques. Table 5.4 Results of MR-CT fusion Entropy Q AB F STD SSIM PSNR DCT DWT FFT IHS Proposed

15 MR CT MR-CT fused MR CT MR-CT fused MR CT MR-CT fused MR CT MR-CT fused Figure 5.9 Examples of MR-CT fusion using proposed technique 81

16 Entropy STD SSIM PSNR 10 0 DCT DWT FFT IHS Proposed Figure 5.10 Results of MR-CT fusion for Case 1 dataset Entropy STD SSIM PSNR 0 DCT DWT FFT IHS Proposed Figure 5.11 Results of MR-CT fusion for Case 2 dataset 82

17 5.3.4 MR-T1-T2 Fusion In MR-T1-T2 fused modality, which provides T1 and T2 weighted slices in a single image. The proposed OSMF technique is tested for 270 patients MR- T1 and MR-T2 images. The average values of fusion quality matrices for MR-T1- MR-T2 in comparison with the states of art fusion techniques are given in Table 5.5. The examples of four MR-T1-T2 image fusions among the 270 sets are given in Figure 5.4. The performance analysis shows that the OSMF based fusion has grater performance than that of conventional transform techniques. The PSNR value of conventional FFT is whereas the proposed spectrum masking has The SSIM value of conventional FFT is while the proposed OSMF has The edge quality of OSMF is while the conventional FFT based fusion has The obtained results show that the proposed OSMF has greater amount of fusion in terms of structural mapping, edge quality, contrast enhancement and retained information than that of other existing fusion techniques. Table 5.5 Results of MR-T1-MR-T2 fusion Entropy Q AB F STD SSIM PSNR DCT DWT FFT IHS Proposed

18 MR-T1 MR-T2 MR-T1-MR-T2 fused MR-T1 MR-T2 MR-T1-MR-T2 fused MR-T1 MR-T2 MR-T1-MR-T2 fused MR-T1 MR-T2 MR-T1-MR-T2 fused Figure 5.12 Examples of MR-T1-MR-T2 fusion using proposed technique 84

19 Entropy STD SSIM PSNR 10 0 DCT DWT FFT IHS Proposed Figure 5.13 Results of MR-T1-MR-T2 fusion for Case 1 dataset Entropy STD SSIM PSNR 0 DCT DWT FFT IHS Proposed Figure 5.14 Results of MR-T1-MR-T2 fusion for Case 2 dataset 85

20 5.4 CONCLUSION In this chapter, presented an Optimum Spectrum Mask Fusion (OSMF) for multi modal medical image fusion.the objective of this technique is to provide contrast improved fusion. The comparison results show that the proposed OSMF provides better quantitative and qualitative values in terms of redundant information, mutual information as well as edge quality with various state of art fusion techniques. The optimization of scale values is selected using GWO. The GWO algorithm has performed optimum selection of scale values irrespective of input modalities. The comparative results are analysed for various image fusion modalities and the average value is compared with existing fusion techniques. The proposed OSMF technique reduced the fusion artefacts in MR- SPECT, MR-PET, MR-CT and MR-T1-T2 fusion. In next chapter, cascaded mask based medical image fusion technique is introduced. The cascaded laplacian wavelet mask can dynamically enhance the soft tissues information as well as hard tissues information in single fusion technique itself. 86