REVIEW ON MEDICAL IMAGE FUSION BASED ON NEURO-FUZZY APPROACH

777 REVIEW ON MEDICAL IMAGE FUSION BASED ON NEURO-FUZZY APPROACH Lakhwinder Singh 1, Dr. Sunil Agrawal 2, Mrs. Preeti Gupta 3 1 Electronics and Communication Engineering, UIET, Chandigarh 2 Electronics and Communication Engineering, UIET, Chandigarh 3 Electronics and Communication Engineering, UIET, Chandigarh ABSTRACT Image fusion is very popular topic nowadays for researchers so it is widely used by many researchers. Image fusion is used in many application like remote sensing, medical fields etc. As CT and MRI are more accurately images which can give more useful information and this pattern is increasingly used. More detailed information can be obtained from a combined image than found in a single image. Need of storage also decrease as it generate and saves single image instead of saving two different kinds of images. To provide detail that is invisible to human eye. Increases robustness and enhances accuracy in biomedical research and clinical diagnosis. Many techniques have been developed and research is not stopped yet. Keywords - CT - MRI, Medical image fusion, Neuro Fuzzy approach, Image fusion. I. INTRODUCTION Image fusion is method in which two or more images are taken from same area and with different modalities are combined together forming single image. The resulting image will be more informative than any of the input images. It is widely used in many applications like medical imaging, biometrics, automatic change detection, machine vision, navigation aid, military applications and remote sensing etc. [6] Image fusion can be done at four levels: signal level, pixel level, feature level and decision level. Medical image fusions regularly utilize the pixel level fusion techniques. At pixel level fusion famous algorithms include Average Method, Maximum Selection Method, Principle Component Analysis, and Laplacian Pyramid Method. [2] In medical image fusion method different multi modalities images are taken and then image fusion is done using different techniques. There are two different modalities are categorized: anatomical and functional which includes CT, X-ray, DSA, MRI, PET, SPECT etc. All modalities are different information characteristics which can provide bodily structure and detecting or measuring changes in metabolism, blood flow, regional chemical composition, and absorption. For example, CT can clearly reflect the anatomical structure of bone tissues. It provides detailed crosssectional views of all types of tissues. MRI can clearly reflect the anatomical structure of soft tissues, organs and blood vessels. And with high spatial resolution can provide anatomical structure information of organs [5]. Which makes CT - MRI fusion is most popular fusion modalities because these two modalities having complimentary information and best information characteristics. Purpose of medical image fusion, there are many reasons to fuse image from different modalities. In the clinical diagnosis and treatment, the problems about the comparison and synthesis between image CT, SPECT and MRI were frequently encountered [3]. Doctor could easily find the position of illness after medical image fusion. Doctors manually combine the CT and MRI medical images of a patient with a tumor to make a more accurate diagnosis but it is very difficult task to complete this job and more important is doctors with different experience make inconsistent decisions. Manual process is subject to human error, and requires years of experience and time consuming [4]. Thus, it is necessary to develop the efficiently automatic image fusion system to decrease doctor s workload and improve the consistence of diagnosis [5]. Iterative image fusion technique is very useful in medical imaging. It is used where quality of the image is more important than the real time application. Output image can go further for again fusion with preferred input image to get a better image quality depending on the requirement. When two or more than two images are given as inputs to the neuro fuzzy system they have equal share pixel-wise in final fused output [2]. Iterative approach is used for giving priority to some images which then fused to output image more than once for the final output image. Suppose we have three image sources where one is a normal image, the second is MRI image and the third is image from some other source and in the final image, we want to have

778 more effect from MRI image. Then we fuse the new output image with the second MRI input image once again and the process is repeated if required [2]. Neuro-Fuzzy technique is the combination of artificial neural networks (ANN) and Fuzzy logic for the combination of two images. Neuro-Fuzzy is better from conventional techniques in decision making as the neurons can be trained in this technique. Membership functions in fuzzy logic can also be applied for better decision making. Further, neurofuzzy technique help maintain more texture feature of images. Fuzzy logic usually takes a lot of time to design and tune the membership functions and neural network learning techniques can automate this process and reduce development time and cost while improving performance [1]. II. REVIEW In this rapidly changing digital world image processing gives tremendous advantages to our day to day life. Many techniques have been developed till now and many applications from digital image processing are used using neuro-fuzzy techniques which are as following explained: A. Multimodal Medical Image Fusion Based on Integer Wavelet Transform and Neuro-Fuzzy[1] In this paper, registered medical CT and MRI images are fused to improve the image information so to provide precise information to the doctor and clinical treatment planning system. Two techniques has been used are Integer Wavelet Transform (IWT) and Neuro- Fuzzy. Images are decomposed using Integer Wavelet Transform. And wavelet coefficients are then fused using neuro-fuzzy algorithm. Inverse Integer Wavelet Transform (IIWT) is applied on the fused coefficients to get desired output image. Wavelet theory improves spatial resolution and spectral characteristics. IWT maintains the characteristics of wavelet, and also has the features of fast operational speed and occupies less memory. The performance of this algorithm is compared with Discrete Wavelet Transform (DWT) and neuro-fuzzy using entropy metric parameters. Fusion Symmetry (FS) and Fusion Factor (FF) also calculated to compare the method. B. CT and MRI Image Fusion based on Wavelet Transform and Neuro-Fuzzy concepts[2] In this research paper, related to medical imaging modalities has been combined two imaging modalities used are CT and MRI because single image features cannot give desired characteristics and information. That s solved by image fusion Fused image gives advantage of efficient disease diagnoses, retrieval of images, undergo surgery treatment, tumor identification etc. paper proposed two fusion techniques Iterative Neuro-Fuzzy Approach (INFA), Lifting Wavelet Transform and Neuro-Fuzzy Approach (LWT-NFA). These techniques are applied on CT and MRI and get the fused image. It has been compared with existing technique using quantitative and qualitative measures. The resultant of this method is compared with existing technique Discrete Wavelet Transform (DWT), average method using subjective and objective measures such as Normalized Correlation Coefficient (NCC), Entropy (EN), and Structural Similarity Index (SSIM). From the implementation observed that INFA algorithm provides clear image information based on the measures. C. Neuro-Fuzzy Logic based Fusion Algorithm of Medical Images [3] In this paper, CT, single photon emission computed tomography (SPECT) and nuclear magnetic resonance imaging (MRI) complementary on reflecting human information are taken to fuse together. In the clinical diagnosis and treatment, the problems about the comparison and synthesis between image CT, SPECT and MRI were frequently occurred. To solve the problem, the fusion algorithm based on BP neural network and LMS is purposed in this paper. Because previously in paper used BP network is defective whose training efficiency is low and generalization is poor when establish the models for highly complex nonlinear system. Pixel level fusion method is used to fuse the effective information fusion algorithm based on neuro-fuzzy logic in this paper. And hybrid algorithm which adds BP with least mean square (LMS) algorithm to train the parameters of membership function. And compare the results and fusion simulation with basic BP neural network on the basis of the evaluation standards which are the standard deviation and the information entropy. Resultant fused image having more texture features and also enhance the information characteristics of two original images. D. Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its Applications [6] This paper addresses, Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques. Image fusion method are used in many applications like medical imaging, remote sensing, medical imaging, machine vision, biometrics and military applications where quality and critical information is important. In this paper, image fusion using fuzzy and neuro fuzzy logic approaches to fuse images from various modalities, in order to

779 enhance visualization. Comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation for image fusion like image quality index, mutual information measure, fusion factor, fusion symmetry, fusion index, root mean square error, peak signal to noise ratio, entropy, correlation coefficient and spatial frequency are obtained. Experimental results obtained from fusion process prove that the use of the neuro fuzzy based image fusion approach shows better performance and fuzzy based image fusion technique gives better results. E. Iterative Image Fusion using Fuzzy and Neuro Fuzzy Logic and Applications [7] This paper purposes, image fusion is a process to reduce ambiguity and diffusion while retrieving the valuable information from the original images. Image fusion is having many applications like remote sensing, medical imaging, machine vision and biometrics. The technique in which image is once again fused with the output image using fuzzy and neuro fuzzy so called iterative fuzzy and neuro fuzzy. Resultant output fused image is then checked using quality assessment parameters. And there parameters are compared with normal techniques used are fuzzy and neuro fuzzy logic. Parameters compared using quality assessment metrics for image fusion like image quality index, mutual information measure, fusion factor, fusion symmetry, fusion index, root mean square error, peak signal to noise ratio and spatial frequency. Experimental outcomes attained from proposed method prove that the use of the iterative fuzzy and iterative neuro fuzzy fusion can efficiently retain the information while increasing the spatial information of the remote sensing and medical imaging. F. A Neuro-Fuzzy Approach for Medical Image Fusion [8] According to this paper, a novel approach to the multisensor, multimodal medical image fusion (MIF) problem, employing multi-scale geometric analysis of non-subsampled contourlet transform and fuzzyadaptive reduced pulse-coupled neural network (RPCNN). In the proposed method, coefficients of both low frequency sub-bands (LFSs) and high frequency sub-bands (HFSs) are fused in a similar way using RPCNNs with fuzzy-adaptive linking strengths. The linking strengths of the RPCNNs neurons are adaptively set by modelling them as the fuzzy membership values, representing their significance in the corresponding source image. Use of RPCNN with less complex structure and having less number of parameters, leads to computational efficiency, an important requirement of point-of-care (POC) health care technologies. The proposed scheme is free from the common shortcomings of the state-of-the-art MIF techniques: contrast reduction, loss of image fine details and unwanted image degradations etc. Subjective and objective evaluations show better performance of this new approach compared to existing techniques. G. Medical Image Fusion Based On Hybrid Intelligence [9] In this paper, medical image modalities used are CT, MRI, SPECT and PET which are complementary images for proper diagnosis and surgical planning. Based on the hybrid intelligence system is proposed and paper has integrated the swarm intelligence and neural network to achieve a better fused output. According to this paper importance are given to edges so are detected and optimized by applying ant colony optimization. In ACO is the natural collective behavior of the ant species. Number of ants are utilized to evolve on a 2D image and to construct the pheromone matrix. The detected edges are enhanced and then given as the feeding input to the simplified pulse coupled neural network (spcnn). The firing maps are generated and the maximum fusion rule is applied to get the output image. The performance of the proposed method is compared with the genetic algorithm method, neuro-fuzzy method and also with the modified pulse coupled neural network. The results show that the proposed hybrid intelligent method performs better when compared to the existing computational and hybrid intelligent methods. H. Medical Image Fusion Using Multi-Level Local Extrema [10] This paper purposes, the new fusion algorithm for multi-modal medical images based on multi-level local extrema (MLE) method which is to have many advantages over conventional image representation methods. Input image is decomposition into coarse and detailed layers which preserves more details in the source images and improves the quality of the output fused image. Resultant image which is fused is taken from the superposition of selected coefficients in the coarse and detailed layers. Three groups of medical images has shown from different sources as experimental subjects and also compare this method with other techniques using cumulative mutual information, the objective image fusion performance measure, spatial frequency, and a blind quality index. Experimental results show that our method achieves a superior performance in both subjective and objective assessment criteria.

780 I. Neuro-Fuzzy Logic Based Fusion Algorithm For Multimodality Images [11] In This paper, Image fusion is the combination of two or more images into one single relevant information fused image to get parameters how the images are related to original and can further used for application like Digital imaging, Aerial and Satellite imaging, Robot vision, Multi-focus imaging, Medical imaging etc. In medical image fusion, it is necessary for diagnosis diseases from multimodality, multidimensional and multi parameter type of images. Images taken are CT and MRI. Many issues and lose of information occurs during image fusion like false edges, dark spots in tissue part and spatial distortion problem detected so to solve this kind of errors this paper applied a Neuro-Fuzzy logic based fusion algorithm has been proposed. After proposed method, calculations have been made and some parameters are evaluated mean, standard deviation and entropy. J. Medical Image Fusion Techniques for Remote Surgery[12] In this paper, image fusion techniques are broadly classified into five categories, for each category comparative quantitative analysis is calculated. And the application of image fusion techniques to remote surgeries and various aspects related to it. Fast data transfer rate in remote surgery applications is needed. The comparison of different fusion techniques like Neural network, Fuzzy Logic, Neuro-fuzzy, DWT, LWT (Lifting Wavelet Transform), contourlet, curvelet, IWT and NF (Integer Wavelet Transform and Neuro-Fuzzy), Fuzzy and CT (Fuzzy and Contourlet Transform) based on some quality evaluation metrics which are mean, entropy, standard deviation, CEN and PSNR with experimental images. Results show that, medical image fusion using contourlet gives better results. Although a method based on two added techniques are contourlet transform and artificial intelligence improves content of fused image with less computation cost. K. Comparison of Registered Multimodal Medical Image fusion Techniques [13] This paper explains Multimodal medical image fusion is an important task to get the image which provides much information of the same organ at the same time. Storing or single image is good for less storage capacity required rather than two images. The proposed method fuses the coefficient based on maximum selection rule. Comparison between existing image fusion techniques and the proposed multilevel fusion techniques is done in this paper. Three different sets of multimodal medical images of brain X-ray, magnetic resonance imaging (MRI) and computed tomography (CT) are used for experiments purpose. The hybrid transforms fusion technique which is combination of spatial and frequency domain method is visually and quantitatively compared with the existing methods. In this hybrid transform first spatial based fusion is applied and then frequency. And resulting three parameters are calculated which are peak signal to noise ratio (PSNR), Entropy and Mutual Information. After Comparison results show that hybrid transform fusion method works better than any of the existing fusion methods which are Simple Average, Principal Component Analysis (PCA), Index of Fuzziness (IOF), Discrete Wavelet Transform (DWT), and Daubechies Complex Wavelet Transform. III. CONCLUSION CT and MRI are mostly used modalities in most of the research papers because they give more information. MRI is safe for pregnant women s and babies as it does not expose to radiation but takes long time process. It takes soft tissue structure of organs like brain, heart etc with high accuracy. Where CT is short time process and with high imaging resolution it can distinguish between tissue and physical density with high resolution. Many techniques and methods are used by many researchers. Pixel level is used in medical image fusion. Integer wavelet transform used to fuse image using neuro-fuzzy algorithm and then inverse integer wavelet transform for output. Wavelet transform is used to fuse image with neuro-fuzzy like there is main focus on neuro-fuzzy method as it is fast and easy method for researchers and gives faster and good performance results. After applying different approaches on images quality and quantative evalution are used to calculate the pixel value weather image after fused gathered more pixels or not. Parameters are mean, entropy, standard deviation, CEN and PSNR etc. There are many benefits of fusion need less space to store one image instead of both and medical diagnosis for doctors to find exact blockage and size of tumor in organ. REFERENCES [1] C. T. Kavitha, C.Chellamuthu, Multimodal Medical Image Fusion Based on Integer Wavelet Transform and Neuro-Fuzzy 978-1- 4244-8594-9/10, 2010 International Conference on Signal and Image Processing. [2] S Rajkumal, P. Bardhan, et al, CT and MRI Image Fusion based on Wavelet Transform and Neuro-Fuzzy concepts with quantitative

781 analysis, School Computing Science and Engineering, VI T University, Vellore, India. [3] J. Teng, S. Wang, et al, Neuro-Fuzzy Logic Based Fusion Algorithm of Medical Images, 2010 3rd International Congress on Image and Signal Processing (CISP2010), College of automation Northwestern Polytechnical University(NPU) Xi an, China. [4] S. Das, M. K. Kundu, Senior Member, IEEE, A Neuro-Fuzzy Approach for Medical Image Fusion, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 12, DECEMBER 2013. [5] Hari Om Shankar Mishra, Smriti Bhatnagar, Amit Shukla, Amit Tiwari, Medical Image Fusion Based on Wavelet Transform, International Journal of Scientific & Engineering Research, Volume 5, Issue 2, February-2014 772 - ISSN 2229-5518 - IJSER 2014. [6] Srinivasa Rao D, Seetha M, Krishna Prasad M. H. M, Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its Applications, International Journal of Computer Applications (0975 8887) - Volume 43 No.20, April 2012. [7] SrinivasaSrao D, Seetha M, Krishna Prasad M. H. M., Quality assessment parameters for iterative image fusion using fuzzy and neuro fuzzy logic and applications, 8 th international conference inter disciplinarily in engineering, INTER-ENG 2014, 9-10 october 2014, Tirgu- Mures, Romania. [8] S. Das, and M. K. Kundu, Senior Member, IEEE., A Neuro-Fuzzy Approach for Medical Image Fusion, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. XX, NO. X, MONTH 2013. [9] Kavitha C.T., Chellamuthu C., Medical image fusion based on hybrid intelligence, Applied Soft Computing 20 (2014) 83 94. [10] Zhiping Xu, Medical image fusion using multi-level local extrema, Information Fusion 19 (2014) 38 48. [11] R. RADHIKA, S. KAVITHA, NEURO- FUZZY LOGIC BASED FUSION ALGORITHM FOR MULTIMODALITY IMAGES, ASAR International Conference, Coimbatore Chapter- 12th May 2013, ISBN: 978-81-927147-4-5. [12] Mangesh Balpande, Urmila Shrawanccckar, Medical Image Fusion Techniques for Remote Surgery, 2013 Annual IEEE India Conference (INDICON). [13] Sonia Kuruvilla, J. Anitha, Comparison of Registered Multimodal Medical Image fusion Techniques, 2014 International Conference on Electronics and Communication System (ICECS -2014).