Medical image fusion is the process of combining two different modality images into single image. This resultant image is helpful in medical field for efficient disease diagnoses, retrieval of images, undergo surgery treatment, tumor identification etc. This single image features cannot be obtained from the single modality medical images and it can be resolved by image fusion. This paper has proposed two fusion techniques Iterative Neuro-Fuzzy Approach (INFA), Lifting Wavelet Transform and Neuro-Fuzzy Approach (LWT-NFA). By using proposed techniques, Computed Tomography (CT) and Magnetic Resonance Image (MRI) images are fused and it has been compared with existing technique using quantitative and qualitative measures. From the implementation observed that INFA algorithm provides clear image information based on the measures. © 2014 IEEE.

Rajkumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Bardhan P

Akkireddy S.K

Munshi C.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

CT and MRI image fusion based on Wavelet Transform and Neuro-Fuzzy concepts with quantitative analysis

2014 International Conference on Electronics and Communication Systems (ICECS)

Image fusion aims to integrate complementary information from multiple modalities into a single image with none distortion and loss of data. Image fusion is important in medical imaging, specifically for the purpose of detecting the tumor and identification of diseases. In this article, completely unique discrete wavelet transform (DWT) and intuitionistic fuzzy sets (IFSs) based fusion method (DWT-IFS) is proposed. For fusion, initially, all source images are fused using DWT with the average, maximum, and entropy fusion rules. Besides, on the fused image IFS is applied. In the IFS process images are converted into intuitionistic fuzzy images (IFIs) by selecting an optimum value for the parameter in membership, non-membership, and hesitation degree function using entropy. Then, the resulting IFIs are decomposed into the blocks, and the corresponding blocks of the images are fused using the intersection and union operations of IFS. The efficiency of the proposed DWT-IFS fusion method is recognized by examining it with other existing methods, such as Averaging (AVG), Principal Component Analysis (PCA), Laplacian Pyramid Approach (LPA), Contrast Pyramid Approach (CPA), Discrete Wavelet Transform (DWT), Morphological Pyramid Approach (MPA), Redundancy Discrete Wavelet Transform (RDWT), Contourlet Transform (CONTRA), and Intuitionistic Fuzzy Set (IFS) using subjective and objective performance evaluation measures. The experimental results reveal that the proposed DWT-IFS fusion method provides higher quality of information in terms of physical properties and contrast as compared to the existing methods. © 2017 Wiley Periodicals, Inc.

International Journal of Imaging Systems and Technology

An Efficient DWT and Intuitionistic Fuzzy Based Multimodality Medical Image Fusion

Medical image fusion is the process of deriving imperative information from multimodality medical images. This derived information can be used for various purposes like, diagnosing diseases, detecting the tumor, surgery treatment and so on. This type of information cannot be obtained using single modality image. Therefore, the drawbacks of single modality medical image has paved the way for the process of combining different modality images such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Single Photon Emission Computed Tomography (SPECT) into a single image. Hence, the above described procedure of combining multimodality images in to a single fused image can be done using image fusion techniques. This paper proposes a new image fusion technique namely; Discrete Wavelet Transform-Averaging-Entropy- Principle Component Analysis method [DWT-A-EN-PCA] and the results of proposed system are compared with other existing fusion techniques using quantitative metrics such as, Entropy (EN), Signal to Noise Ratio (SNR) and Fusion Symmetric (FS) for performance evaluation. From the experimental result it is observed that Entropy (EN) provides better quality of information for proposed method, Signal to Noise Ratio (SNR) shows less noise ratio for proposed method, and Fusion Symmetric (FS) shows very less information loss during the fusion of proposed method. © 2013 IEEE.

2013 International Conference on Communication and Signal Processing

Hybrid method for multimodality medical image fusion using Discrete Wavelet Transform and Entropy concepts with quantitative analysis

Image fusion is basically a process where multiple images (more than one) are combined to form a single resultant fused image. This fused image is more productive as compared to its original input images. The fusion technique in medical images is useful for resourceful disease diagnosis purpose. This paper illustrates different multimodality medical image fusion techniques and their results assessed with various quantitative metrics. Firstly two registered images CT (anatomical information) and MRI-T2 (functional information) are taken as input. Then the fusion techniques are applied onto the input images such as Mamdani type minimum-sum-mean of maximum (MIN-SUM-MOM) and Redundancy Discrete Wavelet Transform (RDWT) and the resultant fused image is analyzed with quantitative metrics namely Over all Cross Entropy(OCE), Peak Signal-to- Noise Ratio (PSNR), Signal to Noise Ratio (SNR), Structural Similarity Index(SSIM), Mutual Information(MI). From the derived results it is inferred that Mamdani type MIN-SUM-MOM is more productive than RDWT and also the proposed fusion techniques provide more information compared to the input images as justified by all the metrics. © 2012 IEEE.

2012 International Conference on Recent Advances in Computing and Software Systems

Medical image fusion based on redundancy DWT and Mamdani type min-sum mean-of-max techniques with quantitative analysis

Image fusion is the process of combining relevant information from two or more images into a single fused image. The resulting image will be more informative than any of the input images. The fusion in medical images is necessary for efficient diseases diagnosis from multimodality, multidimensional and multiparameter type of images. This paper describes a multimodality medical image fusion system using different fusion techniques and the resultant is analysed with quantitative measures. Initially, the registered images from two different modalities such as CT (anatomical information) and MRI - T2, FLAIR (pathological information) are considered as input, since the diagnosis requires anatomical and pathological information. Then the fusion techniques namely Redundancy Discrete Wavelet Transform (RDWT) and Contourlet Transform are applied. Further the fused image is analyzed with five types of quantitative metrics such as Standard Deviation (SD), Entropy (EN), Overall Cross Entropy (OCE), Ratio of Spatial Frequency Error (RSFE), and Power Signal to Noise Ratio (PSNR) for performance evaluation. From the experimental results we observed that RDWT method provides better information (quality) using EN metric and the Contourlet Transform gives the difference in source to the fused image using OCE metric and also the fused image obtained from the proposed fusion techniques has more information than the source images are proved through all metrics. © 2010 IEEE.

Journal	Data powered by Typeset2014 International Conference on Electronics and Communication Systems (ICECS)
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Open Access	0