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.

Rajkumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Prakash C

Chandra Mouli P.V.S.S.R.

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

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

2012 International Conference on Recent Advances in Computing and Software Systems

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

The contrast enhancement, brightness preserving and natural look of the output image plays an important role in an infrared (IR) image enhancement process. There are several IR image enhancement algorithms that have been developed, but they may produce artificial looking images, and the images derived from these methods are not suitable for applications such as surveillance, environmental monitoring and so on. In this paper, an algorithm using new top-hat transform and multi-scale sequential toggle operator has been proposed. Firstly, a new white top-hat transform and new black top-hat transform is generated to extract the light and dark regions of the image. Then, the multi-scale sequential toggle operator is used on the extracted light and dark regions to get a sequence of regions. Ultimately, the final image is extracted from a sequence of images. To test the proposed method, the experiment is carried out on the OSU thermal bench mark database images. The enhanced image results have been compared with the popular existing methods such as Histogram Equalization (HE) and Adaptive Histogram Equalization (AHE), max-median (3 × 3) using various evaluation measures. From the results, it is observed that the proposed method works better than the existing methods. © 2017 IEEE.

2017 Innovations in Power and Advanced Computing Technologies (i-PACT)

Adaptive new top-hat transform and multi-scale sequential toggle operator based infrared image enhancement

Image Fusion is a methodology of joining useful data of infrared and visible images into a single huge informative image. The infrared images are mostly dark and only more heat emitting objects can identify. The visible image provides more information about their surroundings. So to capture the both objects and surroundings we need to fuse both the images. In this paper we are analyzing and comparing different spatial and transform domain fusion methods like Average, Principal Component Analysis (PCA), Laplacian Pyramid Approach (LPA), Contrast Pyramid Approach (CPA), Discrete Wavelet Transform (DWT), Morphological Pyramid Approach (MPA) with Quantitative measures like Entropy (EN) and Peak Signal to Noise Ratio (PSNR). From the experimental results observed that Principal Component Analysis provided better information using EN and PSNR measures for all the image sets is same and satisfied with the visualization of fused image also. © 2014 IEEE.

2014 International Conference on Electronics and Communication Systems (ICECS)

Quantitative analysis of fused infrared and visible images using spatial and transform domain techniques

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.

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

Image fusion is the process to derive the useful information from the scene captured by infrared (IR) and visible images. This derived information is used to improve the image content by enhancing the image visualization. Human identification or any living object identification in IR images is a challenging task. This paper proposes two fusion techniques namely Discrete Wavelet Transform with Neuro-Fuzzy (NF) and Entropy (EN) (DWT-NF-EN) and Integer Wavelet Transform with Neuro-Fuzzy and Entropy (IWT-NF-EN) and their results are compared and analyzed with existing fusion techniques using different quantitative measures. Subjective and objective evaluation of the results obtained is compared with other fusion techniques namely Redundancy Discrete Wavelet Transform (RDWT) and Integer Wavelet Transform and Neuro-Fuzzy (IWT-NF). The objective evaluation is done using the quantitative measures Entropy (EN), Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC). From the experimental results it is observed that proposed methods provided better information (quality) using EN, PSNR and NCC measures for majority of the test images and the same is justified with the subjective results.

ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I Advances in Intelligent Systems and Computing

Infrared and Visible Image Fusion Using Entropy and Neuro-Fuzzy Concepts

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 Typeset2012 International Conference on Recent Advances in Computing and Software Systems
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