Medical Image Fusion Techniques Research Articles

Optimum Wavelet-Based Homomorphic Medical Image Fusion Using Hybrid Genetic–Grey Wolf Optimization Algorithm

Medical image fusion techniques have been widely used in various clinical applications. Generalized homomorphic filters have Fourier domain features of input image. In multimodal medical image fusion discrete wavelet transform-based techniques provides more features and is performed over Fourier spectrum. In this paper, we proposed a homomorphic wavelet fusion which is called optimum homomorphic wavelet fusion (OHWF) using hybrid genetic–grey wolf optimization (HG-GWO) Algorithm. In OHWF, which consists of logarithmic and wavelet domain information of input images. The wavelet-based homomorphic fusion consists of multilevel decomposition features of input image. In our proposal, the approximation coefficients of modality1 (anatomical structure) and optimum scaled detailed coefficients of modality2 are given to adder1. In adder 2, the optimum scaled detailed coefficients of modality 1 and approximation coefficients of modality 2 are added together. The resultants of adder 1 and adder 2 are fused together using pixel based averaging rule. First, the proposed fusion approach is validated for MR-SPECT, MR-PET, MR-CT, and MR T1-T2 image fusion using various fusion evaluation indexes. Later, the conventional grey wolf optimization is modified with genetic operator. Experimental results show that the proposed approach outperforms state-of-the-art fusion algorithms in terms of both structural and the functional information in the fused image.

Enhancement of Hybrid Multimodal Medical Image Fusion Techniques for Clinical Disease Analysis

Multimodal medical image fusion is one the most significant and useful disease analytic techniques. This research article proposes the hybrid multimodality medical image fusion methods and discusses the most essential advantages and disadvantages of these methods. The hybrid multimodal medical image fusion algorithms are used to improve the quality of fused multimodality medical image. Magnetic resonance imaging, positron emission tomography, and single photon emission computed tomography are the input multimodal therapeutic images used for fusion process. An experimental result of proposed hybrid fusion techniques provides the fused multimodal medical images of highest quality, shortest processing time, and best visualization. Both traditional and hybrid multimodal medical image fusion algorithms are evaluated using several quality metrics. Compared with existing techniques the proposed result gives the better processing performance in both qualitative and quantitative evaluation criteria. This is favorable, especially for helping in accurate clinical disease analysis.

A Hybrid Multimodal Medical Image Fusion Technique for CT and MRI Brain Images

Estimating the type, size, location and spread of a brain tumor is vital in the diagnosis and treatment of brain cancer. Fused CT and MRI brain images assist in faster detection and diagnosis of brain tumors. They provide superior results in comparison to individual CT or MRI images. Multiscale transforms (MSTs) are widely used in fusing multimodal images like CT and MRI. However, they have a few drawbacks like reduced contrast, poor edge detection, redundancy and high computation time. This article describes how MSTs coupled with sparse representation (SR) aims to overcome the drawbacks. Non-Subsampled Contourlet Transform (NSCT) is widely used on MSTs for fusing multifocal images. Therefore, a novel technique using NSCT and SR is proposed for better quality fused CT and MRI images. The experimental results show superior performance.

Medical image fusion using PCNN and Poisson-hidden Markov model

The combination of multiple images into a single image without loss of any generosity, is known as image fusion. In medical diagnosis, the magnetic resonance imaging (MRI) gives the brain tissue anatomy without any functional information whereas the Positron emission tomography (PET) image gives the brain function with low spatial resolution. Combination of anatomical and functional tomographic images is required for better diagnosis purpose. Image fusion technique combines the spatial resolution of the functional images by merging them with a high-resolution anatomic image. This paper proposes a novel medical image fusion technique based on pulse coupled neural net (PCNN) and Poisson hidden Markov model (PHMM) that satisfies fusion criterion. The excellency of the proposed approach is verified by the comparison with some of the state-of-the-art techniques in terms of several quantitative fusion evaluation indexes.

Medical image fusion using PCNN and Poisson-hidden Markov model

The combination of multiple images into a single image without loss of any generosity, is known as image fusion. In medical diagnosis, the magnetic resonance imaging (MRI) gives the brain tissue anatomy without any functional information whereas the Positron emission tomography (PET) image gives the brain function with low spatial resolution. Combination of anatomical and functional tomographic images is required for better diagnosis purpose. Image fusion technique combines the spatial resolution of the functional images by merging them with a high-resolution anatomic image. This paper proposes a novel medical image fusion technique based on pulse coupled neural net (PCNN) and Poisson hidden Markov model (PHMM) that satisfies fusion criterion. The excellency of the proposed approach is verified by the comparison with some of the state-of-the-art techniques in terms of several quantitative fusion evaluation indexes.

Medical Image Fusion Using Fuzzy Logic and DHT Based Feature Extraction

Abstract The traditional medical image fusion techniques suffer from various issues such as these techniques did not detect the relevant information from the image as the whole image is considered as important whether it is meaningful to the user or not. Thus a novel fuzzy based image fusion technique is developed in this work to fuse the MRI and PET images. Before fusing the images, the 2DHT, HIS transformation and ROI is applied to the input images and then the image fusion is done on the basis of the selected ROI from the PET images. A comparison analysis of proposed and traditional image fusion techniques is driven in the terms of discrepancy value, average gradient and overall performance. After simulating the work, it is observed that the proposed work outperforms the traditional techniques. To ensure the quality of the performance of the proposed work, the analysis is also done by considering the 100 various sample images. Keywords: Image Fusion, Medical Images, 2DHT, IHS transformation, Fuzzy Inference System.

Quantitative Analysis based on Supervised Classification of Medical Image Fusion Techniques

Quantitative Analysis based on Supervised Classification of Medical Image Fusion Techniques

Optimum spectrum mask based medical image fusion using Gray Wolf Optimization

Medical image fusion techniques have been extensively used in clinical diagnosis. Scaling based techniques are well known techniques in multimodal image fusion, the generalized scaling has static scale value selection which brims the quality of fusion. In this paper, we proposed an Optimum Spectrum Mask Fusion (OSMF) for medical image fusion using conventional Gray Wolves Optimization (GWO) algorithm. The GWO algorithm, indulges swiftest and dynamic scale selection. The spectrum masks technique, contrary to conventional spatial domain and transform domain fusion algorithms in terms of image contrast and edge quality. The optimum mask caters more information in the multi-modality fusion. The proposed OSMF is tested for MR-SPECT, MR-PET, MR-CT and MR: T1-T2 of brain images. Experimental results show, our technique pageantries the improved results than other conventional pixel based fusion techniques.

Structure tensor and nonsubsampled shearlet transform based algorithm for CT and MRI image fusion

Multimodal medical image fusion technique contributes to the reduction of information uncertainty and improves the clinical diagnosis accuracy, the aim of which is to preserve salient image features and detail information of multiple source images to produce a visual enhanced single fused image. In this paper, by taking full advantages of structure tensor and nonsubsampled shearlet transform (NSST) to effectively extract geometric features, a novel unified optimization model is proposed for fusing computed tomography and magnetic resonance imaging images. The proposed model includes two terms, which constrain the gradient and NSST coefficients of the final fused image close to the pre-fused gradient and NSST coefficients. The pre-fused gradient is obtained from weighted structure tensor and the pre-fused NSST coefficients are generated by fusing NSST coefficients of source images with proposed fusion rules. The final fused image can be obtained by solving the constructed optimization problem via conjugate gradient method. Experimental results demonstrate that the proposed approach outperforms the compared multi-resolution and gradient based methods in both visual effects and quantitative assessments.

Multispectral MRI Image Fusion for Enhanced Visualization of Meningioma Brain Tumors and Edema Using Contourlet Transform and Fuzzy Statistics

This paper proposes a multispectral magnetic resonance imaging (MRI) image fusion scheme for improved visualization of anatomical and pathological information of meningioma (MG) brain tumors that combines the contourlet transform and fuzzy statistics. The proposed fusion technique mainly targets the tumor and its surrounding hyperintense (edema) region, which leads to improved brain imaging informatics for radiologists. The developed methodology mainly consists of the contourlet transform for multiscale and directional decomposition, fuzzy entropy for fusing approximation coefficients, and region-based fuzzy energy for fusing detailed coefficients of two input images with the same orientations. Two fusion rules are established here in order to fuse corresponding lower- and higher-frequency subbands of images. The proposed methodology is applied to five various combinations (such as T1-weighted and T2-weighted, T1 post-contrast and T2-weighted etc.) generated from four modalities of MRI images (T1-weighted, T1 post-contrast, T2-weighted, and fluid-attenuated inversion recovery (FLAIR)). A total of 150 MRI images (30 images from each of five combinations) are considered from 20 cases of MG brain tumors. A quantitative evaluation of the proposed method is performed in terms of three performance measures. The performance is compared with that of existing medical image fusion techniques tested on the same dataset. Experimental results show the superiority of the proposed methodology in terms of both qualitative and quantitative measures, which also indicates that fused images contain enriched diagnostic information that can aid the detection of tumors and edema. A fusion of post-contrast T1-weighted MRI images with FLAIR and T2-weighted MRI images provided clinically relevant information.

Comparative Analysis of Similarity Measure Performance for Multimodality Image Fusion using DTCWT and SOFM with Various Medical Image Fusion Techniques

Objectives: In this paper, the performance of similarity measures such as Edge Based Similarity Measure and Structural Similarity Index Measure is evaluated and also compared with the existing medical image fusion techniques. Materials and Methods: Multimodality Medical Image fusion is the process of fusing two Medical images obtained from two different sensors for better diagnosis. Medical image fusion combines and merges all relevant and complementary information from multiple source images into single composite image which facilitates more precise diagnosis and better treatment. The fused image should convey a better description of the scene than the individual images. The performance of the fused image is evaluated by various metrics such as Peak Signal to Noise Ratio (PSNR), Entropy, Standard deviation, Edge Based Similarity Measure (EBSM) and Structural Similarity Index Measure (SSIM). This paper proposes a method for fusion of Medical images using Dual Tree Complex Wavelet Transform (DTCWT) and Self Organizing Feature Map (SOFM). Findings: The performance of the proposed fusion algorithm is evaluated over pairs of CT and MR images obtained from patients in comparison with existing fusion techniques such as Discrete Wavelet Transform (DWT), Nonsubsampled Contourlet Transform (NSCT) and Fast Discrete Curvelet Transform (FDCT). In this paper, performance is evaluated by using the metric; Edge based Similarity Measure (EBSM), and Structural Similarity Index (SSIM). Applications / Improvements: Through the simulation result, as compared with the DWT, FDCT, NSCT and DTCWT fusion methods, it is concluded that the Multimodality image fusion using DTCWT with Robust Second Order First Moment (SOFM) gives better Edge based similarity measure and Structural similarity index measure.

Medical Image Fusion Algorithm based on Local Average Energy-Motivated PCNN in NSCT Domain

Medical Image Fusion (MIF) can improve the performance of medical diagnosis, treatment planning and image-guided surgery significantly through providing high-quality and rich-information medical images. Traditional MIF techniques suffer from common drawbacks such as: contrast reduction, edge blurring and image degradation. Pulse-coupled Neural Network (PCNN) based MIF techniques outperform the traditional methods in providing high-quality fused images due to its global coupling and pulse synchronization property; however, the selection of significant features that motivate the PCNN is still an open problem and plays a major role in measuring the contribution of each source image into the fused image. In this paper, a medical image fusion algorithm is proposed based on the Non-subsampled Contourlet Transform (NSCT) and the Pulse-Coupled Neural Network (PCNN) to fuse images from different modalities. Local Average Energy is used to motivate the PCNN due to its ability to capture salient features of the image such as edges, contours and textures. The proposed approach produces a high quality fused image with high contrast and improved content in comparison with other image fusion techniques without loss of significant details on both levels: the visual and the quantitative.

Pixel based Medical Image Fusion Techniques using Discrete Wavelet Transform and Stationary Wavelet Transform

Background: Process of combining significant information from 2 or more images to get an output without any artifact or information loss. Also to perceive all information required for accurate diagnosis and to get high spatial resolution with functional and anatomical information. Methods: DWT and SWT are used with biorthogonal 2.6 and db1. Average fusion rule is applied for fusing low frequency coefficients and for high frequency coefficients region energy rule is applied. Results: Eight sets of real time medical images are used for the analysis. Comparing the fusion of SWT with DWT, Stationary wavelet transform method performs well than the Discrete wavelet transform as the information loss occurs due to down sampling in each of the DWT sub bands which caused in the relevant sub bands are minimized by SWT. Application: Easy to diagnose for a physician in the field of biomedical.

Multimodality Medical Image Fusion Technique using Hybrid Method

medical image fusion is used to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. In this paper a hybrid method based on the non-subsampled contourlet transform (NSCT) and fractional fourier transform (FRFT) is proposed. CT & MR images of different cases have been used to test the proposed method and results are compared with those of the other conventional image fusion methods by both visual analysis and quantitative analysis. Keywords-subsampled contourlet transform, fractional fourier transform, phase congruency and directive contrast. p p p

Comparative Study and Analysis of Medical Image Fusion Techniques

The term fusion that means in general an approach of extraction of information acquired from several domains. Basically, Image Fusion can be defines as a process of combining information from multiple input images in such way that final fused image having good quality information then individual image. Medical imaging field demands images with high resolution and higher information contents for necessary disease diagnosis and visualization. Therefore, in practical scenario we required more complementary information for necessary disease diagnosis purpose. In this paper, Image Fusion techniques are broadly classified as spatial domain fusion techniques and Transform domain fusion techniques. Various techniques in each class are discussed in short description. In this study Performance of various techniques are evaluated using quality measures such as Entropy, Peak Signal to Noise Ratio, Signal to Noise Ratio, and Structural Similarity Index. For analysis purpose one can considered CT and MRI images of same scene. General Terms Medical image fusion, Image fusion Techniques, Fuzzy Inference System

Fusion of Multi-slice MR-Scan Images with Genetic Algorithm with Curvelet-transform

On considerable progress on Medical Image fusion techniques, many algorithm are used from intensity-huesaturation (IHS), principal component analysis (PCA), multi resolution analysis based method & Artificial Neural Network (ANN), but all these algorithm had heavily degrade the brightness of the input images. In IHS fusion, it converts a low resolution color images from RGB space into the IHS color space and a combined IHS with PCA to improve fused image quality but it generates several drawback, (a) The Pixel-level fusion- method are sensitive to accuracy (gives low accuracy), (b) The color channel of input spectrum should be less than or equal to IHS transform (created very large spectrum) and in ANN-based fusion include: (1) The long iteration time of the ANN and the need to manually parameterize it before each fusion; (2) An occasional failure to converge, such as in the PCNN-based method when some neurons did not fire during the whole iteration.