Multimodal Medical Image Fusion by Adaptive Manifold Filter

Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images.

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Medical Image Fusion Based on Rolling Guidance Filter and Spiking Cortical Model

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/156043 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 6

Author(s):

Liu Shuaiqi ◽

Zhao Jie ◽

Shi Mingzhu

Keyword(s):

Image Fusion ◽

Medical Image ◽

Medical Images ◽

Cortical Model ◽

Image Guided Radiotherapy ◽

Medical Image Fusion ◽

Fused Image ◽

Fusion Methods ◽

Mean And Variance ◽

The Mean

Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. Although numerous medical image fusion methods have been proposed, most of these approaches are sensitive to the noise and usually lead to fusion image distortion, and image information loss. Furthermore, they lack universality when dealing with different kinds of medical images. In this paper, we propose a new medical image fusion to overcome the aforementioned issues of the existing methods. It is achieved by combining with rolling guidance filter (RGF) and spiking cortical model (SCM). Firstly, saliency of medical images can be captured by RGF. Secondly, a self-adaptive threshold of SCM is gained by utilizing the mean and variance of the source images. Finally, fused image can be gotten by SCM motivated by RGF coefficients. Experimental results show that the proposed method is superior to other current popular ones in both subjectively visual performance and objective criteria.

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OPTIMIZED DUAL-TREE COMPLEX WAVELET TRANSFORM AND FUZZY ENTROPY FOR MULTI-MODAL MEDICAL IMAGE FUSION: A HYBRID META-HEURISTIC CONCEPT

Journal of Mechanics in Medicine and Biology ◽

10.1142/s021951942150024x ◽

2021 ◽

pp. 2150024

Author(s):

N. NAGARAJA KUMAR ◽

T. JAYACHANDRA PRASAD ◽

K. SATYA PRASAD

Keyword(s):

Image Fusion ◽

High Frequency ◽

Medical Image ◽

Low Frequency ◽

Fuzzy Entropy ◽

Complex Wavelet Transform ◽

Swarm Optimization ◽

Medical Image Fusion ◽

Fused Image

In recent times, multi-modal medical image fusion has emerged as an important medical application tool. An important goal is to fuse the multi-modal medical images from diverse imaging modalities into a single fused image. The physicians broadly utilize this for precise identification and treatment of diseases. This medical image fusion approach will help the physician perform the combined diagnosis, interventional treatment, pre-operative planning, and intra-operative guidance in various medical applications by developing the corresponding information from clinical images through different modalities. In this paper, a novel multi-modal medical image fusion method is adopted using the intelligent method. Initially, the images from two different modalities are applied with optimized Dual-Tree Complex Wavelet Transform (DT-CWT) for splitting the images into high-frequency subbands and low-frequency subbands. As an improvement to the conventional DT-CWT, the filter coefficients are optimized by the hybrid meta-heuristic algorithm named as Hybrid Beetle and Salp Swarm Optimization (HBSSO) by merging the Salp Swarm Algorithm (SSA), and Beetle Swarm Optimization (BSO). Moreover, the fusion of the source images’ high-frequency subbands was done by the optimized type-2 Fuzzy Entropy. The upper and lower membership limits are optimized by the same hybrid HBSSO. The optimized type-2 fuzzy Entropy automatically selects high-frequency coefficients. Also, the fusion of the low-frequency sub-images is performed by the Averaging approach. Further, the inverse optimized DT-CWT on the fused image sets helps to obtain the final fused medical image. The main objective of the optimized DT-CWT and optimized type-2 fuzzy Entropy is to maximize the SSIM. The experimental results confirm that the developed approach outperforms the existing fusion algorithms in diverse performance measures.

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DEPSO With DTCWT Algorithm for Multimodal Medical Image Fusion

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2021100105 ◽

2021 ◽

Vol 12 (4) ◽

pp. 78-97

Author(s):

Hassiba Talbi ◽

Mohamed-Khireddine Kholladi

Keyword(s):

Wavelet Transform ◽

Image Fusion ◽

High Frequency ◽

Medical Image ◽

Low Frequency ◽

Weighted Average ◽

Optimization Method ◽

Absolute Maximum ◽

Complex Wavelet Transform ◽

Medical Image Fusion

In this paper, the authors propose an algorithm of hybrid particle swarm with differential evolution (DE) operator, termed DEPSO, with the help of a multi-resolution transform named dual tree complex wavelet transform (DTCWT) to solve the problem of multimodal medical image fusion. This hybridizing approach aims to combine algorithms in a judicious manner, where the resulting algorithm will contain the positive features of these different algorithms. This new algorithm decomposes the source images into high-frequency and low-frequency coefficients by the DTCWT, then adopts the absolute maximum method to fuse high-frequency coefficients; the low-frequency coefficients are fused by a weighted average method while the weights are estimated and enhanced by an optimization method to gain optimal results. The authors demonstrate by the experiments that this algorithm, besides its simplicity, provides a robust and efficient way to fuse multimodal medical images compared to existing wavelet transform-based image fusion algorithms.

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Multimodal Medical Image Fusion using Guided Filter in NSCT Domain

Biomedical & Pharmacology Journal ◽

10.13005/bpj/1566 ◽

2018 ◽

Vol 11 (4) ◽

pp. 1937-1946

Author(s):

Nancy Mehta ◽

Sumit Budhiraja

Keyword(s):

Image Fusion ◽

Medical Image ◽

Similarity Index ◽

Low Frequency ◽

Structural Similarity ◽

Relevant Information ◽

Phase Congruency ◽

Edge Preserving ◽

Medical Image Fusion ◽

Fused Image

Multimodal medical image fusion aims at minimizing the redundancy and collecting the relevant information using the input images acquired from different medical sensors. The main goal is to produce a single fused image having more information and has higher efficiency for medical applications. In this paper modified fusion method has been proposed in which NSCT decomposition is used to decompose the wavelet coefficients obtained after wavelet decomposition. NSCT being multidirectional,shift invariant transform provide better results.Guided filter has been used for the fusion of high frequency coefficients on account of its edge preserving property. Phase congruency is used for the fusion of low frequency coefficients due to its insensitivity to illumination contrast hence making it suitable for medical images. The simulated results show that the proposed technique shows better performance in terms of entropy, structural similarity index, Piella metric. The fusion response of the proposed technique is also compared with other fusion approaches; proving the effectiveness of the obtained fusion results.

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Multimodality Medical Image Fusion Using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy

Oncology ◽

10.4018/978-1-5225-0549-5.ch018 ◽

2017 ◽

pp. 519-541

Author(s):

Satishkumar S. Chavan ◽

Sanjay N. Talbar

Keyword(s):

Wavelet Transform ◽

Image Fusion ◽

Frequency Domain ◽

Medical Image ◽

Treatment Plan ◽

Complex Wavelet Transform ◽

Medical Image Fusion ◽

Daubechies Complex Wavelet Transform ◽

Fused Image ◽

Complex Wavelet

The process of enriching the important details from various modality medical images by combining them into single image is called multimodality medical image fusion. It aids physicians in terms of better visualization, more accurate diagnosis and appropriate treatment plan for the cancer patient. The combined fused image is the result of merging of anatomical and physiological variations. It allows accurate localization of cancer tissues and more helpful for estimation of target volume for radiation. The details from both modalities (CT and MRI) are extracted in frequency domain by applying various transforms and combined them using variety of fusion rules to achieve the best quality of images. The performance and effectiveness of each transform on fusion results is evaluated subjectively as well as objectively. The fused images by algorithms in which feature extraction is achieved by M-Band Wavelet Transform and Daubechies Complex Wavelet Transform are superior over other frequency domain algorithms as per subjective and objective analysis.

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Local energy match based non-sub sampled contourlet Transform for multi modal medical image fusion

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.31.13432 ◽

2018 ◽

Vol 7 (2.31) ◽

pp. 165

Author(s):

M Shyamala Devi ◽

P Balamurugan

Keyword(s):

Image Fusion ◽

High Frequency ◽

Medical Image ◽

Gabor Filter ◽

Low Frequency ◽

Local Energy ◽

Fusion Algorithm ◽

Medical Image Fusion ◽

Fused Image ◽

Image Averaging

Image processing technology requires moreover the full image or the part of image which is to be processed from the user’s point of view like the radius of object etc. The main purpose of fusion is to diminish dissimilar error between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the concerned objects is more important than extra information. So preserving the edge features of the image is worth for investigating the image fusion. The image with higher contrast contains more edge-like features. Here we propose a new medical image fusion scheme namely Local Energy Match NSCT based on discrete contourlet transformation, which is constructive to give the details of curve edges. It is used to progress the edge information of fused image by dropping the distortion. This transformation lead to crumbling of multimodal image addicted to finer and coarser details and finest details will be decayed into unusual resolution in dissimilar orientation. The input multimodal images namely CT and MRI images are first transformed by Non Sub sampled Contourlet Transformation (NSCT) which decomposes the image into low frequency and high frequency elements. In our system, the Low frequency coefficient of the image is fused by image averaging and Gabor filter bank algorithm. The processed High frequency coefficients of the image are fused by image averaging and gradient based fusion algorithm. Then the fused image is obtained by inverse NSCT with local energy match based coefficients. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Correlation Coefficient parameters are used in this work .

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A Review of Multimodal Medical Image Fusion Techniques

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/8279342 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Bing Huang ◽

Feng Yang ◽

Mengxiao Yin ◽

Xiaoying Mo ◽

Cheng Zhong

Keyword(s):

Image Fusion ◽

Medical Image ◽

Development Trend ◽

Research Field ◽

Imaging Modalities ◽

Data Set ◽

Medical Image Fusion ◽

Multiple Imaging ◽

Fused Image ◽

Fusion Methods

The medical image fusion is the process of coalescing multiple images from multiple imaging modalities to obtain a fused image with a large amount of information for increasing the clinical applicability of medical images. In this paper, we attempt to give an overview of multimodal medical image fusion methods, putting emphasis on the most recent advances in the domain based on (1) the current fusion methods, including based on deep learning, (2) imaging modalities of medical image fusion, and (3) performance analysis of medical image fusion on mainly data set. Finally, the conclusion of this paper is that the current multimodal medical image fusion research results are more significant and the development trend is on the rise but with many challenges in the research field.

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A Novel Fusion Rule for Medical Image Fusion in Complex Wavelet Transform Domain

International Journal of Image and Graphics ◽

10.1142/s0219467816500224 ◽

2016 ◽

Vol 16 (04) ◽

pp. 1650022 ◽

Cited By ~ 4

Author(s):

Deepak Gambhir ◽

Meenu Manchanda

Keyword(s):

Wavelet Transform ◽

Image Fusion ◽

Medical Image ◽

Fusion Rule ◽

Complex Wavelet Transform ◽

Fusion Rules ◽

Medical Image Fusion ◽

Fused Image ◽

Complex Wavelet ◽

Complex Coefficients

Medical image fusion is being used at large by clinical professionals for improved diagnosis and treatment of diseases. The main aim of image fusion process is to combine complete information from all input images into a single fused image. Therefore, a novel fusion rule is proposed for fusing medical images based on Daubechies complex wavelet transform (DCxWT). Input images are first decomposed using DCxWT. The complex coefficients so obtained are then fused using normalized correlation based fusion rule. Finally, the fused image is obtained by inverse DCxWT with all combined complex coefficients. The performance of the proposed method has been evaluated and compared both visually and objectively with DCxWT based fusion methods using state-of art fusion rules as well as with existing fusion techniques. Experimental results and comparative study demonstrate that the proposed fusion technique generates better results than existing fusion rules as well as with other fusion techniques.

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Multimodality Medical Image Fusion Using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy

Medical Imaging ◽

10.4018/978-1-5225-0571-6.ch015 ◽

2017 ◽

pp. 389-412

Author(s):

Satishkumar S. Chavan ◽

Sanjay N. Talbar

Keyword(s):

Wavelet Transform ◽

Image Fusion ◽

Frequency Domain ◽

Medical Image ◽

Treatment Plan ◽

Complex Wavelet Transform ◽

Medical Image Fusion ◽

Daubechies Complex Wavelet Transform ◽

Fused Image ◽

Complex Wavelet

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Medical image fusion based on quaternion wavelet transform

Journal of Algorithms & Computational Technology ◽

10.1177/1748302620931297 ◽

2020 ◽

Vol 14 ◽

pp. 174830262093129

Author(s):

Zhang Zhancheng ◽

Luo Xiaoqing ◽

Xiong Mengyu ◽

Wang Zhiwen ◽

Li Kai

Keyword(s):

Wavelet Transform ◽

Image Fusion ◽

High Frequency ◽

Medical Image ◽

Low Frequency ◽

Frequency Component ◽

High Frequency Component ◽

Medical Image Fusion ◽

Quaternion Wavelet Transform ◽

Fused Image

Medical image fusion can combine multi-modal images into an integrated higher-quality image, which can provide more comprehensive and accurate pathological information than individual image does. Traditional transform domain-based image fusion methods usually ignore the dependencies between coefficients and may lead to the inaccurate representation of source image. To improve the quality of fused image, a medical image fusion method based on the dependencies of quaternion wavelet transform coefficients is proposed. First, the source images are decomposed into low-frequency component and high-frequency component by quaternion wavelet transform. Then, a clarity evaluation index based on quaternion wavelet transform amplitude and phase is constructed and a contextual activity measure is designed. These measures are utilized to fuse the high-frequency coefficients and the choose-max fusion rule is applied to the low-frequency components. Finally, the fused image can be obtained by inverse quaternion wavelet transform. The experimental results on some brain multi-modal medical images demonstrate that the proposed method has achieved advanced fusion result.

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