Computed tomography image denoising based on multi dose CT image fusion and extended sparse techniques

2021 ◽  
Author(s):  
Anuyogam Venkataraman
Keyword(s):  
Computed Tomography ◽  
Image Fusion ◽  
Region Of Interest ◽  
Source Image ◽  
High Dose ◽  
Fusion Algorithm ◽  
Quantitative Metrics ◽  
Fused Image ◽  
Fusion Methods ◽  
Computed Tomography Image

With the increasing utilization of X-ray Computed Tomography (CT) in medical diagnosis, obtaining higher quality image with lower exposure to radiation is a highly challenging task in image processing. Sparse representation based image fusion is one of the sought after fusion techniques among the current researchers. A novel image fusion algorithm based on focused vector detection is proposed in this thesis. Firstly, the initial fused vector is acquired by combining common and innovative sparse components of multi-dosage ensemble using Joint Sparse PCA fusion method utilizing an overcomplete dictionary trained using high dose images of the same region of interest from different patients. And then, the strongly focused vector is obtained by determining the pixels of low dose and medium dose vectors which have high similarity with the pixels of the initial fused vector using certain quantitative metrics. Final fused image is obtained by denoising and simultaneously integrating the strongly focused vector, initial fused vector and source image vectors in joint sparse domain thereby preserving the edges and other critical information needed for diagnosis. This thesis demonstrates the effectiveness of the proposed algorithms when experimented on different images and the qualitative and quantitative results are compared with some of the widely used image fusion methods.

Computed tomography image denoising based on multi dose CT image fusion and extended sparse techniques

2021 ◽  
Author(s):  
Anuyogam Venkataraman
Keyword(s):  
Computed Tomography ◽  
Image Fusion ◽  
Region Of Interest ◽  
Source Image ◽  
High Dose ◽  
Fusion Algorithm ◽  
Quantitative Metrics ◽  
Fused Image ◽  
Fusion Methods ◽  
Computed Tomography Image

With the increasing utilization of X-ray Computed Tomography (CT) in medical diagnosis, obtaining higher quality image with lower exposure to radiation is a highly challenging task in image processing. Sparse representation based image fusion is one of the sought after fusion techniques among the current researchers. A novel image fusion algorithm based on focused vector detection is proposed in this thesis. Firstly, the initial fused vector is acquired by combining common and innovative sparse components of multi-dosage ensemble using Joint Sparse PCA fusion method utilizing an overcomplete dictionary trained using high dose images of the same region of interest from different patients. And then, the strongly focused vector is obtained by determining the pixels of low dose and medium dose vectors which have high similarity with the pixels of the initial fused vector using certain quantitative metrics. Final fused image is obtained by denoising and simultaneously integrating the strongly focused vector, initial fused vector and source image vectors in joint sparse domain thereby preserving the edges and other critical information needed for diagnosis. This thesis demonstrates the effectiveness of the proposed algorithms when experimented on different images and the qualitative and quantitative results are compared with some of the widely used image fusion methods.

scholarly journals Computed tomography image denoising based on multi dose CT image fusion and extended sparse techniques

2021 ◽  
Author(s):  
Anuyogam Venkataraman
Keyword(s):  
Computed Tomography ◽  
Image Fusion ◽  
Region Of Interest ◽  
Source Image ◽  
High Dose ◽  
Fusion Algorithm ◽  
Quantitative Metrics ◽  
Fused Image ◽  
Fusion Methods ◽  
Computed Tomography Image

With the increasing utilization of X-ray Computed Tomography (CT) in medical diagnosis, obtaining higher quality image with lower exposure to radiation is a highly challenging task in image processing. Sparse representation based image fusion is one of the sought after fusion techniques among the current researchers. A novel image fusion algorithm based on focused vector detection is proposed in this thesis. Firstly, the initial fused vector is acquired by combining common and innovative sparse components of multi-dosage ensemble using Joint Sparse PCA fusion method utilizing an overcomplete dictionary trained using high dose images of the same region of interest from different patients. And then, the strongly focused vector is obtained by determining the pixels of low dose and medium dose vectors which have high similarity with the pixels of the initial fused vector using certain quantitative metrics. Final fused image is obtained by denoising and simultaneously integrating the strongly focused vector, initial fused vector and source image vectors in joint sparse domain thereby preserving the edges and other critical information needed for diagnosis. This thesis demonstrates the effectiveness of the proposed algorithms when experimented on different images and the qualitative and quantitative results are compared with some of the widely used image fusion methods.

scholarly journals Computed tomography image denoising based on multi dose CT image fusion and extended sparse techniques

2021 ◽  
Author(s):  
Anuyogam Venkataraman
Keyword(s):  
Computed Tomography ◽  
Image Fusion ◽  
Region Of Interest ◽  
Source Image ◽  
High Dose ◽  
Fusion Algorithm ◽  
Quantitative Metrics ◽  
Fused Image ◽  
Fusion Methods ◽  
Computed Tomography Image

With the increasing utilization of X-ray Computed Tomography (CT) in medical diagnosis, obtaining higher quality image with lower exposure to radiation is a highly challenging task in image processing. Sparse representation based image fusion is one of the sought after fusion techniques among the current researchers. A novel image fusion algorithm based on focused vector detection is proposed in this thesis. Firstly, the initial fused vector is acquired by combining common and innovative sparse components of multi-dosage ensemble using Joint Sparse PCA fusion method utilizing an overcomplete dictionary trained using high dose images of the same region of interest from different patients. And then, the strongly focused vector is obtained by determining the pixels of low dose and medium dose vectors which have high similarity with the pixels of the initial fused vector using certain quantitative metrics. Final fused image is obtained by denoising and simultaneously integrating the strongly focused vector, initial fused vector and source image vectors in joint sparse domain thereby preserving the edges and other critical information needed for diagnosis. This thesis demonstrates the effectiveness of the proposed algorithms when experimented on different images and the qualitative and quantitative results are compared with some of the widely used image fusion methods.

Multifocus image fusion using multiscale transform and convolutional sparse representation

2020 ◽  
pp. 2050061
Author(s):  
Chengfang Zhang
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Low Frequency ◽  
Activity Level ◽  
Source Image ◽  
Level Measurement ◽  
Fused Image ◽  
Fusion Methods ◽  
Low Pass ◽  
Multiscale Transform

Multifocus image fusion can obtain an image with all objects in focus, which is beneficial for understanding the target scene. Multiscale transform (MST) and sparse representation (SR) have been widely used in multifocus image fusion. However, the contrast of the fused image is lost after multiscale reconstruction, and fine details tend to be smoothed for SR-based fusion. In this paper, we propose a fusion method based on MST and convolutional sparse representation (CSR) to address the inherent defects of both the MST- and SR-based fusion methods. MST is first performed on each source image to obtain the low-frequency components and detailed directional components. Then, CSR is applied in the low-pass fusion, while the high-pass bands are fused using the popular “max-absolute” rule as the activity level measurement. The fused image is finally obtained by performing inverse MST on the fused coefficients. The experimental results on multifocus images show that the proposed algorithm exhibits state-of-the-art performance in terms of definition.

scholarly journals Infrared and Visible Image Fusion through Details Preservation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4556 ◽  
Author(s):  
Yaochen Liu ◽  
Lili Dong ◽  
Yuanyuan Ji ◽  
Wenhai Xu
Keyword(s):  
Image Fusion ◽  
Subjective Evaluation ◽  
Objective Assessment ◽  
Decomposition Methods ◽  
Source Image ◽  
Fusion Method ◽  
Visible Image ◽  
Weighting Method ◽  
Fused Image ◽  
Fusion Methods

In many actual applications, fused image is essential to contain high-quality details for achieving a comprehensive representation of the real scene. However, existing image fusion methods suffer from loss of details because of the error accumulations of sequential tasks. This paper proposes a novel fusion method to preserve details of infrared and visible images by combining new decomposition, feature extraction, and fusion scheme. For decomposition, different from the most decomposition methods by guided filter, the guidance image contains only the strong edge of the source image but no other interference information so that rich tiny details can be decomposed into the detailed part. Then, according to the different characteristics of infrared and visible detail parts, a rough convolutional neural network (CNN) and a sophisticated CNN are designed so that various features can be fully extracted. To integrate the extracted features, we also present a multi-layer features fusion strategy through discrete cosine transform (DCT), which not only highlights significant features but also enhances details. Moreover, the base parts are fused by weighting method. Finally, the fused image is obtained by adding the fused detail and base part. Different from the general image fusion methods, our method not only retains the target region of source image but also enhances background in the fused image. In addition, compared with state-of-the-art fusion methods, our proposed fusion method has many advantages, including (i) better visual quality of fused-image subjective evaluation, and (ii) better objective assessment for those images.

Research and Development of Non Multi-Scale to Pixel-Level Image Fusion

2013 ◽  
Vol 448-453 ◽  
pp. 3621-3624 ◽  
Author(s):  
Ming Jing Li ◽  
Yu Bing Dong ◽  
Xiao Li Wang
Keyword(s):  
Image Fusion ◽  
Basic Principle ◽  
Weighted Average ◽  
Source Image ◽  
Fusion Method ◽  
Fusion Algorithm ◽  
Multi Scale ◽  
Imaging Characteristics ◽  
Fusion Methods ◽  
Image Fusion Method

Image fusion method based on the non multi-scale take the original image as object of study, using various fusion rule of image fusion to fuse images, but not decomposition or transform to original images. So, it can also be called simple multi sensor image fusion methods. Its advantages are low computational complexity and simple principle. Image fusion method based on the non multi-scale is currently the most widely used image fusion methods. The basic principle of fuse method is directly to select large gray, small gray and weighted average among pixel on the source image, to fuse into a new image. Simple pixel level image fusion method mainly includes the pixel gray value being average or weighted average, pixel gray value being selected large and pixel gray value being selected small, etc. Basic principle of fusion process was introduced in detail in this paper, and pixel level fusion algorithm at present was summed up. Simulation results on fusion are presented to illustrate the proposed fusion scheme. In practice, fusion algorithm was selected according to imaging characteristics being retained.

Hybrid Multimodality Medical Image Fusion Using Various Fusion Techniques With Quantitative and Qualitative Analysis

2019 ◽  
pp. 206-233 ◽  
Author(s):  
Rajalingam B. ◽  
Priya R. ◽  
Bhavani R.
Keyword(s):  
Computed Tomography ◽  
Image Fusion ◽  
Medical Image ◽  
Single Photon ◽  
Photon Emission ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Visual Description ◽  
The Individual

In this chapter, different types of image fusion techniques have been studied and evaluated in the medical applications. The ultimate goal of this proposed method is to obtain the fused image without any loss of similar information and preserve all special features present in the input medical images. This method is used to improve the fused image quality for better diagnosis of critical disease analysis. The fused hybrid multimodal medical image should convey better visual description than the individual input images. This chapter proposes the method for multimodal medical image fusion using the hybrid fusion algorithm. The computed tomography, magnetic resonance imaging, positron emission tomography, and single photon emission computed tomography are the input images used for this experimental work. In this chapter, experimental results discovered that the proposed techniques provide better visualization of fused image and gives the superior results compared to various existing traditional algorithms.

scholarly journals Multifocus Image Fusion Using Biogeography-Based Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Ping Zhang ◽  
Chun Fei ◽  
Zhenming Peng ◽  
Jianping Li ◽  
Hongyi Fan
Keyword(s):  
Image Fusion ◽  
Fitness Function ◽  
Block Size ◽  
Source Image ◽  
Morphological Filter ◽  
Fusion Algorithm ◽  
Image Block ◽  
Chaotic Search ◽  
Mutation Operation ◽  
Fused Image

For multifocus image fusion in spatial domain, sharper blocks from different source images are selected to fuse a new image. Block size significantly affects the fusion results and a fixed block size is not applicable in various multifocus images. In this paper, a novel multifocus image fusion algorithm using biogeography-based optimization is proposed to obtain the optimal block size. The sharper blocks of each source image are first selected by sum modified Laplacian and morphological filter to contain an initial fused image. Then, the proposed algorithm uses the migration and mutation operation of biogeography-based optimization to search the optimal block size according to the fitness function in respect of spatial frequency. The chaotic search is adopted during iteration to improve optimization precision. The final fused image is constructed based on the optimal block size. Experimental results demonstrate that the proposed algorithm has good quantitative and visual evaluations.

scholarly journals Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xi-Cheng Lou ◽  
Xin Feng
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Hadamard Product ◽  
Subjective Evaluation ◽  
Low Rank ◽  
Source Image ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Low Rank Representation

A multimodal medical image fusion algorithm based on multiple latent low-rank representation is proposed to improve imaging quality by solving fuzzy details and enhancing the display of lesions. Firstly, the proposed method decomposes the source image repeatedly using latent low-rank representation to obtain several saliency parts and one low-rank part. Secondly, the VGG-19 network identifies the low-rank part’s features and generates the weight maps. Then, the fused low-rank part can be obtained by making the Hadamard product of the weight maps and the source images. Thirdly, the fused saliency parts can be obtained by selecting the max value. Finally, the fused saliency parts and low-rank part are superimposed to obtain the fused image. Experimental results show that the proposed method is superior to the traditional multimodal medical image fusion algorithms in the subjective evaluation and objective indexes.

scholarly journals The Generalised Image Fusion Toolkit (GIFT)

2006 ◽  
Author(s):  
Dan Mueller
Keyword(s):  
Medical Imaging ◽  
Image Fusion ◽  
Worked Examples ◽  
Source Image ◽  
Discrete Wavelet ◽  
Human Visual Perception ◽  
Quadrature Mirror Filter ◽  
Multi Scale ◽  
Fused Image ◽  
Fusion Methods

Image fusion provides a mechanism to combine multiple images into a single representation to aid human visual perception and image processing tasks. Such algorithms endeavour to create a fused image containing the salient information from each source image, without introducing artefacts or inconsistencies. Image fusion is applicable for numerous fields including: defence systems, remote sensing and geoscience, robotics and industrial engineering, and medical imaging. In the medical imaging domain, image fusion may aid diagnosis and surgical planning tasks requiring the segmentation, feature extraction, and/or visualisation of multi-modal datasets.This paper discusses the implementation of an image fusion toolkit built upon the Insight Toolkit (ITK). Based on an existing architecture, the proposed framework (GIFT) offers a ‘plug-and-play’ environment for the construction of n-D multi-scale image fusion methods. We give a brief overview of the toolkit design and demonstrate how to construct image fusion algorithms from low-level components (such as multi-scale methods and feature generators). A number of worked examples for medical applications are presented in Appendix A, including quadrature mirror filter discrete wavelet transform (QMF DWT) image fusion.

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