Color PET-MRI Medical Image Fusion Combining Matching Regional Spectrum in Shearlet Domain
13 Feb 2019-International Journal of Image and Graphics (World Scientific Publishing Company)-Vol. 19, Iss: 01, pp 1950004
TL;DR: This work presents a novel fusion technique for color PET-MRI medical images using Two-Dimensional Discrete Fourier-Karhunen–Loeve transform and singular value decomposition (SVD) in shearlet domain and uses the inverse shearlett transformation (IST) to obtain the fused image.
Abstract: The color PET-MRI medical image fusion is a growing research area in medical image processing domain. MRI imagery provides the picture of the anatomy of brain tissues without any functional informa...
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TL;DR: In this article, a multi-modality algorithm for medical image fusion based on the Adolescent Identity Search Algorithm (AISA) for the Non-Subsampled Shearlet Transform is proposed to obtain image optimization and to reduce the computational cost and time.
103 citations
TL;DR: A detailed literature panorama of medical image fusion is presented in this paper, where pixel-level, feature-level and decision-level fusion methods are highlighted and discussed with several approaches in each category Theories behind fusion algorithms are explored aiming to address challenges and limitations of each classes.
75 citations
TL;DR: Wang et al. as mentioned in this paper proposed an image fusion method based on three-layer decomposition and sparse representation, where the source image is first decomposed into the high-frequency and low-frequency components, and the sparse reconstruct error parameter is adaptively designed according with the noise level.
Abstract: Image fusion has been received much attentions in recent years. However, solving both noise-free image fusion and noise-perturbed image fusion problems remains a big challenge. To solve the weak performance and low computational efficiency for current image fusion methods when dealing with the case of noisy source images, an image fusion method based on three-layer decomposition and sparse representation is proposed in this paper. In view of the high-pass characteristics of noise, the source image is first decomposed into the high-frequency and low-frequency components, and the sparse reconstruct error parameter is adaptively designed according with the noise level, so as to realize the fusion and denoising for high-frequency components simultaneously. To make full use of the details and energy in the low-frequency component, the structure–texture decomposition model is carried out and two fusion rules are carefully designed to fuse them. The fused image can be reconstructed by the perfused high-frequency, low-frequency structure and low-frequency texture layers. Experimental results demonstrate that the proposed method can effectively address the clean and noisy image fusion problems, and yield better performance than some state-of-the-art methods in terms of subjective visual and quantitative evaluations.
43 citations
DOI•
28 Sep 2021
TL;DR: New ideas for solving the problems of uneven distribution of categories and misclassification costs in MRI brain medical images are provided so as to develop a brain disease auxiliary diagnosis system with stronger generalization ability, thereby improving the diagnosis of brain tumors.
Abstract: The development of medical images has facilitated the diagnosis of brain diseases. The diagnosis of brain medical images has the characteristics of uneven distribution of categories and different costs of misclassification. Therefore, traditional classification algorithms are used in clinically confirmed MRI brains. When a medical image is used as a training set to construct a classification model, the classification effect is poor and it is easy to be insensitive to the positive class, which makes it difficult for the brain disease auxiliary diagnosis system to have high accuracy and weak generalization ability. The research purpose of this paper is to study the assistant diagnosis system of brain diseases based on the uneven distribution of medical image categories. In order to improve the performance of the assistant diagnosis system of brain diseases, this paper designs a cost-sensitive probabilistic neural network CS-PNN brain by introducing cost-sensitive this system is an auxiliary diagnosis system for diseases, and the reliability of the system is verified by experiments. It can be known from experiments that the cost-sensitive probabilistic neural network CS-PNN assisted diagnosis system for brain diseases designed in this paper increases with the cost of positive misclassifications and negative misclassifications, and the classification accuracy rate of CS-PNN continues to increase. (01) = 4 achieves the best classification performance of 97%. The research in this article provides new ideas for solving the problems of uneven distribution of categories and misclassification costs in MRI brain medical images, so as to develop a brain disease auxiliary diagnosis system with stronger generalization ability, thereby improving the diagnosis of brain tumors. Accuracy and reduce missed diagnosis.
7 citations
Cites methods from "Color PET-MRI Medical Image Fusion ..."
...Compared with traditional FCM, the algorithm has stronger robustness and anti-noise ability [5-6]....
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TL;DR: A new data augmentation method based on image fusion technique to construct large dataset on all existing tones suitable for dermoscopic images and can be used as a solution to the lack of dark skin images in the dataset.
Abstract: Deep learning models yield remarkable results in skin lesions analysis. However, these models require considerable amounts of data, while accessibility to the images with annotated skin lesions is often limited, and the classes are often imbalanced. Data augmentation is one way to alleviate the lack of labeled data and class imbalance. This paper proposes a new data augmentation method based on image fusion technique to construct large dataset on all existing tones. The fusion method consists of a pulse-coupled neural network fusion strategy in a non-subsampled shearlet transform domain and consists of three steps: decomposition, fusion, and reconstruction. The dermoscopic dataset is obtained by combining ISIC2019 and ISIC2020 Challenge datasets. A comparative study with current algorithms was performed to access the effectiveness of the proposed one. The first experiment results indicate that the proposed algorithm best preserves the lesion dermoscopic structure and skin tones features. The second experiment, which consisted of training a convolutional neural network model with the augmented dataset, indicates a more significant increase in accuracy by 15.69%, and 15.38% respectively for tanned, and brown skin categories. The model precision, recall, and F1-score have also been increased. The obtained results indicate that the proposed augmentation method is suitable for dermoscopic images and can be used as a solution to the lack of dark skin images in the dataset.
5 citations
References
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TL;DR: A technique for image encoding in which local operators of many scales but identical shape serve as the basis functions, which tends to enhance salient image features and is well suited for many image analysis tasks as well as for image compression.
Abstract: We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space. Pixel-to-pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself. The result is a net data compression since the difference, or error, image has low variance and entropy, and the low-pass filtered image may represented at reduced sample density. Further data compression is achieved by quantizing the difference image. These steps are then repeated to compress the low-pass image. Iteration of the process at appropriately expanded scales generates a pyramid data structure. The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus, the code tends to enhance salient image features. A further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression. Fast algorithms are described for coding and decoding.
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TL;DR: In this paper, the authors describe approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform and the curvelet transform, which offer exact reconstruction, stability against perturbations, ease of implementation, and low computational complexity.
Abstract: We describe approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform and the curvelet transform. Our implementations offer exact reconstruction, stability against perturbations, ease of implementation, and low computational complexity. A central tool is Fourier-domain computation of an approximate digital Radon transform. We introduce a very simple interpolation in the Fourier space which takes Cartesian samples and yields samples on a rectopolar grid, which is a pseudo-polar sampling set based on a concentric squares geometry. Despite the crudeness of our interpolation, the visual performance is surprisingly good. Our ridgelet transform applies to the Radon transform a special overcomplete wavelet pyramid whose wavelets have compact support in the frequency domain. Our curvelet transform uses our ridgelet transform as a component step, and implements curvelet subbands using a filter bank of a/spl grave/ trous wavelet filters. Our philosophy throughout is that transforms should be overcomplete, rather than critically sampled. We apply these digital transforms to the denoising of some standard images embedded in white noise. In the tests reported here, simple thresholding of the curvelet coefficients is very competitive with "state of the art" techniques based on wavelets, including thresholding of decimated or undecimated wavelet transforms and also including tree-based Bayesian posterior mean methods. Moreover, the curvelet reconstructions exhibit higher perceptual quality than wavelet-based reconstructions, offering visually sharper images and, in particular, higher quality recovery of edges and of faint linear and curvilinear features. Existing theory for curvelet and ridgelet transforms suggests that these new approaches can outperform wavelet methods in certain image reconstruction problems. The empirical results reported here are in encouraging agreement.
2,244 citations
TL;DR: Experimental results clearly indicate that this metric reflects the quality of visual information obtained from the fusion of input images and can be used to compare the performance of different image fusion algorithms.
Abstract: A measure for objectively assessing the pixel level fusion performance is defined. The proposed metric reflects the quality of visual information obtained from the fusion of input images and can be used to compare the performance of different image fusion algorithms. Experimental results clearly indicate that this metric is perceptually meaningful.
1,446 citations
TL;DR: The results show that the measure represents how much information is obtained from the input images and is meaningful and explicit.
Abstract: Mutual information is proposed as an information measure for evaluating image fusion performance. The proposed measure represents how much information is obtained from the input images. No assumption is made regarding the nature of the relation between the intensities in both input modalities. The results show that the measure is meaningful and explicit.
1,059 citations
TL;DR: This paper presents a comprehensive framework, the general image fusion (GIF) method, which makes it possible to categorize, compare, and evaluate the existing image fusion methods.
Abstract: There are many image fusion methods that can be used to produce high-resolution multispectral images from a high-resolution panchromatic image and low-resolution multispectral images Starting from the physical principle of image formation, this paper presents a comprehensive framework, the general image fusion (GIF) method, which makes it possible to categorize, compare, and evaluate the existing image fusion methods Using the GIF method, it is shown that the pixel values of the high-resolution multispectral images are determined by the corresponding pixel values of the low-resolution panchromatic image, the approximation of the high-resolution panchromatic image at the low-resolution level Many of the existing image fusion methods, including, but not limited to, intensity-hue-saturation, Brovey transform, principal component analysis, high-pass filtering, high-pass modulation, the a/spl grave/ trous algorithm-based wavelet transform, and multiresolution analysis-based intensity modulation (MRAIM), are evaluated and found to be particular cases of the GIF method The performance of each image fusion method is theoretically analyzed based on how the corresponding low-resolution panchromatic image is computed and how the modulation coefficients are set An experiment based on IKONOS images shows that there is consistency between the theoretical analysis and the experimental results and that the MRAIM method synthesizes the images closest to those the corresponding multisensors would observe at the high-resolution level
793 citations