MRI-PET image fusion based on NSCT transform using local energy and local variance fusion rules

Amini, Nasrin; Fatemizadeh, Emad; Behnam, Hamid

doi:10.3109/03091902.2014.904014

Cited by 35 publications

(11 citation statements)

References 12 publications

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“…We compared some other methods with our method in the spatial domain such as pixel averaging, HIS based, CNN, and the proposed method (VGG19), as well as in transform domain such as Laplacian pyramid, wavelet transform, curvelet transform (CVT), contourlet transform, and nonsubsampled contourlet transform (NSCT). [ 9 ] In our method and CNN, we used features from the first layer of the VGG19 network. All images in the transform domain are decomposed into four levels by DWT (db2), CVT, NSCT, and our method.…”

Section: Resultsmentioning

confidence: 99%

“…[ 1 2 ] In recent years, deep learning networks have many applications in different fields such as computer vision and image processing problems such as classification,[ 3 ] segmentation,[ 4 ] registration,[ 5 ] super-resolution. [ 6 ] There are a lot of methods that fused medical images as PET, SPECT, MRI, and CT.[ 7 8 9 10 ] Image fusion based on deep learning methods has also become a new common topic. These methods have also been used in digital photography, multi-focus image fusion, multi-modality imaging, medical image fusion, and infrared/visible image fusion.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning approach for fusion of magnetic resonance imaging-positron emission tomography image based on extract image features using pretrained network (VGG19)

Amini

Mostaar

2022

J Med Signals Sens

Self Cite

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Background: The fusion of images is an interesting way to display the information of some different images in one image together. In this paper, we present a deep learning network approach for fusion of magnetic resonance imaging (MRI) and positron emission tomography (PET) images. Methods: We fused two MRI and PET images automatically with a pretrained convolutional neural network (CNN, VGG19). First, the PET image was converted from red-green-blue space to hue-saturation-intensity space to save the hue and saturation information. We started with extracting features from images by using a pretrained CNN. Then, we used the weights extracted from two MRI and PET images to construct a fused image. Fused image was constructed with multiplied weights to images. For solving the problem of reduced contrast, we added the constant coefficient of the original image to the final result. Finally, quantitative criteria (entropy, mutual information, discrepancy, and overall performance [OP]) were applied to evaluate the results of fusion. We compared the results of our method with the most widely used methods in the spatial and transform domain. Results: The quantitative measurement values we used were entropy, mutual information, discrepancy, and OP that were 3.0319, 2.3993, 3.8187, and 0.9899, respectively. The final results showed that our method based on quantitative assessments was the best and easiest way to fused images, especially in the spatial domain. Conclusion: It concluded that our method used for MRI-PET image fusion was more accurate.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning approach for fusion of magnetic resonance imaging-positron emission tomography image based on extract image features using pretrained network (VGG19)

Amini

Mostaar

2022

J Med Signals Sens

Self Cite

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“…This indicates improved performance for CSID, as it remain capable of extracting enriched information from the input source images, thereby, preserving enhanced edges details and yields enhanced visual quality. In the past few decades, non-invasive applications (like multimodal fusion) have gained tremendous popularity among the healthcare professionals that adds ease and accuracy to the diagnostic process [57,58]. CSID aims to enhance clinical diagnostics by improving the multimodal fusion.…”

Section: Qualitative Analysis Of the Given Set Of Algorithms For Multmentioning

confidence: 99%

CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

et al. 2020

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Technology-assisted clinical diagnosis has gained tremendous importance in modern day healthcare systems. To this end, multimodal medical image fusion has gained great attention from the research community. There are several fusion algorithms that merge Computed Tomography (CT) and Magnetic Resonance Images (MRI) to extract detailed information, which is used to enhance clinical diagnosis. However, these algorithms exhibit several limitations, such as blurred edges during decomposition, excessive information loss that gives rise to false structural artifacts, and high spatial distortion due to inadequate contrast. To resolve these issues, this paper proposes a novel algorithm, namely Convolutional Sparse Image Decomposition (CSID), that fuses CT and MR images. CSID uses contrast stretching and the spatial gradient method to identify edges in source images and employs cartoon-texture decomposition, which creates an overcomplete dictionary. Moreover, this work proposes a modified convolutional sparse coding method and employs improved decision maps and the fusion rule to obtain the final fused image. Simulation results using six datasets of multimodal images demonstrate that CSID achieves superior performance, in terms of visual quality and enriched information extraction, in comparison with eminent image fusion algorithms.

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“…NSCT is achieved with Non-Subsampled Pyramid Filter Bank (NSPFB) [12], [13]. In each of the decomposition levels, the singularities in the image are captured by using one low as well as one high frequency components.The decomposition results in sub images with high frequency images with one low frequency image, with indicating the number of decomposition levels [14], [15].…”

Section: Non-subsampled Contourlet Transform (Nsct)mentioning

confidence: 99%

An Efficient PET-MRI Medical Image Fusion based on IHS NSCT PCA Integrated Method

Karki

2019

IJEAT

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Merging of multiple imaging modalities leads to a single image that acquire high information content. These find useful applications in disease diagnosis and treatment planning. IHS-PCA method is a spatial domain approach for fusion that offersfinestvisibility but demands vast memory and it lacks steering information. We propose an integrated approach that incorporates NSCT combined with PCA utilizing IHS space and histogram matching. The fusion algorithm is applied on MRI with PET image and improved functional property was obtained. The IHS transform is a sharpening technique that converts multispectral image from RGB channels to Intensity Hue and Saturation independent values. Histogram matching is performed with intensity values of the two input images. Pathological details in images can be emphasized in multi-scale and multi-directions by using PCA withNSCT. Fusion rule applied is weighted averaging andprincipal components are used for dimensionality reduction. Inverse NSCT and Inverse IHS are performed so as to obtain the fused image in new RGB space. Visual and subjective investigation is compared with existing methods which demonstrate that our proposed technique gives high structural data content with high spatial and spectral resolution compared withearlier methods.

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MRI-PET image fusion based on NSCT transform using local energy and local variance fusion rules

Cited by 35 publications

References 12 publications

Deep learning approach for fusion of magnetic resonance imaging-positron emission tomography image based on extract image features using pretrained network (VGG19)

Deep learning approach for fusion of magnetic resonance imaging-positron emission tomography image based on extract image features using pretrained network (VGG19)

CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

An Efficient PET-MRI Medical Image Fusion based on IHS NSCT PCA Integrated Method

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