Fusion of <scp>MRI</scp> and <scp>CT</scp> images using guided image filter and image statistics

Bavirisetti, Durga Prasad; Kollu, Vijayakumar; Xiao, Gang; Dhuli, Ravindra

doi:10.1002/ima.22228

Cited by 73 publications

(46 citation statements)

References 59 publications

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“…Similarly for high frequency, features like variance, gradient, entropy, SD, energy, average value, maximum and minimum values, waveform length, average amplitude change, log detector, maximum fractal length, and mean square error (MSE) are extracted. These descriptors are extensively used in the literature for developing machine learning models . Formulation procedures of these features are given in Table .…”

Section: Methodsmentioning

confidence: 99%

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Neuro‐wavelet based intelligent medical image fusion

Hassan

Murtza

Khan

et al. 2019

Int J Imaging Syst Tech

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Imaging based sensitive clinical diagnosis is critically depending on image quality. In this article, the problem of enhancing fundus images is addressed by a novel fusion technique. The proposed approach utilizes the representation capability of wavelet transform and the learning ability of artificial neural networks. In this approach, input images are decomposed into wavelet transform followed by appropriate feature extraction for training of neural networks to obtain fused image. To ensure homogeneity, it employs consistency verification for minimizing the fusion artifacts. The visual and quantitative performance of the proposed approach is assessed using a number of experiments performed on the standard datasets of DRIVE and DRION‐DB. The experimental results demonstrate that the proposed fusion technique offers high average structural similarity of “0.99.” The proposed approach outperforms state‐of‐the‐art techniques which validates its effectiveness. The developed approach might be applied by the clinical diagnosis system for fundus related diseases.

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Section: Methodsmentioning

confidence: 99%

“…These descriptors are extensively used in the literature for developing machine learning models. 59,60 Formulation procedures of these features are given in Table 1.…”

Section: Feature Extractionmentioning

confidence: 99%

Neuro‐wavelet based intelligent medical image fusion

Hassan

Murtza

Khan

et al. 2019

Int J Imaging Syst Tech

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“…MIF using alternating guided filter (AGFF) reduces blocking artefacts with the use of gradient feature filtering [45]. Guided filter and image statistics in [46] show the improvement in the fusion performance and the drawback is that there is some loss in image details. Focused region extraction through the guided filter (GFDF) showed in [47], extracts the focused region by a mean filter and the decision map is refined by a guided filter.…”

Section: Introductionmentioning

confidence: 99%

Hybridisation of single‐image super‐resolution with edge‐aware multi‐focus image fusion for edge enrichment

Gopalakrishnan¹,

Ovireddy²

2020

IET image process

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To break the curtailment of digital imaging and retrieve appropriate information with different focused images, a novel edge-aware multi-focus image fusion is proposed by integrating the single-image super-resolution (SISR) method along with the edge-preserving filters. Initially, the multi-focus images are converted to high resolution images by estimating missing high frequency details from its blurred versions. With acquired high resolution images, smoothing by median and the anisotropic diffusion filters are performed to extract focused regions. An initial weight map is constructed by using maximum selection of pixel intensities of the difference images obtained with filtering. The precision of estimated weight map is further improved by exhibiting morphological operations and guided filter. Finally, the images are fused based on the optimised decision map. Simulation results of proposed fusion work are evaluated with seven metrics and the values are compared with different stateof-the-art methods. Both the quantitative and qualitative analyses showed the excellence of proposed work over other fusion methods.

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“…Today many kinds of modalities of medical images are existing, such as computed tomography (CT), magnetic resonance angiography (MRA), magnetic resonance imaging (MRI), positron emission tomography (PET) and single photon emission tomography (SPECT) [1][2][3]. Different modality medical images can provide different perspectives on the human body, such as CT image can provide sense structures like bones and implants with less distortion, while the MRI image can provide normal and pathological soft tissue information [1][2][3][4][5]. Therefore, in order to fully diagnose the condition of patients, it is desired to fusing different modality medical images into a single image, called image fusion, such that all the information is available.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the fusion methods used wavelet and contourlet transform, many researches also have introduced the structure-preserving smoothing filter into their fusion methods. Such as, Li et.al [15], Bavirisetti et.al [3] and Zhan et.al [16] use the guide filter (GF) to obtain the fusion image, Kumar et.al [6] introduce the cross bilateral filter (CBF) into their fusion scheme.…”

Section: Introductionmentioning

confidence: 99%

Mri and Ct Image Fusion Based Structure-Preserving Filter

Li¹,

Chen²,

Zhang³

et al. 2018

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Medical image fusion plays an important role in clinical application such as image-guided radiotherapy and surgery, and treatment planning. The main purpose of the medical image fusion is to fuse different multi-modal images, such as MRI and CT, into a single image. In this paper, a novel fusion method is proposed based on a fast structure-preserving filter for medical image MRI and CT of a brain. The fast structure preserving filter is a novel double weighted average image filter (SGF) which enables to smooth out high-contrast detail and textures while preserving major image structures very well. The workflow of the proposed method is as follows: first, the detail layers of two source images are obtained by using the structurepreserving filter. Second, compute the weights of each source image by calculating from the detail layer with the help of image statistics. Finally, fuse source images by weighted average using the computed weights. Experimental results show that the proposed method is superior to the existing medical image fusion method in terms of subjective evaluation and objective evaluation.

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Fusion of MRI and CT images using guided image filter and image statistics

Cited by 73 publications

References 59 publications

Neuro‐wavelet based intelligent medical image fusion

Neuro‐wavelet based intelligent medical image fusion

Hybridisation of single‐image super‐resolution with edge‐aware multi‐focus image fusion for edge enrichment

Mri and Ct Image Fusion Based Structure-Preserving Filter

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