Multispectral MRI Image Fusion for Enhanced Visualization of Meningioma Brain Tumors and Edema Using Contourlet Transform and Fuzzy Statistics

Koley, Subhranil; Galande, Ashwini; Kelkar, Bhooshan; Sadhu, Anup; Sarkar, Debranjan; Chakraborty, Chandan

doi:10.1007/s40846-016-0149-5

Cited by 9 publications

(7 citation statements)

References 32 publications

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“…Koley et al [15] had designed a fusion technique using multispectral MRI image to enhance the visualization of pathological and anatomical information of meningioma (MG) brain tumours by combining contourlet transform and fuzzy statistics. This technique used contourlet transform for the decomposition, whereas fuzzy entropy and regionbased fuzzy energy were utilized for fusing approximation coefficients and detailed coefficients, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

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FWFusion: Fuzzy Whale Fusion model for MRI multimodal image fusion

Patil¹,

Shirbahadurkar

2018

Sādhanā

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Medical treatment and diagnosis require information that is taken from several modalities of images like Magnetic Resonance Imaging (MRI), Computerized Tomography and so on. The information obtained for certain ailments is often incomplete, invisible and lacking in consistent scanner performance. Hence, to overcome these issues in the image modalities, image fusion schemes are developed in the literature. This paper proposes a hybrid algorithm using fuzzy concept and a novel P-Whale algorithm, called Fuzzy Whale Fusion (FWFusion), for the fusion of MRI multimodal images. Two multimodal images from MRI (T1, T1C, T2 and FLAIR) are considered as the source images, which are fed as inputs to a wavelet transform. The transform utilized converts the images into four different bands, which are fused using two newly derived fusion factors, fuzzy fusion and whale fusion, in a weighted function. The proposed P-Whale approach combines Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) for the effective selection of whale fusion factors. The performance of FWFusion model is compared to those of the existing strategies using Mutual Information (MI), Peak Signal-to-Noise Ratio (PSNR) and Root Mean Squared Error (RMSE), as the evaluation metrics. From the mean performance evaluation, it is observed that the proposed approach can achieve MI of 1.714, RMSE of 1.9 and PSNR of 27.9472.

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Section: Literature Reviewmentioning

confidence: 99%

“…Nevertheless, there is no guarantee of finding global maxima and it takes time for convergence. In [15], multispectral MRI image has been developed to enhance the visualization of pathological and anatomical information. Nonetheless, it requires consistent scanner performance and a high degree of quality control.…”

Section: Research Gaps and Challengesmentioning

confidence: 99%

FWFusion: Fuzzy Whale Fusion model for MRI multimodal image fusion

Patil¹,

Shirbahadurkar

2018

Sādhanā

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“…Chavan et al [3] presented a non-subsampled rotated complex wavelet transform to fuse CT and MRI for the diagnosis of neurocysticercosis. Koley et al [4] com-bined the contourlet transform and fuzzy statistics to fuse the multispectral MRI images for improving the visualisation of pathological information of brain tumours. Hou et al [5] designed a fusion scheme for CT and MRI based on convolutional neural networks (CNNs) and a dual-channel spiking cortical model in non-subsampled shearlet transform domain.…”

Section: Introductionmentioning

confidence: 99%

Brain medical image fusion scheme based on shuffled frog‐leaping algorithm and adaptive pulse‐coupled neural network

Miao

Chunyu

Yazhuo

2020

IET Image Processing

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Aiming at the problems of low contrast and blurred edge textures in medical image fusion, a new fusion scheme in non‐subsampled contourlet transform (NSCT) domain is proposed to improve the quality of fused brain images which is based on pulse‐coupled neural network (PCNN) and shuffled frog‐leaping algorithm (SFLA). First, the source images are decomposed into low‐frequency (LF) and high‐frequency (HF) subbands using NSCT; if one of the source images is multicolour, then hue, saturation and brightness (HSI) transform is needed first. Second, different PCNN fusion rules are designed for LF and HF subbands according to their features, respectively. Parameters including decay time constants and amplification factors are optimised by SFLA. Finally, the fused image is reconstructed by inverse NSCT; and if necessary, an inverse HSI transform is needed. Visual and quantitative analysis of experimental results show that the fused image preserves more information of the source images, and the ability of edge retention is strong. The scheme has prominent advantages in mutual information and QAB/F for multimodal brain images, including MRI‐PET, MRI‐SPECT, and CT‐MRI, which proves that it can obtain better visual effect and have strong robustness as well as wide applications.

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“…In term of the frequency domain enhancement methods, the wavelet transform is a widely used approach. Based on the theory of wavelet, the curvelet transform and contourlet transform are proposed, these two methods have been widely used in the field of medical image enhancement and fusion, and have achieved good results . The contourlet transform is using the laplacian pyramid decomposition (LP) and directional filter bank (DFB) to realize the multiresolution image representation.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the theory of wavelet, the curvelet transform 10,11 and contourlet transform 12,13 are proposed, these two methods have been widely used in the field of medical image enhancement and fusion, and have achieved good results. 14,15 The contourlet transform is using the laplacian pyramid decomposition (LP) and directional filter bank (DFB) to realize the multiresolution image representation. However, due to the existence of up-sampling and downsampling mechanisms in LP and DFB, the contourlet does not have translation invariance.…”

Section: Introductionmentioning

confidence: 99%

A novel brain image enhancement method based on nonsubsampled contourlet transform

Jia

2017

Int J Imaging Syst Tech

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In this article, a novel brain image enhancement approach based on nonsubsampled contourlet transform (NSCT) is proposed. First, the image is decomposed into a low‐frequency component and several high‐frequency components by the NSCT; Second, the gamma correction is applied to deal with the low‐frequency sub‐band coefficients, and the adaptive threshold is used to remove the noise of the high‐frequency sub‐bands coefficients; Third, the inverse nonsubsampled contourlet transform is adopted to reconstruct the processed coefficients; Finally, the unsharp filter is used to enhance the reconstructed image. The experimental results demonstrate that the performance of the proposed method is superior to the state‐of‐the‐art algorithms in terms of brain image enhancement.

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Multispectral MRI Image Fusion for Enhanced Visualization of Meningioma Brain Tumors and Edema Using Contourlet Transform and Fuzzy Statistics

Cited by 9 publications

References 32 publications

FWFusion: Fuzzy Whale Fusion model for MRI multimodal image fusion

FWFusion: Fuzzy Whale Fusion model for MRI multimodal image fusion

Brain medical image fusion scheme based on shuffled frog‐leaping algorithm and adaptive pulse‐coupled neural network

A novel brain image enhancement method based on nonsubsampled contourlet transform

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