CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain

Ganasala, Padma; Kumar, Vinod

doi:10.1007/s10278-013-9664-x

Cited by 95 publications

(70 citation statements)

References 32 publications

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“…Finally, on an average the Computation Time for proposed approach is observed to be 2.0083 sec, where it is of 2.3360 sec for NSCT [1], 3.128 sec for NSCT-2 [4] and 3.2471 sec for NSCT-3 [6].…”

Section: Resultsmentioning

confidence: 98%

“…These effects will reduce the visual fidelity of the fused image. To achieve more efficient results, the medical image fusion is shifted towards the transform domain through the accomplishment of MST, including the discrete wavelet transform (DWT) [3,7,37], framelet transform [16], contourlet transform [17], and non-sub sampled contourlet transform (NSCT) [1,4,6]. By focusing on the properties of wavelet filters, some extended wavelet based image fusion approaches are proposed based on Wavelet Packet Transform (WPT) [18] and Wavelet Frame Transform (WFT) [19].…”

Section: Literature Surveymentioning

confidence: 99%

“…Image fusion methods are usually divided into transform domain [5][6][7] and spatial domain [8][9][10] techniques. Fusion methods in the spatial domain are directly on pixel graylevel or color space from the source images for fusion operation, so the spatial domain fusion methods are also known as singlescale fusion method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Sub Band Filtering Based Medical Image Fusion Through Non-subsampled Contourlet Transform

Harijana¹,

Gandluru²,

Nanjappa³

2018

IJIES

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Abstract:Medical image fusion is a powerful tool in the medical oriented applications such as diagnosis, treatment planning and image-guided radiotherapy. In this paper, a new medical image fusion approach is proposed based on Non-Subsampled Contourlet Transform (NSCT) and Adaptive sub band filtering. Though there are so many approaches proposed in earlier to find a more and clear fused image, they didn't focus on the computational complexity. Since the computational complexity directly related to the feature count, the rise in the level number of sub band images rises the computational complexity. The adaptive sub band filtering mechanism retrieves the informative sub band images only form all the obtained high frequency sub band images. Further to improve the fusion performance, the proposed approach fuses the low frequency subband images based on phase congruency rule and the high frequency subband images based on Log-Gabor energy rule. Visually and quantitatively experimental results indicate that the proposed fusion method can obtain more effective and accurate fusion results of medical images than other algorithms.

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Section: Resultsmentioning

confidence: 98%

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Sub Band Filtering Based Medical Image Fusion Through Non-subsampled Contourlet Transform

Harijana¹,

Gandluru²,

Nanjappa³

2018

IJIES

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“…The down-sampling step is removed in NSCT to avoid drawbacks in contourlets. However, NSCT requires high computational complexity and a considerable amount of time when managing actual medical images, which limits further development [13] [14].…”

Section: State Of the Artmentioning

confidence: 99%

Medical Image Fusion Combined with Accelerated Non-negative Matrix Factorization and Expanded Laplacian Energy in Shearlet Domain

Lai¹,

Wang²,

He³

et al. 2017

JESTR

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Shearlet transform has been widely applied in related fields due to its admirable properties in image approximation. Image fusion method based on accelerated non-negative matrix factorization (ANMF) and expanded energy of Laplace (EEOL) rules was proposed in this study to integrate the complementary information of medical images with multiple modalities and improve the accuracy of clinical diagnosis and therapy. First, the registered medical images were decomposed into low-and high-frequency sub-band coefficients in shearlet domain. Then, the ANMF rule was used in merging low-frequency coefficients. Next, the visual-contrast-based EEOL rule was adopted in extracting details of source images from high-frequency coefficients. Finally, the ultimate fused image was reconstructed by applying inverse shearlet transform. Experimental results reveal that aside from visual effect, the proposed method achieves the best in three of five criteria and the run time is reduced by 29.21% compared with a method based on non-subsampled contourlet transform (NSCT) in computed tomography (CT)-magnetic resonance imaging (MRI) fusion. Moreover, the proposed method takes the first place in four of five criteria with run time reduced by 48.32% and 24.55% compared with two shearlet-based methods in a MRI-positron emission tomography (PET) case. This study indicates that the proposed method is superior to the selected approaches in visual and statistical evaluation, which is conducive to clinical practice of medical image fusion.

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“…3 Fusion at pixel-level is performed directly on the values derived from the pixels of the input images on a pixel-by-pixel basis to generate a single fused image. Hence, pixel-based fusion methods become the most popular fusion techniques, while only few fusion algorithms use feature and decision level due to their complexity and di±culty of implementation.…”

Section: Introductionmentioning

confidence: 99%

A novel image fusion algorithm based on 2D scale-mixing complex wavelet transform and Bayesian MAP estimation for multimodal medical images

Bengueddoudj

Messali

Mosorov

2017

J. Innov. Opt. Health Sci.

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In this paper, we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform (2D-SMCWT). The fusion of the detail 2D-SMCWT coe±cients is performed via a Bayesian Maximum a Posteriori (MAP) approach by considering a trivariate statistical model for the local neighboring of 2D-SMCWT coe±cients. For the approximation coe±cients, a new fusion rule based on the Principal Component Analysis (PCA) is applied. We conduct several experiments using three di®erent groups of multimodal medical images to evaluate the performance of the proposed method. The obtained results prove the superiority of the proposed method over the state of the art fusion methods in terms of visual quality and several commonly used metrics. Robustness of the proposed method is further tested against di®erent types of noise. The plots of fusion metrics establish the accuracy of the proposed fusion method.

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CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain

Cited by 95 publications

References 32 publications

An Adaptive Sub Band Filtering Based Medical Image Fusion Through Non-subsampled Contourlet Transform

An Adaptive Sub Band Filtering Based Medical Image Fusion Through Non-subsampled Contourlet Transform

Medical Image Fusion Combined with Accelerated Non-negative Matrix Factorization and Expanded Laplacian Energy in Shearlet Domain

A novel image fusion algorithm based on 2D scale-mixing complex wavelet transform and Bayesian MAP estimation for multimodal medical images

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