Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain

Gomathi, P.; Kalaavathi, B.

doi:10.4236/cs.2016.78139

Cited by 35 publications

(24 citation statements)

References 35 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: 99%

“…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%

“…However, single multiple modality cannot satisfy the demand of images with high resolution and visualization for disease diagnosis. In this regard, medical image fusion [1][2][3][4] is a useful and powerful technique for integrating complementary information from multimodality images to improve the diagnostic accuracy. Besides, the fused images are more suitable for assisting the doctors in diagnosis and treatment planning: fusing MR and CT images can generate the images which can describe the soft tissue and bone in order to concurrently represent anatomical and physiological features of the human body.…”

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: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

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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“…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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“…For example, the computed tomography (CT) images provide better information on dense tissue, the positron emission tomography (PET) images supply better information on blood flow and tumor activity with low space resolution, and the magnetic resonance (MR) images show better information on soft tissue. Moreover, the MR-T1 images give more detailed information about anatomical structures, whereas the MR-T2 images contain a greater contrast between the normal and abnormal tissues [ 1 – 4 ]. However, single multiple modality cannot satisfy the demand of images with high resolution and visualization for disease diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Fusion Based on Feature Extraction and Sparse Representation

Yin

Gao

Song

2017

International Journal of Biomedical Imaging

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As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods.

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Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain

Cited by 35 publications

References 35 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

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

Medical Image Fusion Based on Feature Extraction and Sparse Representation

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