Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains

Bhateja, Vikrant; Patel, Himanshi; Krishn, Abhinav; Sahu, Akanksha; Lay-Ekuakille, A.

doi:10.1109/jsen.2015.2465935

Cited by 169 publications

(59 citation statements)

References 41 publications

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“…Exams and imaging systems [192] merge medical use data images through stationary wavelet transformation techniques and Non-Sampled Contourlet Transformed (NSCT). Both techniques improve the variance information and the fused image phase, still using Principal Component Analysis (PCA) and fusion rules to minimize redundancy, offering enhanced contrast and restoration of morphological details.…”

Section: Biomedical Applicationsmentioning

confidence: 99%

Sensor Fusion and Smart Sensor in Sports and Biomedical Applications

Mendes

Vieira

Pires

et al. 2016

Sensors

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The following work presents an overview of smart sensors and sensor fusion targeted at biomedical applications and sports areas. In this work, the integration of these areas is demonstrated, promoting a reflection about techniques and applications to collect, quantify and qualify some physical variables associated with the human body. These techniques are presented in various biomedical and sports applications, which cover areas related to diagnostics, rehabilitation, physical monitoring, and the development of performance in athletes, among others. Although some applications are described in only one of two fields of study (biomedicine and sports), it is very likely that the same application fits in both, with small peculiarities or adaptations. To illustrate the contemporaneity of applications, an analysis of specialized papers published in the last six years has been made. In this context, the main characteristic of this review is to present the largest quantity of relevant examples of sensor fusion and smart sensors focusing on their utilization and proposals, without deeply addressing one specific system or technique, to the detriment of the others.

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Section: Biomedical Applicationsmentioning

confidence: 99%

Sensor Fusion and Smart Sensor in Sports and Biomedical Applications

Mendes

Vieira

Pires

et al. 2016

Sensors

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“…As a result, clear textures and smooth edges are unavailable when positioning contour or sketching region and pseudo-Gibbs distortion may occur [10][11]. Bhateja et al introduced a two-stage multimodal fusion framework using the combination of stationary WT and NSCT domains for computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) images; they also depicted the visual and quantitative superiority of the obtained fusion results [12]. The down-sampling step is removed in NSCT to avoid drawbacks in contourlets.…”

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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“…In image processing applications, the most well-known multi-scale analysis tools are wavelet [7], contourlet [8], ridgelet [9], and other transform methods. Shearlet transform is a new multi-scale analysis tool which integrates the advantages of wavelet and contourlet [10].…”

Section: Introductionmentioning

confidence: 99%

Non-linear adaptive image enhancement in wireless sensor networks based on non-subsampled shearlet transform

Tong

Chen

2017

J Wireless Com Network

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Acquiring clear images is a crucial precondition in many image-related applications, such as wireless sensor network, industrial inspection, and machine vision. In this paper, a multi-scale image adaptive enhancement algorithm for image sensors in wireless sensor networks based on non-subsampled shearlet transform is presented. The images are decomposed into different scales of coefficients. Then the coefficients are enhanced by a non-linear enhancement function. We set two thresholds for this function. One is used to classify the coefficients between the set to be denoised and the set to be enhanced; the other is to control the enhanced intensity of the coefficients. The thresholds are selected adaptively according to the decomposition scale and the standard deviation of the coefficients. The performance of the proposed algorithm is evaluated both objectively and subjectively. And the results show that the visibility of the images is enhanced significantly.

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Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains

Cited by 169 publications

References 41 publications

Sensor Fusion and Smart Sensor in Sports and Biomedical Applications

Sensor Fusion and Smart Sensor in Sports and Biomedical Applications

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

Non-linear adaptive image enhancement in wireless sensor networks based on non-subsampled shearlet transform

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