Hybrid method for multimodality medical image fusion using Discrete Wavelet Transform and Entropy concepts with quantitative analysis

Sharmila, K.; Rajkumar, S.; Vijayarajan, V.

doi:10.1109/iccsp.2013.6577102

Cited by 22 publications

(9 citation statements)

References 4 publications

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“…Many of the existing algorithm has been used for IF process such as Electrical Capacitance Tomography (ECT) algorithm [8], Non-Subsample Contour let Transform (NSCT) [9], sparse representation and decision [10], Curvelet transform [11], hybrid Entropy concept [12], hybrid Dual tree complex wavelet transform [13], and hybrid IF and image registration [14]. The main problem with these methods is information loss.…”

Section: Introductionmentioning

confidence: 99%

VLSI Implementation of Image Fusion Using DWT- PCA Algorithm with Maximum Selection Rule

Borra¹,

Panakala²,

Kumar³

2019

IJIES

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Nowadays, all the medical diagnosis is achieved by using the Digital Image Processing (DIP). Because, the usage of DIP is more important in the medical field to identify the activities of the patients related to various diseases. Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) scan images are used to perform the fusion process. In brain medical image, MRI scan is used to show the brain structural information without functional data. But, CT scan image is included the functional data with brain activity. To improve the low dose CT scan, Hybrid algorithm is introduced in this paper which is implemented in FPGA. The main objective of this work is to optimize performances of the hardware. This work is implemented in FPGA. The combination of Discrete Wavelet Transform (DWT) and Principle Component Analysis (PCA) is known as hybrid algorithm. The Maximum Selection Rule (MSR) is used to select the high frequency component from DWT. These three algorithms have RTL architecture which is implemented by Verilog code. Application Specified Integrated Chips (ASIC) and Field Programmable Gate Array (FPGA) performances analysed for the different methods. In 180nm technology, DWT-PCA-IF architecture achieved 5.145mm2 area, 298.25mW power, and 124ms delay. From the fused medical image, Mean, Standard Deviation (SD), Entropy, and Mutual information (MI) performances are evaluated for DWT-PCA method which has better performance than conventional methods.

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Section: Introductionmentioning

confidence: 99%

VLSI Implementation of Image Fusion Using DWT- PCA Algorithm with Maximum Selection Rule

Borra¹,

Panakala²,

Kumar³

2019

IJIES

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“…Average (AVG) method is the most effective linear spatial fusion method . This method takes the average of pixels from two images, resulting in an image having efficiently reduced contrast.…”

Section: Introductionmentioning

confidence: 99%

An Efficient DWT and Intuitionistic Fuzzy Based Multimodality Medical Image Fusion

Soundrapandiyan

Karuppiah

Kumari

et al. 2017

Int J Imaging Syst Tech

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Image fusion aims to integrate complementary information from multiple modalities into a single image with none distortion and loss of data. Image fusion is important in medical imaging, specifically for the purpose of detecting the tumor and identification of diseases. In this article, completely unique discrete wavelet transform (DWT) and intuitionistic fuzzy sets (IFSs) based fusion method (DWT-IFS) is proposed. For fusion, initially, all source images are fused using DWT with the average, maximum, and entropy fusion rules. Besides, on the fused image IFS is applied. In the IFS process images are converted into intuitionistic fuzzy images (IFIs) by selecting an optimum value for the parameter in membership, non-membership, and hesitation degree function using entropy. Then, the resulting IFIs are decomposed into the blocks, and the corresponding blocks of the images are fused using the intersection and union operations of IFS.

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“…In Fig. 2, images are ordered column wise -First column CT image, second column MRI image, third column to twelfth column -fused images (result of different fusion method) arranged as average [3], maximum [3], minimum [2], contrast pyramid [11], laplacian pyramid [12], morphological [2], PCA [12], ratio pyramid [2], DWT [1] and proposed DWT-type2 fuzzy. From the result it is observed that the visibility of tumor region is increased in the image results of proposed DWT-type2 fuzzy when compared with the results of other methods.…”

Section: Subjective Analysismentioning

confidence: 99%

“…The pixel level fusion techniques are classified into spatial fusion methods and transform fusion methods. The spatial fusion methods include the average method, minimum method, maximum method, contrast pyramid, principal component analysis (PCA) method, laplacian pyramid and so on [3,4]. These methods are directly applied on the image pixels and these reduce the signal-to-noise ratio (SNR) in the resultant images but introduce spatial and spectral distortion in the fused image.…”

Section: Introductionmentioning

confidence: 99%

CT and MRI Image Fusion Based on Discrete Wavelet Transform and Type-2 Fuzzy Logic

Agarwal¹,

Soundrapandiyan²,

Karuppiah³

et al. 2017

IJIES

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Medical imaging requires capturing different aspects of a human body. To capture tissues, cartilages, bones and nerves one needs different sensors and different modalities which results in different images from each modality of the same body part. Image fusion is a process by which one can fuse such images from different modalities in a single image. In this paper, a novel DWT-type2 fuzzy method is proposed to fuse two images (CT and MRI images). In this method, initially, the source images are decomposed into low-level and high-level subbands by discrete wavelet transformation (DWT). As the second step, for fusion, Type-2 fuzzy technique is applied on a low-level subband and average fusion method is applied on the high-level subbands in order to enhance the most prominent features present in CT image and MRI image. Finally, the fused low-level subband and highlevel subbands are reconstructed to form the final fused image using inverse-DWT. To test the proposed fusion method, the experiment is carried out on the MRI-T2 image, functional MRI image and CT image. The fused images have been subjectively and objectively evaluated based on certain evaluation measures and the obtained results have been presented in the paper. From the results, one can observe that the proposed method provides an improvement over other primitive fusion methods.

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Hybrid method for multimodality medical image fusion using Discrete Wavelet Transform and Entropy concepts with quantitative analysis

Cited by 22 publications

References 4 publications

VLSI Implementation of Image Fusion Using DWT- PCA Algorithm with Maximum Selection Rule

VLSI Implementation of Image Fusion Using DWT- PCA Algorithm with Maximum Selection Rule

An Efficient DWT and Intuitionistic Fuzzy Based Multimodality Medical Image Fusion

CT and MRI Image Fusion Based on Discrete Wavelet Transform and Type-2 Fuzzy Logic

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