Medical Image Fusion using Combined Discrete Wavelet and Ripplet Transforms

Kavitha, C.T.; Chellamuthu, C.; Rajesh, R

doi:10.1016/j.proeng.2012.06.102

Cited by 30 publications

(14 citation statements)

References 14 publications

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“…For example, Das and Kundu [83] present an image fusion method, where the low-frequency coefficients are fused using the max selection rule while PCNN fuses high-frequency coefficients. Besides DRT and PCNN, Kavitha et al [84] first decompose source image with discrete wavelet transform, and low-frequency coefficients are decomposed by DRT. Then PCNN is used to fuse all coefficients from DRT.…”

Section: Other X-let Transformsmentioning

confidence: 99%

Review of Image Fusion Based on Pulse-Coupled Neural Network

Wang

Zhu

et al. 2015

Arch Computat Methods Eng

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Recently, many researchers have paid their more attention to image fusion technique based on pulse coupled neural network (PCNN). In order to make the researchers to rapidly understand the research development of image fusion based on PCNN, it is systematically reviewed in the paper. On the basis of statistical analysis on published papers, firstly, PCNN and some modified models are introduced. Then we review the PCNN's applications in the field of image fusion. Subsequently, some existing problems are summarized, while we give some suggestions for the future research.

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Section: Other X-let Transformsmentioning

confidence: 99%

Review of Image Fusion Based on Pulse-Coupled Neural Network

Wang

Zhu

et al. 2015

Arch Computat Methods Eng

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“…C.T.Kavitha ,C. Chellamuthu, R. Rajesh, [1] proposed method using the combined effect of DWT and DRT. The DWT could detect local features of images.…”

Section: Related Workmentioning

confidence: 99%

“…Many MGA tools were proposed, such as Curvelet, Contourlet, Ripplet etc. Which have higher directional sensitivity [1].…”

Section: Introductionmentioning

confidence: 99%

Medical Image Fusion using Combined Multi-Resolution and Multi-Scaling Transform

Rana¹,

Degadwala²

2014

IJCA

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Image Fusion is a process of combining the relevant information from a set of images into a single image, where the resultant fused image will be more informative and complete than any of the input images. Image fusion techniques can improve the quality. This paper uses MRI and CT images for fusion which contains complementary information helpful for diagnosis of disease. Proposed approach, medical image fusion based on the combined effect of Discrete Wavelet Transform (DWT) and Discrete Ripplet Transform (DRT). The images are initially transformed into multi-resolution image using DWT. The approximation image are further transformed using DRT. Then get ripplet coefficients and these are applied with Image Blocking method. The proposed method can be helpful for better medical diagnosis.

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“…The majority of the MIF techniques based on PCNN use the normalized single value of the pixel in the spatial domain or the coefficient in the transform domain as the feeding input to the PCNN which leads to contrast reduction and loss of directional information respectively [19,[21][22][23][24]. Moreover, using a single pixel/coefficient value as stimuli for a PCNN neuron is not effective, since the human visual system is more sensitive to the variations in images such as edges, contours and directional features.…”

Section: Introductionmentioning

confidence: 99%