Wavelet Based Medical Image Fusion Using Filter Masks

Vekkot, Susmitha

doi:10.1007/978-3-642-15810-0_38

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…The primary concept used by the wavelet based image fusion [61,26,27,32,62,63,64,65,66,40,29,30,67,68,33,69,70,71,72,32,73,74,75,59,76,77,78,79,80,34,81,82,83,84] is to extract the detail information from one image and inject it into another. The detail information in images is usually in the high frequency and wavelets would have the ability to select the frequencies in both space and time.…”

Section: Wavelet Based Methodsmentioning

confidence: 99%

Medical image fusion: A survey of the state of the art

2014

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Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

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Section: Wavelet Based Methodsmentioning

confidence: 99%

Medical image fusion: A survey of the state of the art

2014

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“…This paper is in sequel to [13] which introduces the concept of filter masking in wavelet based image fusion. Here SWT based algorithm is used for fusion which involves filtering of detail coefficients of decomposition using a randomly generated Gaussian low-pass filter.…”

Section: Resultsmentioning

confidence: 99%

Gaussian filter based à-trous algorithm for image fusion

Vekkot

Shukla

2015

Proceedings of the Third International Symposium on Women in Computing and Informatics

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Image fusion integrates complementary information from various perspectives in order to provide a meaningful interpretation of useful features and textures in multisource images. Here, we present a multiresolution algorithm based on Stationary Wavelet Transform for fusion of two test images of same size. The algorithm uses a Gaussian low-pass filtering technique for the high frequency subbands of SWT decomposition. The new approach gave sharper edges and structural enhancement than region based approaches involving calculation of energy around salient features. The key feature of Gaussian filtering is the flexibility of using filters with different values for standard deviation depending on the application and the range of detail necessary for processing.

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“…The primary concept used by the wavelet based image fusion [26][27], [59,[61][62][63][64][65][66][67][68][69], [40], [70][71][72][73][74][75][76][77][78][79][80][81][82][83][84], [29,30,[32][33][34] is to extract the detail information from one image and inject it into another. The detail information in images is usually in the high frequency and wavelets would have the ability to select the frequencies in both space and time.…”

Section: Wavelet Based Methodsmentioning

confidence: 99%