A Framework for Edge Detection and Linking Using Wavelets and Image Fusion

Akhtar, Pervez; Ali, Tariq Javid; Bhatti, Muhammad Iqbal; Muqeet, Mohd. Abdul

doi:10.1109/cisp.2008.203

Cited by 7 publications

(14 citation statements)

References 17 publications

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“…These methods lack the ability to surpass impulsive noise, and are incapable of recognizing slow varying intensities. Wavelet methods [45], [58], [9], [62], [2], on the other hand, utilize the natural behaviour of wavelets to divide a signal into different frequency bands: low frequency (approximation), and high frequency (in 3 different orientations). In addition, wavelets are highly localized and well capable of recognizing discontinuities in a signal, and respond better to impulsive noise components in a signal.…”

Section: Conventional Edge Detection Methodsmentioning

confidence: 99%

“…1 Different atoms have different pre-measured spin numbers 2 It is of importance for the reader to recognize that the spin direction of each single nucleus is not the same as B, rather the net direction of the collective spins; the bulk, is the same.…”

Section: Physics Of Nuclear Magnetizationmentioning

confidence: 99%

“…In order to encode signal information spatially an MRI system uses the so called gradient fields. These are smaller magnetic fields produced by gradient coils, in addition to the strong static magnetic field 2 Modern day MRI systems produce gradient fields in three directions, and are denoted by G x , G y , and G z consistent with their direction. Gradient fields enable slice selective imaging that makes MRI a tomographic imaging technique.…”

Section: Spatial Localization and K-space Spatial Localizationmentioning

confidence: 99%

“…In current imaging techniques, one gradient (normally G z ) is used for slice selection 1 while the other two are 1 A 3-Dimensional pixel 2 The gradient fields can work to add and subtract from the static magnetic field. 1 Here z direction is assumed to provide an axial image with the z-axis pointing from the head to toe of a subject.…”

Section: Spatial Localization and K-space Spatial Localizationmentioning

confidence: 99%

“…Therefore, the goal in designing a good sampling matrix is to get µ as close as possible to 1. 1 , 2 A final note on the sampling process should be provided on the number of samples acquired. As describer before, a signal f is said to be K − sparse if there are only K significant components out of the possible N with f ∈ R N .…”

Section: Incoherence and Random Samplingmentioning

confidence: 99%

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A novel framework for compressed sensing brain MRI edge detection and de-noising

Zamyadi¹

2021

Preprint

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In this thesis a novel edge detection technique is developed that employs compressed sensing image reconstruction techniques. The ability of compressed sensing noise reduction is combined with wavelet transforms, acting both as a sparsifying transform as well as an edge detection media. The proposed design was implemented and simulated on a brain phantom. The simulation results were provided for a variety of different sets of variables, and the differences were explained. The results obtained are compared with other edge detection techniques already in use. One important comparison criteria is the visual quality of images; according to which the proposed technique presents improved noise reduction and edge preservation. In addition to qualitative evaluation a method of quantitative measurement based on structural content is also utilized. It is found that the values for such a measure of the proposed method is 1.0755, 1.0174 and 0.5590 for Gaussian, Speckle, and Salt & Pepper noise types respectively. These results indicate that this novel method also improves edge preservation, while the visual quality inspection indicates how much noise has been suppressed.

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