Wavelet transforms and edge detectors on digital images

Schmeelk, John

doi:10.1016/j.mcm.2004.12.002

Cited by 13 publications

(8 citation statements)

References 6 publications

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“…3c ). Since pixel gray levels were found to vary considerably within the same fascicle (appearing not completely connected together in the location plane but being separated by many voids and discontinuities), the fascicle edges cannot be directly extracted by using the existing edge detection operator method or region growing method 15 16 17 . To be able to automatically extract fascicle edges, these voids and discontinuities have to be excluded.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, since pixel gray levels vary considerably within the same fascicle (being separated by many voids and discontinuities), edges of the nerve fascicles cannot be obtained directly by adopting some existing methods, e.g. edge detection operator method and region growth method 15 16 17 . Here, after testing with the classical GVF-Snake model with normal and tangent energy part, it was found that the model can be simplified because the tangent energy part is nearly zero while the edge is enveloping the fascicle.…”

Section: Discussionmentioning

confidence: 99%

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Three-dimensional Reconstruction of Peripheral Nerve Internal Fascicular Groups

Zhong

Wang

Dong

et al. 2015

Sci Rep

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Peripheral nerves are important pathways for receiving afferent sensory impulses and sending out efferent motor instructions, as carried out by sensory nerve fibers and motor nerve fibers. It has remained a great challenge to functionally reconnect nerve internal fiber bundles (or fascicles) in nerve repair. One possible solution may be to establish a 3D nerve fascicle visualization system. This study described the key technology of 3D peripheral nerve fascicle reconstruction. Firstly, fixed nerve segments were embedded with position lines, cryostat-sectioned continuously, stained and imaged histologically. Position line cross-sections were identified using a trained support vector machine method, and the coordinates of their central pixels were obtained. Then, nerve section images were registered using the bilinear method, and edges of fascicles were extracted using an improved gradient vector flow snake method. Subsequently, fascicle types were identified automatically using the multi-directional gradient and second-order gradient method. Finally, a 3D virtual model of internal fascicles was obtained after section images were processed. This technique was successfully applied for 3D reconstruction for the median nerve of the hand-wrist and cubital fossa regions and the gastrocnemius nerve. This nerve internal fascicle 3D reconstruction technology would be helpful for aiding peripheral nerve repair and virtual surgery.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Three-dimensional Reconstruction of Peripheral Nerve Internal Fascicular Groups

Zhong

Wang

Dong

et al. 2015

Sci Rep

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“…Many groups have examined multiscale wavelet transform for edge detection [3][4][5] . Computational complexity of the wavelet transform is low and its denoise ability is strong, so we choose the wavelet transform to detect edge width of objects in the image [6,7] .…”

Section: Detecting Toolmentioning

confidence: 99%

A Method for Threshold Selection in Edge Width Detection of Objects in the Image

Shi

Zhang

Lü

et al. 2009

2009 International Conference on Signal Processing Systems

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A method for threshold selection to detect edge width of objects in the image was presented. Three order spline wavelet was choosed to complete multiscale wavelet edge detection and wavelet edge module values were obtained. After properties of these data was analyzed, a method for threshold selection was proposed using half of maximum of wavelet edge module values. Edge width of objects in the image was given a defination and can be calculated by area and girth of the transition edge using the threshold selected by the method proposed. Experiment results show that the algorithm is effective and performs better.

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“…Wavelet transform provides a natural window for signals that requires high time resolution at high frequencies and high frequency resolution at low frequencies based on the dilation property of a wavelet and does not require preselecting a time window, which is essential in STFT (Sinha et al, 2009). The instantaneous attributes obtained by wavelet analysis have certain effects on the analysis of non-stationary signals such as edge detection (see, for example, Aydin et al, 1996;Schmeelk, 2005) and image compression (see, for example, Lewis and Knowles, 1992;Boix and Cantó, 2010), the time-frequency distribution in time series (see, for example, Farge, 1992), fault diagnosis (see, for example, Jena and Panigrahi, 2012) and lithological characteristics identification (see, for example, Perez-Muñoz et al, 2013) and gas detection(see, for example, Kazemeini et al, 2009).…”

Section: Introductionmentioning

confidence: 99%