Gradient vector flow: Difference between revisions

Revision as of 19:15, 4 April 2020

Revision as of 19:24, 4 April 2020

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| first4 = M. | last4 = Rudzsky | title = Fast geodesic active contours | journal = IEEE Transactions on Image Processing | year = 2001

| volume = {10 | pages = 1467-1475 | issue = 10}}</ref> for rapid computation of this segmentation method. The uniqueness and existence of this

combined model were proven in&nbsp:;<ref name=":GuixCPAA09">{{Cite journal | first1 = L. | last1 = Guilot | first2 = M. | last2 = Bergounioux

| title = Existence and uniqueness results for the gradient vector flow and geodesic active contours mixed model |

journal = Communications on Pure and Applied Analysis | year = 2009 | volume = 8 | issue = 4 | pages = 1333-1349}}</ref>.

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to achieve even better segmentation for images with complex geometric objects.

GVF has been used to find both inner, central, and central cortical surfaces in the analysis of brain images&nbsp;<ref name=":HanxNI04"/>, as shown in Figure&nbsp;4. The

process first finds the inner surface using a three-dimensional geometric deformable model with conventional forces. Then the central

central, and central cortical surfaces in the analysis of brain

surface is found by exploiting the central tendency property of GVF. In particular, the cortical membership function of the human brain

images~\cite{HanxNI04}, as shown in Figure~\ref{fig:Cortex}. The

cortex, derived using a fuzzy classifier, is used to compute GVF as if itself were a thick edge map. The computed GVF vectors point towards

process first finds the inner surface using a three-dimensional

the center of the cortex and can then be used as external forces to drive the inner surface to the central surface. Finally, another

geometric deformable model with conventional forces. Then the central

geometric deformable model with conventional forces is used to drive the central surface to a position on the outer surface of the cortex.

surface is found by exploiting the central tendency property of GVF.

In particular, the cortical membership function of the human brain

cortex, derived using a fuzzy classifier, is used to compute GVF as if

itself were a thick edge map. The computed GVF vectors point towards

the center of the cortex and can then be used as external forces to

drive the inner surface to the central surface. Finally, another

geometric deformable model with conventional forces is used to drive

the central surface to a position on the outer surface of the cortex.

Several notable recent applications of GVF include constructing graphs for optimal surface segmentation in spectral-___domain optical coherence

tomography volumes~\cite{&nbsp;<ref name=":MirxCMIG17}"/>, a learning based probabilistic GVF active contour formulation to give more weights to objects

optimal surface segmentation in spectral-___domain optical coherence

of interest in ultrasound image segmentation~\cite{&nbsp;<ref name =":HafxCBM14}"/>, and an adaptive multi-feature GVF active contour

tomography volumes~\cite{MirxCMIG17}, a learning based probabilistic GVF active contour

for improved ultrasound image segmentation without hand tuned paramaters&nbsp;<ref name=":RodxJVCIR13>{{Cite journal | title=Multi-feature gradient vector flow snakes for adaptive segmentation of the ultrasound images of breast cancer | last1 = Rodtook | first1 = A. | last2 = Makhanov | first2 = S.S. | journal=Journal of Visual Communication and Image Representation | volume=24 | issue = 8 | pages=1414-1430 | year=2013 | publisher=Elsevier}}</ref>

formulation to give more weights to objects of interst in ultrasound image

segmentation~\cite{HafxCBM14}, and an adaptive multi-feature GVF active contour

for improved ultrasound image segmentation without hand tuned

paramaters~\cite{RodxJVCIR13}.

==Related Concepts==