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Alternative parametric boundary reconstruction method for biomedical imaging.

Kolibal J, Howard D - J. Biomed. Biotechnol. (2008)

Bottom Line: Determining the outline or boundary contour of a two-dimensional object, or the surface of a three-dimensional object poses difficulties particularly when there is substantial measurement noise or uncertainty.The technique is applied to parametric boundary data and has potential applications in biomedical imaging.It should be considered as one of several techniques to improve the visualization of images.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, College of Science and Technology, The University of Southern Mississippi, Hattiesburg, MS 39406-0001, USA. joseph.kolibal@usm.edu

ABSTRACT
Determining the outline or boundary contour of a two-dimensional object, or the surface of a three-dimensional object poses difficulties particularly when there is substantial measurement noise or uncertainty. By adapting the mathematical approach of stochastic function recovery to this task, it is possible to obtain usable estimates for these boundaries, even in the presence of large amounts of noise. The technique is applied to parametric boundary data and has potential applications in biomedical imaging. It should be considered as one of several techniques to improve the visualization of images.

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Related in: MedlinePlus

Recovery ofthe boundary of the star specified by 100 points with Gaussian noise of ν = 8 as in Figure 4. The recovered curve is shown asa thicker line.
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Related In: Results  -  Collection


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fig5: Recovery ofthe boundary of the star specified by 100 points with Gaussian noise of ν = 8 as in Figure 4. The recovered curve is shown asa thicker line.

Mentions: The shape of the star in Figure 5 hasbecome more wiggly using the smoothing parameters the same as in the case oflesser noise. The problem is a classical one: there is no means to discern theshape of the figure from the noisy data, except to note that an acceptableshape is determined by the smoothness of the boundary that is intrinsic to thefigure. In this case, changing the smoothing can accommodate this subjectiveassessment, as illustrated in Figure 6. Note that the recovered boundary isconsistent with the curve recovered from the less noisy data set: compare Figures4 and 6 as shown in Figure 7.


Alternative parametric boundary reconstruction method for biomedical imaging.

Kolibal J, Howard D - J. Biomed. Biotechnol. (2008)

Recovery ofthe boundary of the star specified by 100 points with Gaussian noise of ν = 8 as in Figure 4. The recovered curve is shown asa thicker line.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2366076&req=5

fig5: Recovery ofthe boundary of the star specified by 100 points with Gaussian noise of ν = 8 as in Figure 4. The recovered curve is shown asa thicker line.
Mentions: The shape of the star in Figure 5 hasbecome more wiggly using the smoothing parameters the same as in the case oflesser noise. The problem is a classical one: there is no means to discern theshape of the figure from the noisy data, except to note that an acceptableshape is determined by the smoothness of the boundary that is intrinsic to thefigure. In this case, changing the smoothing can accommodate this subjectiveassessment, as illustrated in Figure 6. Note that the recovered boundary isconsistent with the curve recovered from the less noisy data set: compare Figures4 and 6 as shown in Figure 7.

Bottom Line: Determining the outline or boundary contour of a two-dimensional object, or the surface of a three-dimensional object poses difficulties particularly when there is substantial measurement noise or uncertainty.The technique is applied to parametric boundary data and has potential applications in biomedical imaging.It should be considered as one of several techniques to improve the visualization of images.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, College of Science and Technology, The University of Southern Mississippi, Hattiesburg, MS 39406-0001, USA. joseph.kolibal@usm.edu

ABSTRACT
Determining the outline or boundary contour of a two-dimensional object, or the surface of a three-dimensional object poses difficulties particularly when there is substantial measurement noise or uncertainty. By adapting the mathematical approach of stochastic function recovery to this task, it is possible to obtain usable estimates for these boundaries, even in the presence of large amounts of noise. The technique is applied to parametric boundary data and has potential applications in biomedical imaging. It should be considered as one of several techniques to improve the visualization of images.

Show MeSH
Related in: MedlinePlus