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Fast evolution of image manifolds and application to filtering and segmentation in 3D medical images.
IEEE Trans Vis Comput Graph. 2004 Sep-Oct; 10(5):525-35.IT

Abstract

In many instances, numerical integration of space-scale PDEs is the most time consuming operation of image processing. This is because the scale step is limited by conditional stability of explicit schemes. In this work, we introduce the unconditionally stable semi-implicit linearized difference scheme that is fashioned after additive operator split (AOS) [1], [2] for Beltrami and the subjective surface computation. The Beltrami flow [3], [4], [5] is one of the most effective denoising algorithms in image processing. For gray-level images, we show that the flow equation can be arranged in an advection-diffusion form, revealing the edge-enhancing properties of this flow. This also suggests the application of AOS method for faster convergence. The subjective surface [6] deals with constructing a perceptually meaningful interpretation from partial image data by mimicking the human visual system. However, initialization of the surface is critical for the final result and its main drawbacks are very slow convergence and the huge number of iterations required. In this paper, we first show that the governing equation for the subjective surface flow can be rearranged in an AOS implementation, providing a near real-time solution to the shape completion problem in 2D and 3D. Then, we devise a new initialization paradigm where we first "condition" the viewpoint surface using the Fast-Marching algorithm. We compare the original method with our new algorithm on several examples of real 3D medical images, thus revealing the improvement achieved.

Authors+Show Affiliations

Deschamps T
Mathematics Department, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, USA. TDeschamps@lbl.gov
Malladi R
No affiliation info available
Ravve I
No affiliation info available

MeSH

AlgorithmsArtificial IntelligenceComputer GraphicsComputer SimulationDiagnostic ImagingHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalInformation Storage and RetrievalModels, BiologicalNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedSignal Processing, Computer-AssistedSubtraction TechniqueUser-Computer Interface

Pub Type(s)

Comparative Study
Evaluation Study
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

15794135

Citation

Deschamps, Thomas, et al. "Fast Evolution of Image Manifolds and Application to Filtering and Segmentation in 3D Medical Images." IEEE Transactions On Visualization and Computer Graphics, vol. 10, no. 5, 2004, pp. 525-35.
Deschamps T, Malladi R, Ravve I. Fast evolution of image manifolds and application to filtering and segmentation in 3D medical images. IEEE Trans Vis Comput Graph. 2004;10(5):525-35.
Deschamps, T., Malladi, R., & Ravve, I. (2004). Fast evolution of image manifolds and application to filtering and segmentation in 3D medical images. IEEE Transactions On Visualization and Computer Graphics, 10(5), 525-35.
Deschamps T, Malladi R, Ravve I. Fast Evolution of Image Manifolds and Application to Filtering and Segmentation in 3D Medical Images. IEEE Trans Vis Comput Graph. 2004 Sep-Oct;10(5):525-35. PubMed PMID: 15794135.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Fast evolution of image manifolds and application to filtering and segmentation in 3D medical images. AU - Deschamps,Thomas, AU - Malladi,Ravi, AU - Ravve,Igor, PY - 2005/3/30/pubmed PY - 2005/4/22/medline PY - 2005/3/30/entrez SP - 525 EP - 35 JF - IEEE transactions on visualization and computer graphics JO - IEEE Trans Vis Comput Graph VL - 10 IS - 5 N2 - In many instances, numerical integration of space-scale PDEs is the most time consuming operation of image processing. This is because the scale step is limited by conditional stability of explicit schemes. In this work, we introduce the unconditionally stable semi-implicit linearized difference scheme that is fashioned after additive operator split (AOS) [1], [2] for Beltrami and the subjective surface computation. The Beltrami flow [3], [4], [5] is one of the most effective denoising algorithms in image processing. For gray-level images, we show that the flow equation can be arranged in an advection-diffusion form, revealing the edge-enhancing properties of this flow. This also suggests the application of AOS method for faster convergence. The subjective surface [6] deals with constructing a perceptually meaningful interpretation from partial image data by mimicking the human visual system. However, initialization of the surface is critical for the final result and its main drawbacks are very slow convergence and the huge number of iterations required. In this paper, we first show that the governing equation for the subjective surface flow can be rearranged in an AOS implementation, providing a near real-time solution to the shape completion problem in 2D and 3D. Then, we devise a new initialization paradigm where we first "condition" the viewpoint surface using the Fast-Marching algorithm. We compare the original method with our new algorithm on several examples of real 3D medical images, thus revealing the improvement achieved. SN - 1077-2626 UR - https://www.unboundmedicine.com/medline/citation/15794135/Fast_evolution_of_image_manifolds_and_application_to_filtering_and_segmentation_in_3D_medical_images_ DB - PRIME DP - Unbound Medicine ER -