Scale-based image filtering of magnetic resonance data

Image analysis – Applications – Biomedical applications

Reexamination Certificate

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C382S264000, C324S310000, C378S023000

Reexamination Certificate

active

06885762

ABSTRACT:
The invention provides novel scale-based filtering methods that use local structure size or “object scale” information to arrest smoothing around fine structures and across even low-gradient boundaries. One method teaches a weighted average over a scale-dependent neighborhood; while another employs scale-dependent diffusion conductance to perform filtering. Both methods adaptively modify the degree of filtering at any image location depending on local object scale. This permits a restricted homogeneity parameter to be accurately used for filtering in regions with fine details and in the vicinity of boundaries, while at the same time, a generous filtering parameter is used in the interiors of homogeneous regions.

REFERENCES:
patent: 5528365 (1996-06-01), Gonatas
patent: 5560360 (1996-10-01), Filler et al.
patent: 5644646 (1997-07-01), Du et al.
patent: 5743266 (1998-04-01), Levene et al.
patent: 5969524 (1999-10-01), Pierpaoli et al.
patent: 5991701 (1999-11-01), Triano
patent: 6159445 (2000-12-01), Klaveness et al.
patent: 6249121 (2001-06-01), Boskamp et al.
patent: 6556720 (2003-04-01), Avinash
Gerig G, O Kilbler, R Kikinis, FA jolesz, “Nonlinear Anisotropic Filtering Of MRI Data,” IEEE Transactions on Medical Imaging, 11(2):221-232 (1992).*
Ahn CB, YC Song, DJ Park, “Adaptive Template For Signal To Noise Ratio Enhancement In Magnetic Resonance Imaging,”IEEE Transactions on Medical Imaging18(6):549-556 (1999).
Bajla I, M Srámek “Improvement Of 3D Visualization Of The Brain Using Anisotropic Diffusion Smoothing Of MR Data,”Proc. MEDINFOPart 1:683-686 (1995).
Chan P, JL Lim, “One Dimensional Processing For Adaptive Image Restoration,”IEEE Transactions on Acoustic, Speech, and Signal Processing33(1): 117-126 (1985).
Falcão AX, JK Udupa, S Samarasekera, S Sharma, “User-Steered Image Segmentation Paradigms: Live Wire and Live Lane,”Graphical Models Image Processing60:233-260 (1998).
Gerig G, O Kilbler, R Kikinis, Fa Jolesz, “Nonlinear Anisotropic Filtering Of MRI Data,”IEEE Transactions on Medical Imaging, 11(2):221-232 (1992).
Jackson EF, PA Narayanan, JS Wolinskiy, TJ Doyle, “Accuracy And Reproducibility In Volumetric Analysis Of Multiple Sclerosis Lesions,”J. Computer Assisted Tomography17(2):200-205 (1993).
Kaufmann A,Introduction To The Theory Of Fuzzy Subsets1:1-7, Academic Press (1975).
Lee JS “Digital Image Enhancement And Noise Filtering By Use Of Local Statistics,”IEEE Transactions On Acoustic, Speech, And Signal Processing, vol. 2(2), 165-168 (1980).
Lifshitz LM, SM Pizer, “A Multiresolution Hierarchical Approach To Image Segmentation Based On Image Extrema,”IEEE Transactions On Pattern Analysis And Machine Intelligence12(6):529-540 (1990).
Lindeberg T,Scale-Space Theory In Computer Vision, Pp. 1-21(1994).
Maintz J, P Van Den Elsen, M Viergever, “Comparison Of Edge-Based And Ridge-Based Registration Of CT and MR Brain Images,”Medical Image Analysis1: 1-35-51 (1996).
Mcauliffe MJ, D Eberly, DS Fritsch, EL Chaney, SM Pizer, “Scale-Space Boundary Evolution Initialized By Cores,”Proc. Visual. In Biomed. Computing, Lecture Notes In Computer Science1131:173-182 (1996).
Parker GJM, JA Schnabel, “Enhancement Of Anisotropic Diffusive Filtering Of MR Images Using Approximate Entropy,”Proc. Intl. Soc. Of Magnet Resonance In Medicine7:175 (1999).
Perona P, J Malik, “Scale-Space And Edge Detection Using Anisotropic Diffusion,”IEEE Transactions On Pattern Analysis And Machine Analysis12(7):629-639 (1990).
Pizer SM, D Eberly, DS Fritsch, BS Morse, “Zoom-Invariant Vision Of Figural Shape: The Mathematics Of Cores,”Computer Vision And Image Understanding69(1):55-71 (1998).
Rank K, R Unbehauen, “An Adaptive Recursive 2D Filter For Removal Of Gaussian Noise In Images,”IEEE Transactions On Image Processing1(3):431-436 (1992).
Rosenfeld A, Ac Kak,Digital Picture Processing, Academic Press 1:236-267 (1982).
Rosner B,Fundamentals Of Biostatistics, Duxbury Press (1995).
Saha PK, JK Udupa, “Scale-Based Filtering Of Medical Images,”Medical Imaging 2000: Image ProcessingInProceedings Of SPIE3979:735-746 (2000).
Saha PK, JK Udupa, D Odhner, “Scale-Based Fuzzy Connected Image Segmentation: Theory, Algorithms, And Validation,”Computer Vision And Image Understanding77:145-174 (2000).
Sahoo PK, S Soltani, AKC Wong,YC Chen, “A Survey Of Thresholding Techniques,”Computer Vision Graphics And Image Processing41:223-260 (1988).
Udupa JK, “Three-Dimensional Visualization And Analysis Methodologies: A Current Perspective,”Radiographics19:783-806 (1999).
Udupa JK, S Samarasekera, “Fuzzy Connectedness And Object Definition: Theory, Algorithms, And Applications In Image Segmentation,”Graphical Models And Image Processing53(3):246-261 (1996).
Wang DCC, AH Vagucci, CC LI, “Gradient Inverse Weighted Scheme And The Evaluation Of Its Performance,”Computer Graphics And Image Processing15:167-181 (1981).
Wang X, “On Gradient Inverse Weighted Filter,”IEEE Transactions On Signal Processing40(2):482-484 (1992).
Wells WM, P Viola, H Atsumi, S Nakajima, R Kikinis, “Multimodal Image Registration By Maximization Of Mutual Information,”Medical Image Analysis1:151-161 (1996).

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