A decomposition scheme for 3D fuzzy objects based on fuzzy distance information

A decomposition scheme for 3D fuzzy objects is presented. The decomposition is guided by a fuzzy distance transform (FDT) of the fuzzy object and aims to decompose the fuzzy object into simpler parts. Relevant voxels, corresponding to the ''centres'' of the parts, are detected on the FDT and suitably grouped, using a hierarchical clustering technique, into significant seeds for the decomposition. A region growing process is then applied to the seeds. The region growing process makes use of the reverse fuzzy distance transform, which is introduced in this manuscript. The decomposition scheme is illustrated using real data from different applications of which one, namely the identification of the three parts of the Immunoglobulin G antibody imaged using cryo electron tomography, is described more in detail.

[1]  Susana Mata,et al.  Using grey-level and shape information for decomposing proteins in 3D images , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[2]  Punam K. Saha,et al.  Fuzzy Distance Transform: Theory, Algorithms, and Applications , 2002, Comput. Vis. Image Underst..

[3]  C Wählby,et al.  Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections , 2004, Journal of microscopy.

[4]  T. Creighton.  Proteins: Structures and Molecular Properties , 1986 .

[5]  Lennart Thurfjell,et al.  A new three-dimensional connected components labeling algorithm with simultaneous object feature extraction capability , 1992, CVGIP Graph. Model. Image Process..

[6]  Mats Nilsson,et al.  Molecular tools for a molecular medicine: analyzing genes, transcripts and proteins using padlock and proximity probes , 2004, Journal of molecular recognition : JMR.

[7]  J. Gower.  A comparison of some methods of cluster analysis. , 1967, Biometrics.

[8]  Ingela Nyström,et al.  Synthesising Objects and Scenes Using the Reverse Distance Transformation in 2D and 3D , 1995, ICIAP.

[9]  Pierre Soille,et al.  Morphological Image Analysis , 1999 .

[10]  Joakim Lindblad,et al.  Robust Cell Image Segmentation Methods , 2004 .

[11]  Gabriella Sanniti di Baja,et al.  Finding local maxima in a pseudo-Euclidian distance transform , 1988, Comput. Vis. Graph. Image Process..

[12]  Stina Svensson,et al.  Fuzzy Distance Based Hierarchical Clustering Calculated Using the A* Algorithm , 2006, IWCIA.

[13]  Azriel Rosenfeld,et al.  Image enhancement and thresholding by optimization of fuzzy compactness , 1988, Pattern Recognit. Lett..

[14]  Gunilla Borgefors,et al.  On Digital Distance Transforms in Three Dimensions , 1996, Comput. Vis. Image Underst..

[15]  P. Rios,et al.  Freezing immunoglobulins to see them move. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[16]  A Engel,et al.  Visualizing 3D data obtained from microscopy on the Internet. , 1999, Journal of structural biology.

[17]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[18]  Sara Sandin,et al.  Structure and flexibility of individual immunoglobulin G molecules in solution. , 2004, Structure.

[19]  U. Landegren,et al.  In situ genotyping individual DNA molecules by target-primed rolling-circle amplification of padlock probes , 2004, Nature Methods.

[20]  David G. Stork,et al.  Pattern Classification , 1973 .

[21]  G. C. Cheng.  Pictorial pattern recognition , 1969, Pattern Recognit..

[22]  Luc Vincent,et al.  Morphological grayscale reconstruction in image analysis: applications and efficient algorithms , 1993, IEEE Trans. Image Process..

[23]  J. Turner,et al.  Algorithms for automated characterization of cell populations in thick specimens from 3‐D confocal fluorescence microscopy data , 1994, Journal of microscopy.

[24]  Jayaram K. Udupa,et al.  Fuzzy connectedness and image segmentation , 2003, Proc. IEEE.

[25]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Gabriella Sanniti di Baja,et al.  Using distance transforms to decompose 3D discrete objects , 2002, Image Vis. Comput..

[27]  Azriel Rosenfeld,et al.  Fuzzy Digital Topology , 1979, Inf. Control..

[28]  Stina Svensson,et al.  Using a fuzzy framework for delineation and decomposition of Immunoglobulin G in cryo electron tomographic images , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[29]  Ewert Bengtsson,et al.  Abnormal expression pattern of cyclin E in tumour cells , 2003, International journal of cancer.