Self-organizing map for segmenting 3D biological images

Author

Cinque, L. ; Romangnoli, R. ; Levialdi, S. ; Nguyen, P.T.A. ; Guan, L.

Author_Institution

Dipt. di Sci. dell´´Inf., Rome Univ., Italy

Volume

1

fYear

1998

fDate

16-20 Aug 1998

Firstpage

471

Abstract

An image processing method for features extraction and segmentation from three-dimensional (3D) image datasets is presented. Kohonen´s self-organizing map (SOM) is used to perform segmentation. Previously, the segmentation method worked on a 2D dataset based on a projection of the three-dimensional dataset (Nguyen et al., 1998). Our 3D approach to segment biological images preserves the 3D object orientations with respect to the surrounding cell volume. A few examples from genetics and brain analysis are provided in order to demonstrate the performance of the proposed method

Keywords

biology computing; feature extraction; image segmentation; self-organising feature maps; unsupervised learning; 3D biological images; 3D object orientations; Kohonen´s self-organizing map; brain analysis; features extraction; genetics; Automation; Electrical capacitance tomography; Genetics; Humans; Image analysis; Image processing; Image segmentation; Read only memory; Remuneration; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on

Conference_Location

Brisbane, Qld.

ISSN

1051-4651

Print_ISBN

0-8186-8512-3

Type

conf

DOI

10.1109/ICPR.1998.711183

Filename

711183