A volume data classification method based on 3D spherical filters

Author

Suzuki, Motofumi T.

Author_Institution

Nat. Inst. of Multimedia Educ., Chiba

fYear

2007

fDate

7-10 Oct. 2007

Firstpage

1095

Lastpage

1100

Abstract

This paper describes a method to classify 3D volume data by a technique based on fractal dimension analysis. To estimate fractal dimension of the 3D solid textures, the Hurst analysis technique is applied to 3D volume data for classifying texture patterns. In the analysis, popular two dimensional circular Hurst operators are extended to three dimensional spherical operators. The extension of the Hurst operator makes it possible to extract pattern features directly from 3D volume data, whereas typical circular Hurst operators extract pattern features only from 2D image data. An experimental database of 3D volume data is synthesized by Perlins´ noise functions. The database is used for testing three dimensional spherical Hurst operators. Preliminary experimental results show the efficiency of the technique for classifications and segmentations of 3D volume data.

Keywords

feature extraction; filtering theory; fractals; image classification; image texture; 2D circular Hurst operators; 3D solid textures; 3D spherical filters; 3D spherical operators; 3D volume data classification; 3D volume data segmentation; Hurst analysis technique; Perlins´ noise functions; experimental database; fractal dimension analysis; fractal dimension estimiation; pattern feature extraction; texture pattern classification; Data mining; Feature extraction; Filters; Fractals; Image databases; Image segmentation; Pattern analysis; Solids; Spatial databases; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on

Conference_Location

Montreal, Que.

Print_ISBN

978-1-4244-0990-7

Electronic_ISBN

978-1-4244-0991-4

Type

conf

DOI

10.1109/ICSMC.2007.4413605

Filename

4413605