COMPARISON OF DISCRETE WAVELET TRANSFORM AND COMPLEX WAVELET TRANSFORM IN HYBRID SKELETONIZATION BASED ON CVANN

Yıl 2010, Cilt: 2 Sayı: 7, 27 - 51, 01.09.2010

Murat Ceylan Osman Nuri Ucan Yüksel Özbay Rachid Jennane Gabriel Aufort Claude Laurent Benhamou

Öz

Curve and surface thinning are widely-used skeletonization techniques
for modeling objects in 3 dimensions. In the case of disordered porous media
analysis, however, neither is really efficient since the internal geometry of the
object is usually composed of both rod and plate shapes. This paper concludes
an application of discrete wavelet transform (WT) and complex wavelet
transform (CWT) in image processing problem such as hybrid skeletonization
of trabecular bone images. Hybrid skeleton combines 2D surfaces and 1D curve
to represent respectively the plate-shaped and rod-shaped parts of the object.
For hybrid skeletonization, two cascade structures are proposed. In these
structures, features of images were extracted with discrete wavelet transform
and complex wavelet transform. After that, obtained features were used as
inputs of complex-valued artificial neural network (CVANN) which is multilayered
artificial neural networks with two dimensions (real and imaginary
parts). Effects of the feature extraction methods are compared for ability of the
hybrid skeletonization on a trabecular bone sample. Results show that the CWT
succeeded to hybrid skeletonization with lower error rate than WT.

Anahtar Kelimeler

Wavelet transform, complex wavelet transform, complex-valued artificial neural network, hybrid skeletonization.