Morphometrical data analysis using wavelets

  • Authors:

  • C. M. Takemura;R. M. Cesar-, Jr.;R. A. T. Arantes;L. da F. Costa;E. Hingst-Zaher;V. Bonato;S. F. dos Reis

  • Affiliations:

  • Departamento de Ciência da Computação, Instututo de Matemática e Estatística da Universidade de São Paulo (USP-IME), 1010 Rua do Matão, Cidade Universitária ...;Departamento de Ciência da Computação, Instututo de Matemática e Estatística da Universidade de São Paulo (USP-IME), 1010 Rua do Matão, Cidade Universitária ...;USP-IFSC, Av. Trabalhador Sãocarlense, 400Caixa Postal 369, CEP 13560-970, São Carlos, SP, Brazil;USP-IFSC, Av. Trabalhador Sãocarlense, 400Caixa Postal 369, CEP 13560-970, São Carlos, SP, Brazil;USP-MZUSP, Av. Nazaré, 481 Bairro do Ipiranga,CEP 04263-000, São Paulo, SP, Brazil;CREUPI-ICB, Av. Hélio Vergueiro Leite s/n,CEP 13990-000, Espírito Santo do Pinhal, SP, Brazil;UNICAMP-IB, Cidade Universitária "Zeferino Vaz", CEP 13083-970, Campinas, SP, Brazil

  • Venue:
  • Real-Time Imaging - Special issue on imaging in bioinformatics: Part III
  • Year:
  • 2004

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Abstract

In this paper, we present a new shape analysis approach using the well-known wavelet transform and exploring shape representation by landmarks. First, we describe the approach adopted to represent the landmarks data as parametric signals. Then, we show the relation of the derivatives of Gaussian wavelet transform applied to the signal-to-differential properties of the shape that it represents. We present experimental results using real data to show how it is possible to characterize shapes through multiscale and differential signal-processing techniques in order to relate morphological variables with phylogenetic signal, environmental factors and sexual dimorphism. The goal of this research is to develop an effective wavelet transform-based method to represent and classify multiple classes of shapes given by landmarks.