Rotation invariant fuzzy shape contexts based on eigenshapes and fourier transforms for efficient radiological image retrieval

Authors:
Alaidine Ben Ayed;Mustapha Kardouchi;Sid-Ahmed Selouani
Affiliations:
Université de Moncton, Moncton, NB, Canada;Université de Moncton, Moncton, NB, Canada;Université de Moncton, Shippagan, NB, Canada
Venue:
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Year:
2012

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Abstract

This paper proposes a new descriptor for radiological image retrieval. The proposed approach is based on fuzzy shape contexts, Fourier transforms and Eigenshapes. First, fuzzy shape context histograms are computed. Then, a 2D FFT is performed on each 2D histogram to achieve rotation invariance. Finally, histograms are projected onto a lower dimensionality feature space whose basis is formed by a set of vectors called Eigenshapes. They highlight the most important variations between shapes. The proposed approach is translation, scale and rotation invariant. Classes of the medical IRMA database are used for experiments. Comparison with the known approach rotation invariant shape contexts based on feature-space Fourier transformation proves that the proposed method is faster, more efficient, and robust to local deformations.