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

This paper proposes a new descriptor for radiological image retrieval. The proposed approach is based on fuzzy shape contexts, Fourier transforms and Eigenshapes. At first, fuzzy shape context histograms are computed. Then, a 2D FFT is performed on each 2D histogram to achieve rotation invariance. Next, histograms are projected onto a lower dimensionality feature space. The new space is more representative. It highlights the most important variations between shapes. Eigenshapes are the principal components for radiological images. 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 robust to local deformations and more efficient.