Effectiveness of Contourlet vs Wavelet Transform on Medical Image Compression: a Comparative Study
Discrete Wavelet Transform (DWT) has demonstrated
far superior to previous Discrete Cosine Transform (DCT) and
standard JPEG in natural as well as medical image compression. Due
to its localization properties both in special and transform domain,
the quantization error introduced in DWT does not propagate
globally as in DCT. Moreover, DWT is a global approach that avoids
block artifacts as in the JPEG. However, recent reports on natural
image compression have shown the superior performance of
contourlet transform, a new extension to the wavelet transform in two
dimensions using nonseparable and directional filter banks,
compared to DWT. It is mostly due to the optimality of contourlet in
representing the edges when they are smooth curves. In this work, we
investigate this fact for medical images, especially for CT images,
which has not been reported yet. To do that, we propose a
compression scheme in transform domain and compare the
performance of both DWT and contourlet transform in PSNR for
different compression ratios (CR) using this scheme. The results
obtained using different type of computed tomography images show
that the DWT has still good performance at lower CR but contourlet
transform performs better at higher CR.
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