Most of the information in a typical image is concentrated in a few regions. These are dominated by structures characterized by discontinuities in intensity. Inherent to the identification of significant structures is determining the location of the structures and assigning saliency values to them. In this paper, we present a wavelet based algorithm that identifies significant structures in an image. We first decompose the image using the wavelet transform. Then, for each pixel, a combining process computes a function that collects all the information available at the sub-band scales. The rationale of the method relies on the assumption that images and volumes possess scale-coherent structures. A structure is scale-coherent if it persists across the scales, or contains all frequencies. The detection of these features forms the core of our approach. We construct a combining function of the image by multiplying the sub-bands obtained from the wavelet transform. The result of this operation is a mask that delineates regions comprising of significant structures. This mask can then be used to provide saliency values at each pixel. Additional processing and image enhancement algorithms can now concentrate their efforts in regions where the mask has high values. We show how our method can be employed to remove noise, enhance, and smooth features in images arising from medical imaging modalities including MRI and mammography.

Date Published: 30 October 1997
PDF: 12 pages
Proc. SPIE 3169, Wavelet Applications in Signal and Image Processing V, (30 October 1997); doi: 10.1117/12.279701

Published in SPIE Proceedings Vol. 3169:
Wavelet Applications in Signal and Image Processing V
Akram Aldroubi; Andrew F. Laine; Michael A. Unser, Editor(s)