26 October 2004 Texture segmentation and analysis for tissue characterization
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Abstract
Early detection of tissue changes in a disease process is of utmost interest and a challenge for non-invasive imaging techniques. Texture is an important property of image regions and many texture descriptors have been proposed in the literature. In this paper we introduce a new approach related to texture descriptors and texture grouping. There exist some applications, e.g. shape from texture, that require a more dense sampling as provided by the pseudo-Wigner distribution. Therefore, the first step to the problem is to use a modular pattern detection in textured images based on the use of a Pseudo-Wigner Distribution (PWD) followed by a PCA stage. The second scheme is to consider a direct local frequency analysis by splitting the PWD spectra following a "cortex-like" structure. As an alternative technique, the use of a Gabor multiresolution approach was considered. Gabor functions constitute a family of band-pass filters that gather the most salient properties of spatial frequency and orientation selectivity. This paper presents a comparison of time-frequency methods, based on the use of the PWD, with sparse filtering approaches using a Gabor-based multiresolution representation. Performance the current methods is evaluated for the segmentation for synthetic texture mosaics and for osteoporosis images.
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Rafael Redondo, Sylvain Fischer, Gabriel Cristobal, Manuel Forero, Andres Santos, Javier Hormigo, Salvador Gabarda, "Texture segmentation and analysis for tissue characterization", Proc. SPIE 5559, Advanced Signal Processing Algorithms, Architectures, and Implementations XIV, (26 October 2004); doi: 10.1117/12.561112; https://doi.org/10.1117/12.561112
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