26 October 2004 Texture segmentation and analysis for tissue characterization
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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, Rafael Redondo, Sylvain Fischer, Sylvain Fischer, Gabriel Cristobal, Gabriel Cristobal, Manuel Forero, Manuel Forero, Andres Santos, Andres Santos, Javier Hormigo, Javier Hormigo, Salvador Gabarda, 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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