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- W2055648003 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." @default.
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- W2055648003 date "2004-10-26" @default.
- W2055648003 modified "2023-10-14" @default.
- W2055648003 title "Texture segmentation and analysis for tissue characterization" @default.
- W2055648003 doi "https://doi.org/10.1117/12.561112" @default.
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