Abstract-In this paper, we introduce a rotational invariant feature set for texture segmentation and classification, based on an extension of fractal dimension (FD) features. The FD extracts roughness information from images considering all available scales at once. In this work, a single scale is considered at a time so that textures with scale-dependent properties are satisfactorily characterized. Single-scale features are combined with multiple-scale features for a more complete textural representation. Wavelets are employed for the computation of single-and multiple-scale roughness features because of their ability to extract information at different resolutions. Features are extracted in multiple directions using directional wavelets, and the feature vector is finally transformed to a rotational invariant feature vector that retains the texture directional information. An iterative -means scheme is used for segmentation, and a simplified form of a Bayesian classifier is used for classification. The use of the roughness feature set results in high-quality segmentation performance. Furthermore, it is shown that the roughness feature set exhibits a higher classification rate than other feature vectors presented in this work. The feature set retains the important properties of FD-based features, namely insensitivity to absolute illumination and contrast.

The available evidence, based mainly on moderate quality RCTs, suggests that the pre-operative clinical examination should always be supplemented with routine DUS mapping before AVF creation. This policy avoids negative surgical explorations and significantly reduces the immediate AVF failure rate.

This study shows that oxidative stress plays a significant role in mediating methamphetamine-induced eccentric left ventricular dilation and systolic dysfunction.

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This paper introduces a technique for synthesizing natural textures, with emphasis on quasiperiodic and structural textures. Textures are assumed to be composed of three components, namely illumination, structure, and stochastic. The contribution of this work is that, in contrast to previous techniques, it proposes a joint approach for handling the texture's global illumination, irregular structure, and stochastic component which may be correlated to the other two components. Furthermore, the proposed technique does not produce verbatim copies in the synthesized texture. More specifically, a top-down approach is used for extraction of texture elements (textons) in which, in contrast to previous texton-based approaches, no assumptions regarding perfect periodicity are made. The structure itself can be modeled as a stochastic process. Consequently, textons are allowed to have irregular and nonidentical shapes. In the synthesis stage, a new nonregular textural structure is designed from the original one that defines the place holders for textons. We call such place holders empty textons (e-textons). The e-textons are filled in by a representative texton. Since e-textons do not have identical shapes, a texton shape-matching procedure is required. After adding the illumination to the structural component, a strictly localized version of a block sampling technique is applied to add the stochastic component. The block sampling technique combined with the addition of the illumination component provides a significant improvement in the appearance of synthesized textures. Results show that the proposed method is successful in synthesizing structural textures visually indistinguishable to the original. Moreover, the method is successful in synthesizing a variety of stochastic textures.

Several important pattern recognition applications are based on feature vector extraction and vector clustering. Directional patterns are commonly represented by rotation-variant vectors Fd formed from features uniformly extracted in M directions. It is often desirable that pattern recognition algorithms are invariant under pattern rotation. This paper introduces a distance measure and a K-means-based algorithm, namely, Circular K-means (CK-means) to cluster vectors containing directional information, such as Fd, in a circular-shift invariant manner. A circular shift of Fd corresponds to pattern rotation, thus, the algorithm is rotation invariant. An efficient Fourier domain representation of the proposed measure is presented to reduce computational complexity. A split and merge approach (SMCK-means), suited to the proposed CK-means technique, is proposed to reduce the possibility of converging at local minima and to estimate the correct number of clusters. Experiments performed for textural images illustrate the superior performance of the proposed algorithm for clustering directional vectors Fd, compared to the alternative approach that uses the original K-means and rotation-invariant feature vectors transformed from Fd.

Long-term anticoagulation in EVAR patients was associated with a statistically significant increase in any endoleak and persisting type II endoleaks, although it was not linked to an increased risk of reintervention. Close monitoring for EVAR patients who require long-term oral anticoagulation is advised.

In this paper, we present an algorithm for the automated removal of nonprecipitation related echoes such as atmospheric anomalous propagation (AP) in the lower elevations of meteorological-radar volume scans. The motivation for the development of this technique is the need for an objective quality control algorithm that minimizes human interaction. The algorithm uses both textural and intensity information obtained from the two lower-elevation reflectivity maps. The texture of the reflectivity maps is analyzed with the help of multifractals. Four multifractal exponents are computed for each pixel of the reflectivity maps and are compared to a "strict" and a "soft" threshold. Pixels with multifractal exponents larger than the strict threshold are marked as "nonrain," and pixels with exponents smaller than the soft threshold are marked as "rain." Pixels with all other exponent values are further examined using intensity information. We evaluate our QC procedure by comparison with the Tropical Rainfall Measurement Mission (TRMM) Ground Validation Project quality control algorithm that was developed by TRMM scientists. Comparisons are based on a number of selected cases where nonprecipitation and a variety of rain events are present, and results show that both algorithms are effective in eliminating nonprecipitation related echoes while maintaining the rain pixels. The principal advantage of our algorithm is that it is automated; therefore, it eases the requirements for the training for the QC analysis and it speeds the data reduction process by eliminating the need for labor-intensive human-interactive software.