Fuzzy Local Binary Patterns for Ultrasound Texture Characterization

Iakovidis, Dimitris K.; Keramidas, Eystratios G.; Maroulis, Dimitris

doi:10.1007/978-3-540-69812-8_74

Cited by 121 publications

(64 citation statements)

References 13 publications

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“…The classification rate of the FLBP slightly decreases, and, generally obtains a low performance. This not only due to the small spatial support of FLBP, but also to the fact that the classifier is trained with noise free data, while in the experiments of Iakovidis et al [12] the training data also contain noise.…”

Section: Methodsmentioning

confidence: 99%

“…In the last experiment, we apply JPEG compression of different quality levels using a compression algorithm of the Independent JPEG Group (IJG) [13] on the color Vistex textures and, classify them using the uncompressed training data. We also tackle these classification problems using Gaussian Markov Random Fields (GMRF) [5], multi-scale LBP [26], and fuzzy LBP [12]. The implementation of the GMRF features is obtained from the MeasTex site [23].…”

Section: Classification Experimentsmentioning

confidence: 99%

“…LBP riu2 (8,1)+(16,2.4) , are uniform and rotation invariant and the 2-dimensional co-occurrence LBP histograms are classified using a non-parametric L-statistic [26]. The fuzzy LBP (FLBP) are calculated in a 3 × 3 neighborhood (P = 8 pixels and radius R = 1 pixel) with fuzzyfication parameter T = 75 as proposed in [12]. The resulting histograms are also classified using the L-statistic.…”

Section: Classification Experimentsmentioning

confidence: 99%

“…For example, Iakovidis et al [12] describe a fuzzy extension to the local binary pattern (LBP) operator [25] to deal with noise in images. An important drawback of their approach is that a parameter to control the degree of fuzziness has to be specified and an optimal value for this parameter is highly content-dependent.…”

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Noise- and compression-robust biological features for texture classification

et al. 2010

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Texture classification is an important aspect of many digital image processing applications such as surface inspection, content-based image retrieval, and biomedical image analysis. However, noise and compression artifacts in images cause problems for most texture analysis methods. This paper proposes the use of features based on the human visual system for texture classification using a semi-supervised, hierarchical approach. The texture feature consists of responses of cells which are found in the visual cortex of higher primates. Classification experiments on different texture libraries indicate that the proposed features obtain a very high classification near 97%. In contrast to other well-established texture analysis methods, the experiments indicate that the proposed features are more robust to various levels of speckle and Gaussian noise. Furthermore, we show that the classification rate of the textures using the presented biologically inspired features is hardly affected by image compression techniques.

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Section: Methodsmentioning

confidence: 99%

Section: Classification Experimentsmentioning

confidence: 99%

Section: Classification Experimentsmentioning

confidence: 99%

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confidence: 99%

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Noise- and compression-robust biological features for texture classification

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“…2. In Fuzzy LBP (FLBP) [6], a fuzzy logic approach is used to improve the robustness of the LBP code. A set of two fuzzy rules are used to represent the confidence on the values assigned to the bit-string.…”

Section: Brief Review Of Related Work On Lbpmentioning

confidence: 99%

Improving Local Binary Patterns Techniques by Using Edge Information

Abdesselam¹

2013

LNSE

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Abstract-Texture analysis plays an important role in computer vision and pattern recognition applications. During the last few decades, the research community has proposed a large number of techniques for describing, retrieving and classifying texture images. Local Binary Patterns (LBP) coding is a state-of-the-art technique characterized by its simplicity and efficiency. Due to its success, several LBP-variants are proposed in recent literature. In this paper we show that the performance of LBP-based methods can be further improved by introducing a simple modification to the feature extraction process. We suggest building two different LBP histograms one for edge pixels and the second for non-edge pixels. The final feature vector is a weighted combination of the two histograms. This idea is mainly inspired by the results of several research works on vision indicating that when looking at objects, human attention focuses more on salient regions (where changes in intensity, color, etc. occur). The experiments that have been conducted on Brodatz and Outex databases show that implementing this modification on LBP-based techniques (LBP, LTP and LBP_V), produces significant improvement in the accuracy of the original methods.Index Terms-Edge information, local binary patterns, texture analysis.

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Local Binary Patterns for Still Images

Pietikäinen

Hadid

Zhao

et al. 2011

Computational Imaging and Vision

154

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The local binary pattern operator is an image operator which transforms an image into an array or image of integer labels describing small-scale appearance of the image. These labels or their statistics, most commonly the histogram, are then used for further image analysis. The most widely used versions of the operator are designed for monochrome still images but it has been extended also for color (multi channel) images as well as videos and volumetric data. This chapter covers the different versions of the actual LBP operator in spatial domain [42,45,53], while Chap. 3 deals with spatiotemporal LBP [88]. Parts II to IV of this book discuss how the labels are then used in different computer vision tasks. Basic LBPThe basic local binary pattern operator, introduced by Ojala et al. [52], was based on the assumption that texture has locally two complementary aspects, a pattern and its strength. In that work, the LBP was proposed as a two-level version of the texture unit [74] to describe the local textural patterns.The original version of the local binary pattern operator works in a 3 × 3 pixel block of an image. The pixels in this block are thresholded by its center pixel value, multiplied by powers of two and then summed to obtain a label for the center pixel. As the neighborhood consists of 8 pixels, a total of 2 8 = 256 different labels can be obtained depending on the relative gray values of the center and the pixels in the neighborhood. See Fig. 1.1 for an illustration of the basic LBP operator. An example of an LBP image and histogram are shown in Fig. 2.1. Derivation of the Generic LBP OperatorSeveral years after its original publication, the local binary pattern operator was presented in a more generic revised form by Ojala et al. [53]. In contrast to the basic M. Pietikäinen et al., Computer Vision Using Local Binary Patterns, Computational Imaging and Vision 40,

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Fuzzy Local Binary Patterns for Ultrasound Texture Characterization

Cited by 121 publications

References 13 publications

Noise- and compression-robust biological features for texture classification

Noise- and compression-robust biological features for texture classification

Improving Local Binary Patterns Techniques by Using Edge Information

Local Binary Patterns for Still Images

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