One dimensional local binary pattern for bone texture characterization

Houam, Lotfi; Hafiane, Adel; Boukrouche, Abdelhani; Lespessailles, Éric; Jennane, Rachid

doi:10.1007/s10044-012-0288-4

Cited by 59 publications

(35 citation statements)

References 31 publications

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“…Other studies have also applied the work of [29] for 1D signal processing as in [30]- [32]. In [30], [31], the authors applied 1DLBP to a 1D signal.…”

Section: One Dimensional Multi-resolution Local Binary Patterns (1mentioning

confidence: 99%

“…In [30], [31], the authors applied 1DLBP to a 1D signal. However in [32], the authors applied it to a 2D image after projecting the image into a 1D space.…”

Section: One Dimensional Multi-resolution Local Binary Patterns (1mentioning

confidence: 99%

“…Unlike conventional 1DLBP in [29]- [32], it is desired to capture both local and global features of a signal. Also, it is preferred to have tolerance towards noise and segments misalignment.…”

Section: ) 1dmrlbp Tolerates Noise and Discovers Local And Global Fementioning

confidence: 99%

“…All proposed 1DLBP features in [29]- [32] use one specific LBP resolution (e.g. Figure 2 calculates p = 4).…”

Section: One Dimensional Multi-resolution Local Binary Patterns (1mentioning

confidence: 99%

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One dimensional multi-resolution Local Binary Patterns features (1DMRLBP) for regular electrocardiogram (ECG) waveform detection

Louis

Hatzinakos

Venetsanopoulos

2014

2014 19th International Conference on Digital Signal Processing

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Feeding a noisy signal to a biometric system degrades its performance. Hence, signal quality measure is used to avoid passing irregular signals to subsequent systems such as biometric systems. To tackle this issue, 1DMRLBP features, which are 1 dimensional signal feature extraction (inspired by the 2 dimensional image Local Binary Patterns) is proposed. 1DMRLBP with its multi-resolution capability captures local and global signal characteristics; and with its histogram extraction avoids segments misalignment and reduces the number of features. Also with some modifications, 1DMRLBP accommodates the problem of unknown amplitude of a signal. 1DMRLBP achieves 91% performance rate in distinguishing between regular and irregular ECG waveforms. MATLAB code and more information are available at www.comm.utoronto.ca/ ∼ wlouis/1DMRLBP.

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“…Other studies have also applied the work of [29] for 1D signal processing as in [30]- [32]. In [30], [31], the authors applied 1DLBP to a 1D signal.…”

Section: One Dimensional Multi-resolution Local Binary Patterns (1mentioning

confidence: 99%

“…In [30], [31], the authors applied 1DLBP to a 1D signal. However in [32], the authors applied it to a 2D image after projecting the image into a 1D space.…”

Section: One Dimensional Multi-resolution Local Binary Patterns (1mentioning

confidence: 99%

Section: ) 1dmrlbp Tolerates Noise and Discovers Local And Global Fementioning

confidence: 99%

“…All proposed 1DLBP features in [29]- [32] use one specific LBP resolution (e.g. Figure 2 calculates p = 4).…”

Section: One Dimensional Multi-resolution Local Binary Patterns (1mentioning

confidence: 99%

See 2 more Smart Citations

One dimensional multi-resolution Local Binary Patterns features (1DMRLBP) for regular electrocardiogram (ECG) waveform detection

Louis

Hatzinakos

Venetsanopoulos

2014

2014 19th International Conference on Digital Signal Processing

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show abstract

“…focuses on designing and evaluating various feature descriptors for characterizing the bone textures [ 7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Histogram of Oriented Gradients and Texture Features for Bone Texture Characterization

Harb¹,

Desuky²,

Mohammed³

et al. 2017

IJCA

Self Cite

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Texture Characterization of Bone radiograph images (TCB) is a challenge in the osteoporosis diagnosis organized for the International Society for Biomedical Imaging. The objective of this paper is to distinguish osteoporotic cases from healthy controls on 2D bone radiograph images, using texture analysis. In this paper, we propose a Bone Texture Characterization method based on texture features (Segmentation-based Fractal Texture Analysis (SFTA), Basic Texture and Gabor filters) and compare these resulted features with HOG features for 2D bone structure evaluation. The classification experiments are tested with linear SVM and decision tree classifiers. The classification performance for HOG features are always higher than other texture features, and show excellent classification performance compared to other existing methods. General TermsImage mining, Artificial Intelligence.

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Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging

Zehani

Ouahabi

Oussalah³

et al. 2020

Int J Imaging Syst Tech

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This paper suggests a new technique for trabecular bone characterization using fractal analysis of X‐Ray and MRI texture images for osteoporosis diagnosis. Osteoporosis is a chronic disease characterized by a decrease in bone density that can lead to fracture and disability. In essence, the proposed fractal model makes use of the differential box‐counting method (DBCM) to estimate the fractal dimension (FD) after an appropriate image preprocessing stage that ensures a robust estimation process. In this study, we showed that within the frequency domain generated through discrete cosine transform (DCT), only a quarter of DCT coefficients are enough to characterize osteoporotic tissues. The algorithmic complexity of the developed approach is of the order of N8log2N8 where N stands for the size of the image, which, in turn, likely yields important gain in terms of medication cost. We report a successful separation of healthy and pathological cases in term of both P − value (using statistical Wilcoxon rank sum test) and margin difference. A comparative statistical analysis has been performed using a publicly available database that contains a set of MRI and X‐Ray texture images of both healthy and osteoporotic bone tissues. The statistical results demonstrated the feasibility and accepted performance level of our fractal model‐based diagnosis to discriminate healthy and unhealthy trabecular bone tissues. The developed approach has been implemented on a medical device prototype.

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One dimensional local binary pattern for bone texture characterization

Cited by 59 publications

References 31 publications

One dimensional multi-resolution Local Binary Patterns features (1DMRLBP) for regular electrocardiogram (ECG) waveform detection

One dimensional multi-resolution Local Binary Patterns features (1DMRLBP) for regular electrocardiogram (ECG) waveform detection

Histogram of Oriented Gradients and Texture Features for Bone Texture Characterization

Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging

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