Texture analysis using complex wavelet decomposition for knee osteoarthritis detection: Data from the osteoarthritis initiative

Riad, Rabia; Jennane, Rachid; Brahim, Abdelbasset; Janvier, Thomas; Toumi, Hechmi; Lespessailles, Éric

doi:10.1016/j.compeleceng.2018.04.004

Cited by 24 publications

(13 citation statements)

References 24 publications

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“…To solve this problem, Hill et al [23] proposed the UDTCWT, the new undecimated versions of the DTCWT, where the subsampling operator of the usual implementation of the DTCWT was removed. In this implementation, instead of image downsampling, the filter responses themselves were upsampled by inserting zeros between the filter coefficients [24].…”

Section: Robust Udtcwt Domain Magnitudes a Undecimated Double Tree Complex Wavelet Transform (Udtcwt)mentioning

confidence: 99%

Blind Image Watermark Decoder in UDTCWT Domain Using Weibull Mixtures-Based Vector HMT

et al. 2020

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As one promising solution, digital watermarking has been proposed to resolve image copyright protection and content authentication, and has been applied successfully in many fields. Owing to their excellent description capability and invariance property, statistical models have become a popular tool for the image watermarking resulting in favorable trade-offs among imperceptibility, robustness and data payload. By modeling the robust undecimated dual tree complex Wavelet transform (UDTCWT) coefficient magnitudes with the Weibull mixtures based vector hidden Markov trees (HMT) and employing maximum likelihood (ML) test criterion, we propose a new image watermarking approach in UDTCWT domain in this paper. Our image watermarking approach consists of two parts, namely, embedding and extracting. In the embedding process, we compute the robust UDTCWT coefficient magnitudes with UDTCWT domain real/imaginary parts, and insert the digital watermark into the significant UDTCWT coefficient magnitude subband. In the extracting phase, robust UDTCWT coefficient magnitudes are firstly modeled by employing the Weibull mixture-based vector HMT, where the statistical properties of UDTCWT magnitudes are captured accurately. Then the expectation/conditional maximization (ECM) approach is introduced to estimate the statistical model parameters. Finally, an image watermark decoder for multiplicative watermarking is developed using the Weibull mixtures based vector HMT and ML test. The experiments show that the proposed method not only improves the imperceptibility, but also increases the robustness performance and outperforms state-of-the-art methods on a set of standard test images.

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Section: Robust Udtcwt Domain Magnitudes a Undecimated Double Tree Complex Wavelet Transform (Udtcwt)mentioning

confidence: 99%

Blind Image Watermark Decoder in UDTCWT Domain Using Weibull Mixtures-Based Vector HMT

et al. 2020

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“…Various studies have focused on quantitative analysis of knee joint radiographs 11,16,[18][19][20][21] , and in particular on SB texture analysis 8,9,[22][23][24][25] . Changes in SB texture in the radiograph have been quantified based on roughness, anisotropy, and orientation of texture elements mostly by fractal methods 1,[3][4][5][6][7][8][9][10][11][12][13][14]26,27 .…”

Section: Introductionmentioning

confidence: 99%

“…Changes in SB texture in the radiograph have been quantified based on roughness, anisotropy, and orientation of texture elements mostly by fractal methods 1,[3][4][5][6][7][8][9][10][11][12][13][14]26,27 . Only a few studies have investigated non-fractal methods for classifying SB texture 9,11,23,[28][29][30] . The potential advantage of using non-fractal methods is to capture statistical characteristics of textures and added discriminative power by distinguish key texture primitives such as edges, corners and uniform regions 31 .…”

Section: Introductionmentioning

confidence: 99%

Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis

Bayramoglu

Tiulpin

Hirvasniemi

et al. 2020

Osteoarthritis and Cartilage

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Objective:The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). Design:Bilateral posterior-anterior knee radiographs were analyzed from the baseline of Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis Study (MOST) datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. We used an oversegmentation strategy for partitioning knee images into the compact regions that follow natural texture boundaries. Local Binary Patterns (LBP), Fractal Dimension (FD), Haralick features, Shannon entropy, and Histogram of Oriented Gradients (HOG) methods were computed within the standard ROI and within the proposed adaptive ROIs. Subsequently, we built logistic regression models to identify and compare the performances of each texture descriptor and each ROI placement method using 5-fold cross validation setting. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset. We used area under the receiver operating characteristic (ROC) curve (AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results. Results:We found that the adaptive ROI improves the classification performance (OA vs. non-OA) over the commonly-used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, LBP yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820]. Conclusion:Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA.

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“…Stage 0 represents normal and healthy knees, stage 1 represents doubtful OA, stage 2 is indicative of mild OA, stage 3 represents moderate OA, and stage 4 is indicative of severe OA. Numerous experiments have demonstrated successful methods to establish the severity of KOA [ 4 , 5 , 6 , 7 , 8 ]. However, there is no accurate method for measuring the severity of KOA in general clinical practice.…”

Section: Introductionmentioning

confidence: 99%

A novel method for determining the Femoral-Tibial Angle of Knee Osteoarthritis on X-ray radiographs: data from the Osteoarthritis Initiative

Wahyuningrum

Purnama

Verkerke

et al. 2020

Heliyon

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Femoral-tibial alignment is a prominent risk factor for Knee Osteoarthritis (KOA) incidence and progression. One way of assessing alignment is by determining the Femoral-Tibial Angle (FTA). Several studies have investigated FTA determination; however, methods of assessment of FTA still present challenges. This paper introduces a new method for semi-automatic measurement of FTA as part of KOA research. Our novel approach combines preprocessing of X-ray images and the use of Active Shape Model (ASM) as the femoral and tibial segmentation method, followed by a thinning process. The result of the thinning process is used to predict FTA automatically by measuring the angle between the intersection of the two vectors of branching points on the femoral and tibial areas. The proposed method is trained on 10 x-ray images and tested on 50 different x-ray images of the Osteoarthritis Initiative (OAI) dataset. The outcomes of this approach were compared with manually obtained FTA measurements from the OAI dataset as the ground truth. Based on experiments, the difference in measurement results between the FTA of the OAI and the FTA obtained using our method is quite small, i.e., below 0.81° for the right FTA and below 0.77° for the left FTA with minimal average errors. This result indicates that this method is clinically suitable for semi-automatic measurement of the FTA.

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Texture analysis using complex wavelet decomposition for knee osteoarthritis detection: Data from the osteoarthritis initiative

Cited by 24 publications

References 24 publications

Blind Image Watermark Decoder in UDTCWT Domain Using Weibull Mixtures-Based Vector HMT

Blind Image Watermark Decoder in UDTCWT Domain Using Weibull Mixtures-Based Vector HMT

Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis

A novel method for determining the Femoral-Tibial Angle of Knee Osteoarthritis on X-ray radiographs: data from the Osteoarthritis Initiative

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