Texture analysis using dual tree M-band and R&amp;#x00E9;nyi entropy. Application to osteoporosis diagnosis on bone radiographs

Hassani, Ahmed Salmi El; Houam, Lotfi; Rziza, Mohammed; Lespessailles, Éric; Jennane, Rachid

doi:10.1109/isbi.2012.6235853

Cited by 5 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The obtained results were compared with the results from the papers on a similar matter conducted by other authors ( Table 4 ). Taking into account the total classification accuracy ( ACC ), it can be seen that the obtained result is one of the highest and is only 1.6% lower than the results achieved in [ 45 , 46 ], where it amounted to 96.6%. However, the majority of results presented in the table are much lower, and in the case of Andersen et al [ 47 ], it amounts only to 71.3%.…”

Section: Discussionmentioning

confidence: 79%

Comparison of the Classification Results Accuracy for CT Soft Tissue and Bone Reconstructions in Detecting the Porosity of a Spongy Tissue

Dzierżak

Omiotek

Tkacz

et al. 2022

JCM

View full text Add to dashboard Cite

The aim of the study was to compare the accuracy of the classification pertaining to the results of two types of soft tissue and bone reconstructions of the spinal CT in detecting the porosity of L1 vertebral body spongy tissue. The dataset for each type of reconstruction (high-resolution bone reconstruction and soft tissue reconstruction) included 400 sponge tissue images from 50 healthy patients and 50 patients with osteoporosis. Texture feature descriptors were calculated based on the statistical analysis of the grey image histogram, autoregression model, and wavelet transform. The data dimensional reduction was applied by feature selection using nine methods representing various approaches (filter, wrapper, and embedded methods). Eleven methods were used to build the classifier models. In the learning process, hyperparametric optimization based on the grid search method was applied. On this basis, the most effective model and the optimal subset of features for each selection method used were determined. In the case of bone reconstruction images, four models achieved a maximum accuracy of 92%, one of which had the highest sensitivity of 95%, with a specificity of 89%. For soft tissue reconstruction images, five models achieved the highest testing accuracy of 95%, whereas the other quality indices (TPR and TNR) were also equal to 95%. The research showed that the images derived from soft tissue reconstruction allow for obtaining more accurate values of texture parameters, which increases the accuracy of the classification and offers better possibilities for diagnosing osteoporosis.

show abstract

Section: Discussionmentioning

confidence: 79%

Comparison of the Classification Results Accuracy for CT Soft Tissue and Bone Reconstructions in Detecting the Porosity of a Spongy Tissue

Dzierżak

Omiotek

Tkacz

et al. 2022

JCM

View full text Add to dashboard Cite

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

View full text Add to dashboard Cite

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.

show abstract

“…1). The current research thus focuses on designing and evaluating various feature descriptors for characterizing the bone textures [3,1,4,5].…”

Section: Introductionmentioning

confidence: 99%

Bone texture characterization with fisher encoding of local descriptors

Song

Cai

Zhang

et al. 2015

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

View full text Add to dashboard Cite

Bone texture characterization is important for differentiating osteoporotic and healthy subjects. Automated classification is however very challenging due to the high degree of visual similarity between the two types of images. In this paper, we propose to describe the bone textures by extracting dense sets of local descriptors and encoding them with the improved Fisher vector (IFV). Compared to the standard bag-of-visualwords (BoW) model, Fisher encoding is more discriminative by representing the distribution of local descriptors in addition to the occurrence frequencies. Our method is evaluated on the ISBI 2014 challenge dataset of bone texture characterization, and we demonstrate excellent classification performance compared to the challenge entries and large improvement over the BoW model.

show abstract

Texture analysis using dual tree M-band and Rényi entropy. Application to osteoporosis diagnosis on bone radiographs

Cited by 5 publications

References 16 publications

Comparison of the Classification Results Accuracy for CT Soft Tissue and Bone Reconstructions in Detecting the Porosity of a Spongy Tissue

Comparison of the Classification Results Accuracy for CT Soft Tissue and Bone Reconstructions in Detecting the Porosity of a Spongy Tissue

Histogram of Oriented Gradients and Texture Features for Bone Texture Characterization

Bone texture characterization with fisher encoding of local descriptors

Contact Info

Product

Resources

About