B-spline interpolation technique for digital signal processing

Svoboda, Marcus; Mâţiu-Iovan, Liliana; Frigura-Iliasa, Flaviu Mihai; Andea, Petru

doi:10.1109/dt.2015.7222998

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…According to the ROI labeled by the radiologist in the original CT images, we used the B‐spline interpolation sampling technique to resample the images with different resolutions to guarantee that all of the images had the same resolution after resampling to [1,1,1] 1 . 9 Different methods (LoG, Sigma, Wavelet, Square, SquareRoot, Logarithm, Exponential, Gradient, LBP2D) were used for image transformation. 10…”

Section: Methodsmentioning

confidence: 99%

Different CT slice thickness and contrast‐enhancement phase in radiomics models on the differential performance of lung adenocarcinoma

et al. 2022

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Background To investigate the effects of computed tomography (CT) reconstruction slice thickness and contrast‐enhancement phase on the differential diagnosis performance of radiomic signature in lung adenocarcinoma. Methods A total of 187 patients who had been pathologically confirmed with lung adenocarcinoma and nonadenocarcinoma were divided into a training cohort (n = 149) and validation cohort (n = 38). All the patients underwent contrast‐enhanced CT and the images were reconstructed with different slice thickness. The radiomic features were extracted from different slice thickness and scan phase. The logistic regression (LR) algorithm was used to build a machine learning model for each group. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve and DeLong test was used to evaluate its discriminating performance. Results Finally, 34 image features and five semantic features were selected to establish a radiomics model. Based on the three contrast‐enhanced CT phases and four reconstruction slice thickness, 12 groups of radiomics models showed good discrimination ability with the AUCs range from 0.9287 to 0.9631, sensitivity range from 0.8349 to 0.9083, specificity range from 0.825 to 0.925 in the training group. Similar results were observed in the validation group. However, there was no statistical significance between the different CT scan phase groups and different slice thickness (p > 0.05). Conclusions The radiomic analysis of contrast‐enhanced CT can be used for the differential diagnosis of lung adenocarcinoma. Moreover, different slice thickness and contrast‐enhanced scan phase did not affect the discriminating ability in the radiomics models.

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Section: Methodsmentioning

confidence: 99%

Different CT slice thickness and contrast‐enhancement phase in radiomics models on the differential performance of lung adenocarcinoma

et al. 2022

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“…Ten image preprocessing methods were used, including Wavelet, LOG, Square, Square Root, Logarithm, Exponential, Gradient, LBP2D, LBP3D, etc, and adjusted the optimal parameters. 20,21 After image processing, a total of 1852 features were obtained from each ROI in our study, including 3 categories: first order feature, shape features, and texture features. 22 First order features (19 features) uses basic first-order statistics such as mean, variance, entropy, standard deviation, etc, to describe the pixel intensity and distribution within the ROI.…”

Section: Image Preprocessing and Feature Extractionmentioning

confidence: 99%

Machine Learning Based Non-Enhanced CT Radiomics for the Identification of Orbital Cavernous Venous Malformations: An Innovative Tool

Han

Yan

et al. 2022

Journal of Craniofacial Surgery

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Purpose: To evaluate the capability of non-enhanced computed tomography (CT) images for distinguishing between orbital cavernous venous malformations (OCVM) and non-OCVM, and to identify the optimal model from radiomics-based machine learning (ML) algorithms. Methods: A total of 215 cases of OCVM and 120 cases of non-OCVM were retrospectively analyzed in this study. A stratified random sample of 268 patients (80%) was used as the training set (172 OCVM and 96 non-OCVM); the remaining data were used as the testing set. Six feature selection techniques and thirteen ML models were evaluated to construct an optimal classification model. Results: There were statistically significant differences between the OCVM and non-OCVM groups in the density and tumor location (P < 0.05), whereas other indicators were comparable (age, gender, sharp, P > 0.05). Linear regression (area under the curve [AUC] ¼ 0.9351; accuracy ¼ 0.8657) and Stochastic Gradient Descent (AUC ¼ 0.9448; accuracy ¼ 0.8806) classifiers, both of which coupled with the f test and L1-based feature selection method, achieved optimal performance. The support vector machine (AUC ¼ 0.9186; accuracy ¼ 0.8806), Random Forest (AUC ¼ 0.9288; accuracy ¼ 0.8507) and eXtreme Gradient Boosting (AUC ¼ 0.9147; accuracy ¼ 0.8507) classifier combined with f test method showed excellent average performance among our study, respectively. Conclusions:The effect of non-enhanced CT images in OCVM not only can help ophthalmologist to find and locate lesion, but also bring great help for the qualitative diagnosis value using radiomicbased ML algorithms.

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“…According to the ROI labeled by the radiologist in the original DICOM image, the volume map of the twodimensional ROI was obtained for feature extraction and quantification. The B-spline interpolation (22) sampling technique sitkbpline was used to resample the images with different resolutions, so that all the images had the same resolution after resampling [1,1,1]. Different image preprocessing methods (wavelet transform, Laplace transform, Gaussian filter transform, etc.)…”

Section: Image Preprocessingmentioning

confidence: 99%

An MSCT-based radiomics nomogram combined with clinical factors can identify Crohn’s disease and ulcerative colitis

Yan

Huang

et al. 2021

Ann Transl Med

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Background: We established and evaluated a radiomics nomogram based on multislice computed tomography (MSCT) arterial phase contrast-enhanced images to distinguish between Crohn's disease (CD) and ulcerative colitis (UC) objectively, quantitatively, and reproducibly. Methods: MSCT arterial phase-enhancement images of 165 lesions (99 CD, 66 UC) in 87 patients with inflammatory bowel disease (IBD) confirmed by endoscopy or surgical pathology were retrospectively analyzed. A total of 132 lesions (80%) were selected as the training cohort and 33 lesions (20%) as the test cohort. A total of 1648 radiomic features were extracted from each region of interest (ROI), and the Pearson correlation coefficient and tree-based method were used for feature selection. Five machine learning classifiers, including logistic regression (LR), support vector machine (SVM), random forest (RF), stochastic gradient descent (SGD), and linear discriminative analysis (LDA), were trained. The best classifier was evaluated and obtained, and the results were transformed into the Rscore. Three clinical factors were screened out from 8 factors by univariate analysis. The logistic regression method was used to synthesize the significant clinical factors and the Rscore to generate the nomogram, which was compared with the clinical model and LR model.Results: Among all machine learning classifiers, LR performed the best (AUC =0.8077, accuracy =0.697, sensitivity =0.8, specificity =0.5385), SGD model had the second best performance (AUC =0.8, accuracy =0.6667, sensitivity =0.75, specificity =0.5385), and the DeLong test results showed that there was no significant difference between LR and SGD (P=0.465>0.05), while the other models performed poorly.Texture features had the greatest impact on classification results among all imaging features. The significant features of the LR model were used to calculate the Rscore. The 3 significant clinical factors were perienteric edema or inflammation, CT value of arterial phase-enhancement (AP-CT value), and lesion location. Finally, a nomogram was constructed based on the 3 significant clinical factors and the Rscore, whose AUC (0.8846) was much higher than that of the clinical model (0.6154) and the LR model (0.8077). Conclusions:The nomogram is expected to provide a new auxiliary tool for radiologists to quickly identify CD and UC.

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B-spline interpolation technique for digital signal processing

Cited by 9 publications

References 5 publications

Different CT slice thickness and contrast‐enhancement phase in radiomics models on the differential performance of lung adenocarcinoma

Different CT slice thickness and contrast‐enhancement phase in radiomics models on the differential performance of lung adenocarcinoma

Machine Learning Based Non-Enhanced CT Radiomics for the Identification of Orbital Cavernous Venous Malformations: An Innovative Tool

An MSCT-based radiomics nomogram combined with clinical factors can identify Crohn’s disease and ulcerative colitis

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