scholarly journals Deep-Analysis of Palmprint Representation Based on Correlation Concept for Human Biometrics Identification

2020 ◽  
Vol 12 (2) ◽  
pp. 40-58
Author(s):  
Raouia Mokni ◽  
Hassen Drira ◽  
Monji Kherallah
Keyword(s):  
Texture Analysis ◽  
Fractal Dimensions ◽  
Texture Features ◽  
Analysis Method ◽  
Frequency Model ◽  
Palmprint Identification ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Biometrics Identification ◽  
Modality Identification

The security of people requires a beefy guarantee in our society, particularly, with the spread of terrorism throughout the world. In this context, palmprint identification based on texture analysis is amongst the pattern recognition applications to recognize people. In this article, the researchers investigated a deep texture analysis for the palmprint texture pattern representation based on a fusion between several texture information extractions through multiple descriptors, such as HOG and Gabor Filters, Fractal dimensions and GLCM corresponding respectively to the frequency, model, and statistical methodologies-based texture features. They assessed the proposed deep texture analysis method as well as the applicability of the dimensionality reduction techniques and the correlation concept between the features-based fusion on the challenging PolyU, CASIA and IIT-Delhi Palmprint databases. The experimental results show that the fusion of different texture types using the correlation concept for palmprint modality identification leads to promising results.

Relationships of Image Texture Properties with Chewing Activity and Mechanical Properties during Mastication of Bread

2016 ◽  
Vol 12 (4) ◽  
pp. 311-321
Author(s):  
Qian Mao ◽  
Yonghai Sun ◽  
Jumin Hou ◽  
Libo Yu ◽  
Yang Liu ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Texture Analysis ◽  
Texture Features ◽  
Image Texture ◽  
Emg Activity ◽  
Analysis Method ◽  
Texture Properties ◽  
Property Changes ◽  
Occurrence Matrix ◽  
Chewing Activity

Abstract The objective of this study was to investigate the relationships of image texture properties with chewing behaviors, and mechanical properties during mastication of bread. Gray-level gradient co-occurrence matrix (GGCM) was used to process the images of boluses. The chewing behaviors were recorded by electromyography (EMG), and the mechanical properties were measured by texture analyzer. The results showed that among the texture features, the inverse difference moment (IDMGGCM) was selected as the main parameter to describe the decomposition of boluses. IDMGGCM was positively related to the weight gain (r = 0.865, p < 0.01), negatively correlated with hardness (r = –0.835, p <0.01) and EMG activity per cycle (r = –0.767, p < 0.01). GGCM is an effective texture analysis method that could correctly identify 70.1–80.8 % of food bolus images to the corresponding chewing cycles. This study provided a new clue for texture analysis of bread bolus images and offered data revealing the bolus property changes during the mastication of bread.

A Novel Texture Analysis Method Based on Reverse Biorthogonal Wavelet and Co-Occurrence Matrix Applied for Classification of Hepatocellular Carcinoma and Hepatic Hemangioma

2018 ◽  
Vol 8 (9) ◽  
pp. 1835-1843 ◽  
Author(s):  
Jia-Jun Qiu ◽  
Yue Wu ◽  
Bei Hui ◽  
Jia Chen ◽  
Lin Ji ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Texture Analysis ◽  
Texture Features ◽  
Ct Images ◽  
Hepatic Hemangioma ◽  
Gray Level ◽  
Biorthogonal Wavelet ◽  
Analysis Method ◽  
Enhanced Ct ◽  
Occurrence Matrix

Purpose: To explore the feasibility of classifying hepatocellular carcinoma (HCC) and hepatic hemangioma (HEM) using texture features of non-enhanced computed tomography (CT) images, especially to investigate the effectiveness of a novel texture analysis method based on the combination of wavelet and co-occurrence matrix. Methods: 269 patients were retrospectively analyzed, including 129 HCCs and 140 HEMs. We cropped tumor regions of interest (ROIs) on non-enhanced CT images, and then used four texture analysis methods to extract quantitative data of the ROIs: gray-level histogram (GLH), gray-level co-occurrence matrix (GLCM), reverse biorthogonal wavelet transform (RBWT), and reverse biorthogonal wavelet co-occurrence matrix (RBCM). The RBCM was a novel method proposed in this study that combined wavelet transform and co-occurrence matrix. It discretized wavelet coefficient matrices based on the statistical characteristics of the training set. Thus, four sets of texture features were obtained. We then conducted classification studies using support vector machine on each set of texture features. 10-fold cross training and testing were used in the classifications, and their results were evaluated and compared. In addition, we tested the significant differences in the texture features of the RBCM method and explored the possible relationships between textures and lesion types. Results: The RBCM method achieved the best classification performance: its average accuracy was 82.14%; its average AUC (area under the receiver operating characteristic curve) was 0.8423. In addition, using the methods of GLH, GLCM, and RBWT, their average accuracies were 75.81%, 78.79%, and 78.8%, respectively. Conclusions: It indicates that the developed texture analysis methods are rewarding for computer-aided diagnosis of HCC and HEM based on non-enhanced CT images. Furthermore, the distinguishing ability of the proposed RBCM method is more pronounced.

Dimensionality reduction techniques to analyze heating systems in buildings

2015 ◽  
Vol 294 ◽  
pp. 553-564 ◽  
Author(s):  
Manuel Domínguez ◽  
Serafín Alonso ◽  
Antonio Morán ◽  
Miguel A. Prada ◽  
Juan J. Fuertes
Keyword(s):  
Dimensionality Reduction ◽  
Heating Systems ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

scholarly journals T1 mapping-derived signature of myocardial involvement in idiopathic inflammatory myopathy compared to acute viral myocarditis: a texture-based analysis

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
E Bollache ◽  
AT Huber ◽  
J Lamy ◽  
E Afari ◽  
TM Bacoyannis ◽  
...  
Keyword(s):  
Contrast Agent ◽  
Texture Analysis ◽  
T1 Mapping ◽  
Inflammatory Myopathy ◽  
Viral Myocarditis ◽  
Texture Features ◽  
Idiopathic Inflammatory Myopathy ◽  
Post Contrast ◽  
Myocardial T1 ◽  
Myocardial Involvement

Abstract Funding Acknowledgements Type of funding sources: None. Background. Recent studies revealed the ability of MRI T1 mapping to characterize myocardial involvement in both idiopathic inflammatory myopathy (IIM) and acute viral myocarditis (AVM), as compared to healthy controls. However, neither myocardial T1 nor T2 maps were able to discriminate between IIM and AVM patients, when considering conventional myocardial mean values and derived indices such as lambda and extracellular volume. Purpose. To investigate the ability of T1 mapping-derived texture analysis to differentiate IIM from AVM. Methods. Forty patients, 20 with IIM (51 ± 17 years, 9 men) and 20 with AVM (34 ± 13 years, 16 men) underwent 1.5T MRI T1 mapping using a modified Look-Locker inversion-recovery sequence before and 15 minutes after injection of a gadolinium contrast agent. After manual delineation of endocardial and epicardial borders and co-registration of all inversion time images, native and post-contrast T1 maps were estimated. Myocardial texture analysis was performed on native T1 maps. Textural features such as: autocorrelation, contrast, dissimilarity, energy and sum entropy were used to build a least squares-based linear regression model. Finally, receiver operating characteristic (ROC) analysis was used to investigate the ability of such texture features score to classify IIM vs. AVM patients, compared to the performance of mean myocardial T1. A Wilcoxon rank-sum test was also used to test difference significance between groups. Results. Both native and post-contrast mean myocardial T1 values were comparable between IIM (native: 1022 ± 43 ms; post-contrast: 319 ± 44 ms) and AVM (1056 ± 59 ms, p = 0.07; 318 ± 35 ms, p = 0.90, respectively) groups. Results of ROC analyses are provided in the Table, indicating that a better discrimination between IIM and AVM patients was obtained when using texture features, with higher AUC and accuracy than mean T1 values (Figure). Conclusion. Texture analysis derived from MRI T1 maps without contrast agent injection was able to discriminate between IIM and AVM with higher accuracy, sensitivity and specificity than conventional T1 indices. Such analysis could provide a useful myocardial signature to help diagnose and manage cardiac alterations associated with IIM in patients presenting with myocarditis and primarily suspected of AVM. Table Area under curve (AUC) Accuracy Sensitivity Specificity Native T1 0.67 0.70 0.65 0.75 Post-contrast T1 0.49 0.60 0.25 0.95 Texture features score 0.85 0.82 0.90 0.75 ROC analyses for classification between IIM and AVM patients Abstract Figure

scholarly journals MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer

2021 ◽  
Vol 10 (2) ◽  
pp. 237
Author(s):  
Jung Hyun Park ◽  
Byung Se Choi ◽  
Jung Ho Han ◽  
Chae-Yong Kim ◽  
Jungheum Cho ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Brain Metastases ◽  
Stereotactic Radiosurgery ◽  
Texture Analysis ◽  
Multivariable Analysis ◽  
Texture Features ◽  
Progression Free Survival ◽  
Clinical Parameter ◽  
Imaging Biomarker ◽  
Local Progression

This study aims to evaluate the utility of texture analysis in predicting the outcome of stereotactic radiosurgery (SRS) for brain metastases from lung cancer. From 83 patients with lung cancer who underwent SRS for brain metastasis, a total of 118 metastatic lesions were included. Two neuroradiologists independently performed magnetic resonance imaging (MRI)-based texture analysis using the Imaging Biomarker Explorer software. Inter-reader reliability as well as univariable and multivariable analyses were performed for texture features and clinical parameters to determine independent predictors for local progression-free survival (PFS) and overall survival (OS). Furthermore, Harrell’s concordance index (C-index) was used to assess the performance of the independent texture features. The primary tumor histology of small cell lung cancer (SCLC) was the only clinical parameter significantly associated with local PFS in multivariable analysis. Run-length non-uniformity (RLN) and short-run emphasis were the independent texture features associated with local PFS. In the non-SCLC (NSCLC) subgroup analysis, RLN and local range mean were associated with local PFS. The C-index of independent texture features was 0.79 for the all-patients group and 0.73 for the NSCLC subgroup. In conclusion, texture analysis on pre-treatment MRI of lung cancer patients with brain metastases may have a role in predicting SRS response.

scholarly journals Hepatocellular Carcinoma Drug-Eluting Bead Transarterial Chemoembolization (DEB-TACE): Outcome Analysis Using a Model Based On Pre-Treatment CT Texture Features

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 956
Author(s):  
Marcello Andrea Tipaldi ◽  
Edoardo Ronconi ◽  
Elena Lucertini ◽  
Miltiadis Krokidis ◽  
Marta Zerunian ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Clinical Data ◽  
Transarterial Chemoembolization ◽  
Prognostic Value ◽  
Texture Features ◽  
Response To Therapy ◽  
Survival Prediction ◽  
Ct Examination ◽  
Drug Eluting ◽  
Pre Treatment

(1) Introduction and Aim: The aim of this study is to investigate the prognostic value, in terms of response and survival, of CT-based radiomics features for patients with HCC undergoing drug-eluting beads transarterial chemoembolization (DEB-TACE). (2) Materials and Methods: Pre-treatment CT examinations of 50 patients with HCC, treated with DEB-TACE were manually segmented to obtain the tumor volumetric region of interest, extracting radiomics features with TexRAD. Response to therapy evaluation was performed basing on post-procedural CT examination compared to pre-procedural CT, using modified RECIST criteria for HCC. The prognostic value of texture analysis was evaluated, investigating the correlation between radiomics features, response to therapy and overall survival. Three models based on texture and clinical variables and a combination of them were finally built; (3) Results: Entropy, skewness, MPP and kurtosis showed a significant correlation with complete response (CR) to TACE (all p < 0.001). A predictive model to identify patients with a high and low probability of CR was evaluated with an ROC curve, with an AUC of 0.733 (p < 0.001). The three models built for survival prediction yielded an HR of 2.19 (95% CI: 2.03–2.35) using texture features, of 1.7 (95% CI: 1.54–1.9) using clinical data and of 4.61 (95% CI: 4.24–5.01) combining both radiomics and clinical data (all p < 0.0001). (4) Conclusion: Texture analysis based on pre-treatment CT examination is associated with response to therapy and survival in patients with HCC undergoing DEB-TACE, especially if combined with clinical data.

scholarly journals A generalization of t-SNE and UMAP to single-cell multimodal omics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Van Hoan Do ◽  
Stefan Canzar
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Cell Types ◽  
High Dimensional ◽  
Omics Data ◽  
Relative Contribution ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Concise Representation ◽  
Cellular Identity

AbstractEmerging single-cell technologies profile multiple types of molecules within individual cells. A fundamental step in the analysis of the produced high-dimensional data is their visualization using dimensionality reduction techniques such as t-SNE and UMAP. We introduce j-SNE and j-UMAP as their natural generalizations to the joint visualization of multimodal omics data. Our approach automatically learns the relative contribution of each modality to a concise representation of cellular identity that promotes discriminative features but suppresses noise. On eight datasets, j-SNE and j-UMAP produce unified embeddings that better agree with known cell types and that harmonize RNA and protein velocity landscapes.

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