scholarly journals Performances of Whole Tumor Texture Analysis Based on MRI: Predicting Preoperative T Stage of Rectal Carcinomas

2021 ◽  
Vol 11 ◽  
Author(s):  
Jia You ◽  
Jiandong Yin
Keyword(s):  
Texture Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Texture Features ◽  
Roc Curves ◽  
Support Vector ◽  
Classification Models ◽  
Entire Volume ◽  
First Order ◽  
T Stage ◽  
Combined Application

ObjectiveTo determine whether there is a correlation between texture features extracted from high-resolution T2-weighted imaging (HR-T2WI) or apparent diffusion coefficient (ADC) maps and the preoperative T stage (stages T1–2 versus T3–4) in rectal carcinomas.Materials and MethodsOne hundred and fifty four patients with rectal carcinomas who underwent preoperative HR-T2WI and diffusion-weighted imaging were enrolled. Patients were divided into training (n = 89) and validation (n = 65) cohorts. 3D Slicer was used to segment the entire volume of interest for whole tumors based on HR-T2WI and ADC maps. The least absolute shrinkage and selection operator (LASSO) was performed to select feature. The significantly difference was tested by the independent sample t-test and Mann-Whitney U test. The support vector machine (SVM) model was used to develop classification models. The correlation between features and T stage was assessed by Spearman’s correlation analysis. Multivariate logistic regression analysis was performed to identify independent predictors of tumor invasion. The performance of classifiers was evaluated by the receiver operating characteristic (ROC) curves.ResultsThe wavelet HHH NGTDM strength (RS = -0.364, P < 0.001) from HR-T2WI was an independent predictor of stage T3–4 tumors. The shape maximum 2D diameter column (RS = 0.431, P < 0.001), log σ = 5.0 mm 3D first-order maximum (RS = 0.276, P = 0.009), and log σ = 5.0 mm 3D first-order interquartile range (RS = -0.229, P = 0.032) from ADC maps were independent predictors. In training cohorts, the classification models from HR-T2WI, ADC maps and the combination of two achieved the area under the ROC curves (AUCs) of 0.877, 0.902 and 0.941, with the accuracy of 79.78%, 89.86% and 89.89%, respectively. In validation cohorts, the three models achieved AUCs of 0.845, 0.881 and 0.910, with the accuracy of 78.46%, 83.08% and 87.69%, respectively.ConclusionsTexture analysis based on ADC maps shows more potential than HR-T2WI in identifying preoperative T stage in rectal carcinomas. The combined application of HR-T2WI and ADC maps may help to improve the accuracy of preoperative diagnosis of rectal cancer invasion.

scholarly journals Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

2021 ◽  
Vol 11 ◽  
Author(s):  
Bin Zhang ◽  
Lirong Song ◽  
Jiandong Yin
Keyword(s):  
Texture Features ◽  
Roc Curves ◽  
Breast Tumors ◽  
Support Vector ◽  
Feature Subset ◽  
Classification Models ◽  
Training Cohort ◽  
Dce Mri ◽  
Malignant Breast ◽  
Tumor Area

PurposeTo evaluate the potential of the texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intratumoral subregions to distinguish benign from malignant breast tumors.Materials and MethodsA total of 299 patients with pathologically verified breast tumors who underwent breast DCE-MRI examination were enrolled in this study, including 124 benign cases and 175 malignant cases. The whole tumor area was semi-automatically segmented on the basis of subtraction images of DCE-MRI in Matlab 2018b. According to the time to peak of the contrast agent, the whole tumor area was partitioned into three subregions: early, moderate, and late. A total of 467 texture features were extracted from the whole tumor area and the three subregions, respectively. Patients were divided into training (n = 209) and validation (n = 90) cohorts by different MRI scanners. The least absolute shrinkage and selection operator (LASSO) method was used to select the optimal feature subset in the training cohort. The Kolmogorov-Smirnov test was first performed on texture features selected by LASSO to test whether the samples followed a normal distribution. Two machine learning methods, decision tree (DT) and support vector machine (SVM), were used to establish classification models with a 10-fold cross-validation method. The performance of the classification models was evaluated with receiver operating characteristic (ROC) curves.ResultsIn the training cohort, the areas under the ROC curve (AUCs) for the DT_Whole model and SVM_Whole model were 0.744 and 0.806, respectively. In contrast, the AUCs of the DT_Early model (P = 0.004), DT_Late model (P = 0.015), SVM_Early model (P = 0.002), and SVM_Late model (P = 0.002) were significantly higher: 0.863 (95% CI, 0.808–0.906), 0.860 (95% CI, 0.806–0.904), 0.934 (95% CI, 0.891–0.963), and 0.921 (95% CI, 0.876–0.954), respectively. The SVM_Early model and SVM_Late model achieved better performance than the DT_Early model and DT_Late model (P = 0.003, 0.034, 0.008, and 0.026, respectively). In the validation cohort, the AUCs for the DT_Whole model and SVM_Whole model were 0.670 and 0.708, respectively. In comparison, the AUCs of the DT_Early model (P = 0.006), DT_Late model (P = 0.043), SVM_Early model (P = 0.001), and SVM_Late model (P = 0.007) were significantly higher: 0.839 (95% CI, 0.747–0.908), 0.784 (95% CI, 0.601–0.798), 0.890 (95% CI, 0.806–0.946), and 0.865 (95% CI, 0.777–0.928), respectively.ConclusionThe texture features from intratumoral subregions of breast DCE-MRI showed potential in identifying benign and malignant breast tumors.

scholarly journals Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Magda Marcon ◽  
Alexander Ciritsis ◽  
Cristina Rossi ◽  
Anton S. Becker ◽  
Nicole Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Texture Analysis ◽  
Area Under The Curve ◽  
Texture Features ◽  
Breast Ultrasound ◽  
Support Vector ◽  
Maximum Area ◽  
Svm Algorithm ◽  
Automated Breast Ultrasound

Abstract Background Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification. Methods This ethically approved retrospective pilot study included 54 women with benign (n = 38) and malignant (n = 32) solid breast lesions who underwent ABUS. After manual region of interest placement along the lesions’ margin as well as the surrounding fat and glandular breast tissue, 47 texture features (TFs) were calculated for each category. Statistical analysis (ANOVA) and a support vector machine (SVM) algorithm were applied to the texture feature to evaluate the accuracy in distinguishing (i) lesions versus normal tissue and (ii) benign versus malignant lesions. Results Skewness and kurtosis were the only TF significantly different among all the four categories (p < 0.000001). In subsets (i) and (ii), a maximum area under the curve of 0.86 (95% confidence interval [CI] 0.82–0.88) for energy and 0.86 (95% CI 0.82–0.89) for entropy were obtained. Using the SVM algorithm, a maximum area under the curve of 0.98 for both subsets was obtained with a maximum accuracy of 94.4% in subset (i) and 90.7% in subset (ii). Conclusions TA in combination with ML might represent a useful diagnostic tool in the evaluation of breast imaging findings in ABUS. Applying ML techniques to TFs might be superior compared to the analysis of single TF.

Application of texture analysis based on T2-weighted magnetic resonance images in discriminating Gleason scores of prostate cancer

2020 ◽  
Vol 28 (6) ◽  
pp. 1207-1218
Author(s):  
Ruigen Pan ◽  
Xueli Yang ◽  
Zhenyu Shu ◽  
Yifeng Gu ◽  
Lihua Weng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Texture Analysis ◽  
Gleason Score ◽  
Texture Features ◽  
Roc Curves ◽  
Magnetic Resonance Images ◽  
Diagnostic Efficiency ◽  
Energy Value ◽  
Tumor Region

OBJECTIVE: To investigate the value of texture analysis in magnetic resonance images for the evaluation of Gleason scores (GS) of prostate cancer. METHODS: Sixty-six prostate cancer patients are retrospective enrolled, which are divided into five groups namely, GS = 6, 3 + 4, 4 + 3, 8 and 9–10 according to postoperative pathological results. Extraction and analysis of texture features in T2-weighted MR imaging defined tumor region based on pathological specimen after operation are performed by texture software OmniKinetics. The values of texture are analyzed by single factor analysis of variance (ANOVA), and Spearman correlation analysis is used to study the correlation between the value of texture and Gleason classification. Receiver operating characteristic (ROC) curve is then used to assess the ability of applying texture parameters to predict Gleason score of prostate cancer. RESULTS: Entropy value increases and energy value decreases as the elevation of Gleason score, both with statistical difference among five groups (F = 10.826, F = 2.796, P < 0.05). Energy value of group GS = 6 is significantly higher than that of groups GS = 8 and 9–10 (P < 0.005), which is similar between three groups (GS = 3 + 4, 8 and 9–10). The entropy and energy values correlate with GS (r = 0.767, r = –0.692, P < 0.05). Areas under ROC curves (AUC) of combination of entropy and energy are greater than that of using energy alone between groups GS = 6 and ≥7. Analogously, AUC of combination of entropy and energy are significantly higher than that of using entropy alone between groups GS≤3 + 4 and ≥4 + 3, as well as between groups GS≤4 + 3 and ≥8. CONCLUSION: Texture analysis on T2-weighted images of prostate cancer can evaluate Gleason score, especially using the combination of entropy and energy rendering better diagnostic efficiency.

MULTI-RESOLUTION CROWD DENSITY ESTIMATION BASED ON TEXTURE ANALYSIS AND LEARNING FROM DEMONSTRATION

2007 ◽  
Vol 04 (01) ◽  
pp. 1-14 ◽  
Author(s):  
GUOYUAN LIANG ◽  
KA KEUNG LEE ◽  
YANGSHENG XU
Keyword(s):  
Texture Analysis ◽  
Density Estimation ◽  
Texture Feature ◽  
Texture Features ◽  
Input Image ◽  
Support Vector ◽  
Surveillance Systems ◽  
Regression Problem ◽  
Crowd Density Estimation ◽  
Crowd Density

Crowd density estimation is very important for intelligent surveillance systems in public places. This paper presents an automatic method of estimating crowd density using texture analysis and machine learning. First the crowd scene is modeled as a series of multi-resolution image cells based on perspective projection. The cell size is normalized to obtain a uniform representation of texture features. Then the feature vectors of textures are extracted from each input image cell and the support vector machine (SVM) method is utilized to solve the regression problem for calculating the crowd density. In order to diminish the instability of texture feature measurements, a technique of searching the extrema in the Harris–Laplacian space is applied. Finally, the SVM method is used again to detect some abnormal situations caused by the changes in density distribution. Experiments on real crowd videos show the effectiveness of the proposed system.

Static Signature Verification Based on Texture Analysis Using Support Vector Machine

2017 ◽  
Vol 8 (2) ◽  
pp. 22-32 ◽  
Author(s):  
Subhash Chandra ◽  
Sushila Maheshkar
Keyword(s):  
Support Vector Machine ◽  
Texture Features ◽  
Signature Verification ◽  
Support Vector ◽  
Gray Level ◽  
Global Level ◽  
First Order ◽  
Svm Model ◽  
Level Information ◽  
Occurrence Matrix

Off-line hand written signature verification performs at the global level of image. It processes the gray level information in the image using statistical texture features. The textures and co-occurrence matrix are analyzed for features extraction. A first order histogram is also processed to reduce different writing ink pens used by signers. Samples of signature are trained with SVM model where random and skilled forgeries have been used for testing. Experimental results are performed on two databases: MCYT-75 and GPDS Synthetic Signature Corpus.

scholarly journals Texture Analysis of 18F-FDG PET/CT for Differential Diagnosis Spinal Metastases

2021 ◽  
Vol 7 ◽  
Author(s):  
Xin Fan ◽  
Han Zhang ◽  
Yuzhen Yin ◽  
Jiajia Zhang ◽  
Mengdie Yang ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Diagnostic Performance ◽  
Spinal Metastases ◽  
Texture Features ◽  
Classification Model ◽  
Support Vector ◽  
Kaplan Meier ◽  
Texture Parameters ◽  
Pet Ct ◽  
Fdg Pet Ct

Purpose: To evaluate the value of texture analysis for the differential diagnosis of spinal metastases and to improve the diagnostic performance of 2-deoxy-2-[fluorine-18]fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) for spinal metastases.Methods: This retrospective analysis of patients who underwent PET/CT between December 2015 and January 2020 at Shanghai Tenth People's Hospital due to high FDG uptake lesions in the spine included 45 cases of spinal metastases and 44 cases of benign high FDG uptake lesions in the spine. The patients were randomly divided into a training group of 65 and a test group of 24. Seventy-two PET texture features were extracted from each lesion, and the Mann-Whitney U-test was used to screen the training set for texture parameters that differed between the two groups in the presence or absence of spinal metastases. Then, the diagnostic performance of the texture parameters was screened out by receiver operating characteristic (ROC) curve analysis. Texture parameters with higher area under the curve (AUC) values than maximum standardized uptake values (SUVmax) were selected to construct classification models using logistic regression, support vector machines, and decision trees. The probability output of the model with high classification accuracy in the training set was used to compare the diagnostic performance of the classification model and SUVmax using the ROC curve. For all patients with spinal metastases, survival analysis was performed using the Kaplan-Meier method and Cox regression.Results: There were 51 texture parameters that differed meaningfully between benign and malignant lesions, of which four had higher AUC than SUVmax. The texture parameters were input to build a classification model using logistic regression, support vector machine, and decision tree. The accuracy of classification was 87.5, 83.34, and 75%, respectively. The accuracy of the manual diagnosis was 84.27%. Single-factor survival analysis using the Kaplan-Meier method showed that intensity was correlated with patient survival.Conclusion: Partial texture features showed higher diagnostic value for spinal metastases than SUVmax. The machine learning part of the model combined with the texture parameters was more accurate than manual diagnosis. Therefore, texture analysis may be useful to assist in the diagnosis of spinal metastases.

scholarly journals Texture features in the Shearlet domain for histopathological image classification

2020 ◽  
Vol 20 (S14) ◽  
Author(s):  
Sadiq Alinsaif ◽  
Jochen Lang
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Texture Features ◽  
Feature Space ◽  
Feature Representation ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Computational Techniques ◽  
Histopathological Images

Abstract Background A various number of imaging modalities are available (e.g., magnetic resonance, x-ray, ultrasound, and biopsy) where each modality can reveal different structural aspects of tissues. However, the analysis of histological slide images that are captured using a biopsy is considered the gold standard to determine whether cancer exists. Furthermore, it can reveal the stage of cancer. Therefore, supervised machine learning can be used to classify histopathological tissues. Several computational techniques have been proposed to study histopathological images with varying levels of success. Often handcrafted techniques based on texture analysis are proposed to classify histopathological tissues which can be used with supervised machine learning. Methods In this paper, we construct a novel feature space to automate the classification of tissues in histology images. Our feature representation is to integrate various features sets into a new texture feature representation. All of our descriptors are computed in the complex Shearlet domain. With complex coefficients, we investigate not only the use of magnitude coefficients, but also study the effectiveness of incorporating the relative phase (RP) coefficients to create the input feature vector. In our study, four texture-based descriptors are extracted from the Shearlet coefficients: co-occurrence texture features, Local Binary Patterns, Local Oriented Statistic Information Booster, and segmentation-based Fractal Texture Analysis. Each set of these attributes captures significant local and global statistics. Therefore, we study them individually, but additionally integrate them to boost the accuracy of classifying the histopathology tissues while being fed to classical classifiers. To tackle the problem of high-dimensionality, our proposed feature space is reduced using principal component analysis. In our study, we use two classifiers to indicate the success of our proposed feature representation: Support Vector Machine (SVM) and Decision Tree Bagger (DTB). Results Our feature representation delivered high performance when used on four public datasets. As such, the best achieved accuracy: multi-class Kather (i.e., 92.56%), BreakHis (i.e., 91.73%), Epistroma (i.e., 98.04%), Warwick-QU (i.e., 96.29%). Conclusions Our proposed method in the Shearlet domain for the classification of histopathological images proved to be effective when it was investigated on four different datasets that exhibit different levels of complexity.

scholarly journals CT Texture Analysis of Pulmonary Neuroendocrine Tumors—Associations with Tumor Grading and Proliferation

2021 ◽  
Vol 10 (23) ◽  
pp. 5571
Author(s):  
Hans-Jonas Meyer ◽  
Jakob Leonhardi ◽  
Anne Kathrin Höhn ◽  
Johanna Pappisch ◽  
Hubert Wirtz ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Neuroendocrine Tumors ◽  
Texture Features ◽  
Positive Association ◽  
Proliferation Index ◽  
Imaging Biomarkers ◽  
Tumor Grading ◽  
Ki 67 ◽  
Discrimination Analysis ◽  
First Order

Texture analysis derived from computed tomography (CT) might be able to provide clinically relevant imaging biomarkers and might be associated with histopathological features in tumors. The present study sought to elucidate the possible associations between texture features derived from CT images with proliferation index Ki-67 and grading in pulmonary neuroendocrine tumors. Overall, 38 patients (n = 22 females, 58%) with a mean age of 60.8 ± 15.2 years were included into this retrospective study. The texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. In discrimination analysis, “S(1,1)SumEntrp” was significantly different between typical and atypical carcinoids (mean 1.74 ± 0.11 versus 1.79 ± 0.14, p = 0.007). The correlation analysis revealed a moderate positive association between Ki-67 index with the first order parameter kurtosis (r = 0.66, p = 0.001). Several other texture features were associated with the Ki-67 index, the highest correlation coefficient showed “S(4,4)InvDfMom” (r = 0.59, p = 0.004). Several texture features derived from CT were associated with the proliferation index Ki-67 and might therefore be a valuable novel biomarker in pulmonary neuroendocrine tumors. “Sumentrp” might be a promising parameter to aid in the discrimination between typical and atypical carcinoids.

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

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