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•Bidimensional multiscale entropy analysis utilized to capture textural variations in lateral ventricles.•Entropy features are able to discriminate AD conditions at multiple spatial ...scales.•Results achieve high performance in detecting the MCI and AD stages.•Texture analysis on lateral ventricles found feasible for differentiating MCI stage.
Alzheimer’s Disease (AD) is a progressive fatal neurodegenerative disorder that causes cognitive decline in affected people. Image processing of brain MR images can aid in identifying significant imaging biomarkers for detection of AD and its prodromal stage Mild Cognitive Impairment (MCI). Bidimensional multiscale entropy-based texture analysis is a new approach to quantify the textural variations in images at multiple scales. This work is based on the application of bidimensional multiscale entropy for analyzing AD induced textural alterations in lateral ventricles of the brain MR images. For this T1 weighted MR brain images of normal, MCI and AD subjects are obtained from public database. Lateral ventricles (LV) are delineated using reaction–diffusion level set technique from transaxial image slice with high accuracy. Bidimensional multiscale entropy is then applied on segmented LV to extract entropy features at multiple image scales and complexity indices are evaluated for each scale to study textural variations. The parameters such as tolerance factor, window lengths and scales for computation of multiscale entropy for significant differentiation amongst the healthy and diseased subjects are experimentally evaluated. The obtained entropy values from healthy subjects are observed to be significantly lower from the pathological subjects across scales. Classification with extracted features using a linear discriminant classifier achieves an accuracy of 80.1% and 87.6% for Normal vs MCI and Normal vs AD classes, respectively. The proposed multiscale entropy-based approach captures the textural alterations in lateral ventricles of brain MR images and furthermore, can be used as automated tool for early diagnosis of AD.
Fruit by-products are a valuable source of ingredients, in the formulation of what is known by “upcycled foods”. Orange pomace, a by-product of orange juice industry, is a dietary fibre source. In ...this work, a powdered ingredient with soluble fibre obtained from orange pomace was used as replacement of inulin in the formulation of source of fibre “flan” like puddings. Four different formulations were analysed using Flash Profile and instrumental texture: 100% inulin, 70% inulin: 30% orange fibre, 30% inulin: 70% orange fibre, 100% orange fibre. The replacement of 30% of pudding's total fibre with the new ingredient helped to improve the texture and general appearance of the dessert. Greater percentages imparted non-desirable flavour attributes, such as bitterness and acidity. The use of this ingredient as a replacement of commercial inulin in the formulation of source of fibre puddings is possible. However, further research is needed to reduce the off flavours.
Purpose
The study evaluated the usefulness of magnetic resonance imaging (MRI) texture parameters in differentiating clear cell renal carcinoma (CC-RCC) from non-clear cell carcinoma (NC-RCC) and in ...the histological grading of CC-RCC.
Materials and methods
After institutional ethical approval, this retrospective study analyzed 33 patients with 34 RCC masses (29 CC-RCC and five NC-RCC; 19 low-grade and 10 high-grade CC-RCC), who underwent MRI between January 2011 and December 2012 on a 1.5-T scanner (Avanto, Siemens, Erlangen, Germany). The MRI protocol included T2-weighted imaging (T2WI), diffusion-weighted imaging DWI; at b 0, 500 and 1000 s/mm
2
with apparent diffusion coefficient (ADC) maps and T1-weighted pre and postcontrast corticomedullary (CM) and nephrographic (NG) phase acquisition. MR texture analysis (MRTA) was performed using the TexRAD research software (Feedback Medical Ltd., Cambridge, UK) by a single reader who placed free-hand polygonal region of interest (ROI) on the slice showing the maximum viable tumor. Filtration histogram-based texture analysis was used to generate six first-order statistical parameters mean intensity, standard deviation (SD), mean of positive pixels (MPP), entropy, skewness and kurtosis at five spatial scaling factors (SSF) as well as on the unfiltered image. Mann–Whitney test was used to compare the texture parameters of CC-RCC versus NC-RCC, and high-grade versus low-grade CC-RCC.
P
value < 0.05 was considered significant. A 3-step feature selection was used to obtain the best texture metrics for each MRI sequence and included the receiver-operating characteristic (ROC) curve analysis and Pearson’s correlation test.
Results
The best performing texture parameters in differentiating CC-RCC from NC-RCC for each sequence included (area under the curve in parentheses): entropy at SSF 4 (0.807) on T2WI, SD at SSF 4 (0.814) on DWI b500, SD at SSF 6 (0.879) on DWI b1000, mean at SSF 0 (0.848) on ADC, skewness at SSF 2 (0.854) on T1WI and skewness at SSF 3 (0.908) on CM phase. In differentiating high from low-grade CC-RCC, the best parameters were: entropy at SSF 6 (0.823) on DWI b1000, mean at SSF 3 (0.889) on CM phase and MPP at SSF 5 (0.870) on NG phase.
Conclusion
Several MR texture parameters showed excellent diagnostic performance (AUC > 0.8) in differentiating CC-RCC from NC-RCC, and high-grade from low-grade CC-RCC. MRTA could serve as a useful non-invasive tool for this purpose.
Alkali-Silica Reaction (ASR), commonly known as ‘concrete cancer,’ is an expansive reaction occurring over time between aggregate constituents and alkaline hydroxides from cement. As a destructive ...phenomenon, the need to detect the onset of ASR in concrete structures to ensure their long-term durability and structural integrity is thus evidenced. In the structural health monitoring field, vision-based approaches have been found to be viable, fast, and cost-effective in diagnosing numerous types of cracks using physical attributes and surface patterns. However, achieving high accuracy in detecting ASR cracks using traditional visual inspection techniques is challenging and time-consuming. Inspired by artificial intelligence technology, this paper proposes and evaluates a two-phase computer vision procedure for detecting and classifying ASR cracks utilizing a collection of ASR images recorded from several bridges in Queensland, Australia. In the first phase, the procedure compares common pre-trained CNN models to investigate their capability in classifying ASR cracks and to select the best-performed model. In the second phase, a novel Feature Enhancement Process (FEP) was first proposed to increase the contrast between ASR cracks and the heavily textured backgrounds within the images. Next, to better highlight the ASR crack features, the feature-adjusted images are processed further through different texture analysis algorithms including: (i) Texture Morphology, (ii) Adaptive thresholding, and (iii) Local range filtering. The study shows that the proposed FEP can improve the ASR crack classification accuracy of InceptionV3, which is the best CNN model selected from Phase 1, from 90.9% to 92.48%. Furthermore, by combining FEP with texture morphology, a robust two-stage tool for assessing ASR cracks can be made with an impressive validation accuracy of 94.07%. This research contributes towards the application of novel AI deep learning technology in providing cost-effective autonomous ASR crack classification tools to support the owners and managers of civil public works assets and other constructed infrastructures.
Background
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) improves motor deficits in advanced Parkinson's disease (PD) patients, but the degree of motor improvement varies across ...individuals. PD pathology involves the changes of iron spatial distribution in the deep gray matter nuclei.
Purpose
To explore the relationship between the iron spatial distribution and motor improvement among PD patients who underwent STN‐DBS surgery in three regions: substantia nigra (SN), STN, and dentate nucleus (DN).
Study Type
Prospective.
Subjects
Forty PD patients (49.7 ± 8.8 years, 22 males/18 females) who underwent bilateral STN‐DBS.
Field Strength/Sequence
A 3 T preoperative three‐dimensional spoiled bipolar‐readout multi‐echo gradient recalled echo and two‐dimensional fast spin echo sequences.
Assessment
Movement Disorder Society‐sponsored revision of the Unified Parkinson's Disease Rating Scale part III (MDS‐UPDRS III) scores were assessed 2–3 days before and 6 months after STN‐DBS. The first‐ and second‐order texture features in regions of interest were measured on susceptibility maps.
Statistical Tests
Intraclass correlation coefficient was used to determine the consistency of the region of interest volumes delineated by the two raters. Pearson or Spearman's correlation coefficients were used to assess the relationship between motor improvement after DBS and texture features. A P‐value <0.05 was considered statistically significant.
Results
MDS‐UPDRS III scores were reduced by 59.9% after STN‐DBS in 40 PD patients. Motor improvement correlated with second‐order texture parameters in the SN including angular second moment (r = −0.449), correlation (rho = 0.326), sum of squares (r = 0.402), sum of entropy (rho = 0.421), and entropy (r = 0.410). Additionally, DBS outcome negatively correlated with mean susceptibility values in the DN (r = −0.400).
Data Conclusion
PD patients with a more homogeneous iron distribution throughout the SN or a higher iron concentration in the DN responded worse to STN‐DBS.
Level of Evidence
2
Technical Efficacy
Stage 1
Objectives/Hypothesis
To determine if commonly used radiomics features have an association with histological findings in vestibular schwannomas (VS).
Study Design
Retrospective case‐series.
Methods
...Patients were selected from an internal database of those who had a gadolinium‐enhanced T1‐weighted MRI scan captured prior to surgical resection of VS. Texture features from the presurgical magnetic resonance image (MRI) were extracted, and pathologists examined the resected tumors to assess for the presence of mucin, lymphocytes, necrosis, and hemosiderin and used a validated computational tool to determine cellularity. Sensitivity, specificity, and positive likelihood ratios were also computed for selected features using the Youden index to determine the optimal cut‐off value.
Results
A total of 45 patients were included. We found significant associations between multiple MRI texture features and the presence of mucin, lymphocytes, hemosiderin, and cellularity. No significant associations between MRI texture features and necrosis were identified. We were able to identify significant positive likelihood ratios using Youden index cut‐off values for mucin (2.3; 95% CI 1.2–4.3), hemosiderin (1.5; 95% CI 1.04–2.1), lymphocytes (3.8; 95% CI 1.2–11.7), and necrosis (1.5; 95% CI 1.1–2.2).
Conclusions
MRI texture features are associated with underlying histology in VS.
Level of Evidence
3 Laryngoscope, 131:E2000–E2006, 2021
Dan Zhang,1,2 Xiaojiao Li,1,2 Liang Lv,1 Jiayi Yu,1,2 Chao Yang,1,2 Hua Xiong,1,2 Ruikun Liao,1,2 Bi Zhou,1,2 Xianlong Huang,1 Xiaoshuang Liu,3 Zhuoyue Tang1,2 1Department of Radiology, Chongqing ...General Hospital, University of Chinese Academy of Sciences, Chongqing 400014, People's Republic of China; 2Molecular and Functional Imaging Laboratory, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 400014, People's Republic of China; 3Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of ChinaCorrespondence: Zhuoyue TangDepartment of Radiology, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 400014, People's Republic of ChinaEmail zhuoyue_tang@ucas.ac.cnObjective: Theaimofthisstudywasto explore and validate the diagnostic performance of whole-volume CT texture features in differentiating the common benign and malignant epithelial tumors of theparotid gland.Materials and Methods: Contrast-enhanced CT images of 83 patients with common benign and malignant epithelial tumors of theparotid gland confirmed by histopathology were retrospectively analyzed, including 50 patients with pleomorphic adenoma (PA) and 33 patients with malignant epithelial tumors. Quantitative texture features of tumors were extracted from CT images of arterial phase. The diagnostic performance of texture features was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC). The specificity and sensitivity were respectivelydiscussed by the maximum Youden's index.Results: All the texture features were subject to normal distribution and homoscedasticity. Energy, mean, correlation, and sum entropy of epithelial malignancy group were significantly higher than those of PA group (P< 0.05). There were no statistically significant differences between PA group and epithelial malignancy group in uniformity, entropy, skewness, kurtosis, contrast, and difference entropy (P> 0.05). The AUC of each texture feature and joint diagnostic model was 0.887 (energy), 0.734 (mean), 0.739 (correlation), 0.623 (sum entropy), 0.888 (energy-mean), 0.883 (energy-correlation), 0.784 (mean-correlation). The diagnostic efficiency of energy-mean was the best. Based on the maximum Youden's index, the specificity of energy-correlation was the highest (97%) and the sensitivity of energy was the highest (97%).Conclusion: Energy, mean, correlation, and sum entropy can be the effective quantitative texture features to differentiate the benign and malignant epithelial tumors of theparotid gland. With higher AUC, energy and energy-mean are superior to other indexes or joint diagnostic models in differentiating the benign and malignant epithelial tumors of theparotid gland. CT texture analysis can be used as a noninvasive and valuable means of preoperative assessment of parotid epithelial tumors without additional cost to the patients.Keywords: texture analysis, epithelial tumors, parotid gland
In patients with breast cancer (BC), lymphovascular invasion (LVI) status is considered an important prognostic factor. We aimed to develop machine learning (ML)-based radiomics models for the ...prediction of LVI status in patients with BC, using preoperative MRI images.
This retrospective study included patients with BC with known LVI status and preoperative MRI. The dataset was split into training and unseen testing sets by stratified sampling with a 2:1 ratio. 2D and 3D radiomic features were extracted from contrast-enhanced T1 weighted images (C+T1W) and apparent diffusion coefficient (ADC) maps. The reliability of the features was assessed with two radiologists' segmentation data. Dimension reduction was done with reliability analysis, multi-collinearity analysis, removal of low-variance features, and feature selection. ML models were created with base, tuned, and boosted random forest algorithms.
A total of 128 lesions (LVI-positive, 76; LVI-negative, 52) were included. The best model performance was achieved with tunning and boosting model based on 3D ADC maps and selected four radiomic features. The area under the curve and accuracy were 0.726 and 63.5% in the training data, 0.732 and 76.7% in the test data, respectively. The overall sensitivity and positive predictive values were 68% and 69.6% in the training data, 84.6% and 78.6% in the test data, respectively.
ML and radiomics based on 3D segmentation of ADC maps can be used to predict LVI status in BC, with satisfying performance.
Massive cerebral infarction (MCI) is a devastating condition and associated with high rate of morbidity and mortality. Hemorrhagic transformation (HT) is a common complication after acute MCI, and ...often results in poor outcomes. Although several predictors of HT have been identified in acute ischemic stroke (AIS), the association between the predictors and HT remains controversial. Therefore, we aim to explore the value of texture analysis on magnetic resonance image (MRI) for predicting HT after acute MCI. This retrospective study included a total of 98 consecutive patients who were admitted for acute MCI between January 2019 and October 2020. Patients were divided into the HT group (
n
= 44) and non-HT group (
n
= 54) according to the follow-up computed tomography (CT) images. A total of 11 quantitative texture features derived from images of diffusion-weighted image (DWI) or T2-weighted-Fluid-Attenuated Inversion Recovery (T2/FLAIR) were extracted for each patient. Receiver operating characteristic (ROC) analysis were performed to determine the predictive performance of textural features, with HT as the outcome measurement. There was no significant difference in the baseline demographic and clinical characteristics between the two groups. The distribution of atrial fibrillation and National Institutes of Health Stroke Scale (NIHSS) were significantly higher in patients with HT than those without HT. Among the textural parameters extracted from DWI images, six parameters, f2 (contrast), f3 (correlation), f4 (sum of squares), f5 (inverse difference moment), f10 (difference variance), and f11 (difference entropy), differs significantly between the two groups (
p
< 0.05). Moreover, five of six parameters (f2, f3, f5, f10, and f11) have good predictive performances of HT with the area under the ROC curve (AUC) values of 0.795, 0.779, 0.791, 0.780, and 0.797, respectively. However, the texture features f2, f3, and f10 in T2/FLAIR images were the only three significant predictors of HT in patients with acute MCI, but with a relatively low AUC values of 0.652, 0.652, and 0.670, respectively. In summary, our preliminary results showed DWI-based texture analysis has a good predictive validity for HT in patients with acute MCI. Multiparametric MRI texture analysis model should be developed to improve the prediction performance of HT following acute MCI.
Nut butter can be recognized as a functional food substitute for the animal butter. This study aimed to investigate the effects of mono- and diglycerides and lecithin on the physicochemical ...properties and sensory characteristics of hazelnut butter. For this purpose, mono- and di-glycerides, and lecithin were employed in the hazelnut butter formulation at 0, 1, and 2 g/100 g addition levels. The proximate composition, acidity, peroxide value, and texture parameters were evaluated. Although adding mono- and di-glycerides and lecithin to the hazelnut butter formulation did not significantly change the adhesiveness, it increased their hardness. The sensory analysis revealed that lecithin and mono- and di-glycerides did not significantly affect the color, taste, and flavor of the butters. The highest texture, spreadability, and overall acceptance scores were observed when lecithin was used at the level of 2 g/100 g. The lowest acidity had butter containing mono- and di-glycerides at the level of 1 g/100 g. The peroxide values showed no significant changes during the 90-day storage. The principal component analysis (PCA) allowed discriminating among the features. The partial least squares regression (PLSR) models were applied to find the relationship between sensory and instrumental data. Thus, chemometric approach appears to be a promising technique for the analysis of hazelnut butter.
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