Texture Analysis Of CT Images Research Articles

Differentiation between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment using Multi-Scale Texture Analysis of CT Images.

Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent type of pancreas cancer with a high mortality rate and its staging is highly dependent on the extent of involvement between the tumor and surrounding vessels, facilitating treatment response assessment in PDAC. This study aims at detecting and visualizing the tumor region and the surrounding vessels in PDAC CT scan since, despite the tumors in other abdominal organs, clear detection of PDAC is highly difficult. This retrospective study consists of three stages: 1) a patch-based algorithm for differentiation between tumor region and healthy tissue using multi-scale texture analysis along with L1-SVM (Support Vector Machine) classifier, 2) a voting-based approach, developed on a standard logistic function, to mitigate false detections, and 3) 3D visualization of the tumor and the surrounding vessels using ITK-SNAP software. The results demonstrate that multi-scale texture analysis strikes a balance between recall and precision in tumor and healthy tissue differentiation with an overall accuracy of 0.78±0.12 and a sensitivity of 0.90±0.09 in PDAC. Multi-scale texture analysis using statistical and wavelet-based features along with L1-SVM can be employed to differentiate between healthy and pancreatic tissues. Besides, 3D visualization of the tumor region and surrounding vessels can facilitate the assessment of treatment response in PDAC. However, the 3D visualization software must be further developed for integrating with clinical applications.

Texture Analysis of CT Images in Head and Neck Tumors Differentiation

Objective: to determine the diagnostic significance of computed tomography texture analysis (CTTA) in differentiating head and neck tumors.Material and methods. The study included 118 patients aged from 4 to 80 years with a verified diagnosis of benign and malignant (37 and 81, respectively) head and neck tumors. CTTA was performed using the LIFEx program, version 6.30. Thirty eight (38) texture indices extracted from routine CT images were tested by regression analysis with creation of logistic texture models with associations of four indices as independent predictors.Results. The possibility of using derived models – probability textural indices for benign and malignant tumors differentiation was established: area under ROC-curve (AUC) 0.854 ± 0.035 (p < 0.001); for differentiation of locally spread from locally limited tumors: AUC 0.840 ± 0.049 (p < 0.001); for differentiation of moderately, poorly, and undifferentiated cancer (G2, G3, G4) from well-differentiated (G1) head and neck cancer: AUC 0.826 ± 0.085 (p < 0.001).Conclusion. CT images texture analysis allows to make non-invasive prognosis of benign or malignant nature of a visualized head and neck tumor, as well as to determine the extent and degree of tumor malignancy.

Study of texture segmentation and classification for grading small hepatocellular carcinoma based on CT images

To grade Small Hepatocellular CarCinoma (SHCC) using texture analysis of CT images, we retrospectively analysed 68 cases of Grade II (medium-differentiation) and 37 cases of Grades III and IV (high-differentiation). The grading scheme follows 4 stages: (1) training a Super Resolution Generative Adversarial Network (SRGAN) migration learning model on the Lung Nodule Analysis 2016 Dataset, and employing this model to reconstruct Super Resolution Images of the SHCC Dataset (SR-SHCC) images; (2) designing a texture clustering method based on Gray-Level Co-occurrence Matrix (GLCM) to segment tumour regions, which are Regions Of Interest (ROIs), from the original and SR-SHCC images, respectively; (3) extracting texture features on the ROIs; (4) performing statistical analysis and classifications. The segmentation achieved accuracies of 0.9049 and 0.8590 in the original SHCC images and the SR-SHCC images, respectively. The classification achieved an accuracy of 0.838 and an Area Under the ROC Curve (AUC) of 0.84. The grading scheme can effectively reduce poor impacts on the texture analysis of SHCC ROIs. It may play a guiding role for physicians in early diagnoses of medium-differentiation and high-differentiation in SHCC.

Pulmonary adenocarcinoma appearing as ground-glass opacity nodules identified using non-enhanced and contrast-enhanced CT texture analysis: A retrospective analysis.

The present study aimed to investigate the ability of CT-based texture analysis to differentiate invasive adenocarcinoma (IA) from pre-invasive lesions (PIL) or minimally IA (MIA) appearing as ground-glass opacity (GGO) nodules, and to further compare the performance of non-enhanced CT (NECT) images with that of contrast-enhanced CT (CECT) images. A total of 77 patients with GGO nodules and surgically confirmed pulmonary adenocarcinoma were included in the present retrospective study. Each GGO nodule was manually segmented and its texture features were extracted from NECT and CECT images using in-house developed software coded in MATLAB (MathWorks). The independent-samples t-test was used to select the texture features with statistically significant differences between IA and MIA/PIL. Multivariate logistic regression and receiver operating characteristics (ROC) curve analyses were performed to identify predictive features. Of the 77 GGO nodules, 12 were atypical adenomatous hyperplasia or adenocarcinoma in situ (15.6%), 36 were MIA (46.8%) and 29 were IA (37.7%). IA and MIA/PIL exhibited significant differences in most histogram features and gray-level co-occurrence matrix features (P<0.05). Multivariate logistic regression and ROC curve analyses revealed that smaller energy and higher entropy were significant differentiators of IA from MIA and PIL, irrespective of whether NECT images [area under the curve (AUC): 0.839, 0.859] or CECT images (AUC: 0.818, 0.820) are used. Texture analysis of CT images, regardless of whether NECT or CECT is used, has the potential to distinguish IA from PIL or MIA, particularly the parameters of energy and entropy. Furthermore, NECT images were simpler to obtain and no contrast agent was required; thus, analysis with NECT may be a preferred choice.

Assessment of Primary Solid Renal Mass using Texture Analysis of CT Images of Kidney by Active Contour Method: A Novel Methody

Assessment of Primary Solid Renal Mass using Texture Analysis of CT Images of Kidney by Active Contour Method: A Novel Methody

Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.

To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). This single-institutional retrospective study included 58 patients with pathologically proven small renal mass (17 in AMLwvf and 41 in RCC groups). Texture features were extracted from the largest possible tumorous regions of interest (ROIs) by manual segmentation in preoperative three-phase CT images. Interobserver reliability and the Mann-Whitney U test were applied to select features preliminarily. Then support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were adopted to establish discriminative classifiers, and the performance of classifiers was assessed. Of the 42 extracted features, 16 candidate features showed significant intergroup differences (P < 0.05) and had good interobserver agreement. An optimal feature subset including 11 features was further selected by the SVM-RFE method. The SVM-RFE+SMOTE classifier achieved the best performance in discriminating between small AMLwvf and RCC, with the highest accuracy, sensitivity, specificity and AUC of 93.9 %, 87.8 %, 100 % and 0.955, respectively. Machine learning analysis of CT texture features can facilitate the accurate differentiation of small AMLwvf from RCC. • Although conventional CT is useful for diagnosis of SRMs, it has limitations. • Machine-learning based CT texture analysis facilitate differentiation of small AMLwvf from RCC. • The highest accuracy of SVM-RFE+SMOTE classifier reached 93.9 %. • Texture analysis combined with machine-learning methods might spare unnecessary surgery for AMLwvf.

A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT.

This paper proposes a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated within nodular regions by means of feature covariances, which leverage rich intra- and inter-variations of the feature space dimensions. When compared to the classical use of the average for feature aggregation, feature covariances preserve spatial co-variations between features. The obtained Riesz-covariance descriptors lie on a manifold governed by Riemannian geometry allowing geodesic measurements and differentiations. The latter property is incorporated both into a kernel for support vector machines (SVM) and a manifold-aware sparse regularized classifier. The effectiveness of the presented models is evaluated on a dataset of 110 patients with non-small cell lung carcinoma (NSCLC) and cancer recurrence information. Disease recurrence within a timeframe of 12 months could be predicted with an accuracy of 81.3-82.7%. The anatomical location of recurrence could be discriminated between local, regional and distant failure with an accuracy of 78.3-93.3%. The obtained results open novel research perspectives by revealing the importance of the nodular regions used to build the predictive models.

Texture analysis of CT images in the characterization of oral cancers involving buccal mucosa

The aim of this study was to investigate the usefulness of texture analysis in the characterization of oral cancers involving the buccal mucosa and to assess its effectiveness in differentiating between the various grades of the tumour. Contrast enhanced CT examination was carried out in 21 patients with carcinoma of the buccal mucosa who had consented to retrospective analysis during a research study that was approved by the institutional review board. Two regions of interest (ROIs) were created, one at the site of the lesion and the other at the contralateral normal side. Texture analysis measures of fractal dimension (FD), lacunarity and grey level co-occurrence matrix (GLCM) were computed for each ROI. The numeric data from the two ROIs were compared and were correlated with the tumour grade as confirmed by biopsy. The difference between the mean FD and GLCM parameters of the lesion vs the normal ROI were statistically significant (p < 0.05); no significant difference was observed between the three grades of tumour for any of the parameters (p > 0.05). Texture analysis on CT images is a potential method in the characterization of oral cancers involving the buccal mucosa and deserves further investigation as a predictor of tumour aggression.

Trabecular texture analysis of CT images in the relationship with spinal fracture.

To evaluate age-related changes in trabecular texture and bone mineral density (BMD) and to determine the usefulness of texture analysis in the assessment of the risk of fracture. The vertebral trabecular BMD was measured in 209 women aged 18-86 years. Run length analysis was applied to computed tomographic images obtained with 2-mm section thickness. The width of the trabecula (T-texture) and the width of the intertrabecular space (I-texture) were calculated. I-texture significantly increased with age in women 50 years of age or older (P < .0001). Women with fracture had significantly lower BMD (P < .0001 at 60-69 years, P < .05 at 70-79 years) and significantly higher I-texture (P < .05 at 60-69 years, P < .01 at 70-79 years). For fracture risk, BMD had the highest sensitivity and I-texture had the highest specificity in women aged 60-69 years; I-texture had the highest sensitivity and specificity in women aged 70-79 years. Texture analysis was helpful in the assessment of fracture risk, especially in elderly women.

Texture analysis of CT images

The present study has shown some promise in the use of texture for the extraction of diagnostic information from CT images. A number of features are obtained from abdominal CT scans of the liver using the spatial domain statistical texture analysis methods: SGLDM, GLRLM, and GLDM. This study investigated whether (a) the texture could be used to discriminate among the various tissue types that are inaccessible to human perception and, (b) if so, then what are the most useful feature parameters for such an application? The efficacies of the different methods were evaluated from the consistency of the computed values within a class and from their differences with other classes. The study has demonstrated the use of texture for tissue characterization of CT images. In particular, we have been successful in identifying the onset of disease in liver tissue, which can not be recognized even by trained human observers. Three useful features, namely entropy (H), local homogeneity (L) and grey level distribution (GLD), have been found effective for pattern recognition. The performance of these features has been compared on the basis of statistical significance. The results show that, except for L, (Direction 0/spl deg/) all feature parameters perform equally well and detect early malignancy with a confidence level of above 99%-a finding that will not only help in automation, but more importantly, in early detection of malignancy in the liver. >