scholarly journals Differentiation of Pituitary Adenoma from Rathke Cleft Cyst: Combining MR Image Features with Texture Features

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yang Zhang ◽  
Chaoyue Chen ◽  
Zerong Tian ◽  
Yangfan Cheng ◽  
Jianguo Xu
Keyword(s):  
Pituitary Adenoma ◽  
Signal Intensity ◽  
Texture Features ◽  
Binary Logistic Regression ◽  
Image Features ◽  
Image Feature ◽  
Binary Logistic Regression Analysis ◽  
Mr Image ◽  
Rathke Cleft Cyst ◽  
Contrast Enhanced

Objectives. To differentiate pituitary adenoma from Rathke cleft cyst in magnetic resonance (MR) scan by combing MR image features with texture features. Methods. A total number of 133 patients were included in this study, 83 with pituitary adenoma and 50 with Rathke cleft cyst. Qualitative MR image features and quantitative texture features were evaluated by using the chi-square tests or Mann–Whitney U test. Binary logistic regression analysis was conducted to investigate their ability as independent predictors. ROC analysis was conducted subsequently on the independent predictors to assess their practical value in discrimination and was used to investigate the association between two types of features. Results. Signal intensity on the contrast-enhanced image was found to be the only significantly different MR image feature between the two lesions. Two texture features from the contrast-enhanced images (Histo-Skewness and GLCM-Correlation) were found to be the independent predictors in discrimination, of which AUC values were 0.80 and 0.75, respectively. Besides, the above two texture features (Histo-Skewness and GLCM-Contrast) were suggested to be associated with signal intensity on the contrast-enhanced image. Conclusion. Signal intensity on the contrast-enhanced image was the most significant MR image feature in differentiation between pituitary adenoma and Rathke cleft cyst, and texture features also showed promising and practical ability in discrimination. Moreover, two types of features could be coordinated with each other.

scholarly journals Texture Spectrum Coupled with Entropy and Homogeneity Image Features for Myocardium Muscle Characterization

2019 ◽  
Vol 14 (4) ◽  
pp. 295-304
Author(s):  
Luminita Moraru ◽  
Simona Moldovanu ◽  
Anisia-Luiza Culea-Florescu ◽  
Dorin Bibicu ◽  
Nilanjan Dey ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Heart Muscle ◽  
Texture Classification ◽  
Texture Features ◽  
Image Features ◽  
Muscle Disease ◽  
Image Feature ◽  
Ultrasound Images ◽  
Fuzzy C Means ◽  
Chamber View

Background: People in middle/later age often suffer from heart muscle damage due to coronary artery disease associated to myocardial infarction. In young people, the genetic forms of cardiomyopathies (heart muscle disease) are the utmost protuberant cause of myocardial disease. Objective: Accurate early detected information regarding the myocardial tissue structure is a key answer for tracking the progress of several myocardial diseases. associations while known disease-lncRNA associations are required only. Method: The present work proposes a new method for myocardium muscle texture classification based on entropy, homogeneity and on the texture unit-based texture spectrum approaches. Entropy and homogeneity are generated in moving windows of size 3x3 and 5x5 to enhance the texture features and to create the premise of differentiation of the myocardium structures. The texture is then statistically analyzed using the texture spectrum approach. Texture classification is achieved based on a fuzzy c–means descriptive classifier. The proposed method has been tested on a dataset of 80 echocardiographic ultrasound images in both short-axis and long-axis in apical two chamber view representations, for normal and infarct pathologies. Results: The noise sensitivity of the fuzzy c–means classifier was overcome by using the image features. The results established that the entropy-based features provided superior clustering results compared to homogeneity. Conclusion: Entropy image feature has a lower spread of the data in the clusters of healthy subjects and myocardial infarction. Also, the Euclidean distance function between the cluster centroids has higher values for both LAX and SAX views for entropy images.</P>

scholarly journals Usefulness of the Texture Signatures Based on Multiparametric MRI in Predicting Growth Hormone Pituitary Adenoma Subtypes

2021 ◽  
Vol 11 ◽  
Author(s):  
Chen-Xi Liu ◽  
Li-Jun Heng ◽  
Yu Han ◽  
Sheng-Zhong Wang ◽  
Lin-Feng Yan ◽  
...  
Keyword(s):  
Growth Hormone ◽  
Pituitary Adenoma ◽  
Predictive Value ◽  
Region Of Interest ◽  
Texture Features ◽  
Roc Curve Analysis ◽  
Diagnostic Efficacy ◽  
Contrast Enhanced ◽  
Magnetic Resonance Imaging Mri ◽  
Sensitivity Specificity

ObjectiveTo explore the usefulness of texture signatures based on multiparametric magnetic resonance imaging (MRI) in predicting the subtypes of growth hormone (GH) pituitary adenoma (PA).MethodsForty-nine patients with GH-secreting PA confirmed by the pathological analysis were included in this retrospective study. Texture parameters based on T1-, T2-, and contrast-enhanced T1-weighted images (T1C) were extracted and compared for differences between densely granulated (DG) and sparsely granulated (SG) somatotroph adenoma by using two segmentation methods [region of interest 1 (ROI1), excluding the cystic/necrotic portion, and ROI2, containing the whole tumor]. Receiver operating characteristic (ROC) curve analysis was performed to determine the differentiating efficacy.ResultsAmong 49 included patients, 24 were DG and 25 were SG adenomas. Nine optimal texture features with significant differences between two groups were obtained from ROI1. Based on the ROC analyses, T1WI signatures from ROI1 achieved the highest diagnostic efficacy with an AUC of 0.918, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 85.7, 72.0, 100.0, 100.0, and 77.4%, respectively, for differentiating DG from SG. Comparing with the T1WI signature, the T1C signature obtained relatively high efficacy with an AUC of 0.893. When combining the texture features of T1WI and T1C, the radiomics signature also had a good performance in differentiating the two groups with an AUC of 0.908. In addition, the performance got in all the signatures from ROI2 was lower than those in the corresponding signature from ROI1.ConclusionTexture signatures based on MR images may be useful biomarkers to differentiate subtypes of GH-secreting PA patients.

scholarly journals An Effective Contour Detection based Image Retrieval using Multi-Fusion Method and Neural Network

2021 ◽  
Author(s):  
Rohit Raja ◽  
Sandeep Kumar ◽  
Shilpa Choudhary ◽  
Hemlata Dalmia
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Texture Features ◽  
Visual Image ◽  
Image Features ◽  
Contour Detection ◽  
Image Feature ◽  
Digital Platforms ◽  
Color Descriptor ◽  
Dominant Color Descriptor

Abstract Day by day, rapidly increasing the number of images on digital platforms and digital image databases has increased. Generally, the user requires image retrieval and it is a challenging task to search effectively from the enormous database. Mainly content-based image retrieval (CBIR) algorithm considered the visual image feature such as color, texture, shape, etc. The non-visual features also play a significant role in image retrieval, mainly in the security concern and selection of image features is an essential issue in CBIR. Performance is one of the challenging tasks in image retrieval, according to current CBIR studies. To overcome this gap, the new method used for CBIR using histogram of gradient (HOG), dominant color descriptor (DCD) & hue moment (HM) features. This work uses color features and shapes texture in-depth for CBIR. HOG is used to extract texture features. DCD on RGB and HSV are used to improve efficiency and computation. A neural network (NN) is used to extract the image features, which improves the computation using the Corel dataset. The experimental results evaluated on various standard benchmarks Corel-1k, Corel-5k datasets, and outcomes of the proposed work illustrate that the proposed CBIR is efficient for other state-of-the-art image retrieval methods. Intensive analysis of the proposed work proved that the proposed work has better precision, recall, accuracy

scholarly journals Radiomic Analysis of Craniopharyngioma and Meningioma in the Sellar/Parasellar Area with MR Images Features and Texture Features: A Feasible Study

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zerong Tian ◽  
Chaoyue Chen ◽  
Yang Zhang ◽  
Yimeng Fan ◽  
Ridong Feng ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Texture Features ◽  
Binary Logistic Regression ◽  
Roc Curves ◽  
Binary Logistic Regression Analysis ◽  
Fisher Exact Test ◽  
Contrastive Analysis ◽  
Mr Images ◽  
Exact Test ◽  
Significant Difference

Purpose. To investigate the ability of qualitative Magnetic Resonance (MR) images features and quantitative Magnetic Resonance Imaging (MRI) texture features in the contrastive analysis between craniopharyngioma and meningioma. Method. A total number of 127 patients were included in this study (craniopharyngioma = 63; meningioma = 64). All the features analyzed in this study were acquired from preoperative MRI images. Qualitative MR images features were evaluated with chi-square tests or Fisher exact test, while MRI texture features were evaluated with the Mann–Whitney U test with the Benjamini–Hochberg method. Then binary logistic regression analysis for texture features was performed to evaluate their ability as independent predictors, and the diagnostic accuracy was calculated next for these texture features with high abilities as independent predictors using receiver operating characteristic (ROC) curves. Results. Four qualitative MR images features showed significant difference between craniopharyngioma and meningioma, but only cystic alteration could be considered as diagnostic independent predictors. Meanwhile, three quantitative parameters, histogram-based matrix- (HISTO-) Skewness, Grey-level co-occurrence matrix- (GLCM-) Contrast on contrast-enhanced images, and HISTO-Skewness on images of T2-weighted imaging (T2WI), showed promising abilities in the contrastive analysis. Besides, these texture features were found significantly to be relative to cystic alteration. Conclusion. MR images features and texture features were useful in the contrastive analysis of craniopharyngioma and meningioma. Furthermore, qualitative MR images features and MRI texture features could be related to each other.

Application of ultrasound and contrast-enhanced ultrasound to distinguish salivary focal inflammatory masses from malignant masses: A retrospective observational study

2021 ◽  
pp. 1-12
Author(s):  
Yanan Zhao ◽  
Tao Jiang ◽  
Kun Lv ◽  
Minqiang Pan ◽  
Qing Wen ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Salivary Gland ◽  
Clinical Characteristics ◽  
Clinical Manifestations ◽  
Binary Logistic Regression ◽  
Growth Patterns ◽  
Binary Logistic Regression Analysis ◽  
Contrast Enhanced Ultrasound ◽  
Differential Analysis ◽  
Contrast Enhanced

BACKGROUND: The aim was to retrospectively analyze the ultrasonographic and clinical characteristics of focal inflammatory masses and malignant masses of salivary gland by using B-mode ultrasound (US) and contrast-enhanced ultrasound (CEUS) for differential analysis. METHODS: The features of US and CEUS were retrospectively analyzed for 19 cases of focal salivary inflammatory masses and 45 cases of malignant salivary masses. All cases were confirmed by pathohistological examination. RESULTS: On B-mode US, the incidence of expansive growth patterns of malignant salivary masses (44.4%, 20/45) was significantly higher than that of focal salivary inflammatory masses (15.8%, 3/19) (p = 0.029). The rate of lymphadenopathy surrounding salivary glands of malignant salivary masses (42.2%, 19/45) was significantly higher than that of focal salivary inflammatory masses (15.8%, 3/19) (p = 0.042). On CEUS, clear enhancement margins were more common in malignant salivary masses (44.4%, 20/45) compared to focal salivary inflammatory masses (15.8%, 3/19) (p = 0.029); Rapid washout was more common in malignant salivary masses (82.2%, 37/45) than focal salivary inflammatory masses (31.6%, 6/19) (p <  0.001). Rapid washout on CEUS and craniocaudal diameter were independent predictive factors in differentiating salivary inflammatory masses and malignant masses according to binary logistic regression analysis. US and CEUS achieved a sensitivity of 80.0%, a specificity of 78.9%and an accuracy of 80.0%for discrimination between salivary inflammatory masses and malignant masses. CONCLUSION: Therefore, a multimodal ultrasonographic pathway combining clinical manifestations, B-mode US and CEUS was needed to differentiate between salivary focal inflammatory masses and malignancies to avoid unnecessary biopsies.

Discrimination between pituitary adenoma and craniopharyngioma using MRI-based image features and texture features

2020 ◽  
Vol 38 (12) ◽  
pp. 1125-1134
Author(s):  
Yang Zhang ◽  
Chaoyue Chen ◽  
Zerong Tian ◽  
Jianguo Xu
Keyword(s):  
Pituitary Adenoma ◽  
Texture Features ◽  
Image Features

High-resolution electron microscopy of the atomic structure of some grain boundaries in Au

1986 ◽  
Vol 44 ◽  
pp. 414-415
Author(s):  
W. Krakow ◽  
D. A. Smith
Keyword(s):  
Atomic Structure ◽  
High Resolution Electron Microscopy ◽  
Image Features ◽  
Image Feature ◽  
Resolution Electron ◽  
Tilt Boundaries ◽  
Processing Techniques ◽  
Atom Position ◽  
Structure Configuration

The successful determination of the atomic structure of [110] tilt boundaries in Au stems from the investigation of microscope performance at intermediate accelerating voltages (200 and 400kV) as well as a detailed understanding of how grain boundary image features depend on dynamical diffraction processes variation with specimen and beam orientations. This success is also facilitated by improving image quality by digital image processing techniques to the point where a structure image is obtained and each atom position is represented by a resolved image feature. Figure 1 shows an example of a low angle (∼10°) Σ = 129/[110] tilt boundary in a ∼250Å Au film, taken under tilted beam brightfield imaging conditions, to illustrate the steps necessary to obtain the atomic structure configuration from the image. The original image of Fig. 1a shows the regular arrangement of strain-field images associated with the cores of ½ [10] primary dislocations which are separated by ∼15Å.

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