Application of Gray-Scale Texture Feature in the Diagnosis of Pulmonary Nodules

2011 ◽  
Vol 140 ◽  
pp. 34-37
Author(s):  
Chang Zheng Shi ◽  
Qian Zhao ◽  
Liang Ping Luo
Keyword(s):  
Texture Feature ◽  
Pulmonary Nodules ◽  
Texture Features ◽  
Diagnostic Value ◽  
Image Texture ◽  
Lung Nodules ◽  
Gray Scale ◽  
Gray Correlation ◽  
Correlation Parameters

In this paper, we have proposed a new way to detect lung nodules with image texture features. 104 cases of lung nodules, including 31 benign cases and 73 malignant cases, are collected, and the gray-scale correlation and texture heterogeneity are computed through CT imagings for all patients. We find that the gray correlation parameters are different between benign and malignant nodules. The heterogeneity parameters in malignant nodules are higher than that in benign noduals. The gray-scale texture correlation and heterogeneity parameters have diagnostic value in differentiating benign and malignant lung nodules. This study is an exploring study, which still needs further research.

scholarly journals Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu
Keyword(s):  
Human Body ◽  
Rapid Development ◽  
Texture Feature ◽  
Texture Features ◽  
Dynamics Simulation ◽  
Image Texture ◽  
Whole Body ◽  
Database Modeling ◽  
Rigid Model ◽  
The Subject

AbstractWith the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.

scholarly journals Cotton stubble detection based on wavelet decomposition and texture features

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Yukun Yang ◽  
Jing Nie ◽  
Za Kan ◽  
Shuo Yang ◽  
Hangxing Zhao ◽  
...  
Keyword(s):  
Navigation System ◽  
Wavelet Decomposition ◽  
Back Propagation ◽  
Texture Feature ◽  
Texture Features ◽  
Back Propagation Neural Network ◽  
Visual Navigation ◽  
Image Texture ◽  
Original Image ◽  
Residual Film

Abstract Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. Methods Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. Results The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. Conclusions The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.

TEXTURE ANALYSIS USING LOCAL TEXTURE PATTERNS: A FUZZY LOGIC APPROACH

2011 ◽  
Vol 25 (05) ◽  
pp. 741-762 ◽  
Author(s):  
E. M. SRINIVASAN ◽  
K. RAMAR ◽  
A. SURULIANDI
Keyword(s):  
Texture Analysis ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Vital Role ◽  
Image Texture ◽  
Feature Extraction Method ◽  
Local Texture ◽  
Texture Model ◽  
Fuzzy Logic Approach

Texture analysis plays a vital role in image processing. The prospect of texture based image analysis depends on the texture features and the texture model. This paper presents a new texture feature extraction method 'Fuzzy Local Texture Patterns (FLTP)' and 'Fuzzy Pattern Spectrum (FPS)', suitable for texture analysis. The local image texture is described by FLTP and the global image texture is described by FPS. The proposed method is tested with texture classification, texture segmentation and texture edge detection. The results show that the proposed method provides a very good and robust performance for texture analysis.

An Advanced Morphological Component Analysis, Steganography, and Deep Learning-Based System to Transmit Secure Textual Data

2021 ◽  
Vol 13 (2) ◽  
pp. 40-62
Author(s):  
Binay Kumar Pandey ◽  
Digvijay Pandey ◽  
Subodh Wairya ◽  
Gaurav Agarwal
Keyword(s):  
Signal To Noise Ratio ◽  
Texture Feature ◽  
Texture Features ◽  
Performance Measure ◽  
Previous Method ◽  
Image Texture ◽  
Least Significant Bit ◽  
Suggested Approach ◽  
Morphological Component Analysis ◽  
Morphological Component

A potential to extract detailed textual image texture features is a key characteristic of the suggested approach, instead of using a single spatial texture feature. For the generation of MCs, four textured characteristics (including horizontal and vertical) are assumed in this paper that are content, coarseness, contrast, and directionality. The morphological parts of a clandestine text-based image were further segmented and then usually inserted into the least significant bit in cover pixels utilising spatial steganography. This same reverse process for steganography and MCA is conducted on the recipient side after transmission. The results demonstrate that the proposed method based on fusion of MCA and steganography provides a higher performance measure, for instance peak signal-to-noise ratio, SSIM, than the previous method.

scholarly journals Bispectrum Texture Feature Manifold for Feature Extraction in Rolling Bear Fault Diagnosis

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Fei Wang ◽  
Liqing Fang
Keyword(s):  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Texture Feature ◽  
Texture Features ◽  
Rolling Bearing ◽  
Image Texture ◽  
Support Vector ◽  
Contour Map ◽  
Low Dimensional ◽  
Degradation Degree

Effectively classify the fault types and the degradation degree of a rolling bearing is an important basis for accurate malfunction detection. A novel feature extract method - bispectrum image texture features manifold (BTM) of the rolling bearing vibration signal is proposed in this paper. The BTM method is realized by three main steps: bispectrum image analysis, texture feature construction and manifold feature dimensionality reduction. In this method, bispectrum analysis is employed to convert the mass vibration signals into bispectrum contour map, the typical texture features were extracted from the contour map by gray level co-occurrence matrix (GLCM), then the manifold dimensionality reduction method liner local tangent space alignment (LLTSA) is used to remove redundant information and reduce the dimension from the extracted texture features and obtain more meaningful low-dimensional information. Furthermore, the low-dimensional texture features were identified by support vector machine (SVM) which was optimized by genetic optimization algorithm (GA). The validity of BTM is confirmed by rolling bear experiments, the result show that the proposed feature extraction method can accurately distinguish different fault types and have a good performance to classify the degradation degree of inner race fault, outer race fault and rolling ball fault.

Predictive role of T2WI and ADC-derived texture parameters in differentiating Gleason score 3 + 4 and 4 + 3 prostate cancer

2021 ◽  
pp. 1-9
Author(s):  
Zhen Kang ◽  
Anhui Xu ◽  
Liang Wang
Keyword(s):  
Prostate Cancer ◽  
Gleason Score ◽  
Area Under The Curve ◽  
Texture Feature ◽  
Texture Features ◽  
Feature Reduction ◽  
Image Texture ◽  
Roc Curve Analysis ◽  
Texture Parameters ◽  
Predictive Role

BACKGROUND: Since Gleason score (GS) 4 + 3 prostate cancer (PCa) has the worse prognosis than GS 3 + 4 PCa, differentiating these two types of PCa is of clinical significance. OBJECTIVE: To assess the predictive roles of using T2WI and ADC-derived image texture parameters in differentiating GS 3 + 4 from GS 4 + 3 PCa. METHODS: Forty-eight PCa patients of GS 3 + 4 and 37 patients of GS 4 + 3 are retrieved and randomly divided into training (60%) and testing (40%) sets. Axial image showing the maximum tumor size is selected in the T2WI and ADC maps for further image texture feature analysis. Three hundred texture features are computed from each region of interest (ROI) using MaZda software. Feature reduction is implemented to obtain 30 optimal features, which are then used to generate the most discriminative features (MDF). Receiver operating characteristic (ROC) curve analysis is performed on MDF values in the training sets to achieve cutoff values for determining the correct rates of discrimination between two Gleason patterns in the testing sets. RESULTS: ROC analysis on T2WI and ADC-derived MDF values in the training set (n = 51) results in a mean area under the curve (AUC) of 0.953±0.025 (with sensitivity 92.74±6.15 and specificity 89.7±6.9), and 0.985±0.013 (with sensitivity 96.36±4.46 and specificity 97.26±2.58), respectively. Using the corresponding MDF cutoffs, 95.3% (ranges from 76.5% to 100%) and 94.1% (ranged from 76.5% to 100%) of test cases (n = 34) are correctly discriminated using T2WI and ADC-derived MDF values, respectively. CONCLUSIONS: The study demonstrates that using T2WI and ADC-derived image texture parameters has a potentially predictive role in differentiating GS 3 + 4 and GS 4 + 3 PCa.

scholarly journals Seismic Damage Recognition Based on Watershed Segmentation of SAR Image Texture Features

2016 ◽  
Author(s):  
Qiang Li ◽  
Lixia Gong ◽  
Jingfa Zhang
Keyword(s):  
Change Detection ◽  
Texture Feature ◽  
Texture Features ◽  
Seismic Damage ◽  
Segmentation Algorithm ◽  
Image Texture ◽  
Sar Image ◽  
Watershed Segmentation ◽  
Sar Images ◽  
Feature Parameters

The information of seismic damage of buildings in SAR images of different time phase, especially in SAR images after earthquake, is easily disturbed by other factors, which affects the accuracy of information discrimination. In order to identify and evaluate the distribution information of the seismic damage accurately and make full use of the abundant texture features in the SAR image. The conventional method of change detection based on texture features usually takes the pixel as the calculating unit. In this paper, a method of texture feature change detection of SAR images based on watershed segmentation algorithm is proposed. Based on the optimization of texture feature parameters, the feature parameters are segmented by the watershed segmentation algorithm, and the feature object image is obtained. This method introduces the idea of object oriented, and carries out the calculation of the difference map at the object level, Finally, the classification threshold value of different types of seismic damage types is selected, and the recognition of building damage is achieved. Taking the ALOS data before and after the earthquake in Yushu as an example to verify the effectiveness of the method, the overall accuracy of the building extraction is 88.9%, Compared with pixel-based methods, it is proved that the proposed method is effective.

scholarly journals Lung nodules classification in CT images using texture descriptors

2019 ◽  
Vol 13 (3) ◽  
pp. 38
Author(s):  
Luís Vinícius De Moura ◽  
Caroline Machado Dartora ◽  
Ana Maria Marques da Silva
Keyword(s):  
Logistic Regression ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Binary Logistic Regression ◽  
Lung Nodules ◽  
Visual Evaluation ◽  
Cad Systems ◽  
Nodule Volume ◽  
Leave One Out

In lung cancer, early diagnosis can improve potentially the prognosis. Accurate interpretation of computed tomography (CT) scans demands significant efforts by radiologists due to the extensive number of slices analyzed in each examination, for each patient. Computer-aided diagnosis (CAD) systems have been applied in several medical fields, but mostly in lung nodules detection and classification. CAD systems for lung lesions classification usually extract different types of features from lesions, such as texture feature, shape and intensity. This exploratory study aims to investigate the performance of lung nodules classification in 2D and 3D CT lesions images using Haralick texture features analysis and binary logistic regression.  Expert radiologists manually segmented from a CT dataset of 17 benign and 20 malignant nodules, which have their anatomopathological results. Haralick features were extracted from 2D lesions images, using the largest cross-section nodule area, and from all nodule volume (3D). Principal Component Analysis (PCA) was applied to reduce texture features dimensionality, showing two and three principal components (PC) can explain 85.8% and 96.25% of data variance for 2D lesions, and 72.4% and 91.7% for 3D lesions, respectively. Binary logistic regression using leave-one-out cross-validation for training and test datasets showed no differences in accuracy (63% - 68%), using two or three PC. The higher sensitivity (75%) was acquired using 2D images with two or three PC, while the higher specificity (65%) was obtained using 3D images with two or three PC. Binary logistic regression using a small number of Haralick texture features showed better accuracy in lung nodules classification than visual evaluation by radiologists, although the limited dataset. Further studies are needed to generalize and improve these results.

scholarly journals A Study and Analysis of Improved Binary Pattern Technique in Dynamic Images

2021 ◽  
Vol 12 (6) ◽  
pp. 2602-2608
Author(s):  
R.Thirumalaisamy, Dr.S.Kother Mohideen
Keyword(s):  
Texture Feature ◽  
Texture Features ◽  
Spatial Arrangement ◽  
Image Texture ◽  
Processing Cost ◽  
Object Movement ◽  
Fast Movement ◽  
Object Features ◽  
Pattern Tracking ◽  
Local Image

A dynamic image has a distinct quantity of object movement from one to another. It can be any object such as a car, person, an object moving from one point X to another point Y. Image consists of a sense of movement. Applications of object tracking are biometrics tracking, AR uses, video surveillance, passage monitoring, vehicle navigation, etc. Challenges in tracking multifaceted objects are fast movement, geometric conversion, blurring, messy background, artifacts, etc. To resolve this problem by merge all small features with nearby texture features. Texture feature describes the plane space and configuration of an area. A mixture of color and texture feature improves the object details and to increase the strength of the object's illustration. In Existing methods such as binary pattern method all object features are removed, so it is difficult to predict the exact pixel movement. The proposed method of improved binary pattern is also tracking the small changes in the pixel difference in one frame to other. Compared with the existing algorithms, IBP method measures the spatial arrangement of local image texture which reduces the overall processing cost and improves the strength of objective image. To track the similarities and difference of the object in each and every frame efficiently and effectively Improved Local Binary Pattern tracking algorithm was proposed. This proposed technique is an effective way to analysis complicated real time situations compared with other methods.

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