A defect detection method for unpatterned fabric based on multidirectional binary patterns and the gray-level co-occurrence matrix

2019 ◽  
Vol 90 (7-8) ◽  
pp. 776-796 ◽  
Author(s):  
Feng Li ◽  
Lina Yuan ◽  
Kun Zhang ◽  
Wenqing Li
Keyword(s):  
Defect Detection ◽  
Detection Threshold ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Level ◽  
Detection Scheme ◽  
Second Stage ◽  
Fabric Defect ◽  
Occurrence Matrix ◽  
Different Characteristics

A new texture-feature description operator, called the multidirectional binary patterns (MDBP) operator, is proposed in this paper. The operator can extract the detailed distribution of textures in local regions by comparing the differences in the gray levels between neighboring pixels. Moreover, the texture expression ability is enhanced by focusing on the texture features in the linear neighborhood of the image in multiple directions. The MDBP operator was modified by introducing a “uniform” pattern to reduce the grayscale values in the image. Combining the “uniform” MDBP operator and the gray-level co-occurrence matrix, an unpatterned fabric-defect detection scheme is proposed, including texture-feature extraction and detection stages. In the first stage, the multidirectional texture-feature matrix of a nondefective fabric image is extracted, and then the detection threshold is determined based on the similarity between the feature matrices. In the second stage, the defect is detected with the detection threshold. The proposed method is adapted to various grayscale textile images with different characteristics and is robust to a wide variety of image-processing operations. In addition, it is invariant to grayscale changes, performs well when representing textures and detecting defects and has lower computational complexity than other methods.

NIMG-56. USING RADIOMIC FEATURES FROM DAILY MAGNETIC RESONANCE IMAGING TO PREDICT RESPONSE TO RADIATION THERAPY IN GLIOBLASTOMA PATIENTS: A PILOT STUDY

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi142-vi142
Author(s):  
Kaylie Cullison ◽  
Garrett Simpson ◽  
Danilo Maziero ◽  
Kolton Jones ◽  
Radka Stoyanova ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Tumor Growth ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Level ◽  
Difference Matrix ◽  
Patient Response ◽  
Significant Difference ◽  
Occurrence Matrix ◽  
Surrounding Areas

Abstract A dilemma in treating glioblastoma is that MRI after chemotherapy and radiation therapy (chemoRT) shows areas of presumed tumor growth in up to 50% of patients. These areas can represent true progression (TP), tumor growth with tumors non-responsive to treatment, or pseudoprogression (PP), edema and tumor necrosis with favorable treatment response. On imaging, TP and PP are usually not discernable. Patients in this study undergo six weeks of chemoRT on a combination MRI/RT device, receiving daily MRIs. The goal of this study is to explore the correlation of radiomics features with progression. The tumor lesion and surrounding areas of growth/edema were manually outlined as regions of interest (ROIs) for each daily T2-weighted MRI scan. The ROIs were used to calculate texture features: statistical features based on the gray-level co-occurrence matrix (GLCM), the gray-level zone size matrix (GLZSM), the gray-level run length matrix (GLRLM), and the neighborhood gray-tone difference matrix (NGTDM). Each of these matrix classes describe the probability of spatial relationships of gray levels occurring within the ROI. Daily texture features were averaged per week of treatment for each patient. Patient response was retrospectively defined as no progression (NP), TP, or PP. A Kruskal-Wallis test was performed to identify texture features that correlated most strongly with patient response. Forty texture features were calculated for 12 patients (19 treated, 7 excluded due to no T2 lesion or progression status unknown, 6 NP, 3 TP, 3 PP). There was a trend of more texture features correlating significantly with response in weeks 4-6 of treatment, compared to weeks 1-3. A particular texture feature, GLSZM Small Zone Low Gray-Level Emphasis, showed increasing difference between PP and TP over time, with significant difference during week 6 of treatment (p=0.0495). Future directions include correlating early outcomes with greater numbers of patients and daily multiparametric MRI.

Bridge Target Detection in SAR Images Based on Texture Feature

2013 ◽  
Vol 347-350 ◽  
pp. 3634-3638 ◽  
Author(s):  
Nan Zheng ◽  
Wei Zheng ◽  
Zhong Lin Xu ◽  
Da Cheng Wang
Keyword(s):  
False Alarm ◽  
Target Detection ◽  
Texture Feature ◽  
Texture Features ◽  
Histogram Equalization ◽  
Experimental Results ◽  
Gray Level ◽  
Contrastive Analysis ◽  
Sar Images ◽  
Occurrence Matrix

This paper carries out an algorithm research on bridge target detection in SAR images and presents a method that combines both texture features and correlation features. The method firstly extracts initial targets by using the algorithm of histogram equalization segmentation, and then conducts a contrastive analysis for targets and their surrounding background textures by using the gray level co-occurrence matrix to get rid of the false alarm target. The experimental results show that the method is simple, effective and has certain algorithm robustness.

Texture Defect Detection Using Gabor Filters

2012 ◽  
Vol 229-231 ◽  
pp. 1176-1179
Author(s):  
Peng Fei Li ◽  
Huan Huan Zhang ◽  
Xue Juan Kang ◽  
Jun Feng Jing
Keyword(s):  
Defect Detection ◽  
Texture Features ◽  
Gabor Filters ◽  
Experimental Results ◽  
Detection Scheme ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Texture Defect Detection ◽  
Regular Texture

This paper aims at investigating a method for tackling the problem of automated fabric defect detection. A new scheme for defect detection using Gabor filters was proposed. In the experiment, texture features is extracted using Gabor filters. The proposed method would automatically segment defects from the regular texture. The experimental results showed the effectiveness of the proposed detection scheme.

scholarly journals Color moment and gray level co-occurrence matrix in classification of soil organic matter for patchouli plantation

2020 ◽  
Vol 19 (2) ◽  
pp. 983
Author(s):  
Candra Dewi ◽  
Akbar Grahadhuita ◽  
Lailil Muflikhah
Keyword(s):  
Organic Matter ◽  
Nearest Neighbor ◽  
Texture Feature ◽  
Texture Features ◽  
Training Data ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
Occurrence Matrix ◽  
Selection Of ◽  
Color And Texture Features

<span>Patchouli is one of the essential plants that have the most potential and widely cultivated in Indonesia. Patchouli is greedily absorbing soil nutrients and organic matter. Therefore, the selection of soil with high organic matter will maximize the patchouli’s productivity. This paper aims to facilitate soil’s organic matter identification by classifying soil image based on the combination of color and texture features. The color feature extraction was done using the Color Moments method and the texture feature was done using Gray Level Co-occurrence Matrix (GLCM) method. The selection of features was performed to obtain the best combination of color and texture features. The selected features then was used as input of classification by using Modified K-Nearest Neighbor (MKNN). The samples of soil that used as data were taken from several districts in Blitar, East Java province. The testing result of this research showed the highest accuracy of 93,33% by using 180 training data, and also particular color and texture feature combination.</span>

scholarly journals Upgraded Spatial Gray Level Dependence Matrices for Textural Analysis in Colon Cancer Tissues

2018 ◽  
Vol 7 (2.20) ◽  
pp. 291 ◽  
Author(s):  
B Saroja ◽  
A Selwin Mich Priyadharson
Keyword(s):  
Colon Cancer ◽  
Texture Feature ◽  
Image Data ◽  
Texture Features ◽  
Gray Level ◽  
Colon Tissue ◽  
Data Set ◽  
Histopathological Image ◽  
Cancer Biopsy ◽  
Cancer Tissues

Colon or Bowel or Colorectal Cancer (CRC) is commonly determined by diagnosing a sample of colon tissue and further analysed by medical imaging. The colon tissue classification method count on specific changes between texture features extracted from benign and malignant regions. The variations in the image acquisition methods effects the colon tissue analysis. In this paper, an Upgraded Spatial Gray Level Dependence Matrices (U-SGLDM) is emphasized to extract textural features. The licensed image set of all applicable types of tissues within colon cancer are used for experimentation. Several texture feature sets are extracted to show the significant differences among the eight colon cancer biopsy images in the image data set. The fractal dimension-Hurst Coefficient is added to U-SGLDM for long range assessment. The Prominence of the analysis evoked in the representation of histopathological image structure over longer periods.  

Magnetic Resonance Imaging Breast Scan Classification based on Texture Features and Long Short-Term Memory Model

2021 ◽  
Vol 19 (7) ◽  
pp. 41-47
Author(s):  
Suha Raheem Hilal ◽  
Hussain S. Hasan ◽  
Ali M. Hasan
Keyword(s):  
Mirror Symmetry ◽  
Short Term Memory ◽  
Texture Feature ◽  
Breast Mri ◽  
Texture Features ◽  
Gray Level ◽  
Short Term ◽  
Term Memory ◽  
Mri Scans ◽  
Long Short Term Memory

The aim of study is building new program for processing MRI images using MATLAB and to investigate different breast MRI detection algorithms that inform normal and abnormal scans of MRI. In this research an algorithm is proposed to extract texture feature and inform normal and abnormal scans of MRI. First, the MRI scans are pre- processed by image enhancement, intensity normalization, background segmentation and detection of mirror symmetry of breast. Second, the proposed gray level co- occurrence matrix (GLCM) and gray level run length matrix (GLRLM) methods are used to extract texture features from MRI T2-weighted and STIR images. Finally, these features are classified into normal and abnormal by using long short term memory (LSTM) model. The research will be validated using 326 datasets that downloaded from cancer imaging archive (TCIA). The achieved classification accuracy was 98.80%.

scholarly journals Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features

2020 ◽  
Vol 12 (3) ◽  
pp. 27-44
Author(s):  
Gulivindala Suresh ◽  
Chanamallu Srinivasa Rao
Keyword(s):  
Feature Vector ◽  
Texture Feature ◽  
Texture Features ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Copy Move Forgery Detection ◽  
Occurrence Matrix ◽  
Differential Excitation

Copy-move forgery (CMF) is an established process to copy an image segment and pastes it within the same image to hide or duplicate a portion of the image. Several CMF detection techniques are available; however, better detection accuracy with low feature vector is always substantial. For this, differential excitation component (DEC) of Weber Law descriptor in combination with the gray level co-occurrence matrix (GLCM) approach of texture feature extraction for CMFD is proposed. GLCM Texture features are computed in four directions on DEC and this acts as a feature vector for support vector machine classifier. These texture features are more distinguishable and it is validated through other two proposed methods based on discrete wavelet transform-GLCM (DWT-GLCM) and GLCM. Experimentation is carried out on CoMoFoD and CASIA databases to validate the efficacy of proposed methods. Proposed methods exhibit resilience against many post-processing attacks. Comparative analysis with existing methods shows the superiority of the proposed method (DEC-GLCM) with regard to detection accuracy.

scholarly journals Quantitative Analysis of Melanosis Coli Colonic Mucosa Using Textural Patterns

2020 ◽  
Vol 10 (1) ◽  
pp. 404 ◽  
Author(s):  
Chung-Ming Lo ◽  
Chun-Chang Chen ◽  
Yu-Hsuan Yeh ◽  
Chun-Chao Chang ◽  
Hsing-Jung Yeh
Keyword(s):  
Quantitative Assessment ◽  
Colonic Mucosa ◽  
Pearson Correlation ◽  
Texture Features ◽  
Adenomatous Polyps ◽  
Gray Level ◽  
Melanosis Coli ◽  
Pearson Correlation Analysis ◽  
Occurrence Matrix

Melanosis coli (MC) is a disease related to long-term use of anthranoid laxative agents. Patients with clinical constipation or obesity are more likely to use these drugs for long periods. Moreover, patients with MC are more likely to develop polyps, particularly adenomatous polyps. Adenomatous polyps can transform to colorectal cancer. Recognizing multiple polyps from MC is challenging due to their heterogeneity. Therefore, this study proposed a quantitative assessment of MC colonic mucosa with texture patterns. In total, the MC colonoscopy images of 1092 person-times were included in this study. At the beginning, the correlations among carcinoembryonic antigens, polyp texture, and pathology were analyzed. Then, 181 patients with MC were extracted for further analysis while patients having unclear images were excluded. By gray-level co-occurrence matrix, texture patterns in the colorectal images were extracted. Pearson correlation analysis indicated five texture features were significantly correlated with pathological results (p < 0.001). This result should be used in the future to design an instant help software to help the physician. The information of colonoscopy and image analystic data can provide clinicians with suggestions for assessing patients with MC.

Sign in / Sign up

Export Citation Format

Share Document