Texture Features Extraction for Indonesian Macroscopic and Microscopic Beef Digital Images Based on Gray-Level Co-Occurrence Matrix

2017 ◽  
Vol 23 (3) ◽  
pp. 2629-2632 ◽  
Author(s):  
Sigit Widiyanto ◽  
Yuli Karyanti ◽  
Dini Tri Wardani
Keyword(s):  
Digital Images ◽  
Texture Features ◽  
Features Extraction ◽  
Gray Level ◽  
Occurrence Matrix

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.

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.

scholarly journals Multi Object and Dynamic Query Based CBIR System using DCT Incorporated with HOG and HTF

2019 ◽  
Vol 9 (2S3) ◽  
pp. 590-592
Keyword(s):  
Retrieval System ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Texture Features ◽  
Gray Level ◽  
Query Image ◽  
User Expectations ◽  
Multi Level ◽  
Occurrence Matrix ◽  
Dynamic Query

This work contributes multi object detection and dynamic query image based retrieval system. Generally, finding relevance and matching user expectations is very critical based on query key information and these results irrelevant responses which will produce low similarity index. Consequently, CBIR system took a major responsibility of identifying new objects, retrieving similar objects or contents based on multi query and dynamic keywords with improved recall and precision as per requirement of the users. At this juncture, Discrete Curvelet Transform with the incorporation of HOG and HTF based approach is proposed to handle commercial image, medical images and types of multi model images. This proposed approach mainly focuses on extracting scaled features for finding correlation among the query and database images. To start with the process, query image is decomposed into multi level sub images to extract set of texture features at two levels. These features are estimated by Gray Level Co-occurrence Matrix (GLCM) and HOG descriptor based techniques is adapted to find scaled vectors with reduced dimensionality. This method outperform compared as compared to existing method is authenticated from experimental results.

scholarly journals Multi Object and Dynamic Query Based CBIR System using DCT Incorporated with HOG and HTF

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1358-1369
Keyword(s):  
Retrieval System ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Texture Features ◽  
Gray Level ◽  
Query Image ◽  
User Expectations ◽  
Multi Level ◽  
Occurrence Matrix ◽  
Dynamic Query

This work contributes multi object detection and dynamic query image based retrieval system. Generally, finding relevance and matching user expectations is very critical based on query key information and these results irrelevant responses which will produce low similarity index. Consequently, CBIR system took a major responsibility of identifying new objects, retrieving similar objects or contents based on multi query and dynamic keywords with improved recall and precision as per requirement of the users. At this juncture, Discrete Curvelet Transform with the incorporation of HOG and HTF based approach is proposed to handle commercial image, medical images and types of multi model images. This proposed approach mainly focuses on extracting scaled features for finding correlation among the query and database images. To start with the process, query image is decomposed into multi level sub images to extract set of texture features at two levels. These features are estimated by Gray Level Co-occurrence Matrix (GLCM) and HOG descriptor based techniques is adapted to find scaled vectors with reduced dimensionality. This method outperform compared as compared to existing method is authenticated from experimental results.

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.

scholarly journals Relationship between Texture Features and Mineralogy Phases in Iron Ore Sinter Based on Gray-level Co-occurrence Matrix

2009 ◽  
Vol 49 (5) ◽  
pp. 709-718 ◽  
Author(s):  
Xuewei Lv ◽  
Chenguang Bai ◽  
Guibao Qiu ◽  
Shengfu Zhang ◽  
Meilong Hu
Keyword(s):  
Iron Ore ◽  
Texture Features ◽  
Gray Level ◽  
Iron Ore Sinter ◽  
Occurrence Matrix

RELATIONSHIP BETWEEN SOIL MOISTURE CONTENT IN PADDY FIELD AND ITS IMAGE TEXTURE

2016 ◽  
Vol 78 (1-2) ◽  
Author(s):  
Siti Khairunniza Bejo ◽  
Nor Hafizah Sumgap ◽  
Siti Nurul Afiah Mohd Johari
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Color Space ◽  
Texture Features ◽  
Image Texture ◽  
Gray Level ◽  
Second Moment ◽  
Variance Homogeneity ◽  
Occurrence Matrix

The aim of this study is to identify the relationship between soil moisture content and its image texture. Soil image was captured and converted into CIELUV color space. These images were later used to develop two dimensional gray level co-occurrence matrix. Eight texture features extracted from gray level co-occurrence matrix namely mean, variance, homogeneity, dissimilarity, entropy, contrast, second moment and correlation was used for the analysis. The results has shown that the image texture properties can be used to relate with soil moisture content, where variance, homogeneity, dissimilarity, entropy, contrast, second moment and correlation gave significant responds to the moisture content. The highest value of correlation was gathered from entropy with r = -0.522.

Analysis of Image Texture Features Based on Gray Level Co-Occurrence Matrix

2012 ◽  
Vol 204-208 ◽  
pp. 4746-4750 ◽  
Author(s):  
Ying Chen ◽  
Feng Yu Yang
Keyword(s):  
Quality Evaluation ◽  
Texture Features ◽  
Image Texture ◽  
Generation Process ◽  
Gray Level ◽  
Analysis Process ◽  
Comprehensive Information ◽  
Occurrence Matrix ◽  
The Relationship ◽  
Secondary Diagonal

Gray level co-occurrence matrix (GLCM) is a second-order statistical measure of image grayscale which reflects the comprehensive information of image grayscale in the direction, local neighborhood and magnitude of changes. Firstly, we analyze and reveal the generation process of gray level co-occurrence matrix from horizontal, vertical and principal and secondary diagonal directions. Secondly, we use Brodatz texture images as samples, and analyze the relationship between non-zero elements of gray level co-occurrence matrix in changes of both direction and distances of each pixels pair by. Finally, we explain its function of the analysis process of texture. This paper can provided certain referential significance in the application of using gray level co-occurrence matrix at quality evaluation of texture image.

scholarly journals Adaptive Multiscale Block Compressed Sensing of Images based on Gray Level Co- Occurrence Matrix

2020 ◽  
Vol 13 (5) ◽  
pp. 169-175
Author(s):  
Jinfeng Li ◽  
◽  
Jinnan Guo ◽  
Shun Cao ◽  
Yutong Zhao
Keyword(s):  
Compressed Sensing ◽  
High Frequency ◽  
Sampling Rate ◽  
Texture Features ◽  
Gray Level ◽  
Limited Sampling ◽  
Block Compressed Sensing ◽  
Frequency Components ◽  
Image Edges ◽  
Occurrence Matrix

In conventional block compressed sensing (BCS), the images are divided into small fixed-size blocks sampled at the same sub-rate. The sparsities and high-frequency components of the images are ignored, and the reconstruction qualities of the complex texture images are poor. An adaptive multiscale variant of the block compressed sensing was proposed to reconstruct the texture details of the images. The texture features of the images were obtained from the high-frequency components by the three-level wavelet transform and analyzed on the basis of the gray level co-occurrence matrix. A mathematical model was established to adjust the block sizes of the images automatically and allocate the limited sampling resource adaptively. The smoothed projected Landweber (SPL) was utilized to reconstruct the images. The accuracy of the proposed algorithm was verified by the simulation experiments. Results demonstrate that the texture details of the reconstructed images are abundant. The image edges are also clear, and the blocking artifacts are effectively eliminated. The reconstruction qualities of images, especially the partial images, are considerably improved at different sub-sampling rates. The proposed algorithm achieves a 2.42–3.3 dB gain in reconstruction PSNR for the Barbara image over the original BCS-SPL at a sub-sampling rate of 0.3. No remarkable differences are noted between the reconstructed and original texture blocks in visual sensation. The proposed algorithm provides evidence for the compression and reconstruction of the images with complex texture details.

scholarly journals IMAGE SEARCH ENGINE BASED ON COMBINED FEATURES OF IMAGE SUB-BLOCKS

2012 ◽  
pp. 98-104
Author(s):  
CHAITHANYA SAGAR KOTAPURI ◽  
MOHAMMAD HAYATH RIVJEE
Keyword(s):  
Euclidean Distance ◽  
Distance Measure ◽  
Texture Features ◽  
Gray Level ◽  
Image Search ◽  
Retrieval Performance ◽  
Target Image ◽  
Image Histogram ◽  
Combined Features ◽  
Occurrence Matrix

In this paper we propose a new and efficient technique to retrieve images based on sum of the values of Local Histogram and GLCM (Gray Level Co-occurrence Matrix) texture of image sub-blocks to enhance the retrieval performance. The image is divided into sub blocks of equal size. Then the color and texture features of each sub-block are computed. Most of the image retrieval techniques used Histograms for indexing. Histograms describe global intensity distribution. They are very easy to compute and are insensitive to small changes in object translations and rotations. Our main focus is on separation of the image bins (histogram value divisions by frequency) followed by calculating the sum of values, and using them as image local features. At first, the histogram is calculated for an image sub-block. After that, it is subdivided into 16 equal bins and the sum of local values is calculated and stored. Similarly the texture features are extracted based on GLCM. The four statistic features of GLCM i.e. entropy, energy, inverse difference and contrast are used as texture features. These four features are computed in four directions (00, 450, 900, and 1350). A total of 16 texture values are computed per an image sub-block. An integrated matching scheme based on Most Similar Highest Priority (MSHP) principle is used to compare the query and target image. The adjacency matrix of a bipartite graph is formed using the sub-blocks of query and target image. This matrix is used for matching the images. Sum of the differences between each bin of the query and target image histogram is used as a distance measure for Local Histogram and Euclidean distance is adopted for texture features. Weighted combined distance is used in retrieving the images. The experimental results show that the proposed method has achieved highest retrieval performance.

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