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Three-dimensional texture analysis of MRI brain datasets
2001
IEEE Transactions on Medical Imaging
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It is based on extended, multisort co-occurrence matrices that employ intensity, gradient and anisotropy image features in a uniform way. ...
The ability of the suggested 3-D texture descriptors is demonstrated on nontrivial classification tasks for pathologic findings in brain datasets. ...
In this paper, we propose a new method for 3-D texture analysis which is based on extended co-occurrence matrices. ...
doi:10.1109/42.925295
pmid:11403201
fatcat:vwvh7wj5andqvcqzwsie76qr5q
A co-occurrence texture semi-invariance to direction, distance, and patient size
2008
Medical Imaging 2008: Image Processing
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Several research studies have shown that the co-occurrence texture model and its Haralick descriptors can be successfully applied to capture the statistical properties of the soft tissues' patterns. ...
Based on the Analysis of Variance (ANOVA) technique and multiple pair-wise comparisons, we found that using only the "near" and "far" displacements is enough to capture the spatial properties of the texture ...
Based on our previous work on liver texture-based segmentation 10 , co-occurrence texture model performs the best among these texture models. ...
doi:10.1117/12.771068
dblp:conf/miip/SusomboonRFJ08
fatcat:juvmlgmbjvb7jki3334a26fghy
Multi-scale gray level co-occurrence matrices for texture description
2013
Neurocomputing
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Gray level co-occurrence matrices (GLCM) have been proved to be an effective texture descriptor. ...
The performance of the proposed approach is evaluated by applying the multi-scale descriptor on five benchmark texture data sets and the results are compared to other well-known texture operators, including ...
Descriptors are extracted from these matrices. The number of rows and columns of the co-occurrence matrix depends only on the gray levels in the texture and not on the image size. ...
doi:10.1016/j.neucom.2012.09.042
fatcat:cupud4zx55flfb77oooea2urcy
A general framework for content-based medical image retrieval with its application to mammograms
2005
Medical Imaging 2005: PACS and Imaging Informatics
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These two groups of ROIs are used to analyze 11 textural features based on gray level co-occurrence matrices. ...
A maximum precision of 51% and recall of 19% were obtained using the gray level co-occurrence matrices and a distance of 5. The averages of precision and recall are 49% and 18% in this experiment. ...
This study concentrates on textural analysis based on gray-level co-occurrence matrices for the content-based retrieval of mammograms. ...
doi:10.1117/12.594929
fatcat:axdwatg2czb4linkftwcednxdu
Statistical Descriptors for Fingerprint Matching
2012
International Journal of Computer Applications
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Statistical descriptors are computed from 16 Gray Level Co-occurrence Matrices (GLCM) from Extracted ROI. The proposed algorithm is evaluated on the FVC2002 DB2 database. ...
This paper presents a novel algorithm for fingerprint matching using statistical descriptors. ...
[14] defines the measures of 14 different textural features, which can be extracted from these co-occurrence matrices. ...
doi:10.5120/9633-4361
fatcat:cjr6dnzeujcx5n2s4ddjjr6dli
Automatic classification of granite tiles through colour and texture features
2012
Expert systems with applications
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The results show that classification based on colour and texture is highly effective and outperforms previous methods based on textural features alone. ...
We discuss issues and possible solutions related to image acquisition, robustness against noise factors, extraction of visual features and classification, with particular focus on the last two. ...
Integrative co-occurrence matrices Integrative co-occurrence matrices (Arvis et al., 2004; Palm, 2004) are based on both intra-and inter-channel features which are computed by extracting the same co-occurrence ...
doi:10.1016/j.eswa.2012.03.052
fatcat:zsczbghqg5hfziswssjpqudqcm
Local versus global texture analysis for lung nodule image retrieval
2008
Medical Imaging 2008: PACS and Imaging Informatics
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For comparison purposes we utilized Manhattan, Euclidean, and Chebyshev distances for one-dimensional feature vectors (global co-occurrence) while for two-dimensional feature comparison (local co-occurrence ...
Global co-occurrence performed the worse at 44% precision yet when co-occurrence was performed locally (local co-occurrence) the precision results improved to 64%. ...
In global co-occurrence the texture descriptors were extracted per nodule image while in local co-occurrence the texture descriptors were extracted for each relevant pixel in the nodule image. ...
doi:10.1117/12.770979
fatcat:uchyk3ztzzbldgj3i36dvkljdy
Co-occurrence Matrix of Covariance Matrices: A Novel Coding Model for the Classification of Texture Images
[chapter]
2017
Lecture Notes in Computer Science
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Main idea Based on the concept of the gray-level co-occurrence matrix (GLCM) used in texture analysis. ...
The co-occurrence matrix of covariance matrices. ...
On going work. → Sensitivity analysis: displacement (∆ x , ∆ y ), number of atoms (K ), patch size, similarity measure. → Extraction of Haralick features. → Fuzzy version of co-occurrence matrices. ...
doi:10.1007/978-3-319-68445-1_85
fatcat:5hmgnhincfabnmlbtzluukzisu
Plant Leaf Identification Using Multi-scale Fractal Dimension
[chapter]
2009
Lecture Notes in Computer Science
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Yielded results show the potential of the approach, which overcomes traditional texture analysis methods, such as Co-occurrence matrices, Gabor filters and Fourier descriptors. ...
This paper presents a novel approach to plant identification based on leaf texture. ...
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94
62.66
Co-occurrence matrices
16
130
86.66
M.S. ...
doi:10.1007/978-3-642-04146-4_17
fatcat:7h4wdzsxrvcshfpp4fd7gghq5u
Texture classification of images from Endoscopic Capsule by using MLP and SVM - A comparative approach
[chapter]
2009
International Federation for Medical and Biological Engineering Proceedings
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Texture descriptors calculated from co-occurrence matrices are then modeled by using third and forth order moments in order to cope with non-Gaussianity, which appears especially in some pathological cases ...
Texture information is coded by second order statistics of color image levels extracted from co-occurrence matrices. The cooccurrence matrices are computed from images rich in texture information. ...
on co-occurrence matrices. ...
doi:10.1007/978-3-642-03904-1_75
fatcat:qp3urxkd7bfvbety7tbrk6uf4u
An ink texture descriptor for NIR-imaged medieval documents
2009
2009 16th IEEE International Conference on Image Processing (ICIP)
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We use co-occurrence matrices of gray-level intensities (GLCM) to model these second-order statistics [11] . ...
The descriptor is enriched by first-order and second-order statistics, with a new feature based on weighed off-diagonal bands and eigen decomposition of the covariant matrix of local joint intensity co-occurrences ...
doi:10.1109/icip.2009.5413820
dblp:conf/icip/LicataPK09
fatcat:cc3lqgglg5aqbddtxpr72zicxy
Hybrid Tow Feature Extraction Descriptor for Shape Pattern Recognition
2018
Australian Journal of Basic and Applied Sciences
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Grey Level Co-Occurrence Matrix (GLCM ): The second method of features extraction is based on grey-level co-occurrence metrics (GLCM). ...
The Angular radial transform (ART) is an image description method based on moment accepted in MPEG-7 as a shape descriptor based on 2D region (Bober, M., 2001) . wavelet descriptors. ...
doi:10.22587/ajbas.2018.12.7.5
fatcat:qtyso3v4gbgwpkn5r5lyn72dzu
Computer Aided Diagnosis of Dental CT images for Bone Quality Assessment
2011
International Journal of Bioscience Biochemistry and Bioinformatics
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This article focuses on comparing the discriminating power of several multiresolution texture analysis methods to evaluate the quality of the bone based on the texture variations of the images obtained ...
a classifier that automatically grades the bone depends on the quality and correlating significant texture parameters with Insertion Torque Values. ...
This yields twenty four texture descriptors (eight for each detail) for every level of resolution. b) Wavelet Co-occurrence Features Secondly, co-occurrence matrices were calculated at each Traditional ...
doi:10.7763/ijbbb.2011.v1.42
fatcat:j2uygmseynac5eou4j5ytwglxi
Plant Texture Classification Using Gabor Co-occurrences
[chapter]
2010
Lecture Notes in Computer Science
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This paper presents a method for comparing and classifying plants based on leaf texture. Joint distributions for the responses from applying different scales of the Gabor filter are calculated. ...
This technique is also applied to the Brodatz texture database, to demonstrate its more general application, and comparison to the results from traditional texture analysis methods is given. ...
For the leaf datasets, the Fourier descriptors outperformed the cooccurrence matrices, whilst for the Brodatz datasets, co-occurrence matrices did better. ...
doi:10.1007/978-3-642-17274-8_65
fatcat:wqn47ko6xvhdxd7b3zjwutj4vy
Haralick feature extraction from LBP images for color texture classification
2008
2008 First Workshops on Image Processing Theory, Tools and Applications
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In this paper, we present a new approach for color texture classification by use of Haralick features extracted from co-occurrence matrices computed from Local Binary Pattern (LBP) images. ...
An iterative procedure then selects among the extracted features, those which discriminate the textures, in order to build a low dimensional feature space. ...
Haralick features extracted from co-occurrence matrices Co-occurrence matrices, introduced by Haralick [20] , are statistical descriptors which both measure the grey scale distribution in an image and ...
doi:10.1109/ipta.2008.4743780
fatcat:uuv52lygjzfq5k5nrub2loygim
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