Improved 3D Co-Occurrence Matrix for Texture Description and Classification

Barburiceanu, Stefania; Terebes, Romulus; Meza, Serban

doi:10.1109/isetc50328.2020.9301123

Cited by 4 publications

(6 citation statements)

References 11 publications

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“…The proposed method covered in the article extends our prior contributions validated in the two conference papers [28] and [29] by fusing the two results and provides a complex means of feature description in 3D. In [28], we proposed the 3D version of BM3DELBP (BM3DELBP_3D) whose feature space is constructed based on the signs of differences between neighbouring pixels without any information regarding the amount of difference such as the contrast or degree of homogeneity in the analysed image.…”

Section: The Proposed Methodsmentioning

confidence: 98%

“…In [28], we proposed the 3D version of BM3DELBP (BM3DELBP_3D) whose feature space is constructed based on the signs of differences between neighbouring pixels without any information regarding the amount of difference such as the contrast or degree of homogeneity in the analysed image. This knowledge is captured by the Improved 3D Gray-Level Co-Occurrence Matrix (IGLCM_3D) introduced by us in [29]. Following, the proposed approach described by the current paper aims at the combination of the two types of texture features in order to increase the discrimination power.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…We proposed in the preliminary work [29] an improved version of the 3D GLCM, the IGLCM_3D. The functional diagram is given in Figure 6.…”

Section: The Improved 3d Gray-level Co-occurrence Matrix (Iglcm_3d) 3...mentioning

confidence: 99%

“…The gradient magnitude-based feature vector is extracted from the GMCM matrix. As gradient-based indicators, we proposed in [29] the following measures:…”

Section: Computation Of the Haralick Feature Vectormentioning

confidence: 99%

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3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

2021

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Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

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Section: The Proposed Methodsmentioning

confidence: 98%

Section: The Proposed Methodsmentioning

confidence: 99%

“…We proposed in the preliminary work [29] an improved version of the 3D GLCM, the IGLCM_3D. The functional diagram is given in Figure 6.…”

Section: The Improved 3d Gray-level Co-occurrence Matrix (Iglcm_3d) 3...mentioning

confidence: 99%

“…The gradient magnitude-based feature vector is extracted from the GMCM matrix. As gradient-based indicators, we proposed in [29] the following measures:…”

Section: Computation Of the Haralick Feature Vectormentioning

confidence: 99%

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3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

2021

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“…Moreover, Tan et al [32] proposed a 3D GLCM-based Convolution neural network (3D-GLCM CNN) model for the clinical task of polyp classification for discriminating volumetric malignant polyps from benign polyps. Besides, Barburiceanu et al [33] proposed an approach to feature extraction from volumetric images by adopting improved 3D GLCM based on illumination information, gradient magnitude, and gradient orientation. This is followed by the extraction of Haralick features, gradient-based and orientation-based proposed indicators, which showed that the improved 3D Co-Occurrence Matrix increases the discrimination power and the classification performance.…”

Section: Texture Featuresmentioning

confidence: 99%

Liver Tumor Decision Support System on Human Magnetic Resonance Images: A Comparative Study

Alquran¹,

Al-Issa²,

Alslatie³

et al. 2023

Computer Systems Science and Engineering

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Liver cancer is the second leading cause of cancer death worldwide. Early tumor detection may help identify suitable treatment and increase the survival rate. Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs. Magnetic Resonance Imaging (MRI), in particular, uses magnetic fields and radio waves to differentiate internal human organs tissue. However, the interpretation of medical images requires the subjective expertise of a radiologist and oncologist. Thus, building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses. This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images. This paper proposed two models; the first one employed the 3D features while the second exploited the 2D features. The first system uses 3D texture attributes, 3D shape features, and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories. On top of that, the proposed method is applied to 2D slices for comparison purposes. The proposed approach attained 100% accuracy in discriminating between all types of tumors, 100% Area Under the Curve (AUC), 100% sensitivity, and 100% specificity and precision as well in 3D liver tumors. On the other hand, the performance is lower in 2D classification. The maximum accuracy reached 96.4% for two classes and 92.1% for four classes. The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes. The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input, besides comparing the performance between 2D and 3D classification. In the future, the presented work can be extended to be used

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Fine-Grained Correlation Representation for Graph-Based Point Cloud Attribute Compression

Song

Yang

et al. 2022

2022 IEEE International Conference on Multimedia and Expo (ICME)

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Improved 3D Co-Occurrence Matrix for Texture Description and Classification

Cited by 4 publications

References 11 publications

3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

Liver Tumor Decision Support System on Human Magnetic Resonance Images: A Comparative Study

Fine-Grained Correlation Representation for Graph-Based Point Cloud Attribute Compression

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