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

Barburiceanu, Stefania; Terebes, Romulus; Meza, Serban

doi:10.3390/app11052332

Cited by 43 publications

(20 citation statements)

References 30 publications

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“…For example, for the image shown in Fig 5, take the interval = 1 , and the gray co-occurrence matrix in all directions is shown in Fig 6. We created the corresponding GLCM based on the CCT image, the offset in this process is determined and the creation process is shown in Fig 7 . We first convert the preprocessed image to its corresponding grayscale image [29], that is, the intensity of each pixel with a depth of N bits ranging from 0 to 255. We then define the offset of the symbiotic comparison.…”

Section: Glcmmentioning

confidence: 99%

Gray level co-occurrence matrix and Schmitt neural network for Covid-19 diagnosis

Pi¹

2021

EAI Endorsed Transactions on e-Learning

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INTRODUCTION: When COVID-19 spreads to most of the world, chest CT imaging is widely regarded as a convenient and feasible method for the diagnosis of suspected patients. In the traditional diagnosis method, doctors and experts judge these CT images and draw conclusions. However, with the surge in the number of suspected patients, relying solely on traditional manual diagnosis methods can no longer meet people's demand for efficiency. OBJECTIVES: A number of previous studies have shown that it is possible to use machine learning methods to help people diagnose suspected SARS-CoV-2 patients. However, the accuracy of the existing scheme still needs to be improved. METHODS: In order to make a more accurate diagnosis of suspected patients with SARS-CoV-2, we proposed a new model. We first preprocessed the CCT images of the tested objects to seek for higher accuracy, then extracted the texture features from the processed CCT images, and finally divided the CCT images of the tested objects into two categories: sick and normal using Schmidt neural network. RESULTS: The accuracy of the proposed model is (76.33±1.18%), while the accuracy of the existing model RBFNN, ELM-BA, WEBBO and GLCM-SVM are (73.45±0.69%), (74.88±0.86%), (70.48±0.81%) and (64.42±0.88%), respectively. Compared with the existing RBFNN, ELM-BA, WEBBO and GLCM-SVM models, the accuracy of our proposed model is 1.45% higher than that of the best ELM-BA model. More importantly, the proposed model has better stability. CONCLUSION: The model we proposed is feasible for the corresponding diagnosis of suspected patients. This is not only conducive to timely treatment of patients, but more importantly, effective isolation of confirmed patients as soon as possible can prevent the further spread of the epidemic.

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Section: Glcmmentioning

confidence: 99%

Gray level co-occurrence matrix and Schmitt neural network for Covid-19 diagnosis

Pi¹

2021

EAI Endorsed Transactions on e-Learning

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“…In order to reduce the computational time needed for the curve to evolve towards its final state which corresponds to object boundaries, selective diffusion as it is proposed in (Terebes et al, 2018) can be also applied. Moreover, object texture (Barburiceanu, Terebes & Meza, 2021) can be also embedded within the curve evolution process in a similar manner as the proposed approach. This shows once again that the active contour model remains an important tool for image segmentation.…”

Section: Active Contour Models Driven By Cnnmentioning

confidence: 99%

Active Contours Driven by Cellular Neural Networks for Image Segmentation in Biomedical Application

Belean¹

2021

STUD INFORM CONTROL

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This paper proposes a novel approach for image segmentation in the context of biomedical applications. The medical images considered for this analysis are both microarray images and images recorded from microfluidics devices. In case of microarray images, microarray spots represented as circular shapes are localised and used further on for the estimation of gene expression levels, based on the average pixel intensities. Considering the microfluidic devices images, the features of cells or clusters of cells are determined by using the proposed image segmentation approach. The novelty of this approach lies in the fact that this is a segmentation procedure which uses an edge-based active contour model (ACM) driven by cellular neural networks (CNNs). Thus, a predefined curve is evolved towards the edges of the image objects (i.e., circular microarray spots and irregular shapes representing clusters of cells). In the curve evolution process, the classic representation of image edges by using the gradient vector is replaced by an edge-based feature template determined by the CNN for each image object. The benefits of the proposed segmentation method are illustrated for both image processing applications, by using specific quality measures for the characterization of object features within the image under analysis.

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“…These co-occurrences are computed for a grey-level image by enumerating the frequency of pixel pair values. Such spatial information, extracted in different directions for each pixel, can be used to improve representation learning in CNN-based architectures [ 29 ]. Also, the fusion of specific Haralick features with other handcrafted and in-depth features extracted through deep learning models has been used, revealing a high impact on the lung cancer classification performance [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Deep fusion of gray level co-occurrence matrices for lung nodule classification

2022

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Lung cancer is a serious threat to human health, with millions dying because of its late diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for early detection and classification of lung nodules. The requirement for high accuracy in analyzing CT scan images is a significant challenge in detecting and classifying lung cancer. In this paper, a new deep fusion structure based on the long short-term memory (LSTM) has been introduced, which is applied to the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCMs), classifying the nodules into benign, malignant, and ambiguous. Also, an improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. WSA-Otsu thresholding can overcome the fixed thresholds and time requirement restrictions in previous thresholding methods. Extended experiments are used to assess this fusion structure by considering 2D-GLCM based on 2D-slices and approximating the proposed 3D-GLCM computations based on volumetric 2.5D-GLCMs. The proposed methods are trained and assessed through the LIDC-IDRI dataset. The accuracy, sensitivity, and specificity obtained for 2D-GLCM fusion are 94.4%, 91.6%, and 95.8%, respectively. For 2.5D-GLCM fusion, the accuracy, sensitivity, and specificity are 97.33%, 96%, and 98%, respectively. For 3D-GLCM, the accuracy, sensitivity, and specificity of the proposed fusion structure reached 98.7%, 98%, and 99%, respectively, outperforming most state-of-the-art counterparts. The results and analysis also indicate that the WSA-Otsu method requires a shorter execution time and yields a more accurate thresholding process.

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

Cited by 43 publications

References 30 publications

Gray level co-occurrence matrix and Schmitt neural network for Covid-19 diagnosis

Gray level co-occurrence matrix and Schmitt neural network for Covid-19 diagnosis

Active Contours Driven by Cellular Neural Networks for Image Segmentation in Biomedical Application

Deep fusion of gray level co-occurrence matrices for lung nodule classification

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