Texture classification using combination of LBP and GLRLM features along with KNN and multiclass SVM classification

Das, Sourajit; Jena, Uma Ranjan

doi:10.1109/ccintels.2016.7878212

Cited by 17 publications

(4 citation statements)

References 11 publications

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“…Gray‐level co‐occurrence matrix (GLCM)‐based and gray‐level run‐length matrix (GLRLM)‐based texture features are the best‐known radiomics analysis features . These texture features represent the statistical relationships between a collection of adjacent pixels in four particular directions (0°, 45°, 90°, and 135°), while ignore the statistical relationships in other directions, such as 30°, 60°, and so on.…”

Section: Introductionmentioning

confidence: 99%

A sparse representation‐based radiomics for outcome prediction of higher grade gliomas

Shi

Chen

et al. 2018

Medical Physics

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Purpose: Accurately predicting outcome (i.e., overall survival (OS) time) for higher grade glioma (HGG) has great clinical value and would provide optimized guidelines for treatment planning. Radiomics focuses on revealing underlying pathophysiological information in biomedical images for disease analysis and demonstrates promising prognostic clinical performance. In this paper, we propose a novel sparse representation-based radiomics framework to predict if HGG patients would have long or short OS time. Methods: First, taking advantages of the scale invariant feature transform (SIFT) feature in image characterizing, we developed a sparse representation-based method to convert a local SIFT descriptor into a global tumor feature. Next, because preserving sample structure is beneficial for feature selection, we proposed a locality preserving projection and sparse representation-combined feature selection method to select more discriminative features for tumor classification. Finally, we employed a multifeature collaborative sparse representation classification to combine the information of multimodal images to classify OS time. Results: Three experiments were performed on the two datasets provided by different institutions. Specifically, the proposed model was trained and independently tested on dataset 1 (135 subjects), on dataset 2 (86 subjects), and on the combination of dataset 1 and dataset 2, respectively. Experimental results demonstrated that the proposed method achieved encouraging prediction performance, exhibiting a testing accuracy of 93.33% on dataset 1 (one modality), 92.31% on dataset 2 (two modalities), and 87.93% on the combined dataset (one modality). Conclusions: The sparse representation theory provides reasonable solutions to feature extraction, feature selection, and classification for radiomics. This study provides a promising tool to enhance the prediction performance of HGG patient's outcome.

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Section: Introductionmentioning

confidence: 99%

A sparse representation‐based radiomics for outcome prediction of higher grade gliomas

Shi

Chen

et al. 2018

Medical Physics

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“…Features extracted from SEM and AFM images were fed to five classifiers: decision tree (DT) (Mohanty et al, 2013), random forest (RF) (Raman et al, 2014), support vector machine (SVM) (Fernandez-Lozano et al, 2015) with kernel function RBF, k -nearest neighbor (KNN) (Das & Jena, 2016), and neural network (NN) (Mala et al, 2015). These classifiers were evaluated using a tenfold cross-validation approach.…”

Section: Methodsmentioning

confidence: 99%

Elucidation of Differential Nano-Textural Attributes for Normal Oral Mucosa and Pre-Cancer

et al. 2019

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Computational analysis on altered micro-nano-textural attributes of the oral mucosa may provide precise diagnostic information about oral potentially malignant disorders (OPMDs) instead of an existing handful of qualitative reports. This study evaluated micro-nano-textural features of oral epithelium from scanning electron microscopic (SEM) images and the sub-epithelial connective tissue from light microscopic (LM) and atomic force microscopic (AFM) images for normal and OPMD (namely oral sub-mucous fibrosis, i.e., OSF). Objective textural descriptors, namely discrete wavelet transform, gray-level co-occurrence matrix (GLCM), and local binary pattern (LBP), were extracted and fed to standard classifiers. Best classification accuracy of 87.28 and 93.21%; sensitivity of 93 and 96%; specificity of 80 and 91% were achieved, respectively, for SEM and AFM. In the study groups, SEM analysis showed a significant (p < 0.01) variation for all the considered textural descriptors, while for AFM, a remarkable alteration (p < 0.01) was only found in GLCM and LBP. Interestingly, sub-epithelial collagen nanoscale and microscale textural information from AFM and LM images, respectively, were complementary, namely microlevel contrast was more in normal (0.251) than OSF (0.193), while nanolevel contrast was more in OSF (0.283) than normal (0.204). This work, thus, illustrated differential micro-nano-textural attributes for oral epithelium and sub-epithelium to distinguish OPMD precisely and may be contributory in early cancer diagnostics.

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“…Cetiner et al [158] proposed a method of feature extraction based on the wavelet moment and defect image classification based on KNN, which can be used in automatic defect classification systems in the forest industry. Das and Jena [159] presented a method combining image texture feature extraction techniques. First, LBP and the grey level run length matrix (GLRLM) were combined to extract image features, and then KNN and an SVM were used for classification.…”

Section: Classificationmentioning

confidence: 99%

State of the Art in Defect Detection Based on Machine Vision

Ren

Fang

Yan

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

302

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Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

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Texture classification using combination of LBP and GLRLM features along with KNN and multiclass SVM classification

Cited by 17 publications

References 11 publications

A sparse representation‐based radiomics for outcome prediction of higher grade gliomas

A sparse representation‐based radiomics for outcome prediction of higher grade gliomas

Elucidation of Differential Nano-Textural Attributes for Normal Oral Mucosa and Pre-Cancer

State of the Art in Defect Detection Based on Machine Vision

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