In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM)

We present our latest work on in vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). The in vivo skin capacitive images were taken by a capacitance based fingerprint sensor, the skin capacitive images were then analysed by GLCM. Four different GLCM feature vectors, angular second moment (ASM), entropy (ENT), contrast (CON) and correlation (COR), are selected to describe the skin texture. The results show that angular second moment increases as age increases, and entropy decreases as age increases. The results also suggest that the angular second moment values and the entropy values reflect more about the skin texture, whilst the contrast values and the correlation values reflect more about the topically applied solvents. The overall results shows that the GLCM is an effective way to extract and analyse the skin texture information, which can potentially be a valuable reference for evaluating effects of medical and cosmetic treatments.

Computer-aided diagnosis of liver tumors on computed tomography images

BACKGROUND AND OBJECTIVE

Liver cancer is the tenth most common cancer in the USA, and its incidence has been increasing for several decades. Early detection, diagnosis, and treatment of the disease are very important. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver cancer. CT scanners can provide multiple-phase sequential scans of the whole liver. In this study, we proposed a computer-aided diagnosis (CAD) system to diagnose liver cancer using the features of tumors obtained from multiphase CT images.

METHODS

A total of 71 histologically-proven liver tumors including 49 benign and 22 malignant lesions were evaluated with the proposed CAD system to evaluate its performance. Tumors were identified by the user and then segmented using a region growing algorithm. After tumor segmentation, three kinds of features were obtained for each tumor, including texture, shape, and kinetic curve. The texture was quantified using 3 dimensional (3-D) texture data of the tumor based on the grey level co-occurrence matrix (GLCM). Compactness, margin, and an elliptic model were used to describe the 3-D shape of the tumor. The kinetic curve was established from each phase of tumor and represented as variations in density between each phase. Backward elimination was used to select the best combination of features, and binary logistic regression analysis was used to classify the tumors with leave-one-out cross validation.

RESULTS

The accuracy and sensitivity for the texture were 71.82% and 68.18%, respectively, which were better than for the shape and kinetic curve under closed specificity. Combining all of the features achieved the highest accuracy (58/71, 81.69%), sensitivity (18/22, 81.82%), and specificity (40/49, 81.63%). The Az value of combining all features was 0.8713.

CONCLUSIONS

Combining texture, shape, and kinetic curve features may be able to differentiate benign from malignant tumors in the liver using our proposed CAD system.

Texture analysis of pulmonary parenchymateous changes related to pulmonary thromboembolism in dogs - a novel approach using quantitative methods

Background

Diagnosis of pulmonary thromboembolism (PTE) in dogs relies on computed tomography pulmonary angiography (CTPA), but detailed interpretation of CTPA images is demanding for the radiologist and only large vessels may be evaluated. New approaches for better detection of smaller thrombi include dual energy computed tomography (DECT) as well as computer assisted diagnosis (CAD) techniques. The purpose of this study was to investigate the performance of quantitative texture analysis for detecting dogs with PTE using grey-level co-occurrence matrices (GLCM) and multivariate statistical classification analyses.

CT images from healthy (n = 6) and diseased (n = 29) dogs with and without PTE confirmed on CTPA were segmented so that only tissue with CT numbers between −1024 and −250 Houndsfield Units (HU) was preserved. GLCM analysis and subsequent multivariate classification analyses were performed on texture parameters extracted from these images.

Results

Leave-one-dog-out cross validation and receiver operator characteristic (ROC) showed that the models generated from the texture analysis were able to predict healthy dogs with optimal levels of performance. Partial Least Square Discriminant Analysis (PLS-DA) obtained a sensitivity of 94% and a specificity of 96%, while Support Vector Machines (SVM) yielded a sensitivity of 99% and a specificity of 100%. The models, however, performed worse in classifying the type of disease in the diseased dog group: In diseased dogs with PTE sensitivities were 30% (PLS-DA) and 38% (SVM), and specificities were 80% (PLS-DA) and 89% (SVM). In diseased dogs without PTE the sensitivities of the models were 59% (PLS-DA) and 79% (SVM) and specificities were 79% (PLS-DA) and 82% (SVM).

Conclusion

The results indicate that texture analysis of CTPA images using GLCM is an effective tool for distinguishing healthy from abnormal lung. Furthermore the texture of pulmonary parenchyma in dogs with PTE is altered, when compared to the texture of pulmonary parenchyma of healthy dogs. The models’ poorer performance in classifying dogs within the diseased group, may be related to the low number of dogs compared to texture variables, a lack of balanced number of dogs within each group or a real lack of difference in the texture features among the diseased dogs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12917-017-1117-1) contains supplementary material, which is available to authorized users.

Retinal fundus image classification for diabetic retinopathy using SVM predictions

Diabetic Retinopathy (DR) is one of the leading causes of blindness in all age groups. Inadequate blood supply to the retina, retinal vascular exudation, and intraocular hemorrhage cause DR. Despite recent advances in the diagnosis and treatment of DR, this complication remains a challenging task for physicians and patients. Hence, a comprehensive and automated technique for DR screening is necessary, which will give early detection of this disease. The proposed work focuses on 16 class classification method using Support Vector Machine (SVM) that predict abnormalities individually or in combination based on the selected class. Our proposed work comprises Gaussian mixture model (GMM), K-means, Maximum a Posteriori (MAP) algorithm, Principal Component Analysis (PCA), Grey level co-occurrence matrix (GLCM), and SVM for disease diagnosis using DR. The proposed method provides an accuracy of 77.3% on DIARETDB1 dataset. We expect this low computational cost will be helpful in the medicine and diagnosis of DR.

Modeling of texture quantification and image classification for change prediction due to COVID lockdown using Skysat and Planetscope imagery

This research work models two methods together to provide maximum information about a study area. The quantification of image texture is performed using the “grey level co-occurrence matrix (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{GLCM}$$\end{document}GLCM)” technique. Image classification-based “object-based change detection (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{OBCD}$$\end{document}OBCD)” methods are used to visually represent the developed transformation in the study area. Pre-COVID and post-COVID (during lockdown) panchromatic images of Connaught Place, New Delhi, are investigated in this research work to develop a model for the study area. Texture classification of the study area is performed based on visual texture features for eight distances and four orientations. Six different image classification methodologies are used for mapping the study area. These methodologies are “Parallelepiped classification (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{PC}$$\end{document}PC),” “Minimum distance classification (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{MDC}$$\end{document}MDC),” “Maximum likelihood classification (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{MLC}$$\end{document}MLC),” “Spectral angle mapper (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{SAM}$$\end{document}SAM),” “Spectral information divergence (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{SID}$$\end{document}SID)” and “Support vector machine (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{SVM}$$\end{document}SVM).” \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{GLCM}$$\end{document}GLCM calculations have provided a pattern in texture features contrast, correlation, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{ASM}$$\end{document}ASM, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{IDM}$$\end{document}IDM. Maximum classification accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$83.68\%$$\end{document}83.68% and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$73.65\%$$\end{document}73.65% are obtained for pre-COVID and post-COVID image data through \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{MLC}$$\end{document}MLC classification technique. Finally, a model is presented to analyze before and after COVID images to get complete information about the study area numerically and visually.

Real-time snowy weather detection based on machine vision and vehicle kinematics: A non-parametric data fusion analysis protocol

INTRODUCTION

This study introduces a new analysis protocol for detecting real-time snowy weather conditions on freeways by utilizing trajectory-level data extracted from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) dataset. The data include parameters reduced from a real-time image feature extraction technique, time series data collected from external sensors, and CANbus data collected by the NDS ego-vehicles. To provide flexibility in winter maintenance, two segmentation types of one-minute and one-mile segments were used to sample snowy trips and their matched clear weather trips.

METHOD

In this study, four non-parametric models were developed using six data assemblies to detect snowy weather on freeways. The data assemblies are arranged based on three data sources, including image database extracted from an in-vehicle video camera, sensors, and CANbus data, to examine the effectiveness of snow detection models for different data types considering real-time availability of data.

RESULTS

Overall, the developed models successfully detected snowy weather on freeways with an accuracy ranging between 76% to 89%. Results indicated that high accuracy of estimating snowy weather can be accomplished using the data fusion between external sensors data and texture parameters of images, without accessing to CANbus data.

PRACTICAL APPLICATIONS

Practical applications can be driven with respect to the time or distance coordinates, using different data fusion assemblies, and data availability. The study proves the importance of employing vehicles as weather sensors in the Connected Vehicles (CV) applications and Variable Speed Limit (VSL) to improve traffic safety on freeways.