Now showing 1 - 9 of 9
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    Differentiation of digital TB images using multi-fractal analysis
    (17-10-2011)
    Priya, E.
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    Srinivasan, S.
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    Microscopic examination of stained sputum smears has remained the cornerstone of pulmonary TB (tuberculosis) diagnosis throughout the world. In this work, an attempt has been made to differentiate such digital TB positive and negative sputum smear images using multi-fractal analysis. The digital TB images (N50) used for this analysis were captured for this analysis were captured using a fluorescence microscope from auramine stained slides. The multi-fractal analysis of the captured original images and cropped ones were subjected to multi-fractal analysis. Distinct variations were observed from the cropped images in terms of the multi-fractal signatures, maximum and minimum values of local and global information (a, f(a)). The spectral width was found to vary significant for positive and negative images. The results seem to be clinically useful for mass screening of TB images. © 2011 IEEE.
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    Performance of SURF and SIFT keypoints for the automated differentiation of abnormality in chest radiographs
    (01-07-2021)
    Govindarajan, Satyavratan
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    In this work, automated abnormality detection using keypoint information from Speeded-Up Robust feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors in chest Radiographic (CR) images is investigated and compared. Computerized image analysis using artificial intelligence is crucial to detect subtle and non-specific alterations of Tuberculosis (TB). For this, the healthy and TB CRs are subjected to lung field segmentation. SURF and SIFT keypoints are extracted from the segmented lung images. Statistical features from keypoints, its scale and orientation are computed. Discrimination of TB from healthy is performed using SVM. Results show that the SURF and SIFT methods are able to extract local keypoint information in CRs. Linear SVM is found to perform better with precision of 88.9% and AUC of 91% in TB detection for combined features. Hence, the application of keypoint techniques is found to have clinical relevance in the automated screening of non-specific TB abnormalities using CRs. © 2021 European Federation for Medical Informatics (EFMI) and IOS Press.
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    Differentiation of digital tb images using texture analysis and RBF classifier
    (01-01-2012)
    Priya, E.
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    Srinivasan, S.
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    In this work, differentiation of positive and negative images of Tuberculosis (TB) sputum smear has been attempted using statistical method based on Gray Level Co-occurrence Matrix (GLCM). The sputum smear images (N=100) recorded under standard image acquisition protocol are considered for this work. Second order statistical texture analysis is performed on the acquired images using GLCM method and a set of nineteen features are derived. Principal Component Analysis (PCA) is then employed to reduce feature sets, to enhance the efficiency of differentiation and to reduce the redundancy. These feature sets are further classified using Radial Basis Function (RBF) classifier. Results show that GLCM is able to differentiate positive and negative TB images. Correlation is found to be high for many of the parameters. Application of PCA reduced the number of features to four which had maximum magnitude in the first principal component. Higher classification accuracy is achieved using RBF classifier. It appears that this method of texture analysis could be useful to develop automated system for characterization and classification of digital TB sputum smear images. © 2012 All Rights Reserved.
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    Classification of tuberculosis digital images using hybrid evolutionary extreme learning machines
    (17-12-2012)
    Priya, Ebenezer
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    Srinivasan, Subramanian
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    In this work, classification of Tuberculosis (TB) digital images has been attempted using active contour method and Differential Evolution based Extreme Learning Machines (DE-ELM). The sputum smear positive and negative images (N=100) recorded under standard image acquisition protocol are subjected to segmentation using level set formulation of active contour method. Moment features are extracted from the segmented images using Hu's and Zernike method. Further, the most significant moment features derived using Principal Component Analysis and Kernel Principal Component Analysis (KPCA) are subjected to classification using DE-ELM. Results show that the segmentation method identifies the bacilli retaining their shape in-spite of artifacts present in the images. It is also observed that with the KPCA derived significant features, DE-ELM performed with higher accuracy and faster learning speed in classifying the images. © 2012 Springer-Verlag.
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    Shape characterization of mediastinum in tuberculosis chest radiographs using level set segmentation
    (01-04-2021)
    Tulo, Sukanta Kumar
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    Govindarajan, Satyavratan
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    Mediastinum is considered as one of the substantial anatomical regions for the gross diagnosis of several chest related pathologies. The geometric variations of the mediastinum in Chest Radiographs (CXRs) could be utilised as potential image markers in the early detection of Tuberculosis (TB). This study attempts to segment mediastinum in CXRs using level sets for the shape characterization of TB conditions. The CXR images for this study are considered from a public database. An edge-based distance regularized level set evolution is employed to segment the lungs followed by a region-based Chan-Vese model that extracts mediastinum region. Features such as mediastinum area and lungs area are extracted from the segmented images. Further, mediastinum to lungs area ratio is calculated. Statistical analysis is performed on the features to differentiate normal and TB images. Results show that the proposed segmentation approach is able to segment the lungs and extract the mediastinum in CXRs. It is found that features namely mediastinum area and mediastinum to lungs area ratio are statistically significant in the differentiation of TB. Larger mediastinum area is observed in TB images as compared to normal. The performance of lung field segmentation is also observed to be in line with the literature. The mediastinum segmentation approach in CXRs obtains to be a novel method as compared to the existing methods. As the proposed approach based on mediastinum image analysis provides better shape characterization, the study could be clinically useful in the differentiation of TB conditions.
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    An automated approach to differentiate drug resistant tuberculosis in chest X-ray images using projection profiling and mediastinal features
    (01-07-2021)
    Tulo, Sukanta Kumar
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    In this study, an attempt has been made to differentiate Drug Resistant Tuberculosis (DR-TB) in chest X-rays using projection profiling and mediastinal features. DR-TB is a condition which is non-responsive to at least one of anti-TB drugs. Mediastinum variations can be considered as significant image biomarkers for detection of DR-TB. Images are obtained from a public database and are contrast enhanced using coherence filtering. Projection profiling is used to obtain the feature lines from which the mediastinal and thoracic indices are computed. Classification of Drug Sensitive (DS-TB) and DR-TB is performed using three classifiers. Results show that the mediastinal features are found to be statistically significant. Support vector machine with quadratic kernel is able to provide better classification performance values of greater than 93%. Hence, the automated analysis of mediastinum could be clinically significant in differentiation of DR-TB. © 2021 European Federation for Medical Informatics (EFMI) and IOS Press.
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    Publication
    Differentiation of digital TB images using texture analysis and RBF classifier
    (20-08-2012)
    Priya, E.
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    Srinivasan, S.
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    In this work, differentiation of positive and negative images of Tuberculosis (TB) sputum smear has been attempted using statistical method based on Gray Level Co-occurrence Matrix (GLCM). The sputum smear images (N=100) recorded under standard image acquisition protocol are considered for this work. Second order statistical texture analysis is performed on the acquired images using GLCM method and a set of nineteen features are derived. Principal Component Analysis (PCA) is then employed to reduce feature sets, to enhance the efficiency of differentiation and to reduce the redundancy. These feature sets are further classified using Radial Basis Function (RBF) classifier. Results show that GLCM is able to differentiate positive and negative TB images. Correlation is found to be high for many of the parameters. Application of PCA reduced the number of features to four which had maximum magnitude in the first principal component. Higher classification accuracy is achieved using RBF classifier. It appears that this method of texture analysis could be useful to develop automated system for characterization and classification of digital TB sputum smear images.
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    Publication
    Evaluation of Diagnostic Value of Mediastinum for Differentiation of Drug Sensitive, Multi and Extensively Drug Resistant Tuberculosis Using Chest X-Rays
    Background and Objective: The rise of Drug Resistant Tuberculosis (DR TB), particularly Multi DR (MDR), and Extensively DR (XDR) has reduced the rate of control of the disease. Computer aided diagnosis using Chest X-rays (CXRs) can help in mass screening and timely diagnosis of DR TB, which is essential to administer proper treatment regimens. In CXRs, lungs and mediastinum are two significant regions which contain the information about the likelihood of DR TB. The objective of this work is to analyze the shape characteristics of lungs and mediastinum to improve the diagnostics accuracy for differentiation of Drug Sensitive (DS), MDR and XDR TB using computer aided diagnostics system. Methods: The CXR images of DS and DR TB patients are obtained from a public database. The lung fields are segmented from the CXRs using Reaction Diffusion Level Set Evolution. Mediastinum is segmented from the delineated lung masks using Chan Vese model. The shape features from each lung and mediastinum masks are extracted and analysed. The discriminative power of individual and combination of both lung and mediastinum features are evaluated using machine learning techniques for classification of DS vs MDR, MDR vs XDR and DS vs XDR TB images. The performances of classifiers are compared using standard metrics. Results: The proposed segmentation methods are able to delineate lungs and mediastinum from the CXR images. The extracted lung and mediastinum features are found to be statistically significant (p < 0.05) for differentiation of DS and DR TB conditions. Using the combination of both lung and mediastinum features, Multi-Layer Perceptron classifier achieves maximum F-measure of 82.4%, 81.0% and 87.0% for differentiation of DS vs MDR, MDR vs XDR and DS vs XDR, respectively. Conclusion: Analysis of mediastinum along with the lungs in chest X-rays could improve the diagnostic performance for differentiation of drug sensitive and resistant TB conditions. The proposed methodology is able to differentiate DS, MDR and XDR TB, and found to be clinically relevant. Hence, this work is useful for computer-based early detection of DS and DR TB conditions.
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    Publication
    Analysis of Tuberculosis in Chest Radiographs for Computerized Diagnosis using Bag of Keypoint Features
    (01-04-2019)
    Govindarajan, Satyavratan
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    Chest radiography is the most preferred non-invasive imaging technique for early diagnosis of Tuberculosis (TB). However, lack of radiological expertise in TB detection leads to indiscriminate chest radiograph (CXR) screening. A modest classification approach based on the local image description to detect subtle characteristics of TB using CXRs is highly recommended. In this work, an attempt has been made to classify normal and TB CXR images using Bag of Features (BoF) approach with Speeded-Up Robust Feature (SURF) descriptor. The images are obtained from a public database. Lung fields segmentation is performed using Distance Regularized Level Set (DRLS) formulation. The results of segmentation are validated against the ground truth images using similarity, overlap and area correlation measures. BoF approach with SURF keypoint descriptors is implemented to categorize the images using Multilayer Perceptron (MLP) classifier. The obtained results demonstrate that the DRLS method is able to delineate lung fields from CXR images. The BoF with SURF keypoint descriptor is able to characterize local attributes of normal and TB images. The segmentation results are found to be in high correlation with ground truth. MLP classifier is found to provide high Recall, Specificity (Spec), Accuracy, F-score and Area Under the Curve (AUC) values of 87.7%, 85.9%, 87.8%, 87.6% and 94% respectively between normal and abnormal images. The proposed computer aided diagnostic approach is found to perform better as compared to the existing methods. Thus, the study can be of significant assistance to physicians at the point of care in resource constrained regions.