Segmentation of X-ray image using city block distance measure

Roopa, H; Asha, T.

doi:10.1109/iccicct.2016.7987942

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…The features are then computed from the segmented region of interest for classifying the input X-ray as healthy or TB infected. Such techniques used, registration-based method [15], active shape models [16], city block distance measure [17], and distance Regularised level set formulation [7] for segmenting the region of interest. The segmented region of interest is then used for the extraction of multiple feature descriptors that are fed to the classifier for the classification of TB.…”

Section: Related Workmentioning

confidence: 99%

A deep learning approach for the classification of TB from NIH CXR dataset

Zaidi

Akram

Jameel

et al. 2021

IET Image Processing

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In this research, a novel customized deep learning model is proposed to detect Tuberculosis (TB) from chest X‐rays (CXR). The model is utilized for three experimentations: (i) classification of CXR image as healthy or TB infected, (ii) sub‐classification of infected images to TB specific manifestations, and (iii) classification of CXR image to thoracic disease manifestations. The National Institute of Health (NIH) CXR is used for experimentation. For the first two experimentations, the subset of the dataset is used containing only 10 TB specific manifestations, whereas, the entire NIH CXR dataset is used for the third experiment. The F1 score for binary classification of TB in experiment 1 is calculated as 0.92 which is higher than the average F1 score of the radiologists. The average accuracy for classifying TB specific manifestations in experiment 2 is recorded as 0.84. Finally, the average accuracy of the thoracic disease classification is recorded as 0.82 in experiment 3. The proposed system outperformed the existing approaches reporting higher AUC for each manifestation. Whereas, to the best of knowledge it is the first such attempt on NIH CXR dataset for TB and TB specific manifestation classification and the proposed system showed promising results.

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Section: Related Workmentioning

confidence: 99%

A deep learning approach for the classification of TB from NIH CXR dataset

Zaidi

Akram

Jameel

et al. 2021

IET Image Processing

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“…Zyout et al [11] extracted textual pattern from mammogram images and Particle Swarm Optimization (PSO) was applied to select the most discriminative features then SVM was used for classification. Roopa et al [15] used city block distance measure for segmenting chest x-ray image which helps in diagnosis of TB.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Chest X-ray Images and Analysis using PCA and kPCA

Roopa¹,

Asha²

2018

IJECE

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<p class="abstract">Tuberculosis (TB) is an infectious disease caused by mycobacterium which can be diagnosed by its various symptoms like fever, cough, etc. Tuberculosis can also be analyzed by understanding the chest x-ray of the patient which is revealed by an expert physician .The chest x-ray image contains many features which cannot be directly used by any computer system for analyzing the disease. Features of chest x-ray images must be understood and extracted, so that it can be processed to a form to be fed to any computer system for disease analysis. This paper presents feature extraction of chest x-ray image which can be used as an input for any data mining algorithm for TB disease analysis. So texture and shape based features are extracted from x-ray image using image processing concepts. The features extracted are analyzed using principal component analysis (PCA) and kernel principal component analysis (kPCA) techniques. Filter and wrapper feature selection method using linear regression model were applied on these techniques. The performance of PCA and kPCA are analyzed and found that the accuracy of PCA using wrapper approach is 96.07% when compared to the accuracy of kPCA which is 62.50%. PCA performs well than kPCA with a good accuracy.</p>

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Analysis of Feature Ranking Methods on X-Ray Images

Roopa¹,

Asha²

2019

Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

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Segmentation of X-ray image using city block distance measure

Cited by 7 publications

References 6 publications

A deep learning approach for the classification of TB from NIH CXR dataset

A deep learning approach for the classification of TB from NIH CXR dataset

Feature Extraction of Chest X-ray Images and Analysis using PCA and kPCA

Analysis of Feature Ranking Methods on X-Ray Images

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