Chest X-ray segmentation using Sauvola thresholding and Gaussian derivatives responses

Kiran, Mahreen; Ahmed, Imran; Khan, Nazish; Reddy, Alavalapati Goutham

doi:10.1007/s12652-019-01281-7

Cited by 22 publications

(3 citation statements)

References 41 publications

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“…K means clustering method goes in the circle of vector quantization, forms cluster of k in n mining region (Kasu & Saravanan, 2019). The region growing method of chest segmentation is described in Kiran et al (2019). The normalization of the x-ray input chest image is made due to the poor contrast or due to glare (Guendel et al, 2019;Vidya et al, 2019;Hoffman, Kothari, & Wang, 2014).…”

Section: Methodsmentioning

confidence: 99%

A new merged segmentation technique used for X-ray chest images

Perumal¹,

Paul²

2021

3C Tecnología

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Image processing techniques of a chest X-ray image includes noise removal followed by segmentation, feature extraction to locate regions and classification. The chest image appears differently when viewed from different angles or under different lightings. Diagnosing TB stays a challenge. The customary medical specialty, even so count, variety ways, they are gradual and frequently unreliable. In ancient poster anterior chest radiographs, several clinical and diagnostic functions build use of computationally designed algorithms that assist in scientific diagnostic analysis by victimization acquisition of pictures. The Digital image may be a necessary medium for analyzing, annotating, patient's demographics coverage in screening of TB via chest radiography. This paper deals with the essential segmentation methods of TB. In our methodology, this disease can be fastly and accurately identifiable by the merge segmentation methods of K means with Marker-based Watershed segmentation which has highest precision and recall values when compared to the several other segmentation methods which is been is discussed. More than 80 chest Xray images output for recall and precision is discussed here.

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Section: Methodsmentioning

confidence: 99%

A new merged segmentation technique used for X-ray chest images

Perumal¹,

Paul²

2021

3C Tecnología

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“…These techniques can also be contextualized in the analysis of Chest X-rays, in which significant amounts of quantitative data are obtained/extracted and can be used for many diagnostic purposes. Chest X-rays are considered as one of the most basic examination tools in many medical practices [ 13 ]. It is economical and has significant clinical value in diagnosing various infectious diseases of lungs [ 14 ], like, Pneumonia, Tuberculosis, early Lung cancer, and, nowadays, COVID-19.…”

Section: Introductionmentioning

confidence: 99%

An IoT-enabled smart health care system for screening of COVID-19 with multi layers features fusion and selection

2022

Self Cite

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Advancement of smart medical sensors, devices, cloud computing, and health care technologies is getting remarkable attention from academia and the health care industry. As, Internet of things (IoT) has been recognized as one of the promising research topics in the domain of health care, particularly in medical image processing. Researchers utilized various machine and deep learning techniques along with artificial intelligence for analyzing medical images. These developed techniques are used to detect diseases that might assist medical experts in diagnosing diseases at early stages and providing accurate, consistent, effective, and speedy results, and decrease the mortality rate. Nowadays, Coronavirus (COVID-19) has been growing as one of the most rigorous and severe infections and spreading globally. Consequently, an intelligent automated system is required as an active diagnostic choice that might be used to prevent the spread of COVID-19. Thus, this work presented an IoT-enabled smart health care system for the automatic screening and classification of contagious diseases (Pneumonia, COVID-19) in Chest X-ray images. The developed system is based on two different deep learning architectures used with a multi-layers feature fusion and feature selection approach to classify X-ray images of infectious diseases. This work comprises the following steps: to enhance the diversity of the data set, data augmentation is performed, while for feature extraction, deep learning architectures, i.e., VGG-19 and Inception-V3, are used along with transfer learning. For the fusion of extracted features obtained from deep learning architectures, a parallel maximum covariance, and for feature selection, the multi-logistic regression controlled entropy variance approach is applied. For experimentation, a data set is customized containing Chest X-ray images using various publicly available resources. The system provides an overall classification accuracy of 97%.

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“…Full phases of medical image processing are addressed. Researchers [5] [6] established a method using soft calculating and the various techniques pertaining to image processing systems for spotting the lung cancer from CT scanned photo images. Here, for pre-processing, anisotropic diffusion filter as well as Gaussian filter techniques happen to be applied.…”

Section: Introductionmentioning

confidence: 99%

Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach

Bhandary¹,

G²,

Basthikodi³

et al. 2021

IJETT

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Lung cancer begins in the lungs and leading to the reason of cancer demise amid population in the creation. According to the American Cancer Society, which estimates about 27% of the deaths because of cancer. In the early phase of its evolution, lung cancer does not cause any symptoms usually. Many of the patients have been diagnosed in a developed phase where symptoms become more prominent, that results in poor curative treatment and high mortality rate. Computer Aided Detection systems are used to achieve greater accuracies for the lung cancer diagnosis. In this research exertion, we proposed a novel methodology for lung Segmentation on the basis of Fuzzy C-Means Clustering, Adaptive Thresholding, and Segmentation of Active Contour Model. The experimental results are analysed and presented.

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Chest X-ray segmentation using Sauvola thresholding and Gaussian derivatives responses

Cited by 22 publications

References 41 publications

A new merged segmentation technique used for X-ray chest images

A new merged segmentation technique used for X-ray chest images

An IoT-enabled smart health care system for screening of COVID-19 with multi layers features fusion and selection

Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach

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