Medical image processing: A review

Goel, Navnish; Yadav, Akhilendra; Singh, Brij Mohan

doi:10.1109/cipech.2016.7918737

Cited by 48 publications

(35 citation statements)

References 34 publications

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“…Some modalities are organ-specific like retinal photography, and others examine multiple organs such as CT and MRI [4]. Goel et al [5] defined common medical imaging modalities and compared some with respect to availability, cost, radiation effect, data acquisition, speed and resolution. Xrays are 2D images produced by electromagnetic waves penetrating the body and absorbed non-uniformly by tissues and bones.…”

Section: Medical Image Modalitiesmentioning

confidence: 99%

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Farhat

Sakr

Kilany

2020

Machine Vision and Applications

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Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.

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Section: Medical Image Modalitiesmentioning

confidence: 99%

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Farhat

Sakr

Kilany

2020

Machine Vision and Applications

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“…They are two ways of using wavelet thresholding so called soft thresholding and hard thresholding. Comparison of these two methods shows, that more convenient could be soft thresholding [6,7].…”

Section: Related Workmentioning

confidence: 99%

“…Quality of image is comparing with Multi-Variate Gaussian model (MVG). Quality of the distorted image is classified as distance between quality model NSS and MVG model by equation[7]:…”

mentioning

confidence: 99%

Modeling of Kidney Stones from Ultrasound Images based on Hybrid Regional Segmentation with Active Contours

Vilímek¹,

Kubíček²,

Kloudova³

et al. 2019

Acta Mechanica Slov.

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Kidney stones (nephrolithiasis) are among the most common kidney diseases. They are solid stones that arise from minerals dissolved in urine. The treatment of kidney stones depends primarily on the position, size and composition of the stones, as well as on the general health condition of the patient. However, early diagnosis is quite complicated. In this paper, we propose a fully automatic hybrid method for identification and features extraction of the kidney stones. Specifically, model is based on the multiregional segmentation and approximation of the kidney stone area. Methods consequently use concept of the active contours which are focused on extraction of the geometrical features. The method remarkably allowing for an objective monitoring and classification of the kidney stones. These results may play a role to overcome conventionally used clinical procedures where clinicians mark kidney stones manually, without software feedback.

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“…Table -III contains survey of wearable based, sensor based and mobile based systems. Most of the work is based on sensors or wearable devices using which data can be collected, these samples are then compared with existing annotated corpus to predict the results [38], [39], [40]. On the other hand there are systems which make use of inbuilt sensors of mobile device and provide user interface to doctors and patients [34], [35], [36], [37].…”

Section: B Iot In Healthcarementioning

confidence: 99%

Text Mining In Healthcare

2019

IJITEE

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In healthcare, data mining intensively and extensively becoming essential. Data mining applications can benefit all patients and the healthcare professionals. This paper starts with introducing data mining and the healthcare paradigm. This study confers various techniques of data mining in healthcare application domain. As the scope of the study is limited to text mining classification, state of art in particular to healthcare text mining classification is studied in detail with suggested improvements. Various issues and challenges owing to the type of data in healthcare are also discussed in detail with possible solutions. Finally, the paper highlights the need for personalized prescriptive systems for patients and healthcare professionals

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Medical image processing: A review

Cited by 48 publications

References 34 publications

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Modeling of Kidney Stones from Ultrasound Images based on Hybrid Regional Segmentation with Active Contours

Text Mining In Healthcare

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