The Performance of Active-Contour and Region Growing Methods Against Noises in the Segmentation of Computed-Tomography Scans

Mousavi, Mojtaba; Shariaty, Faridoddin; Orooji, Mahdi; Velichko, Elena

doi:10.1007/978-3-030-58868-7_63

Cited by 11 publications

(7 citation statements)

References 26 publications

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“…Due to the development of computer systems, these systems can be used in medicine and the diagnosis of disease (19). In recent years, many advances have been made in computer systems and have been widely used to diagnose lung cancer (20). Recently, due to the prevalence of COVID-19 disease (18), these systems can be used for automatic and early diagnosis of this disease.…”

Section: Covid-19 Detection From Normal Casesmentioning

confidence: 99%

Application of Artificial Intelligence for Rapid Prevention of Epidemic Diseases (COVID-19)

Shariaty

Navvabi

et al. 2022

Preprint

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Background: Epidemic diseases are hazardous in terms of a rapid outbreak. Rapid control of these diseases by finding patients and quarantine and treatment can be the only tool to reduce the number of cases and mortality at the beginning of the outbreak, in the absence of therapy and vaccines. COVID-19 (coronavirus) is a deadly viral disease that causes severe respiratory illness and spreads through the air. Artificial intelligence (AI) technologies have played an essential role in solving complex problems. The use of these technologies in response to the challenges posed by the COVID-19 epidemic can reduce the effects of epidemics in various contexts.Objective: The purpose of this article is to review the applications of artificial intelligence in cases of contagious disease. In this work, COVID-19 disease has been used as an example of dangerous infectious diseases (while the studied methods can be used for all contagious diseases), and a systematic review of the literature on the role of artificial intelligence as COVID-19 has become a comprehensive and critical technology for combating epidemiology, diagnosis, and disease progression.Methods: A complete search of the literature has been done using the databases of PubMed, Scopus, Web of Science, and Google Scholar, and other sources. In this work, the aim is to review articles that the authors believe can be helpful in the prevention of infectious diseases in the event of an outbreak of artificial intelligence in the prevention of more casualties. The first steps needed in a flurry of a disease (including coronavirus) include identifying the primary sufferers and isolating them from the public and examining the illness and how the disease has progressed. In these stages, artificial intelligence can very effectively help the medical community and even the government prevent an epidemic. In this study, the keywords COVID-19 and artificial intelligence and infectious diseases have been used.Results: During our literature search, we came across 73 papers. Researchers analyzed studies examining the diagnostic roles and imaging features of patients with COVID-19. The latter were scanned using CT or ultrasound scans, chest radiographs, or positron emission tomography/computed tomography (PET/CT) scans. Chest x-ray and CT scan are the imaging modalities that are most widely utilized for the diagnosis and management of COVID-19 patients, with chest CT scan being more accurate and sensitive in diagnosing COVID-19 at an early stage. Only a handful of studies have looked into the roles of ultrasonography and PET/CT scans in diagnosing COVID-19 infection.Conclusions: We gathered research from the existing COVID-19 literature that employed artificial intelligence-based methodologies to give insights into various domains of COVID-19 in this systematic review. Our findings indicate critical variables, data formats, and COVID-19 sources to help with clinical research and translation. Findings from this study may also assist in reducing the harm caused by the pandemic in the case of such epidemic diseases in the future.

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Section: Covid-19 Detection From Normal Casesmentioning

confidence: 99%

Application of Artificial Intelligence for Rapid Prevention of Epidemic Diseases (COVID-19)

Shariaty

Navvabi

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Фактически многие методы количественной оценки легочных заболеваний автоматически требуют сегментации легочной паренхимы и узелков на этапе первичной обработки [6]. Методы сегментации можно разделить на несколько классов [7]: полностью основанные на изображении [8][9][10], основанные на модели [11] и гибридные методы [12]. Полностью основанные на изображении методы выполняют сегментацию, основываясь исключительно на информации, имеющейся в изображении.…”

Section: применение модели внешнего вида текстуры для сегментации лег...unclassified

“…Различение тканей с разной плотностью может быть ограничено в результате существующего случайного шума на медицинских изображениях. Шум соль/перец и спекл-шум возникают на изображениях КТ из-за деструктивной и конструктивной когерентной агрегации [8].…”

Section: применение модели внешнего вида текстуры для сегментации лег...unclassified

“…В заключение представлены результаты применения данного алгоритма на нормальных и зашумленных КТ-изображениях. Затем результаты применения представленного алгоритма сравниваются с алгоритмами Region Growing [30], Active Contour [31] и комбинацией алгоритмов Active Contour и Watershed [8].…”

Section: применение модели внешнего вида текстуры для сегментации лег...unclassified

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Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chest

Shariaty

Pavlov

Zavjalov

et al. 2022

Izv. vysš. učebn. zaved. Ross., Radioèlektron.

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Introduction. Lung cancer is one of the most critical diseases globally, with more than 1.6 million new cases registered every year. Early detection of lung cancer is essential; therefore, particular attention should be paid to the development of effective diagnostic and therapeutic procedures. Computer processing of CT scans in the course of lung cancer diagnostics involves the following stages: medical image acquisition, pre-processing of medical images, segmentation, and false-positive reduction. Since segmentation is an essential stage in the process of medical image analysis, the development of novel segmentation approaches is attracting much research interest. Model-based segmentation approaches have recently gained in popularity, largely due to their potential to restore lost information.Aim. To apply a texture appearance model for the segmentation of pulmonary nodules on computed tomography of the chest.Materials and methods. A novel model-based Texture Appearance Model (TAM) is proposed for precise and effective segmentation of all sorts of nodule regions. We taught the TAM for segmentation of a lung nodule in lung CT images using a combination of extracted texture characteristics from CT scans and Texture Representation of Image (TRI).Results. The results of applying the described TAM method to normal and noisy CT images are presented and compared to those obtained using the Region Growing and Active Contour algorithms, as well as the combination of Active Contour and Watershed algorithms. The TAM was tested in 85 nodules from a dataset, yielding an average dice similarity coefficient (DSC) of 84.75 percent.Conclusion. A novel method for segmenting nodules in the lung, which is capable of segmenting all forms of nodules with excellent accuracy, is proposed. This model-based technique, when used with the active loop algorithm, can enhance accuracy and decrease false positives by selecting the initial mask. The precision, dice, accuracy, and specificity of lung nodule segmentation on a normal CT scan are 85.5, 85, 96, and 98, which levels are superior to those produced by the Active Contour, Region Growing and the combination of Active Contour and Watershed algorithms.

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“…Due to the development of computer systems, these systems can be used in medicine and the diagnosis of disease [19]. In recent years, many advances have been made in computer systems and have been widely used to diagnose lung cancer [20]. Recently, due to the prevalence of COVID-19 disease, these systems can be used for automatic and early diagnosis of this disease [18].…”

Section: Covid-19 Detection From Normal Casesmentioning

confidence: 99%

Application of Artificial Intelligence for Rapid Prevention of Epidemic Diseases (COVID-19)

2022

IJHPP

View full text Add to dashboard Cite

Background: Epidemic diseases are hazardous in terms of a rapid outbreak. Rapid control of these diseases by finding patients and quarantine and treatment can be the only tool to reduce the number of cases and mortality at the beginning of the outbreak, in the absence of therapy and vaccines. COVID-19 (coronavirus) is a deadly viral disease that causes severe respiratory illness and spreads through the air. Artificial intelligence (AI) technologies have played an essential role in solving complex problems. The use of these technologies in response to the challenges posed by the COVID-19 epidemic can reduce the effects of epidemics in various contexts. Objective: The purpose of this article is to review the applications of artificial intelligence in cases of contagious disease. In this work, COVID-19 disease has been used as an example of dangerous infectious diseases (while the studied methods can be used for all contagious diseases), and a systematic review of the literature on the role of artificial intelligence as COVID-19 has become a comprehensive and critical technology for combating epidemiology, diagnosis, and disease progression. Methods: A complete search of the literature has been done using the databases of PubMed, Scopus, Web of Science, and Google Scholar, and other sources. In this work, the aim is to review articles that the authors believe can be helpful in the prevention of infectious diseases in the event of an outbreak of artificial intelligence in the prevention of more casualties. The first steps needed in a flurry of a disease (including coronavirus) include identifying the primary sufferers and isolating them from the public and examining the illness and how the disease has progressed. In these stages, artificial intelligence can very effectively help the medical community and even the government prevent an epidemic. In this study, the keywords COVID-19, artificial intelligence, and infectious diseases have been used. Results: During our literature search, we came across 73 papers. Researchers analyzed studies examining the diagnostic roles and imaging features of patients with COVID-19. The latter were scanned using CT or ultrasound scans, chest radiographs, or positron emission tomography/computed tomography (PET/CT) scans. Chest x-ray and CT scan are the imaging modalities that are most widely utilized for the diagnosis and management of COVID-19 patients, with chest CT scan being more accurate and sensitive in diagnosing COVID-19 at an early stage. Only a handful of studies have looked into the roles of ultrasonography and PET/CT scans in diagnosing COVID-19 infection. Conclusions: We gathered research from the existing COVID-19 literature that employed artificial intelligence-based methodologies to give insights into various domains of COVID-19 in this systematic review. Our findings indicate critical variables, data formats, and COVID-19 sources to help with clinical research and translation. Findings from this study may also assist in reducing the harm caused by the pandemic in the case of such epidemic diseases in the future.

show abstract

The Performance of Active-Contour and Region Growing Methods Against Noises in the Segmentation of Computed-Tomography Scans

Cited by 11 publications

References 26 publications

Application of Artificial Intelligence for Rapid Prevention of Epidemic Diseases (COVID-19)

Application of Artificial Intelligence for Rapid Prevention of Epidemic Diseases (COVID-19)

Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chest

Application of Artificial Intelligence for Rapid Prevention of Epidemic Diseases (COVID-19)

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