A new fast morphological geodesic active contour method for lung CT image segmentation

Medeiros, Aldísio G.; Guimarães, Matheus T.; Peixoto, Solon Alves; Santos, Lucas; Barros, Antônio Carlos Souza Albuquerque; Rebouças, Elizângela de S.; Albuquerque, Victor Hugo C. de; Filho, Pedro Pedrosa Rebouças

doi:10.1016/j.measurement.2019.05.078

Cited by 23 publications

(22 citation statements)

References 25 publications

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“…Shakeel et al [78] used a PCT to segment lung CT pictures and then used a deep learning method to identify lung tumours from the tested CT images. Medeiros et al [68] introduced a lung CT image segmentation approach which is based on the active contour method (ACM) and a fuzzy boundary detector. Wang et al [97] proposed an adaptive fully dense (AFD) neural network-based CT image segmentation approach.…”

Section: Segmentationmentioning

confidence: 99%

Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review

Nira

Kumar

2022

Multimed Tools Appl

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As everyone knows that in today's time Artificial Intelligence, Machine Learning and Deep Learning are being used extensively and generally researchers are thinking of using them everywhere. At the same time, we are also seeing that the second wave of corona has wreaked havoc in India. More than 4 lakh cases are coming in 24 h. In the meantime, news came that a new deadly fungus has come, which doctors have named Mucormycosis (Black fungus). This fungus also spread rapidly in many states, due to which states have declared this disease as an epidemic. It has become very important to find a cure for this life-threatening fungus by taking the help of our today's devices and technology such as artificial intelligence, data learning. It was found that the CT-Scan has much more adequate information and delivers greater evaluation validity than the chest X-Ray. After that the steps of Image processing such as pre-processing, segmentation, all these were surveyed in which it was found that accuracy score for the deep features retrieved from the ResNet50 model and SVM classifier using the Linear kernel function was 94.7%, which was the highest of all the findings. Also studied about Deep Belief Network (DBN) that how easy it can be to diagnose a life-threatening infection like fungus. Then a survey explained how computer vision helped in the corona era, in the same way it would help in epidemics like Mucormycosis.

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Section: Segmentationmentioning

confidence: 99%

Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review

Nira

Kumar

2022

Multimed Tools Appl

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“…Shakeel et al [ 10 ] applied a profuse clustering technique (PCT) to segment lung CT images and then employed a deep learning model to detect lung cancers from the tested CT images. Medeiros et al [ 11 ] presented a segmentation method based on active contour method (ACM) with fuzzy border detector to segment lung CT images. Wang et al [ 12 ] presented CT image segmentation method based on adaptive fully dense(AFD) neural network.…”

Section: Introductionmentioning

confidence: 99%

Automatic clustering method to segment COVID-19 CT images

et al. 2021

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Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy.

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“…Segmentation on biomedical images can be classified as conventional non-DNN approaches and DNN approaches. Conventional methods usually design artificial features (such as oriented gradients [14,15], Haar features [14,15], curvature [16,17], and Haralick texture features [18]) on images and then construct various segmentation models (such as graph models [6,19] and shape models [20][21][22][23]) to differentiate abnormal regions. For example, Soliman et al [23] integrated two visual appearances of lungs to form their shape model for lung segmentation on CT chest images.…”

Section: Introductionmentioning

confidence: 99%

Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales

Zhang

et al. 2020

J Digit Imaging

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To evaluate the application of machine learning for the detection of subpleural pulmonary lesions (SPLs) in ultrasound (US) scans, we propose a novel boundary-restored network (BRN) for automated SPL segmentation to avoid issues associated with manual SPL segmentation (subjectivity, manual segmentation errors, and high time consumption). In total, 1612 ultrasound slices from 255 patients in which SPLs were visually present were exported. The segmentation performance of the neural network based on the Dice similarity coefficient (DSC), Matthews correlation coefficient (MCC), Jaccard similarity metric (Jaccard), Average Symmetric Surface Distance (ASSD), and Maximum symmetric surface distance (MSSD) was assessed. Our dual-stage boundary-restored network (BRN) outperformed existing segmentation methods (U-Net and a fully convolutional network (FCN)) for the segmentation accuracy parameters including DSC (83.45 ± 16.60%), MCC (0.8330 ± 0.1626), Jaccard (0.7391 ± 0.1770), ASSD (5.68 ± 2.70 mm), and MSSD (15.61 ± 6.07 mm). It also outperformed the original BRN in terms of the DSC by almost 5%. Our results suggest that deep learning algorithms aid fully automated SPL segmentation in patients with SPLs. Further improvement of this technology might improve the specificity of lung cancer screening efforts and could lead to new applications of lung US imaging.

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A new fast morphological geodesic active contour method for lung CT image segmentation

Cited by 23 publications

References 25 publications

Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review

Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review

Automatic clustering method to segment COVID-19 CT images

Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales

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