Mathematical Techniques for Biomedical Image Segmentation

Diabetic foot ulcer represents one of the leading causes of lower extremity amputation in diabetic patients. The aim of this work is to propose a computational method to carry out segmentation on diabetic foot ulcer images of patients treated with the Heberprot-P. This drug accelerates the wound healing process and reduces the risk of amputation. The used material is a bank of 1176 images provided by the Center for Genetic Engineering and Biotechnology at Havana, and as a method, we propose the use of the model of Mask R-CNN and the concept of learning transfer to automatically locate the region that delimits the ulcer. The proposed model obtained very satisfactory results, and we validated its performance together with specialist physicians, on a set of 1010 images.

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“…This task continues being an open problem [38]. Therefore, quantitative comparison with manual segmentation may provide a useful indication [39].…”

Section: Discussion Quantitative Comparison Of Resultsmentioning

confidence: 99%

Automatic Segmentation of Diabetic foot ulcer from Mask Region-Based Convolutional Neural Networks

PL¹,

Rodríguez²,

Montalvo³

2020

J. biomed. res. clin. investig.

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“…Golden silicon probes NSG30-A, supplied by NT-MDT (Zelenograd, Russia), with a curvature radius of 10 nm and a resonant frequency of 240-440 kHz were used. Improvement and segmentation of coronavirus images by mathematics algorithm (16). In this section brie y described the mathematical algorithms used in the enhancement of microscopic images of the novel coronavirus.…”

Section: Methodsmentioning

confidence: 99%

SARS-CoV-2: Preliminary study of infected human nasopharyngeal tissue by high resolution microscopy.

Mondeja¹,

Valdés

Resik

et al. 2021

Preprint

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BackgroundThe novel coronavirus SARS-CoV-2 is the etiological agent of COVID-19. This virus has become one of the most dangerous in recent times with a very high rate of transmission. At present, several publications show the typical crown-shape of the novel coronavirus grown in cell cultures. However, an integral ultramicroscopy study done directly from clinical specimens has not been published. Methods Nasopharyngeal swabs were collected from two Cuban individuals, one asymptomatic and RT-PCR negative (negative control) and the other from a COVID-19 symptomatic and RT-PCR positive for SARS CoV-2. Samples were treated with an aldehyde solution and processed by Scanning Electron Microscopy (SEM), Confocal Microscopy (CM) and, Atomic Force Microscopy (AFM). Improvement and segmentation of coronavirus images were performed by a novel mathematical image enhancement algorithm. Results The images of the negative control sample showed the characteristic healthy microvilli morphology at the apical region of the nasal epithelial cells. As expected, they do not display virus-like structures. The images of the positive sample showed characteristic coronavirus-like particles and evident destruction of microvilli. In some regions, virions budding through the cell membrane were observed. Microvilli destruction could explain the anosmia reported by some patients. Virus-particles emerging from the cell-surface with a variable size ranging from 80 to 400 nm were observed by scanning electron microscopy (SEM). Viral antigen was identified in the apical cells zone by CM. Conclusions The integral microscopy study showed that SARS-CoV-2 has a similar image to SARS-CoV. The application of several high-resolution microscopy techniques to nasopharyngeal samples awaits future use.

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“…Improvement and segmentation of coronavirus images by mathematics algorithm (16) . In this section, we will slightly expose some details of the mathematical algorithms used in the enhancement of microscopic images of the novel coronavirus.…”

Section: Methodsmentioning

confidence: 99%

SARS-CoV-2: High-resolution microscopy study in human nasopharyngeal samples

Mondeja¹,

Valdés

Resik

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background The novel coronavirus SARS-CoV-2 is the etiological agent of COVID-19. This virus has become one of the most dangerous in recent times with a very high rate of transmission. At present, several publications show the typical crown-shape of the novel coronavirus grown in cell cultures. However, an integral ultramicroscopy study done directly from clinical specimens has not been published. Methods Nasopharyngeal swabs were collected from two Cuban individuals, one asymptomatic and RT-PCR negative (negative control) and the other from a COVID-19 symptomatic and RT-PCR positive for SARS CoV-2. Samples were treated with an aldehyde solution and processed by Scanning Electron Microscopy (SEM), Confocal Microscopy (CM) and, Atomic Force Microscopy (AFM). Improvement and segmentation of coronavirus images were performed by a novel mathematical image enhancement algorithm. Results The images of the negative control sample showed the characteristic healthy microvilli morphology at the apical region of the nasal epithelial cells. As expected, they do not display virus-like structures. The images of the positive sample showed characteristic coronavirus-like particles and evident destruction of microvilli. In some regions, virions budding through the cell membrane were observed. Microvilli destruction could explain the anosmia reported by some patients. Virus-particles emerging from the cell-surface with a variable size ranging from 80 to 400 nm were observed by scanning electron microscopy (SEM). Viral antigen was identified in the apical cells zone by CM. Conclusions The integral microscopy study showed that SARS-CoV-2 has a similar image to SARS-CoV. The application of several high-resolution microscopy techniques to nasopharyngeal samples awaits future use.

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Mathematical Techniques for Biomedical Image Segmentation

Cited by 9 publications

References 16 publications

Automatic Segmentation of Diabetic foot ulcer from Mask Region-Based Convolutional Neural Networks

Automatic Segmentation of Diabetic foot ulcer from Mask Region-Based Convolutional Neural Networks

SARS-CoV-2: Preliminary study of infected human nasopharyngeal tissue by high resolution microscopy.

SARS-CoV-2: High-resolution microscopy study in human nasopharyngeal samples

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