Application of The Full-Width-At-Half-Maximum Image Segmentation Method to Analyse Retinal Vascular Changes in Patients With Diabetic Retinopathy

2021 ◽  
Author(s):  
Bo Lun Xu ◽  
Yi Jie Li ◽  
Wen Li Zhou ◽  
Ke Yun Cheng ◽  
Hai Jing Zhan ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Image Segmentation ◽  
Morphological Changes ◽  
Lumen Diameter ◽  
Outer Diameter ◽  
Full Width ◽  
Half Maximum ◽  
Segmentation Method ◽  
Retinal Arteriolar ◽  
Sd Oct

Abstract The study used spectral domain optical coherence tomography (SD-OCT) and full width at half maximum image segmentation to investigate the morphological changes of retinal blood vessels in patients with diabetic retinopathy (DR).Seventy-five patients with type 2 diabetes mellitus (DM) without DR and 65 patients with DR were studied. The vascular images of superior temporal region B of the retina were obtained by SD-OCT. The edges of retinal vessels were identified by the full-width-at-half-maximum image segmentation method. The lumen diameter, wall thickness (WT), wall cross-sectional area (WCSA), and wall-to-lumen ratio (WLR) were investigated.We found that compared with no-diabetic-retinopathy (NDR) group, patients in DR group had increased retinal arteriolar lumen diameter (RALD), retinal arteriolar outer diameter (RAOD), and WT(128.80 µm vs. 104.88 µm; 147.01 µm vs. 135.60 µm; 18.29 µm vs. 15.26 µm; P < 0.05 respectively). And, the retinal venular lumen diameter, retinal venular outer diameter, and venular WT in the DR group also increased (146.17 µm vs. 133.66 µm; 180.20 µm vs. 156.43 µm; 17.01 µm vs. 11.38 µm; P < 0.05 respectively). The morphological changes of retinal vessels were significantly correlated with DR stage.In conclusion,in diabetic patients with DR, both retinal arteries and veins are widened with increased vascular thickness.

scholarly journals Morphological Changes in Lamellar Macular Holes According to SD-OCT Examination over a Long Observation Period

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1145
Author(s):  
Magdalena Kal ◽  
Izabela Chojnowska-Ćwiąkała ◽  
Mateusz Winiarczyk ◽  
Monika Jasielska ◽  
Jerzy Mackiewicz
Keyword(s):  
Retinal Thickness ◽  
Morphological Changes ◽  
Outer Diameter ◽  
Corrected Visual Acuity ◽  
Macular Holes ◽  
Kolmogorov Smirnov ◽  
Inner Diameter ◽  
Observation Period ◽  
Sd Oct

Background: The aim of this study was to evaluate the quantitative morphological changes in lamellar macular holes (LMHs) based on SD-OCT examinations and to assess the correlations among minimal retinal thickness (MRT), reading vision (RV), and best corrected visual acuity (BCVA) over a 36-month follow-up period. Methods: A group of 40 patients (44 eyes) with LMH was evaluated, with an average age of 69.87 (SD = 10.14). The quantitative parameters monitored in the follow-up period (at 0, 3, 6, 12, 18, 24, 30, and 36 months) were tested for normality of distribution by Shapiro–Wilk and Kolmogorov–Smirnov tests. Results: The RV and BCVA values were stable, and no significant changes were found at any of the check-ups during the 36-month follow-up period (BCVA p = 0.435 and RV p = 0.0999). The analysis of individual quantitative LMH parameters during the 36-month follow-up period did not demonstrate statistically significant differences: MRT (p = 0.461), Max RT temporal (p = 0.051), Max RT nasal (p = 0.364), inner diameter (ID) (p = 0.089), and outer diameter (OD) (p = 0.985). Conclusions: The observations at 0, 6, 12, 18, 24, 30, and 36 months revealed moderate and significant correlations between RV and MRT. No significant correlation between BCVA and MRT was observed.

scholarly journals Retinal Arteriolar Morphometry Based on Full Width at Half Maximum Analysis of Spectral-Domain Optical Coherence Tomography Images

PLoS ONE ◽  
2015 ◽  
Vol 10 (12) ◽  
pp. e0144437 ◽  
Author(s):  
Yu Hua Tong ◽  
Tie Pei Zhu ◽  
Ze Lin Zhao ◽  
Hai Jing Zhan ◽  
Fang Zheng Jiang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Full Width ◽  
Half Maximum ◽  
Retinal Arteriolar

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

scholarly journals Spectroscopic Studies of Laser-Based Far-Ultraviolet Plasma Light Source

2021 ◽  
Vol 11 (15) ◽  
pp. 6919
Author(s):  
Majid Masnavi ◽  
Martin Richardson
Keyword(s):  
Dense Plasma ◽  
Wavelength Region ◽  
Spectroscopic Studies ◽  
Radiation Hydrodynamics ◽  
Full Width ◽  
Half Maximum ◽  
Laser Plasmas ◽  
Series Of Experiments ◽  
Far Ultraviolet ◽  
Radiation Conversion

A series of experiments is described which were conducted to measure the absolute spectral irradiances of laser plasmas created from metal targets over the wavelength region of 123–164 nm by two separate 1.0 μm lasers, i.e., using 100 Hz, 10 ns, 2–20 kHz, 60–100 ns full-width-at-half-maximum pulses. A maximum radiation conversion efficiency of ≈ 3%/2πsr is measured over a wavelength region from ≈ 125 to 160 nm. A developed collisional-radiative solver and radiation-hydrodynamics simulations in comparison to the spectra detected by the Seya–Namioka-type monochromator reveal the strong broadband experimental radiations which mainly originate from bound–bound transitions of low-ionized charges superimposed on a strong continuum from a dense plasma with an electron temperature of less than 10 eV.

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

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