Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Journal of Healthcare Engineering ◽

10.1155/2021/5573381 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Zhike Zhang ◽

Shuixin Zhang ◽

Hongyu Feng

Keyword(s):

Blood Vessels ◽

Input Signal ◽

Medical Images ◽

Vessel Segmentation ◽

New Method ◽

Geometric Transformation ◽

High Contrast ◽

Low Contrast ◽

Resonance Response ◽

Local Image

Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve detection of high-contrast coarse blood vessels and then redefines the input signal as a local image shielding the global detection result to achieve enhanced detection of low-contrast microfine vessels and complete multilevel random resonance segmentation detection. Finally, a random resonance detection method for fundus vessels based on scale decomposition is proposed, in which the images are scale decomposed, the high-frequency signals containing detailed information are randomly resonantly enhanced to achieve microfine vessel segmentation detection, and the final vessel segmentation detection results are obtained after fusing the low-frequency image signals. The optimal stochastic resonance response of the nonlinear model of neurons in the global sense is obtained to detect the high-grade intensity signal; then, the input signal is defined as a local image with high-contrast blood vessels removed, and the parameters are optimized before the detection of the low-grade intensity signal. Finally, the multilevel random resonance response is fused to obtain the segmentation results of the fundus retinal vessels. The sensitivity of the multilevel segmentation method proposed in this paper is significantly improved compared with the global random resonance results, indicating that the method proposed in this paper has obvious advantages in the segmentation of vessels with low-intensity levels. The image library was tested, and the experimental results showed that the new method has a better segmentation effect on low-contrast microscopic blood vessels. The new method not only makes full use of the noise for weak signal detection and segmentation but also provides a new idea of how to achieve multilevel segmentation and recognition of medical images.

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Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

Computational Intelligence and Neuroscience ◽

10.1155/2020/8822407 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yuliang Ma ◽

Xue Li ◽

Xiaopeng Duan ◽

Yun Peng ◽

Yingchun Zhang

Keyword(s):

Blood Vessel ◽

Blood Vessels ◽

Area Under The Curve ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Superior Performance ◽

Retinal Blood Vessel ◽

Low Contrast ◽

Blood Vessel Segmentation ◽

Small Vessels

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

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Segmentation of Retinal Blood Vessels Based on Cake Filter

BioMed Research International ◽

10.1155/2015/137024 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Xi-Rong Bao ◽

Xin Ge ◽

Li-Huang She ◽

Shi Zhang

Keyword(s):

Blood Vessel ◽

Blood Vessels ◽

False Positive Rate ◽

New Method ◽

Real Component ◽

Low Contrast ◽

Small Vessels ◽

Retinal Blood Vessels ◽

Average Accuracy ◽

Positive Rate

Segmentation of retinal blood vessels is significant to diagnosis and evaluation of ocular diseases like glaucoma and systemic diseases such as diabetes and hypertension. The retinal blood vessel segmentation for small and low contrast vessels is still a challenging problem. To solve this problem, a new method based on cake filter is proposed. Firstly, a quadrature filter band called cake filter band is made up in Fourier field. Then the real component fusion is used to separate the blood vessel from the background. Finally, the blood vessel network is got by a self-adaption threshold. The experiments implemented on the STARE database indicate that the new method has a better performance than the traditional ones on the small vessels extraction, average accuracy rate, and true and false positive rate.

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Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation

Electronics ◽

10.3390/electronics10182297 ◽

2021 ◽

Vol 10 (18) ◽

pp. 2297

Author(s):

Toufique A. Soomro ◽

Ahmed Ali ◽

Nisar Ahmed Jandan ◽

Ahmed J. Afifi ◽

Muhammad Irfan ◽

...

Keyword(s):

Blood Vessels ◽

Retinal Disease ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Low Contrast ◽

The Public ◽

Retinal Blood Vessels ◽

Retinal Vessel Segmentation ◽

Automated Screening ◽

Traditional Image

Segmentation of retinal vessels plays a crucial role in detecting many eye diseases, and its reliable computerized implementation is becoming essential for automated retinal disease screening systems. A large number of retinal vessel segmentation algorithms are available, but these methods improve accuracy levels. Their sensitivity remains low due to the lack of proper segmentation of low contrast vessels, and this low contrast requires more attention in this segmentation process. In this paper, we have proposed new preprocessing steps for the precise extraction of retinal blood vessels. These proposed preprocessing steps are also tested on other existing algorithms to observe their impact. There are two steps to our suggested module for segmenting retinal blood vessels. The first step involves implementing and validating the preprocessing module. The second step applies these preprocessing stages to our proposed binarization steps to extract retinal blood vessels. The proposed preprocessing phase uses the traditional image-processing method to provide a much-improved segmented vessel image. Our binarization steps contained the image coherence technique for the retinal blood vessels. The proposed method gives good performance on a database accessible to the public named DRIVE and STARE. The novelty of this proposed method is that it is an unsupervised method and offers an accuracy of around 96% and sensitivity of 81% while outperforming existing approaches. Due to new tactics at each step of the proposed process, this blood vessel segmentation application is suitable for computer analysis of retinal images, such as automated screening for the early diagnosis of eye disease.

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Efficient BFCN for Automatic Retinal Vessel Segmentation

Journal of Ophthalmology ◽

10.1155/2020/6439407 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Yun Jiang ◽

Falin Wang ◽

Jing Gao ◽

Wenhuan Liu

Keyword(s):

Blood Vessels ◽

Spatial Information ◽

Contextual Information ◽

Receptive Fields ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Deep Convolutional Neural Networks ◽

Low Contrast ◽

Retinal Vessel Segmentation ◽

Segmentation Image

Retinal vessel segmentation has high value for the research on the diagnosis of diabetic retinopathy, hypertension, and cardiovascular and cerebrovascular diseases. Most methods based on deep convolutional neural networks (DCNN) do not have large receptive fields or rich spatial information and cannot capture global context information of the larger areas. Therefore, it is difficult to identify the lesion area, and the segmentation efficiency is poor. This paper presents a butterfly fully convolutional neural network (BFCN). First, in view of the low contrast between blood vessels and the background in retinal blood vessel images, this paper uses automatic color enhancement (ACE) technology to increase the contrast between blood vessels and the background. Second, using the multiscale information extraction (MSIE) module in the backbone network can capture the global contextual information in a larger area to reduce the loss of feature information. At the same time, using the transfer layer (T_Layer) can not only alleviate gradient vanishing problem and repair the information loss in the downsampling process but also obtain rich spatial information. Finally, for the first time in the paper, the segmentation image is postprocessed, and the Laplacian sharpening method is used to improve the accuracy of vessel segmentation. The method mentioned in this paper has been verified by the DRIVE, STARE, and CHASE datasets, with the accuracy of 0.9627, 0.9735, and 0.9688, respectively.

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Resistance Monitor and Control for Vacuum Evaporation of Metals and Carbon

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100092827 ◽

1976 ◽

Vol 34 ◽

pp. 572-573

Author(s):

Russell L. Steere ◽

Eric F. Erbe ◽

J. Michael Moseley

Keyword(s):

Electronic Device ◽

Point Sources ◽

High Contrast ◽

Variable Resistor ◽

Quality Of Results ◽

Low Contrast ◽

Evaporation Source ◽

And Control ◽

At Will

We have designed and built an electronic device which compares the resistance of a defined area of vacuum evaporated material with a variable resistor. When the two resistances are matched, the device automatically disconnects the primary side of the substrate transformer and stops further evaporation.This approach to controlled evaporation in conjunction with the modified guns and evaporation source permits reliably reproducible multiple Pt shadow films from a single Pt wrapped carbon point source. The reproducibility from consecutive C point sources is also reliable. Furthermore, the device we have developed permits us to select a predetermined resistance so that low contrast high-resolution shadows, heavy high contrast shadows, or any grade in between can be selected at will. The reproducibility and quality of results are demonstrated in Figures 1-4 which represent evaporations at various settings of the variable resistor.

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Minimum Detectable Change of Visual Acuity Measurements Using ETDRS Charts (Early Treatment Diabetic Retinopathy Study)

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18157876 ◽

2021 ◽

Vol 18 (15) ◽

pp. 7876

Author(s):

María Carmen Sánchez-González ◽

Raquel García-Oliver ◽

José-María Sánchez-González ◽

María-José Bautista-Llamas ◽

José-Jesús Jiménez-Rejano ◽

...

Keyword(s):

Visual Acuity ◽

Diabetic Retinopathy ◽

Early Treatment ◽

High Reliability ◽

Minimal Detectable Change ◽

High Contrast ◽

Detectable Change ◽

Low Contrast ◽

Minimum Detectable Change ◽

The Mean

In our work, we determined the value of visual acuity (VA) with ETDRS charts (Early Treatment Diabetic Retinopathy Study). The purpose of the study was to determine the measurement reliabilities, calculating the correlation coefficient interclass (ICC), the value of the error associated with the measure (SEM), and the minimal detectable change (MDC). Forty healthy subjects took part. The mean age was 23.5 ± 3.1 (19 to 26) years. Visual acuities were measured with ETDRS charts (96% ETDRS chart nº 2140) and (10% SLOAN Contrast Eye Test chart nº 2153). The measurements were made (at 4 m) under four conditions: Firstly, photopic conditions with high contrast (HC) and low contrast (LC) and after 15 min of visual rest, mesopic conditions with high and low contrast. Under photopic conditions and high contrast, the ICC = 0.866 and decreased to 0.580 when the luminosity and contrast decreased. The % MDC in the four conditions was always less than 10%. It was minor under photopic conditions and HC (5.83) and maximum in mesopic conditions and LC (9.70). Our results conclude a high reliability of the ETDRS test, which is higher in photopic and high contrast conditions and lower when the luminosity and contrast decreases.

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Adaptive Integration in the Visual Cortex by Depressing Recurrent Cortical Circuits

Neural Computation ◽

10.1162/neco.2008.06-07-546 ◽

2008 ◽

Vol 20 (7) ◽

pp. 1847-1872 ◽

Cited By ~ 24

Author(s):

Mark C. W. van Rossum ◽

Matthijs A. A. van der Meer ◽

Dengke Xiao ◽

Mike W. Oram

Keyword(s):

Visual Cortex ◽

Physiological Data ◽

High Contrast ◽

Cortical Circuits ◽

Response Latencies ◽

Low Contrast ◽

Visual Areas ◽

Higher Visual Areas ◽

Recurrent Connections

Neurons in the visual cortex receive a large amount of input from recurrent connections, yet the functional role of these connections remains unclear. Here we explore networks with strong recurrence in a computational model and show that short-term depression of the synapses in the recurrent loops implements an adaptive filter. This allows the visual system to respond reliably to deteriorated stimuli yet quickly to high-quality stimuli. For low-contrast stimuli, the model predicts long response latencies, whereas latencies are short for high-contrast stimuli. This is consistent with physiological data showing that in higher visual areas, latencies can increase more than 100 ms at low contrast compared to high contrast. Moreover, when presented with briefly flashed stimuli, the model predicts stereotypical responses that outlast the stimulus, again consistent with physiological findings. The adaptive properties of the model suggest that the abundant recurrent connections found in visual cortex serve to adapt the network's time constant in accordance with the stimulus and normalizes neuronal signals such that processing is as fast as possible while maintaining reliability.

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