An improved threshold method based on histogram entropy for the blood vessel segmentation

Guo, Shuxiang; Yang, Qiuxia; Gao, Baofeng; Zhao, Yan; Xiao, Nan; He, Yanlin; Zhang, Chaonan

doi:10.1109/cbs.2017.8266103

Cited by 2 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…or a black pixels if the image intensity is less than that constant. 22 To segment the blood vessels accurately, local blocking-based thresholding concept is used to classify the blood vessel pixel and another region. Local thresholding will be utilized efficiently when effects of the gradients are smaller relatives to the subimage size chosen.…”

Section: Thresholdingmentioning

confidence: 99%

“…Step 2: The cost value are given in Equation (22). Moreover, the average cross entropy is calculated in Equation (22).…”

Section: Algorithm 2 Training Algorithm Of Mdcnnmentioning

confidence: 99%

“…Since it is preferable to have continuous structure, automatic vessel segmentation frequently produces the worst performance on extraction factor 21 . Setting the models for various components, including as blood arteries, catheters, and guidewires, is a crucial aspect of this system 22 …”

Section: Introductionmentioning

confidence: 99%

“…21 Setting the models for various components, including as blood arteries, catheters, and guidewires, is a crucial aspect of this system. 22 A cross-connected CNN (CcNet) automatically segments the vessels utilizing cross connections via merging multilayer information from the principal and subsidiary paths. On the other hand, this network makes segmenting the arteries around the lesions problematic.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Highly accurate blood vessel segmentation using texture‐based modified K‐means clustering with deep learning model

Lisha¹,

Sulochana

2023

Concurrency and Computation

View full text Add to dashboard Cite

This paper suggests a blood vessel segmentation using the Texture-Based Modified K-means Clustering (TBMKC) technique to lessen the detrimental effects of light lesions. In the suggested method, the K value is chosen automatically by maximizing the local pixel data of the input image. Utilizing the Gabor Filter and Scale-Invariant Feature Transform (SIFT), respectively, texture and statistical characteristics are used to extract the local information. The number of pixels present in each class is used to alter the cluster centroid using Particle Swarm Optimization (PSO) methods. Local adaptive thresholding is then used to separate the blood vessel pattern. The proposed method is invariant to scales and rotations due to SIFT features. The thresholding output is given to the modified DCNN (Deep Convolutional Neural Network). Finally, the performance of the (MDCNN+ TBMKC+ PSO) scheme is computed over other extant schemes. The outcomes of the adopted scheme is determined and tested using the retinal image from DRIVE, STARE, HRF, and CHASE_DBI databases. The accuracy of the proposed method is 13.41%,

show abstract

Section: Thresholdingmentioning

confidence: 99%

“…Step 2: The cost value are given in Equation (22). Moreover, the average cross entropy is calculated in Equation (22).…”

Section: Algorithm 2 Training Algorithm Of Mdcnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Highly accurate blood vessel segmentation using texture‐based modified K‐means clustering with deep learning model

Lisha¹,

Sulochana

2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Thus, the topic of improving the accuracy of fundus image segmentation has attracted a great number of scholars. Retinal blood vessel segmentation methods include tracking detection based on blood vessel orientation, segmentation techniques based on mathematical morphology methods and matched filtering, segmentation techniques using deformation models, and machine learning [6][7][8][9]. With deep learning rapidly advancing in the medical field, retinal blood vessel segmentation has also achieved remarkable results [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Improved U-Net Fundus Image Segmentation Algorithm Integrating Effective Channel Attention

Wang¹,

Yuan²,

Lv³

et al. 2022

jist

View full text Add to dashboard Cite

Fundus blood vessel segmentation is important to obtain the early diagnosis of ophthalmic-related diseases. A great number of approaches have been published, yet micro-vessel segmentation is still not able to deliver the desired results. In this paper, an improved retinal segmentation algorithm incorporating an effective channel attention (ECA) module is presented. Firstly, the ECA module is imported into the downsampling stage of a U-shape neural network (U-Net) to capture the cross-channel interaction information. Secondly, a dilated convolutional module is added to expand the receptive field of the retina, so that more micro-vessel features can be extracted. Experiments were performed on two publicly available datasets, namely DRIVE and CHASE_DB1. Finally, the improved U-Net was used to validate the results. The proposed method achieves high accuracy in terms of the dice coefficient, mean pixel accuracy (mPA) metric and the mean intersection over union (mIoU) metric. The advantages of the algorithm include low complexity and having to use fewer parameters.

show abstract

An improved threshold method based on histogram entropy for the blood vessel segmentation

Cited by 2 publications

References 17 publications

Highly accurate blood vessel segmentation using texture‐based modified K‐means clustering with deep learning model

Highly accurate blood vessel segmentation using texture‐based modified K‐means clustering with deep learning model

Improved U-Net Fundus Image Segmentation Algorithm Integrating Effective Channel Attention

Contact Info

Product

Resources

About