Medical Image Segmentation Using a Combination of Lattice Boltzmann Method and Fuzzy Clustering Based on GPU CUDA Parallel Processing

Suban, Ignasius Boli; Suyoto, Suyoto; Pranowo, Pranowo

doi:10.3991/ijoe.v17i11.24459

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…Medical image segmentation is the basis of medical image processing and analysis. The solution to this problem not only directly affects the successful application of computer graphics and image technology in medicine but also has important theoretical and practical significance [ 6 , 7 ]. Medical image segmentation is a process of extracting regions of interest, and the segmentation results can provide a reference for subsequent disease diagnosis, treatment plan planning, and treatment effect evaluation.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Clinical Application of Shock Wave in the Treatment of Avascular Necrosis of the Femoral Head in the Early and Middle Stages

Zhou

Zhang

et al. 2022

Contrast Media & Molecular Imaging

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In order to observe the clinical efficacy of shock waves in the treatment of avascular necrosis of the femoral head in the early and middle stages, a clinical application method of a shock wave in the treatment of avascular necrosis of the femoral head in the early and middle stages was proposed. The method combines the CT image segmentation technology to further segment the hip joint image, thereby speeding up the treatment speed and achieving a better treatment effect. Experimental results show that CT image segmentation takes 10.9 hours with an average time of 8 seconds, which is faster than other methods. The shock wave is an effective treatment method for early avascular necrosis of the femoral head, and this method will become one of the main methods for the clinical treatment of this kind of disease.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Clinical Application of Shock Wave in the Treatment of Avascular Necrosis of the Femoral Head in the Early and Middle Stages

Zhou

Zhang

et al. 2022

Contrast Media & Molecular Imaging

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show abstract

“…In the formula (1), l k , r k respectively represent the driving left and right visual field edges, x represents the road horizontal line, l b 、 r b respectively represent the recognized projection edges, and according to the above road edge model [3][4] , the road boundary line extraction hypothesis can be made to judge the mapping relationship between the boundary lines. The camera projection area of different road boundaries is different.…”

Section: Road Boundary Line Extraction Based On Image Edge Featuresmentioning

confidence: 99%

Intelligent road boundary identification method based on image segmentation and edge features

Li,

Hussin

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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Conventional road boundary intelligent identification methods mainly use black-and-white optical driving method to generate road boundary detection binary image, which is easily influenced by threshold segmentation, resulting in large deviation parameters of parabola identification. Therefore, it is necessary to design a brand-new road boundary intelligent identification method based on image segmentation and edge features. That is to say, the road boundary is extracted by using the image edge features, and an intelligent road boundary identification algorithm is designed in combination with image segmentation, thus completing the intelligent road boundary identification. The experimental results show that the intelligent road boundary recognition method based on image segmentation and edge features has good recognition effect, reliability and certain application value, and has made certain contributions to improving driving safety.

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Biomedical Image Segmentation Based on Deep Learning Algorithms

NIU,

WANG

2024

J. Mech. Med. Biol.

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A new biomedical image segmentation method based on deep learning networks has been constructed. Under the framework of deep learning, a dual-branch deep learning network structure was designed to perform average pooling and maximum pooling on input image data, respectively. The results of pooling processing are sent to multi-layer convolution for further processing, where 2D convolution and dilated convolution are used, respectively, and the more flexible SoftMax function is selected for activation processing. Multiple fully connected processes were used between each link and between two branches to form better fusion criteria. The experimental results show that our method has a more ideal segmentation effect in both homologous and heterogeneous training segmentation experiments, with AC, SE, and SP indicators reaching over 95%, 84%, and 99%, respectively.

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Medical Image Segmentation Using a Combination of Lattice Boltzmann Method and Fuzzy Clustering Based on GPU CUDA Parallel Processing

Cited by 5 publications

References 29 publications

[Retracted] Clinical Application of Shock Wave in the Treatment of Avascular Necrosis of the Femoral Head in the Early and Middle Stages

[Retracted] Clinical Application of Shock Wave in the Treatment of Avascular Necrosis of the Femoral Head in the Early and Middle Stages

Intelligent road boundary identification method based on image segmentation and edge features

Biomedical Image Segmentation Based on Deep Learning Algorithms

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