The Vese-Chan model without redundant parameter estimation for multiphase image segmentation

Wang, Jie; Xu, Zisen; Pan, Zhenkuan; Wei, Weibo; Wang, Guodong

doi:10.1186/s13640-019-0488-6

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…Chan-Vese model ( 36 ) is a segmentation model based on regional information, which is actually an optimization of the simplified Mumford-Shah (MS) model ( 37 ). MS model does not use the edge detection function based on gradient but directly uses all the gray information inside and outside the active contour.…”

Section: Medical Image Segmentationmentioning

confidence: 99%

A Machine-Learning-Based Medical Imaging Fast Recognition of Injury Mechanism for Athletes of Winter Sports

Liu

Yue

Chen

2022

Front. Public Health

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The Beijing 2022 Winter Olympics will begin soon, which is mainly focused on winter sports. Athletes from different countries will arrive in Beijing one after another for training and competition. The health protection of athletes of winter sports is very important in training and competition. The occurrence of sports injury is characterized by multiple factors, uncertainty, and accidents. This paper mainly pays attention to the head injury with the highest severity. Athletes' high safety awareness is a part of reducing injury, but safety awareness cannot effectively reduce the occurrence of injury in competition, and timely treatment of injured athletes is particularly important. After athletes are injured, a telemedicine image acquisition system can be built, so that medical experts can identify athletes' injuries in time and provide the basis for further diagnosis and treatment. In order to improve the accuracy of medical image processing, a C-support vector machine (SVM) medical image segmentation method combining the Chan-Vese (CV) model and SVM is proposed in this paper. After segmentation, the edge and detail features of the image are more prominent, which meet the requirements of high precision for medical image segmentation. Meanwhile, a high-precision registration algorithm of brain functional time-series images based on machine learning (ML) is proposed, and the automatic optimization of high-precision registration of brain function time-series images is performed by ML algorithm. The experimental results show that the proposed algorithm has higher segmentation accuracy above 80% and less registration time below 40 ms, which can provide a reference for doctors to quickly identify the injury and shorten the time.

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Section: Medical Image Segmentationmentioning

confidence: 99%

A Machine-Learning-Based Medical Imaging Fast Recognition of Injury Mechanism for Athletes of Winter Sports

Liu

Yue

Chen

2022

Front. Public Health

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“…These ensure robustness to images exhibiting noisy and blurred edges [ 27 ]. The most popular formulations include the Mumford-Shah [ 28 ] and Chan-Vese models [ 28 , 29 , 30 ]. We utilize the Chan Vese model, which utilizes the average intensity computed across the image region [ 27 ].…”

Section: Background and Motivationmentioning

confidence: 99%

Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection

Nnolim

2020

Heliyon

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Automatic detection of complex cracks on rough concrete surfaces via image processing is a challenging task. The most current effective methods involve deep learning schemes. These are usually computationally and structurally complex. Recently, relatively simplified algorithms were developed for effective segmentation of crack features. However, these approaches still could not consistently and accurately extract such features from extremely noisy images of rough concrete surfaces with complex crack patterns. This study describes crack feature segmentation algorithms based on wavelet coefficient adjustment, nonlinear filter pre-processing, saturation channel extraction, adaptive threshold-based edge detection and fuzzy clustering-based area classification. Additional modifications include a new energy function for active contour segmentation algorithm. Adaptive localized mask generation is also proposed for automatic region-based segmentation. Furthermore, a binary fusion stage is incorporated for improved edge feature extraction. The quantitative and visual evaluation of the proposed schemes show improvement in results compared to several recent state-of-the-art algorithms.

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Improved single shot multibox detector target detection method based on deep feature fusion

Bai

Sun

Tao

et al. 2021

Concurrency and Computation

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The feature layers of different layers in the single shot multibox detector (SSD) are independently used as the input of the classification network, so it is easy to detect the same object. This article proposes an improved SSD model based on deep feature fusion. In the SSD algorithm, the deep feature fusion between the target detection layer and its adjacent feature layer is used, including convolution kernels and pooling kernels of different sizes, down-sampling of low-level features and up-sampling of deconvolution of high-level features. The network is improved by combining the target frame recommendation strategy in the SSD algorithm and the frame regression algorithm. The experimental results show that the improved SSD algorithm improves the detection accuracy and detection rate of the target, and the effect is more obvious for the relatively small-scale target.

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The Vese-Chan model without redundant parameter estimation for multiphase image segmentation

Cited by 5 publications

References 20 publications

A Machine-Learning-Based Medical Imaging Fast Recognition of Injury Mechanism for Athletes of Winter Sports

A Machine-Learning-Based Medical Imaging Fast Recognition of Injury Mechanism for Athletes of Winter Sports

Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection

Improved single shot multibox detector target detection method based on deep feature fusion

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