Active contours using global models for medical image segmentation

Kashyap, Ramgopal; Tiwari, Vivek

doi:10.1504/ijcsyse.2018.091404

Cited by 33 publications

(8 citation statements)

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“…In shallow feature extraction, usually a larger convolution kernel can learn richer feature information. But the cost is that once the convolution kernel increases, it will adversely affect the training efficiency and speed of the deep network [ 29 ].…”

Section: Denoising Networkmentioning

confidence: 99%

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

Yao

2020

Computational and Mathematical Methods in Medicine

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In order to improve the resolution of magnetic resonance (MR) image and reduce the interference of noise, a multifeature extraction denoising algorithm based on a deep residual network is proposed. First, the feature extraction layer is constructed by combining three different sizes of convolution kernels, which are used to obtain multiple shallow features for fusion and increase the network’s multiscale perception ability. Then, it combines batch normalization and residual learning technology to accelerate and optimize the deep network, while solving the problem of internal covariate transfer in deep learning. Finally, the joint loss function is defined by combining the perceptual loss and the traditional mean square error loss. When the network is trained, it can not only be compared at the pixel level but also be learned at a higher level of semantic features to generate a clearer target image. Based on the MATLAB simulation platform, the TCGA-GBM and CH-GBM datasets are used to experimentally demonstrate the proposed algorithm. The results show that when the image size is set to 190 × 215 and the optimization algorithm is Adam, the performance of the proposed algorithm is the best, and its denoising effect is significantly better than other comparison algorithms. Especially under high-intensity noise levels, the denoising advantage is more prominent.

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Section: Denoising Networkmentioning

confidence: 99%

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

Yao

2020

Computational and Mathematical Methods in Medicine

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“…Novel energy based active contour method with level set is presented by Kashyap and Tiwari [1]; this approach acquires a promotion in segmentation computation time and has excellent segmentation accuracy. Yu et al [2] employ level set, similarity theory and curve evolution to formulate similarity‐based level‐set model; this model reduces iteration times, and it is also not vulnerable to the initial active contour.…”

Section: Introductionmentioning

confidence: 99%

M‐WRSF model for medical image segmentation

et al. 2019

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In this Letter, multichannel weighted region-scalable fitting segmentation model (M-WRSF) is proposed for medical image segmentation. The authors have utilised a new edge detection function to improve the performance of image segmentation approaches that already exists on weak edges and the grey-scale inhomogeneity of some medical images. The M-WRSF model introduces a novel punishment item to improve numerical stability and augments time interval to boost iterative efficiency. The Gaussian kernel function is added on the basis of original model to enhance robustness. On the other side, the original medical images need to make de-noising operation before the multichannel active contour model segment them, and also level-set initial curve has no effect on the segmentation results. The benefits of the new M-WRSF model, specifically, the increased efficiency and usability, are verified in the simulation experiment.

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“…After the object detection step, the boundaries are described by Otsu method, 12 active contour model, 11,13 gradient vector flow, 14 etc. Their results are not only in natural images but also in medical images 15–18 …”

Section: Introductionmentioning

confidence: 99%

“…The results of the learned RPN improve region proposal quality and accurate nearly cost‐free. The local region‐based active contour was proposed in the work of Kashyap and Tiwari 17 . In this research, the authors applied the result of denoising to enhance energy by level set detailing.…”

Section: Introductionmentioning

confidence: 99%

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Object contour based on improving region growing method and context border condition

Binh

Tuyet

Thành

2019

Concurrency and Computation

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Image diagnosis is a very significant medical specialty. Thereby, the doctor will see the form and function of the structure inside the body. The recorded images will be an important basis to help doctors find the disease and treatment more effectively. However, the quality of medical images is a vital factor. Improving the quality of medical images and object contour are the major challenges, because each tiny detail of medical images can contain much information. Object contour in good medical image quality is hard work and it is more difficult in low-quality medical image. The organs in the image are not clear, which is difficult for the doctor to distinguish the border of the organs in the image. In this paper, we proposed a method for object contour based on improving region growing in bandelet domain in low-quality medical images. The method includes two stages: increasing the quality of medical image in bandelet domain and object contour by improving region growing combined with context border condition in medical images in the first stage. The result of the proposed method is better than the other methods.

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Active contours using global models for medical image segmentation

Cited by 33 publications

References 0 publications

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

M‐WRSF model for medical image segmentation

Object contour based on improving region growing method and context border condition

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