Rock Particle Image Segmentation Based on Improved Normalized Cut

Gao, Ting; Wang, Weixing; Liu, Wei; Yang, Donglin

doi:10.14257/ijca.2017.10.4.24

Cited by 7 publications

(2 citation statements)

References 17 publications

(31 reference statements)

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“…Although the U-Net has good segmentation performance on single-cell images, but it does not work well on overlapping cells [21]. There assume that the segmentation between background and cells can be regarded as a binary classification problem of pixels, but it is difficult for U-Net to classify the overlapping areas of cells into two or more cells at the same time [22]. As a result, the researchers turn attention to generative adversarial networks (GAN), to segment cell images by image generation rather than pixels classification.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Cervical Cell Images Based on Generative Adversarial Networks

Huang

Yang

et al. 2021

IEEE Access

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The segmentation of cervical cell in liquid-based smear image plays an important role in cervical cancer detection. Despite of research for many years, it is still a challenge for the complexity of cell images such as poor contrast, cell irregularity, and overlapping. To solve this problem, a novel method is proposed based on Cell-GAN -a generative adversarial network. Firstly, the Cell-GAN is trained to learn a probability distribution of cell morphology by comparing the difference between the generated single-cell images and annotated single-cell images. Thus, the Cell-GAN has the ability to judge the integrity of a cell and treat other cellular information of a cell image, except for overlapping parts, as the background. Then, a complete single-cell image is generated by the trained Cell-GAN for each cell, which is located by a guide factor. The guide factor is constructed by a part of the cell to be segmented, such as the nucleus, to help Cell-GAN locate the cell and avoid generating a multi-cell image in the presence of overlapping, which means the contours of cells still cannot be distinguished. Finally, the segmentation line is defined by the contour of the generated cell, and the input image is cropped using the cell size information. The cropped image is reused for image generation until the area of generated cell varies within a small range.The proposed method is evaluated on the segmentation of single-cell images and overlapping cell images and obtained significant values of 94.3% DC, 7.9% FNR o for single-cell images and 89.9% DC, 6.4% FNR o for overlapping cell images respectively. The experimental results indicate that the proposed method can adaptively approach the boundary lines of cells to handle with different cases of overlapping in cervical cell images through what learned by the Cell-GAN. The proposed method outperforms most current methods in both segmentation accuracy and robustness. INDEX TERMSDeep learning; Generative Adversarial Networks; cervical cell image segmentation; overlapping cells.

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Section: Introductionmentioning

confidence: 99%

Segmentation of Cervical Cell Images Based on Generative Adversarial Networks

Huang

Yang

et al. 2021

IEEE Access

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“…Computed tomography (CT) has the advantages of fast imaging and high image resolution. It is an important electronic imaging technology [1,2]. As an important detection method in routine clinical examinations, it has become a computer-assisted human organ examination, and followup an important basis for medical treatment is currently widely used in clinical medicine [3,4].…”

Section: Introductionmentioning

confidence: 99%

An Improved Image Segmentation Algorithm CT Superpixel Grid Using Active Contour

Wei

Wang

2021

Wireless Communications and Mobile Computing

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The traditional CT image segmentation algorithm is easy to ignore image contour initialization, which leads to the problem of long time consuming and low accuracy. A superpixel mesh CT image improved segmentation algorithm using active contour was proposed. CT image superpixel gridding was carried out first; secondly, on the basis of gridding, the region growth criterion was improved by superpixel processing, the region growth graph was established, the image edge salient graph was calculated based on the growth graph, and the target edge was obtained as the initial contour; finally, the Mumford-Shah model in the active contour model was improved; the energy functional was constructed based on the improved model and transformed into the symbol distance function. The results show that the proposed algorithm takes less time to mesh superpixels, the accuracy of image edge calculation is high, the correct classification coefficient is as high as 0.9, and the accuracy of CT image segmentation is always higher than 90%, which has superiority.

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