Research on Edge Detection Algorithm in Digital Image Processing

Wu, Zheng; Liu, Kun

doi:10.2991/msmee-17.2017.227

Cited by 6 publications

(1 citation statement)

References 9 publications

(9 reference statements)

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“…Traditional segmentation algorithms such as threshold segmentation ( 7 ), edge detection segmentation ( 8 ), and area growth ( 9 , 10 ) can be used only in simple scenarios. For the segmentation of pulmonary lesions in medical images, due to the blurring of the surrounding grey region and the lack of differentiability with the background, traditional segmentation methods encounter several problems, such as missed and false edge detection.…”

Section: Introductionmentioning

confidence: 99%

RAD-UNet: Research on an improved lung nodule semantic segmentation algorithm based on deep learning

Zuo

2023

Front. Oncol.

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ObjectiveDue to the small proportion of target pixels in computed tomography (CT) images and the high similarity with the environment, convolutional neural network-based semantic segmentation models are difficult to develop by using deep learning. Extracting feature information often leads to under- or oversegmentation of lesions in CT images. In this paper, an improved convolutional neural network segmentation model known as RAD-UNet, which is based on the U-Net encoder-decoder architecture, is proposed and applied to lung nodular segmentation in CT images.MethodThe proposed RAD-UNet segmentation model includes several improved components: the U-Net encoder is replaced by a ResNet residual network module; an atrous spatial pyramid pooling module is added after the U-Net encoder; and the U-Net decoder is improved by introducing a cross-fusion feature module with channel and spatial attention.ResultsThe segmentation model was applied to the LIDC dataset and a CT dataset collected by the Affiliated Hospital of Anhui Medical University. The experimental results show that compared with the existing SegNet [14] and U-Net [15] methods, the proposed model demonstrates better lung lesion segmentation performance. On the above two datasets, the mIoU reached 87.76% and 88.13%, and the F1-score reached 93.56% and 93.72%, respectively. Conclusion: The experimental results show that the improved RAD-UNet segmentation method achieves more accurate pixel-level segmentation in CT images of lung tumours and identifies lung nodules better than the SegNet [14] and U-Net [15] models. The problems of under- and oversegmentation that occur during segmentation are solved, effectively improving the image segmentation performance.

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