Efficient and real-time skin lesion image segmentation using spatial-frequency information and channel convolutional networks

Accurate segmentation of skin lesions is essential for physicians to screen in dermoscopy images. However, they commonly face three main limitations: difficulty in accurately processing targets with coarse edges; frequent challenges in recovering detailed feature data; and a lack of adequate capabil...

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Bibliographic Details
Published in:Journal of real-time image processing 2024-10, Vol.21 (5), p.165, Article 165
Main Authors: Liu, Shangwang, Zhou, Bingyan, Lin, Yinghai, Wang, Peixia
Format: Article
Language:English
Subjects:
Accuracy
Coders
Computer Graphics
Computer Science
Datasets
Deep learning
Dermatology
Efficiency
Image analysis
Image Processing and Computer Vision
Image segmentation
Lesions
Medical research
Methods
Multimedia Information Systems
Neural networks
Pattern Recognition
Real time
Redundancy
Semantics
Signal,Image and Speech Processing
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Summary:Accurate segmentation of skin lesions is essential for physicians to screen in dermoscopy images. However, they commonly face three main limitations: difficulty in accurately processing targets with coarse edges; frequent challenges in recovering detailed feature data; and a lack of adequate capability for the effective amalgamation of multi-scale features. To overcome these problems, we propose a skin lesion segmentation network (SFCC Net) that combines an attention mechanism and a redundancy reduction strategy. The initial step involved the design of a downsampling encoder and an encoder composed of Receptive Field (REFC) Blocks, aimed at supplementing lost details and extracting latent features. Subsequently, the Spatial-Frequency-Channel (SF) Block was employed to minimize feature redundancy and restore fine-grained information. To fully leverage previously learned features, an Up-sampling Convolution (UpC) Block was designed for information integration. The network’s performance was compared with state-of-the-art models on four public datasets. Experimental results demonstrate significant improvements in the network’s performance. On the ISIC datasets, the proposed network outperformed D-LKA Net by 4.19%, 0.19%, and 7.75% in F1, and by 2.14%, 0.51%, and 12.20% in IoU. The frame rate (FPS) of the proposed network when processing skin lesion images underscores its suitability for real-time image analysis. Additionally, the network’s generalization capability was validated on a lung dataset.
ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-024-01542-5