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

• 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
• ISSN: 1861-8200; 1861-8219
• DOI: 10.1007/s11554-024-01542-5

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.