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Two-stage selective segmentation method based on exponential weighted geodesic distance driven model and thresholding method
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DOI：10.1016/j.camwa.2025.12.029.png)
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Selective segmentation represents a pivotal image processing technique within the domain of computer vision. Its objective is to facilitate the precise identification and extraction of a region of interest (ROI) within an image, while excluding background regions that are irrelevant to the task at hand. Nevertheless, the challenge of segmentation for images with noise, particularly those with grayscale inhomogeneity, still exists in many state-of-the-art segmentation models. In order to address this challenge, this paper proposes a novel two-step selective segmentation method based on an exponential weighted geodesic distance-driven scheme and a thresholding method inspired by the K-means method. In particular, the exponential weighted geodesic distance is first leveraged to enhance the contrast between the ROI and the background. Subsequently, the segmentation of the ROI is obtained through thresholding on the enhanced image generated in the initial stage. The experimental results demonstrate the efficacy of the proposed method in suppressing the influence of noise and grayscale inhomogeneity to a certain extent. Furthermore, the results show that the proposed method yields a higher segmentation accuracy than several existing state-of-the-art variation-based selective segmentation models and two classical deep learning segmentation models.
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IF:
2.5
论文数:
269
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0
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