人工智能
计算机科学
部分容积
情态动词
分割
噪音(视频)
图像(数学)
计算机视觉
图像分割
模式识别(心理学)
功能(生物学)
数学
进化生物学
生物
化学
高分子化学
作者
Benoît Lelandais,Isabelle Gardin,Laurent Mouchard,Pierre Véra,Su Ruan
标识
DOI:10.1016/j.ijar.2013.10.006
摘要
In imaging, physical phenomena and the acquisition system are responsible for noise and the partial volume effect, respectively, which affect the uncertainty and the imprecision. To address these different imperfections, we propose a method that is based on information fusion and that uses belief function theory for image segmentation in the presence of multiple image sources (multi-modal images). First, the method takes advantage of neighbourhood information from mono-modal images and information from an acquisition system to reduce uncertainty from noise and imprecision due to the partial volume effect. Then, it uses information that arises from each modality of the image to reduce the imprecision that is inherent in the nature of the images, to achieve a final segmentation. The results obtained on simulated images using various signal-to-noise ratios and medical images show its ability to segment correctly multi-modal images in the presence of noise and the partial volume effect.
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