Data field-based transition region extraction and thresholding

Thresholding is a popular image segmentation method that converts a gray level image into a binary image. In this paper, we propose a data field-based method for transition region extraction and thresholding, which involves three major steps, including generating the image data field, deriving the transition region by comparing the potential values, and calculating the threshold from the transition region. Image data field can effectively represent the spatial interactions of neighborhood pixels, and its potential value is a more robust measurement for the gray level change. In addition, we introduce a fully automatic scheme for parameters selection. The approach is validated both quantitatively and qualitatively. Compared with existing relative methods on a variety of synthetic and real images, with or without noisy, the experimental results suggest that the presented method is efficient and effective.

► A data field-based method for transition region extraction and thresholding.
► Image data field effectively represents the spatial interactions of neighborhood pixels.
► The potential value of field is a robust measurement for the gray level change.
► A fully automatic scheme for parameters selection.
► Image data field-based method for segmentation is efficient and effective.