Thresholding is one basic issue in digital image processing. At any level of pixel, property, or entity, the threshold is frequently used in identifying the scope of one image object and the boundary between two different image objects. In this paper, the image threshold is comprehended by identifying its basic characteristics. In theory, image thresholding is self-adaptive spatially, temporally, and spectrally. However, the past contributions, such as histogram-based image thresholding, are mainly made to spectrally or grey-valued adaptive image thresholding, i.e., spatially irrelevant image thresholding. Here an two-step approach to spatially adaptive image thresholding is proposed. First, we make a rough image segmentation with our prior knowledge about the image. Then we make a histogram-like statistics for generating a representative threshold in each one of these segmented image regions. The representative threshold is positioned at the center of that image region. Innovatively, a spatial surface fitting function is given to solve the threshold at any position of the image. The spatial surface fitting function is generated with an orthogonal basis of functions along axes x and y respectively. With the representative thresholds in the initially segmented regions, the parametrics crs of the spatial surface fitting function are estimated according to the criteria of least squared error. As for the result of thresholds, the overall accuracy of image thresholding is evaluated with the mean squared error. Some potential improvements of our approach, including initial image segmentation, initial representative thresholds determination, and higher order basis functions selection, are elaborated for more sound image thresholding.