Adaptive local threshold with shape information and its application to object segmentation

This paper presents a novel local threshold segmentation algorithm for digital images incorporating shape information. In image segmentation, most of local threshold algorithms are only based on intensity analysis. In many applications where an image contains objects with a similar shape, besides the intensity information, prior known shape attributes could be exploited to improve the segmentation. The goal of this work is to design a local threshold algorithm that includes shape information to enhance the segmentation quality. The algorithm can be divided into two steps: adaptively selecting local threshold based on maximum likelihood, and then removing unwanted segmented fragments by a supervised classifier. Shape attribute distributions are learned from typical objects in ground truth images. Local threshold for each object in an image to be segmented is chosen to maximize probabilities of these shape attributes according to learned distributions. After local thresholds are picked, the algorithm applies a supervised classifier trained by shape features to reject unwanted fragments. Experiments on oil sand images have shown that the proposed algorithm has superior performance to local threshold approaches based on intensity information in terms of segmentation quality.