A Novel Approach of Multiple Objects Segmentation Based on Graph Cut

Abstract

Segmentation is a very crucial step in many applications. Actually, there are often more than one object to be segmented in an image or a video. Taking the lung images as an example, pulmonary lesions area and lung parenchyma area are both important basis for a doctor to make diagnoses. Due to the fact that lung lesion areas and lung tissues have close gray values in the image, and the diversity, irregularity and location uncertainty of pulmonary lesions, traditional segmentation methods cannot segment objects of interest accurately, nor can extract them at the same time. In this paper, a novel approach is proposed for multiple objects segmentation based on Graph Cut. The algorithm introduces a multi-layers graph structure to represent different regions from inside to outside in an image. Besides, the foreground and background are modeled by Gaussian Mixture Models (GMMs) which can describe the gray distributions of them accurately. Then the weights of parts of links in the graph can be calculated by the probability distribution of the models. To solve the problem of boundaries leakage when two objects with similar gray value are in close proximity, a shape constraint is added to the energy function. The segmentation is achieved by max-flow/min-cut and all of the objects can be obtained. Experiment results demonstrate that the proposed method in this paper can deal with the CT images of lung with pathologies, and has accuracy and robustness.