A novel level set model initialized with guided filter for automated PET-CT image segmentation

Positron emission tomography (PET) and computed tomography (CT) scanner image analysis plays an important role in clinical radiotherapy treatment. PET and CT images provide complementary cues for identifying tumor tissues. In specific, PET images can clearly denote the tumor tissue, whereas this source suffers from the problem of low spatial resolution. On the contrary, CT images have a high resolution, but they can not recognize the tumor from normal tissues. In this work, we firstly fuse PET and CT images by using the guided filter. Then, a region and edge-based level set model is proposed to segment PET-CT fusion images. At last, a normalization term is designed by combining length, distance and H¹ terms with the aim to improve segmentation accuracy. The proposed method was validated in the robust delineation of lung tumor tissues on 20 PET-CT samples. Both qualitative and quantitative results demonstrate significant improvement compared to both the data-independent and deep learning based segmentation methods.