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Gaussian-based Spatial Hybrid Distances for Detection and Segmentation of Lymphoid Lesions in 3D PET Images

Detection and segmentation of lymphoid lesions from PET/CT images is a crucial task for cancer staging and treatment monitoring. However, it is still a challenging task owing to the fact that there are not fixed locations, obvious feature information, shapes and sizes for lymphomas in PET images. In this work, a spatial hybrid distance (SHD) methodology is proposed for detection and segmentation of 3D PET images. Based on Gaussian kernel function, the SHDs are defined by combining different feature attributes including some priori knowledge about organ distribution, and spatial positions. A clustering method using SHD is proposed to detect and segment the lymphoma. Finally, a series of comparison experiments are performed based on DBSCAN algorithm, and the results show that the propoded SHD can achieve better performance than traditional similarity measures, and it can provide an unsupervised detection and segmentation of Iymphoid lesions in 3D PET images.