Delineation of FDG-PET tumors from heterogeneous background using spectral clustering

This paper explored the feasibility of using spectral clustering to segment FDG-PET tumor in the presence of heterogeneous background. Spectral clustering refers to a class of clustering methods which employ the eigenstructure of a similarity matrix to partition image voxels into disjoint clusters. The similarity between two voxels was measured with the intensity distance scaled by voxel-varying factors capturing local statistics and the number of clusters was inferred based on rotating the eigenvector matrix for the maximally sparse representation. Metrics used to evaluate the segmentation accuracy included: Dice coefficient, Jaccard coefficient, false positive dice, false negative dice, symmetric mean absolute surface distance, and absolute volumetric difference. Comparison of segmentation results between the presented method and the adaptive thresholding method on the simulated PET data shows the former attains an overall better detection accuracy. Applying the presented method on patient data gave segmentation results in fairly good agreement with physician manual annotations. These results indicate that the presented method have the potential to accurately delineate complex shaped FDG-PET tumors containing inhomogeneous activities in the presence of heterogeneous background.