Blob segmentation using joint space-intensity likelihood ratio test: application to 3D tumor segmentation

We propose a novel semi-automatic figure-ground segmentation solution for blob-like objects in multi-dimensional images. The blob-like structure constitutes various objects of interest that are hard to segment in many application domains, such as tumor lesions in 3D medical data. The proposed solution is motivated towards computer-aided diagnosis medical applications, justifying our semi-automatic and figure-ground approach. The efficient segmentation is realized by combining the robust anisotropic Gaussian model fitting and the likelihood ratio test (LRT)-based non-parametric segmentation in joint space-intensity domain. The robustly fitted Gaussian is exploited to estimate the foreground and background likelihoods for both spatial and intensity variables. We demonstrate that the LRT with the bootstrapped likelihoods is assured to be the optimal Bayesian classification while automatically determining the LRT threshold. A 3D implementation of the proposed algorithm is applied to the lung nodule segmentation in CT data and validated with 1310 cases. Our efficient solution segments a target nodule in less than 3 seconds in average.