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Delineation of blurry boundary from medical images is challenging in particular when the target object or region of interest is adjacent to other tissues with similar or overlapping intensity distributions. To address this challenge, we propose a graph model with adaptive global and geodesic constraints to contour the indistinct boundary from CT images. The global factor reflects the appearance affinities...
With the rapid growth of biomedical imaging data, manual delineation of gross tumor volume (GTV) for variety of cancers is becoming less practical due to its low efficiency, non-reproducibility, and inter-observer dependency. In this paper, we propose an automated co-segmentation method using a Bayesian decision theory to correlate the tumor and background similarities from PET and CT images. Our...
In this work, an efficient tumor positioning method is proposed by performing registration based segmentation from 18-FDG PET-CT scanners. At the first stage, the tumor is segmented from PET scans by region growing using the manual seeds which employs the SUV monotonous features, and then the tumor contours are transferred to corresponding CT images automatically by registration method which is based...
Accurate parenchymal lung tumor delineation with PET-CT can be problematic given the inherent tumor heterogeneity and proximity / involvement of extra-parenchymal tissue. In this paper, we propose a tumor delineation approach that is based on new tumor–background likelihood models in PET and CT. By incorporating the intensity downhill feature in PET as a distance cost into the background likelihood...
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