Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vessel Detection by Mean Shift-Based Ray Propagation
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A framework for automated tumor detection in thoracic FDG PET images using texture-based features
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
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PET imagery is a valuable oncology tool for characterizing lesions and assessing lesion response to therapy. These assessments require accurate delineation of the lesion. This is a challenging task for clinicians due to small tumor sizes, blurred boundaries from the large point-spread-function and respiratory motion, inhomogeneous uptake, and nearby high uptake regions. These aspects have led to great variability in lesion assessment amongst clinicians. In this paper, we describe a segmentation algorithm for PET lesions which yields objective segmentations without operator variability. The technique is based on the mean shift algorithm, applied in a spherical coordinate frame to yield a directional assessment of foreground and background and a varying background model. We analyze the algorithm using clinically relevant hybrid digital phantoms and illustrate its effectiveness relative to other techniques.