ICIP 2006, Atlanta, GA
 

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Paper Detail

Paper:MA-L3.6
Session:Biomedical Image Segmentation
Time:Monday, October 9, 11:40 - 12:00
Presentation: Lecture
Topic: Biomedical Imaging: Other
Title: AUTOMATIC HOT SPOT DETECTION AND SEGMENTATION IN WHOLE BODY FDG-PET IMAGES
Authors: Haiying Guan; University of California, Santa Barbara 
 Toshiro Kubota; Siemens Medical Solutions 
 Xiaolei Huang; Siemens Medical Solutions 
 Xiang Sean Zhou; Siemens Medical Solutions 
 Matthew Turk; University of California, Santa Barbara 
Abstract: We present a system for automatic hot spots detection and segmentation in whole body FDG-PET images. The main contribution of our system is threefold. First, it has a novel body-section labeling module based on spatial Hidden-Markov Models (HMM); this allows different processing policies to be applied in different body sections. Second, the Competition Diffusion (CD) segmentation algorithm, which takes into account body-section information, converts the binary thresholding results to probabilistic interpretation and detects hot-spot region candidates. Third, a recursive intensity mode-seeking algorithm finds hot spot centers efficiently, and given these centers, a clinically meaningful protocol is proposed to accurately quantify hot spot volumes. Experimental results show that our system works robustly despite the large variations in clinical PET images.