Automatic Hot Spot Detection and Segmentation in Whole Body FDG-PET Images

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 hotspot region candidates. Third, a recursive intensity modeseeking 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.

• Type: Conference
• Year: 2006
• Where: ICIP
• Authors: Haiying Guan, Toshiro Kubota, Xiaolei Huang, Xiang Sean Zhou, Matthew Turk
• Keywords: Body Fdg-pet Images, Hot Spot Centers, Hot Spot Volumes, Hot Spots Detection, ICIP 2006, Image Processing, Segmentation Algorithm