CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Measuring Lesion Growth from 3D Medical Images
NAM '97 Proceedings of the 1997 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (NAM '97)
Automatic Lesion/Tumor Detection Using Intelligent Mesh-Based Active Contour
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Content-based retrieval of dynamic PET functional images
IEEE Transactions on Information Technology in Biomedicine
Multiresolution detection of spiculated lesions in digital mammograms
IEEE Transactions on Image Processing
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Position Emission Tomography (PET) is increasingly applied in the diagnosis and surgery in patients thanks to its ability of showing nearly all types of lesions including tumour and head injury. However, due to its natures of low resolution and different appearances as a result of different tracers, segmentation of lesions presents great challenges. In this study, a simple and robust algorithm is proposed via additive colour mixture approach. Comparison with the other two methods including Bayesian classified and geodesic active contour is also performed, demonstrating the proposed colouring approach has many advantages in terms of speed, robustness, and user intervention. This research has many medical applications including pharmaceutical trials, decision making for drug treatment or surgery and patients follow-up and shows potential to the development of content-based image databases when coming to characterise PET images using lesion features.