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|Title:||Development of semi-automatic segmentation methods for measuring tibial cartilage volume.|
|Citation:||Proceedings of Digital Image Computing: Techniques and Applications, held in Cairns, Queensland, Australia, 2005: pp.45-52|
|Conference Name:||Digital Image Computing: Techniques and Applications (2005 : Cairns, Australia)|
|James Cheong, David Suter and Flavia Cicuttini|
|Abstract:||Osteoarthritis is a chronic and crippling disease affecting an increasing number of people each year. With no known cure, it is expected to reach epidemic proportions in the coming years. Currently, there is strong interest in developing a fully automated cartilage area/volume measurement method in the medical field to assist both pharmaceutical companies and medical professions in researching the disease. This paper describes the development of two different semi-automatic methods for segmenting and measuring human knee cartilage volume from magnetic resonance imaging (MRI) scans. Two different approaches were adopted, a data driven segmentation technique using directional Canny filters and a model based segmentation method using an improved Active Shape Model (ASM) scheme. The cartilage volume obtained using each method was benchmarked against the current "gold standard" (cartilage volume from manual segmentation).|
|Appears in Collections:||Computer Science publications|
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