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Please use this identifier to cite or link to this item: http://hdl.handle.net/2328/26398

Title: Multi-organ segmentation of CT images using statistical region merging
Authors: Lee, Gobert N
Bajger, Mariusz
Caon, Martin
Keywords: Medical imaging
Computer-aided diagnosis
CT imaging
Issue Date: 2012
Publisher: International Association of Science and Technology for Development
Citation: Lee, G.N., Bajger, M. and Caon, M., 2012. Multi-organ segmentation of CT images using statistical region merging. Proceedings of the Ninth IASTED International Conference on Biomedical Engineering, 199-206.
Abstract: Segmentation is one of the key steps in the process of developing anatomical models for calculation of safe medical dose of radiation for children. This study explores the potential of the Statistical Region Merging segmentation technique for tissue segmentation in CT images. An analytical criterion allowing for an automatic tuning of the method is developed. The experiments are performed using a data set of 54 images from one patient, demonstrating the validity of the proposed criterion. The results are evaluated using the Jaccard index and a measure of border error with tolerance which addresses, application-dependant, acceptable error. The outcome shows that the technique has a great potential to become a method of choice for segmentation of CT images with an overall average boundary precision, for six representative tissues, equal to 0.937.
URI: http://hdl.handle.net/2328/26398
Appears in Collections:Computer Science, Engineering and Mathematics - Collected Works

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