0428537 20250807162218.220140819235959.9 000336726600014 10.1007/s13246-014-0273-x 11 s. P cav_un_epca*03114860158-9938372 (2014)393-403Springer Voxel model Image segmentation Statistical region merging CT dosimetry cav_un_auth*0303661 Caon M. AU cav_un_auth*0239988 Sedlář Jiří Zpracování obrazové informace Department of Image Processing ZOI ZOI UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0303662 Bajger M. AU cav_un_auth*0303663 Lee G. AU http://library.utia.cas.cz/separaty/2014/ZOI/sedlar-0428537.pdf The segmentation of CT images to produce a computational model of anatomy is a time-consuming and laborious process. Here we report a time saving semiautomatic approach. The image-processing technique known as ‘‘statistical region merging’’ (SRM) was used to pre-segment the 54 original CT images of the ADELAIDE data set into regions of related pixels. These regions were amalgamated into organs and tissues by a program operated through a graphical user interface. This combination of SRM and GUI was used to build a voxel computational model of anatomy. The ‘‘new’’ version of ADELAIDE was compared to the ‘‘old’’ version by simulating an abdominal CT procedure on both models and comparing the Monte Carlo calculated organ doses. Seventeen of the 21 SRM– GUI segmented tissues received doses that were within 18 % of the doses received by the manually segmented tissues. 2015 JD 4 RVO:67985556 http://hdl.handle.net/11104/0235488 S ENGINEERINGBIOMEDICAL 0.235 0.446 Q3 9.405 12.500 Q4 Q3 2014 000336726600014 WOS cav_un_epca*0311486 Australasian Physical & Engineering Sciences in Medicine 0158-9938 1879-5447 Roč. 37 č. 2 2014 393 403 Springer