Title :
Integrating automatic and interactive brain tumor segmentation
Author :
Dam, Erik ; Loog, Marco ; Letteboer, Marloes
Author_Institution :
Image Anal. Group, IT Univ. of Copenhagen, Denmark
Abstract :
This paper integrates automatic segmentation based on supervised learning with an interactive multi-scale watershed segmentation method. The combined method automatically provides an initial segmentation that applies the building blocks that the user can use in the interactive method. Thereby the two approaches are seamlessly integrated and the combined method can be used on the full range of problems from very easy to very difficult segmentation tasks resulting in different levels of interaction needed. The method is evaluated for the segmentation of brain tumors.
Keywords :
brain; image classification; image segmentation; learning (artificial intelligence); medical image processing; tumours; automatic segmentation method; image classification; interactive brain tumor segmentation; interactive multiscale watershed segmentation; supervised learning; Biomedical imaging; Brain modeling; Image analysis; Image segmentation; Joining processes; Lesions; Neoplasms; Pattern classification; Shape; Supervised learning;
Conference_Titel :
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
Print_ISBN :
0-7695-2128-2
DOI :
10.1109/ICPR.2004.1334647