Authors:
Gustavo C. Oliveira
1
;
Renato Varoto
2
and
Alberto Cliquet Jr.
2
;
1
Affiliations:
1
University of São Paulo, Brazil
;
2
University of Campinas (UNICAMP), Brazil
Keyword(s):
Image Segmentation, Glioma, Genetic Algorithm, AdaBoost Classifier.
Abstract:
We present a technique for automatic brain tumor segmentation in magnetic resonance images, combining
a modified version of a Genetic Algorithm Clustering method with an AdaBoost Classifier. In a group of
42 FLAIR images, segmentations produced by the algorithm were compared to the ground truth information
produced by radiologists. The mean Dice similarity coefficient reached by the algorithm was 70.3%. In
most cases, the AdaBoost classifier increased the quality of the segmentation, improving, on average, the
DSC in about 10%. Our implementation of the Genetic Algorithm Clustering method presents improvements
compared to the original method. The use of a fixed, small number of groups and smaller population allowed
for less computational effort. In addition, adaptive restriction in the initial segmentation was achieved by using
the information of the groups with highest and 2nd-highest mean intensities. By exploring intensity and spatial
information of the pixels, the AdaBoo
st classifier improved segmentation results.
(More)