Automatic segmentation of hippocampus in histological images of mouse brains using deformable models and random forest

Pablo Mesejo 1 Roberto Ugolotti 1 Ferdinando Di Cunto 2 Stefano Cagnoni 1 Mario Giacobini 2, 3
1 Intelligent Bio-Inspired Systems lab (IBISlab)
2 Molecular Biotechnology Center
3 Department of Veterinary Sciences
Abstract : We perform a two-step segmentation of the hippocam-pus in histological images. First, we maximize the overlap of an empirically-derived parametric Deformable Model with two crucial landmark sub-structures in the brain image using Differential Evolution. Then, the points located in the previous step determine the region where a thresh-olding technique based on Otsu's method is to be applied. Finally, the segmentation is expanded employing Random Forest in the regions not covered by the model. Our approach showed an average segmentation accuracy of the 92.25% and 92.11% on test sets comprising 15 real and 15 synthetic images, respectively.
Keywords : Image Segmentation Deformable Models Random Forest Histological Images
Type de document :
Communication dans un congrès
25th IEEE International Symposium on Computer-Based Medical Systems (CBMS’12), Jun 2012, Rome, Italy. pp.1-4, 2012, 〈10.1109/CBMS.2012.6266318〉
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Contributeur : Pablo Mesejo Santiago <>
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Pablo Mesejo, Roberto Ugolotti, Ferdinando Di Cunto, Stefano Cagnoni, Mario Giacobini. Automatic segmentation of hippocampus in histological images of mouse brains using deformable models and random forest. 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS’12), Jun 2012, Rome, Italy. pp.1-4, 2012, 〈10.1109/CBMS.2012.6266318〉. 〈hal-01221660〉

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