We perform a two-step segmentation of the hippocampus 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 thresholding 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.

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

DI CUNTO, Ferdinando;GIACOBINI, Mario Dante Lucio
2012

Abstract

We perform a two-step segmentation of the hippocampus 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 thresholding 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.
25th IEEE International Symposium on Computer-Based Medical Systems
Rome, Italy
20-22 June 2012
Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems
IEEE
1
4
9781467320498
evolutionary computation; image analysis; Neurosciences
MESEJO P.; UGOLOTTI R.; CAGNONI S.; DI CUNTO F.; GIACOBINI M.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/128310
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