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Automatic segmentation of hippocampus in histological images of mouse brains using deformable models and random forest
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.
Cited literature [11 references]
https://hal.inria.fr/hal-01221660
Contributor : Pablo Mesejo Santiago Connect in order to contact the contributor
Submitted on : Wednesday, October 28, 2015 - 1:27:02 PM
Last modification on : Monday, February 7, 2022 - 4:06:03 PM
Long-term archiving on: : Friday, January 29, 2016 - 1:18:36 PM
Contributor : Pablo Mesejo Santiago Connect in order to contact the contributor
Submitted on : Wednesday, October 28, 2015 - 1:27:02 PM
Last modification on : Monday, February 7, 2022 - 4:06:03 PM
Long-term archiving on: : Friday, January 29, 2016 - 1:18:36 PM
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PID2332607.pdf
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