George Iordanescu1, Palamadai Venkatasubramanian1, Alice Wyrwicz1
1Northwestern University, Evanston, IL, USA
We present an automated method for the selection of the segmentation parameters used for the quantification of amyloid plaques in APP transgenic mice that are models for Alzheimers disease (AD). For this we use support vector machines (SVM) in an unsupervised way. The usual approach for classification is to use the ground truth, in our case real examples of amyloid deposits in MR images. Obtaining such examples is however very difficult, due to the reduced size and low contrast of plaques. To address this, we employ a learning by counter-example approach, by training one class SVM classifier on control datasets.