The automatic localization and segmentation, or parsing, of neuroanatomical brain structures is a key step
in many neuroscience tasks. However, the inherent variability in these brain structures and their appearance
continues to challenge medical image processing methods. The state of the art primarily relies upon local voxelbased
morphometry, Markov random field, and probabilistic atlas based approaches, which limits the ability to
explicitly capture the parts-based structure inherent in the brain. We propose a method that defines a principled
parts-based representation of the sub-cortical brain structures. Our method is based on the pictorial structures
model and jointly models the appearance of each part as well as the layout of the parts as a whole. Inference
is cast as a maximum a posteriori problem and solved in a steepest-descent manner. Experimental results on a
28-case data set demonstrate high accuracy of our method and substantiate our claim that there is significant
promise in a parts-based approach to modeling medical imaging structures.
The problem of object category recognition has long challenged the computer vision community. In this paper, we
address these tasks via learning two-class and multi-class discriminative models. The proposed approach integrates the
Adaboost algorithm into the decision tree structure, called DB-Tree, and each tree node combines a number of weak
classifiers into a strong classifier (a conditional posterior probability). In the learning stage, each boosted classifier in a
tree node is trained to split the training set to left and right sub-trees, and the classifier is thus used not to return the class
of the sample but rather to assign the sample to the left or right sub-tree. Therefore, the DB-Tree can be built up
automatically and recursively. In the testing stage, the posterior probability of each node is computed by the weighted
conditional probability of left and right sub-trees. Thus, the top node of the tree can output the overall posterior
probability. In addition, the multi-class and two-class learning procedures become unified, through treating the multi-class
classification problem as a special two-class classification problem, and either a positive or negative label is
assigned to each class in minimizing the total entropy in each node.
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