Machine learning techniques like pointwise classification are widely used for object detection and segmentation.
However, for large search spaces like CT images, this approach becomes computationally very demanding. Designing
strong yet compact classifiers is thus of great importance for systems that ought to be clinically used as
time is a limiting factor in clinical routine. The runtime of a system plays an important role in the decision about
its application. In this paper we propose a novel technique for reducing the computational complexity of voxel
classification systems based on the well-known AdaBoost algorithm in general and Probabilistic Boosting Trees
in particular. We describe a means of incorporating a measure of hypothesis complexity into the optimization
process, resulting in classifiers with lower evaluation cost. More specifically, in our approach the hypothesis
generation that is performed during the AdaBoost training is no longer based only on the error of a hypothesis
but also on its complexity. This leads to a reduced overall classifier complexity and thus shorter evaluation
times. The validity of the approach is shown in an experimental evaluation. In a cross validation experiment,
a system for automatic segmentation of liver tumors in CT images, that is based on the Probabilistic Boosting
Tree, was trained with and without the proposed extension. In this preliminary study, the evaluation cost for
classifying previously unseen samples could be reduced by 83% using the methods described here without losing
classification accuracy.
In CT angiography images, osseous structures occluding vessels pose difficulties for physicians during diagnosis.
Simple thresholding techniques for removing bones fail due to overlapping CT values of vessels filled with contrast
agent and osseous tissue, while manual delineation is slow and tedious. Thus, we propose to automatically
segment bones using a trainable classifier to label image patches as bone or background. The image features
provided to the classifier are based on grey value statistics and gradients. In contrast to most existing methods,
osseous tissue segmentation in our algorithm works without any prior knowledge of the body region depicted in
the image. This is achieved by using a probabilistic boosting tree, which is capable of automatically decomposing
the input space. The whole system works by partitioning the image using a watershed transform, classifying
image regions as bone or background and refining the result by means of a graph-based procedure. Additionally,
an intuitive way of manually refining the segmentation result is incorporated. The system was evaluated on 15
CTA datasets acquired from various body regions, showing an average correct recognition of bone regions of 80%
at a false positive rate of 0.025% of the background voxels.
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