Proceedings Article | 10 March 2006
KEYWORDS: Feature selection, Bone, Principal component analysis, Image classification, Visualization, Feature extraction, Biomedical optics, Dimension reduction, Image processing, Image segmentation
Multiscale analysis provides a complete hierarchical partitioning of images into visually plausible regions. Each
of them is formally characterized by a feature vector describing shape, texture and scale properties. Consequently,
object extraction becomes a classification of the feature vectors. Classifiers are trained by relevant and
irrelevant regions labeled as object and remaining partitions, respectively. A trained classifier is applicable to
yet uncategorized partitionings to identify the corresponding region's classes. Such an approach enables retrieval
of a-priori unknown objects within a point-and-click interface. In this work, the classification pipeline consists
of a framework for data selection, feature selection, classifier training, classification of testing data, and evaluation.
According to the no-free-lunch-theorem of supervised learning, the appropriate classification pipeline is
determined experimentally. Therefore, each of the steps is varied by state-of-the-art methods and the respective
classification quality is measured. Selection of training data from the ground truth is supported by bootstrapping,
variance pooling, virtual training data, and cross validation. Feature selection for dimension reduction
is performed by linear discriminant analysis, principal component analysis, and greedy selection. Competing
classifiers are k-nearest-neighbor, Bayesian classifier, and the support vector machine. Quality is measured by
precision and recall to reflect the retrieval task. A set of 105 hand radiographs from clinical routine serves as
ground truth, where the metacarpal bones have been labeled manually. In total, 368 out of 39.017 regions are
identified as relevant. In initial experiments for feature selection with the support vector machine have been
obtained recall, precision and F-measure of 0.58, 0.67, and 0,62, respectively.
