A classification framework for content-based extraction of biomedical objects from hierarchically decomposed images

Christian Thies; Marcel Schmidt Borreda; Thomas Seidl; Thomas M. Lehmann

doi:10.1117/12.653503

10 March 2006 A classification framework for content-based extraction of biomedical objects from hierarchically decomposed images

Christian Thies, Marcel Schmidt Borreda, Thomas Seidl, Thomas M. Lehmann

Author Affiliations +

Proceedings Volume 6144, Medical Imaging 2006: Image Processing; 61441N (2006) https://doi.org/10.1117/12.653503
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

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.

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Christian Thies, Marcel Schmidt Borreda, Thomas Seidl, and Thomas M. Lehmann "A classification framework for content-based extraction of biomedical objects from hierarchically decomposed images", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61441N (10 March 2006); https://doi.org/10.1117/12.653503

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