Impact of Feature Selection on Clustering Images ofVertebral Compression Fractures
Non-traumatic Vertebral Compression Fractures (VCFs) are generally
caused by osteoporosis (benign VCFs) or metastatic cancer (malignant
VCFs) and the success of the medical treatment strongly depends on a fast and
correct classification of VCFs. Recently, methods for computer-aided diagnosis
(CAD) based on machine learning have been proposed for classifying VCFs. In
this work, we investigate the problem of clustering images of VCFs and the impact
of feature selection by genetic algorithms, comparing the clustering i)with
all features and ii)with feature selection through the purity results. The analysis
of the clusters helps to understand the results of classifiers and difficulties
of differentiating images of different classes by an expert. The results indicate
that features selection improved the separability of clusters and purity. Feature
selection also helps to understand which attributes are most important for
analysing the images of vertebral bodies.
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