Investigation of PCA as a compression pre-processing tool for X-ray image classification

Image classification has rapidly gained interest in the medical field with the ability to assist practitioners to diagnose a variety of conditions. Due to the critical nature of the application, any pre-processing function that may compromise the fitness of the classifier requires careful assessment. Image compression, albeit necessary in terms of volume-based goals, is an example of such a preprocessing function that can deeply affect data veracity. In this work, the trade-off between volume and veracity in bone fracture classification using X-ray images is investigated. The impacts of the dimensionality reduction technique—via Principal Component Analysis—as a compression tool on X-ray image classification are explored. The effects of the compression technique on the detection of fractures are assessed by evaluating how reductions in principal components of the X-ray image, and subsequently its volume, affect the accuracy of the fracture classification. Varying levels of compression are applied to both healthy and fracture image sets with tests conducted using ANFIS, SVM and ANN classifiers. Results indicate that a potentially feasible compression range exists whereby classification accuracy is acceptably diminished, after which further compression yields are marginal and classification accuracies drastically decrease. Overall results demonstrate the suitability of the method which yields compression levels of up to 94% with a corresponding minimal drop in classification accuracy of 2%.