Behavior of Certain Wavelets in Classification of Orthopaedic Images of Different Modalities

Orthopedicians often identify imaging modality visually out of their experience. To be effective, the process needs to be automated. This paper presents a behavior of wavelets in classification of orthopedic imaging modalities using Artificial Neural Network (ANN). In this work, we have considered orthopedic imaging modalities, namely, X-ray, CT and MRI and Bone scan images. Four wavelets, namely Haar, Daubechies, Symlets and Coiflets are used for sub band decomposition and their approximation co-efficients are recorded. Features, namely, mean standard deviation, median, variance and entropy is drawn from the decomposed images. Results are drawn from the performance of these wavelets at five levels of decomposition. Feature reduction is based on the classification accuracies which are analysed using wavelets. The experimental results show that the proposed method achieves satisfactory results with an average accuracy of 98% for four wavelets and for all the modalities considered. The study can be extended to include other modalities in medical field. The work is useful for orthopaedics practitioners.