An Effective and Reliable Computer Automated Technique for Bone Fracture Detection

Abstract

INTRODUCTION: In the year 1895 the X-ray images were discovered. Since then the medical imaging has got advanced tremendously. Anyhow the methods of interpretation have started progressing only by the evolution of Computer aided Diagnosis(CAD).

OBJECTIVES: To develop a Computer Aided Diagnosis (CAD) system to detect the bone fracture which helps the radiologists (or) the Orthopaedics by interpreting the medical images in short duration.

METHODS: In this paper, an effective automated bone fracture detection is proposed using enhanced Haar Wavelet Transform, Scale-Invariant Feature Transform (SIFT) and back propagation neural network. The former two techniques are used for feature extraction and the latter one is used for classification of fracture images. Simultaneously, the usage of enhanced Haar Wavelet Transforms and SIFT are phenomenally improves the quality of the X-ray image. Further in this work, k-means clustering based ‘Bag of Words’ methods are used to extract enhanced features extracted from SIFT. The classification phase of this proposed technique uses the classical back propagation neural network that contains 1024 neurons in 3-layers.

RESULTS: The experimental validation of this proposed scheme performed using nearly 300 different bone fractures x-ray images confirmed a better classification rate of 93.4%.

CONCLUSIONS: The experimental results of the proposed computer aided technique are proven to be better than the detection technique facilitated with the traditional SIFT technique.