Performance Analysis of Orthogonal and Biorthogonal Wavelets for Edge Detection of X-ray Images

The X-ray images are extensively used by the medical practitioners to detect the minute fractures of bone images as they are painless and economical compared to other image modalities. The minute fractures cannot be identified with nacked eye. So the X-ray images are to be processed for detecting the minute fractures. The orthogonal wavelet transforms like Haar, daubechies etc can be used as edge detector, but a lot of false edge information will be extracted. Edge detection of X-ray images using Multiresolution Analysis(MRA) based biorthogonal wavelets is more preferable when compared with orthogonal wavelets because of more flexibility. Therefore biorthogonal wavelet transforms like bior1.3,bior2.4 are applied to detect the edges and are compared for edge feature extraction. Among all the methods, biorthogonal wavelet bior1.3 performs well in detecting the edges with better quality. The various performance metrics like Ratio of Edge pixels to size of image (REPS), peak signal to noise ratio (PSNR) and computation time are compared for various wavelets for edge detection.