BONE CRACK DETECTION IN MEDICAL IMAGES USING MULTI MODEL IMAGE FUSION

In medical imaging, various modalities provide different features of the human body because they use different physical principles of imaging. CT and MRI images with high spatial resolution provide the anatomical details, while PET and SPECT show the biochemical and physiological information but their spatial resolutions are not good enough. So it is very useful and important to combine images from multi -modality scanning such that the resulting image can provide both functional and anatomical information with high spatial resolution. In this paper we present a wavelet-based image fusion algorithm. The images to be fused are firstly decomposed into high frequency and low frequency bands. We select four groups of images to simulate, and compare our simulation results with the pixel addition, weighted averaging method and wavelet method based on min-max and subtraction based fusion rule. Then, the low and high frequency components are combined by using different fusion rules. Finally, the fused image is instructed by inverse wavelet transform. The various objective and subjective evaluation metrics and Quality are calculated to compare the results. The wavelet based fusion methods using different fusion rules are compared bot h subjectively as well as objectively. The experimental results show that the pixel minimum method is giving the better results in respect of MSE, SNR and using edge based quality metrics addition method observed to be better in preserving the edge information. One Image fusion method can be perfect for one particular application but may not for another application. So it depends on which information to extract, enhance, and reconstruct or retrieve to use the particular fusion method