No abstract

No abstract

No abstract

A method for feature level image fusion for multimodal medical images in second generation wavelet domain (lifting wavelet transform domain) is proposed. The feature fused is edge and boundary information of input images that is extracted using wavelet transform modulus maxima criterion. The image fusion performance is evaluated by standard deviation, entropy, cross entropy and gradient parameters. Experimental results show that the proposed method gives better results for image fusion as image contrast, average information content and detail information of fused image are increased. This method has further advantages of fast implementation, flexibility, saving of auxiliary memory, property of perfect reconstruction and simplicity as we have used lifting wavelet transform. The reduction in computational complexity has been achieved by a factor of two as compared to the nonlifted wavelet transform.

Medical images generally have low contrast and they get complex type of noise due to the use of various devices and applications of various algorithms. However, most of the denoising methods consider only additive noise or some special noise model dependent on their systems and conditions only. Such methods when applied to real medical images yield poor results. The present work proposes a method for denoising of medical images using soft-thresholding in wavelet domain on multiple levels. We have developed a method to compute the threshold values for denoising of medical images, which depend on the median as well as the contrast ratio of the wavelet coefficients and also on the level number. We have performed experiments by adding various proportions of Gaussian, Salt-and-Pepper and Speckle noise, and found that the proposed method performs better for these cases. The method is efficient because the threshold values can be calculated directly and it is adaptive as these values depend on mean, median and standard deviation of wavelet coefficients of the particular image. The proposed method also gives a criterion for level-dependent thresholding. Application of the proposed method to Ultrasound, X-ray and MRI images is demonstrated in experiments. In the present work, we have also done studies on how to select the mother wavelet for a particular problem.