Rajiv Singh

Fusion of multimodal medical images using Daubechies complex wavelet transform - A multiresolution approach

Multimodal medical image fusion is an important task for the retrieval of complementary information from medical images. Shift sensitivity, lack of phase information and poor directionality of real valued wavelet transforms motivated us to use complex wavelet transform for fusion. We have used Daubechies complex wavelet transform (DCxWT) for image fusion which is approximately shift invariant and provides phase information. In the present work, we have proposed a new multimodal medical image fusion using DCxWT at multiple levels which is based on multiresolution principle. The proposed method fuses the complex wavelet coefficients of source images using maximum selection rule. Experiments have been performed over three different sets of multimodal medical images. The proposed fusion method is visually and quantitatively compared with wavelet domain (Dual tree complex wavelet transform (DTCWT), Lifting wavelet transform (LWT), Multiwavelet transform (MWT), Stationary wavelet transform (SWT)) and spatial domain (Principal component analysis (PCA), linear and sharp) image fusion methods. The proposed method is further compared with Contourlet transform (CT) and Nonsubsampled contourlet transform (NSCT) based image fusion methods. For comparison of the proposed method, we have used five fusion metrics, namely entropy, edge strength, standard deviation, fusion factor and fusion symmetry. Comparison results prove that performance of the proposed fusion method is better than any of the above existing fusion methods. Robustness of the proposed method is tested against Gaussian, salt & pepper and speckle noise and the plots of fusion metrics for different noise cases established the superiority of the proposed fusion method.

Mixed scheme based multimodal medical image fusion using Daubechies Complex Wavelet Transform

Multimodal medical image fusion is an important task for retrieving complementary information from different modality of medical images. Image fusion can be performed using either spatial or transform domain methods. Limitations of spatial domain fusion methods led to transform domain methods. Discrete wavelet transform (DWT) based fusion is one of the most widely used transform domain method. But it suffers from shift sensitivity and does not provide any phase information. These disadvantages of DWT motivated us to use complex wavelet transform. In the present work, we have proposed a new multimodal medical image fusion method using Daubechies complex wavelet transform (DCxWT) which applies two separate fusion rules for approximation and detail coefficients. Shift invariance, availability of phase information and multiscale edge information properties of DCxWT improves the quality of fused image. We have compared the proposed method with spatial domain fusion methods (PCA and linear fusion) and transform domain fusion methods (discrete and lifting wavelet transforms). Comparison of results has been done qualitatively as well as by using different fusion metrics (entropy, standard deviation, fusion factor, fusion symmetry and QABF). On the basis of qualitative and quantitative analysis of the obtained results, the proposed method is found to be better than spatial domain fusion methods (PCA and linear fusion) and transform domain fusion methods (discrete and lifting wavelet transforms).

Multiscale Medical Image Fusion in Wavelet Domain

Wavelet transforms have emerged as a powerful tool in image fusion. However, the study and analysis of medical image fusion is still a challenging area of research. Therefore, in this paper, we propose a multiscale fusion of multimodal medical images in wavelet domain. Fusion of medical images has been performed at multiple scales varying from minimum to maximum level using maximum selection rule which provides more flexibility and choice to select the relevant fused images. The experimental analysis of the proposed method has been performed with several sets of medical images. Fusion results have been evaluated subjectively and objectively with existing state-of-the-art fusion methods which include several pyramid- and wavelet-transform-based fusion methods and principal component analysis (PCA) fusion method. The comparative analysis of the fusion results has been performed with edge strength (Q), mutual information (MI), entropy (E), standard deviation (SD), blind structural similarity index metric (BSSIM), spatial frequency (SF), and average gradient (AG) metrics. The combined subjective and objective evaluations of the proposed fusion method at multiple scales showed the effectiveness and goodness of the proposed approach.

DCT-domain robust data hiding using chaotic sequence

In this paper, we propose DCT-domain robust data hiding algorithm using chaotic sequence. The algorithm works by dividing the cover into blocks of equal sizes and then embeds the watermark in middle band of DCT coefficient. Performance evaluation for robustness and imperceptibility of proposed algorithm has been made using the bit error rate (BER) and the peak signal to noise ratio (PSNR) value for cover image and watermarked image. This algorithm is compared to PN sequence based DCT algorithm. The proposed algorithm provides more robustness against several common image processing attacks, such as JPEG compression, low-pass filtering and addition of noise. In case of JPEG compression attacks, for low quality compression (Q-60) it has been analyzed that more than 92% of the hidden data was recovered without any error and for same quality compression in PN sequence based DCT algorithm has more than 89% of the hidden data recovered without any error.

Edge preserving image fusion based on contourlet transform

Image fusion is an emerging area of research having a number of applications in medical imaging, remote sensing, satellite imaging, target tracking, concealed weapon detection and biometrics. In the present work, we have proposed a new edge preserving image fusion method based on contourlet transform. As contourlet transform has high directionality and anisotropy, it gives better image representation than wavelet transforms. Also contourlet transform represents salient features of images such as edges, curves and contours in better way. So it is well suited for image fusion. We have performed experiments on several image data sets and results are shown for two datasets of multifocus images and one dataset of medical images. On the basis of experimental results, it was found that performance of proposed fusion method is better than wavelet transform (Discrete wavelet transform and Stationary wavelet transform) based image fusion methods. We have verified the goodness of the proposed fusion algorithm by well known image fusion measures (entropy, standard deviation, mutual information (MI) and

Multimodal medical image fusion using daubechies complex wavelet transform

Q_{AB}^{F}

Fusion of multifocus noisy images using contourlet transform

). The fusion evaluation parameters also imply that the proposed edge preserving image fusion method is better than wavelet transform (Discrete wavelet transform and Stationary wavelet transform) based image fusion methods.

Objective evaluation of noisy multimodal medical image fusion using Daubechies complex wavelet transform

In the present work, we propose a new weighted fusion scheme using Daubechies complex wavelet transform (DCxWT). Shift sensitivity and lack of phase information in real valued wavelet transforms motivated to use DCxWT for multimodal medical image fusion. It was experimentally found that shift invariance and phase information properties improve the performance of image fusion in complex wavelet domain. Therefore, we used DCxWT for fusion of multimodal medical images. To show the effectiveness of the proposed work, we have compared our method with existing DCxWT, dual tree complex wavelet transform (DTCWT), discrete wavelet transform (DWT), non-sub contourlet transform (NSCT) and contourlet transform (CT) based fusion methods using edge strength and mutual information fusion metrics. Comparison results clearly show that the proposed fusion scheme with DCxWT outperforms existing DCxWT, DTCWT, DWT, NSCT and CT based fusion methods.

Redundant Discrete Wavelet Transform Based Medical Image Fusion

Image fusion is one of the important application areas of image processing. It is used to obtain a single composite image with complementary features of various types of images. Presence of noise degrades the quality of an image and its subsequent fusion results, if it is not handled properly. Therefore, in this paper we have proposed an algorithm that combines denoising with fusion process. The proposed algorithm uses level dependent threshold that changes according to the characteristics of coefficients. The proposed method is compared with stationary wavelet transform and dual tree complex wavelet transform based fusion methods. The visual results show that the proposed algorithm gives better results than other methods. The performance of the algorithm is also tested using four quality metrics (peak signal to noise ratio, entropy, standard deviation and edge strength). Further, with the help of experiments, we have also established the fact that denoising before performing fusion gives effectively better results.

Image fusion based on nonsubsampled contourlet transform

Medical image fusion needs proper attention as images obtained from medical instruments are of poor contrast and corrupted by blur and noise due to imperfection of image capturing devices. Thus, objective evaluation of medical image fusion techniques has become an important task in noisy domain. Therefore, in the present work, we have proposed maximum selection and energy based fusion rules for the evaluation of noisy multimodal medical image fusion using Daubechies complex wavelet transform (DCxWT). Unlike, traditional real valued wavelet transforms, which suffered from shift sensitivity and did not provide any phase information, DCxWT is shift invariant and provides phase information through its imaginary coefficients. Shift invariance and availability of phase information properties of DCxWT have been found useful for fusion of multimodal medical images. The experiments have been performed over several set of noisy medical images at multiple levels of noise for the proposed fusion scheme. Further, the proposed fusion scheme has been tested up to the maximum level of Gaussian, salt & pepper and speckle noise. Objective evaluation of the proposed fusion scheme is performed with fusion factor, fusion symmetry, entropy, standard deviation and edge information metrics. Results have been shown for two sets of multimodal medical images for the proposed method with maximum and energy based fusion rules, and comparison has been done with Lifting wavelet transform (LWT) and Stationary wavelet transform (SWT) based fusion methods. Comparative analysis of the proposed method with LWT and SWT based fusion methods at different noise levels shows the superiority of the proposed scheme. Moreover, the plots of different fusion metrics against the maximum level of Gaussian, salt & pepper and speckle noise show the robustness of the proposed fusion method against noise.