Ashish Khare

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.

Despeckling of medical ultrasound images using Daubechies complex wavelet transform

The paper presents a novel despeckling method, based on Daubechies complex wavelet transform, for medical ultrasound images. Daubechies complex wavelet transform is used due to its approximate shift invariance property and extra information in imaginary plane of complex wavelet domain when compared to real wavelet domain. A wavelet shrinkage factor has been derived to estimate the noise-free wavelet coefficients. The proposed method firstly detects strong edges using imaginary component of complex scaling coefficients and then applies shrinkage on magnitude of complex wavelet coefficients in the wavelet domain at non-edge points. The proposed shrinkage depends on the statistical parameters of complex wavelet coefficients of noisy image which makes it adaptive in nature. Effectiveness of the proposed method is compared on the basis of signal to mean square error (SMSE) and signal to noise ratio (SNR). The experimental results demonstrate that the proposed method outperforms other conventional despeckling methods as well as wavelet based log transformed and non-log transformed methods on test images. Application of the proposed method on real diagnostic ultrasound images has shown a clear improvement over other methods.

Daubechies Complex Wavelet Transform Based Moving Object Tracking

This paper describes a new method for moving object tracking, using complex wavelet transform. Real-valued wavelet transform is widely used in tracking applications, but it suffers from shift-sensitivity. Daubechies complex wavelet transform is more suitable for tracking due to approximate shift-invariance nature. The proposed method is intelligent enough to segment the object from a scene. Segmentation in the first frame has been done by computing multiscale correlation of imaginary component of complex wavelet coefficients and then object is tracked in next frames by computing the energy of complex wavelet coefficients corresponding to the object area and matching this energy to that of the neighborhood area. The proposed method is simple and does not require any other parameter except complex wavelet coefficients for segmentation as well as tracking

Multilevel threshold based image denoising in curvelet domain

In this paper, we propose a multilevel thresholding technique for noise removal in curvelet transform domain which uses cycle-spinning. Most of uncorrelated noise gets removed by thresholding curvelet coefficients at lowest level, while correlated noise gets removed by only a fraction at lower levels, so we used multilevel thresholding on curvelet coefficients. The threshold in the proposed method depends on the variance of curvelet coefficients, the mean and the median of absolute curvelet coefficients at a particular level which makes it adaptive in nature. Results obtained for 2-D images demonstrate an improved performance over other recent related methods available in literature.

Biorthogonal wavelet transform based image fusion using absolute maximum fusion rule

The objective of image fusion is to combine relevant information from two or more images of the same scene into a single composite image which is more informative and is more suitable for human and machine perception. In recent past, different methods of image fusion have been proposed in literature both in spatial domain and wavelet domain. Spatial domain based methods produce spatial distortions in the fused image. Spatial domain distortion can be well handled by the use of wavelet transform based image fusion methods. In this paper, we propose a pixel-level image fusion scheme using multiresolution Biorthogonal wavelet transform (BWT). Wavelet coefficients at different decomposition levels are fused using absolute maximum fusion rule. Two important properties wavelet symmetry and linear phase of BWT have been exploited for image fusion because they are capable to preserve edge information and hence reducing the distortions in the fused image. The performance of the proposed method have been extensively tested on several pairs of multifocus and multimodal images both free from any noise and in presence of additive white Gaussian noise and compared visually and quantitatively against existing spatial domain methods. Experimental results show that the proposed method improves fusion quality by reducing loss of significant information available in individual images. Fusion factor, entropy and standard deviation are used as quantitative quality measures of the fused image.

A New Method for Deblurring and Denoising of Medical Images using Complex Wavelet Transform

Deblurring in the presence of non-Gaussian noise is a hard problem, specially in ultrasonic and CT images. In this paper, a new method of image restoration, using complex wavelet transform, has been devised and applied to deblur in the presence of high speckle noise. It has been shown that the new method outperforms the Weiner filtering and Fourier-wavelet regularized deconvolution (ForWaRD) methods for both ultrasonic and CT images. Unlike Fourier and real wavelet transforms, complex wavelet transform is nearly shift-invariant. This gives complex wavelet transform an edge over other traditional methods when applied simultaneously for deblurring as well as denoising. The proposed method is independent of any assumption about the degradation process. It is adaptive, as it uses shrinkage function based on median and mean of absolute wavelet coefficient as well as standard deviation of wavelet coefficients. Its application on real spiral CT images of inner ear has shown a clear improvement over other methods

DAUBECHIES COMPLEX WAVELET TRANSFORM BASED MULTILEVEL SHRINKAGE FOR DEBLURRING OF MEDICAL IMAGES IN PRESENCE OF NOISE

Deblurring in the presence of noise is a hard problem, especially in ultrasonic and CT images. In this paper, we propose a new method of image deblurring in presence of noise, using symmetric Daubechies complex wavelet transform. The proposed method is based on shrinkage of multilevel Daubechies complex wavelet coefficients, and is adaptive as it uses shrinkage function based on the variance of wavelet coefficients as well as the mean and the median of absolute wavelet coefficients at a particular level. The results obtained after the application of the proposed method demonstrate an improved performance over other related methods available in literature.

Moving object segmentation in Daubechies complex wavelet domain

Motion segmentation is a crucial step in video analysis and is associated with a number of computer vision applications. This paper introduces a new method for segmentation of moving object which is based on double change detection technique applied on Daubechies complex wavelet coefficients of three consecutive frames. Daubechies complex wavelet transform for segmentation of moving object has been chosen as it is approximate shift invariant and has a better directional selectivity as compared to real valued wavelet transform. Double change detection technique is used to obtain video object plane by inter-frame difference of three consecutive frames. Double change detection technique also provides automatic detection of appearance of new objects. The proposed method does not require any other parameter except Daubechies complex wavelet coefficients. Results of the proposed method for segmentation of moving objects are compared with results of other state-of-the-art methods in terms of visual performance and a number of quantitative performance metrics viz. Misclassification Penalty, Relative Foreground Area Measure, Pixel Classification Based Measure, Normalized Absolute Error, and Percentage of Correct Classification. The proposed method is found to have high degree of segmentation accuracy than the other state-of-the-art methods.

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.