Redundancy Discrete Wavelet Transform and Contourlet Transform for Multimodality Medical Image Fusion with Quantitative Analysis
19 Nov 2010-pp 134-139
TL;DR: From the experimental results, it is observed that RDWT method provides better information (quality) using EN metric and the Contour let Transform gives the difference in source to the fusion image using OCE metric and also the fused image obtained from the proposed fusion techniques has more information than the source images are proved through all metrics.
Abstract: Image fusion is the process of combining relevant information from two or more images into a single fused image. The resulting image will be more informative than any of the input images. The fusion in medical images is necessary for efficient diseases diagnosis from multimodality, multidimensional and multiparameter type of images. This paper describes a multimodality medical image fusion system using different fusion techniques and the resultant is analysed with quantitative measures. Initially, the registered images from two different modalities such as CT (anatomical information) and MRI - T2, FLAIR (pathological information) are considered as input, since the diagnosis requires anatomical and pathological information. Then the fusion techniques namely Redundancy Discrete Wavelet Transform (RDWT) and Contour let Transform are applied. Further the fused image is analyzed with five types of quantitative metrics such as Standard Deviation (SD), Entropy (EN), Overall Cross Entropy (OCE), Ratio of Spatial Frequency Error (RSFE), and Power Signal to Noise Ratio (PSNR) for performance evaluation. From the experimental results we observed that RDWT method provides better information (quality) using EN metric and the Contour let Transform gives the difference in source to the fused image using OCE metric and also the fused image obtained from the proposed fusion techniques has more information than the source images are proved through all metrics.
Citations
More filters
TL;DR: In this article, a review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion, concluding that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.
Abstract: Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.
633 citations
TL;DR: In this paper, a review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion, concluding that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.
517 citations
TL;DR: An Optimum Laplacian Wavelet Mask (OLWM) based fusion using Hybrid Cuckoo Search -Grey Wolf Optimization (HCS-GWO) for multi modal medical image fusion shows improved results than other conventional decomposition techniques.
Abstract: Multi scale masking techniques are well known in the field of multi modal medical image fusion. In medical image fusion the quality of complement information is important .In multi modal medical image fusion discrete wavelet transform (db4) based techniques provides greater level of approximation but the edge features available is less. The laplacian filter based techniques provides grater edge features. In this paper we propose an Optimum Laplacian Wavelet Mask (OLWM) based fusion using Hybrid Cuckoo Search -Grey Wolf Optimization (HCS-GWO) for multi modal medical image fusion. The HCS-GWO can automatically select the control parameters of grey wolf algorithm using cuckoo search parameters. First, the proposed fusion approach is validated for MR-SPECT, MR-PET, MR-CT and MR T1-T2 image fusion using various fusion evaluation indexes. Later, the conventional grey wolf optimization is modified with cuckoo search algorithm. Experimental results are analyzed using various performance metrics and our OLWM shows improved results than other conventional decomposition techniques.
66 citations
TL;DR: This paper presents a novel approach to Multimodality Medical Image Fusion (MMIF) used for the analysis of the lesions for the diagnostic purpose and post treatment review of NCC and shows promising and superior results when compared with the state of the art wavelet based fusion algorithms.
50 citations
47 citations
References
More filters
TL;DR: A technique for image encoding in which local operators of many scales but identical shape serve as the basis functions, which tends to enhance salient image features and is well suited for many image analysis tasks as well as for image compression.
Abstract: We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space. Pixel-to-pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself. The result is a net data compression since the difference, or error, image has low variance and entropy, and the low-pass filtered image may represented at reduced sample density. Further data compression is achieved by quantizing the difference image. These steps are then repeated to compress the low-pass image. Iteration of the process at appropriately expanded scales generates a pyramid data structure. The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus, the code tends to enhance salient image features. A further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression. Fast algorithms are described for coding and decoding.
6,975 citations
"Redundancy Discrete Wavelet Transfo..." refers background in this paper
...0 namely are FilterSubtraction-Decimate Pyramid (FSD), Gradient Pyramid, Laplacian Pyramid [9], Discrete Wavelet Transform Pyramid (DWT), Shift Invariant Discrete Wavelet Transform Pyramid (SIDWT) [8], Principle Component Analysis [2], Morphological Pyramid, Ratio Pyramid, Contrast Pyramid, and so on [1]....
[...]
TL;DR: A "true" two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information is pursued and it is shown that with parabolic scaling and sufficient directional vanishing moments, contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves.
Abstract: The limitations of commonly used separable extensions of one-dimensional transforms, such as the Fourier and wavelet transforms, in capturing the geometry of image edges are well known. In this paper, we pursue a "true" two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information. The main challenge in exploring geometry in images comes from the discrete nature of the data. Thus, unlike other approaches, such as curvelets, that first develop a transform in the continuous domain and then discretize for sampled data, our approach starts with a discrete-domain construction and then studies its convergence to an expansion in the continuous domain. Specifically, we construct a discrete-domain multiresolution and multidirection expansion using nonseparable filter banks, in much the same way that wavelets were derived from filter banks. This construction results in a flexible multiresolution, local, and directional image expansion using contour segments, and, thus, it is named the contourlet transform. The discrete contourlet transform has a fast iterated filter bank algorithm that requires an order N operations for N-pixel images. Furthermore, we establish a precise link between the developed filter bank and the associated continuous-domain contourlet expansion via a directional multiresolution analysis framework. We show that with parabolic scaling and sufficient directional vanishing moments, contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves. Finally, we show some numerical experiments demonstrating the potential of contourlets in several image processing applications.
3,948 citations
"Redundancy Discrete Wavelet Transfo..." refers methods in this paper
...Directional Filter Bank is used to link the discontinuities point in linear structures....
[...]
...Construction of LP In addition Directional Filter Bank [7] is used for highpass subbands using quincunx filter to form a tree structure image....
[...]
...The Directional Filter Bank diagram is shown in Fig....
[...]
...1) Transformation Stage In this stage a double filter bank scheme is utilized efficiently for subband decomposition using Laplacian Pyramid (LP) and Directional Filter Bank (DFB)....
[...]
...In addition Directional Filter Bank [7] is used for highpass subbands using quincunx filter to form a tree structure image....
[...]
26 Oct 1997
TL;DR: A novel approach to the fusion of spatially registered images and image sequences is proposed that incorporates a shift invariant extension of the discrete wavelet transform, which yields an overcomplete signal representation.
Abstract: In this paper, we propose a novel approach to the fusion of spatially registered images and image sequences. The fusion method incorporates a shift invariant extension of the discrete wavelet transform, which yields an overcomplete signal representation. The advantage of the proposed method is the improved temporal stability and consistency of the fused sequence compared to other existing fusion methods. We further introduce an information theoretic quality measure based on mutual information to quantify the stability and consistency of the fused image sequence.
403 citations
"Redundancy Discrete Wavelet Transfo..." refers background in this paper
...0 namely are FilterSubtraction-Decimate Pyramid (FSD), Gradient Pyramid, Laplacian Pyramid [9], Discrete Wavelet Transform Pyramid (DWT), Shift Invariant Discrete Wavelet Transform Pyramid (SIDWT) [8], Principle Component Analysis [2], Morphological Pyramid, Ratio Pyramid, Contrast Pyramid, and so on [1]....
[...]
TL;DR: A new fusion algorithm for multimodal medical images based on contourlet transform is proposed, which can provide a more satisfactory fusion outcome compared with conventional image fusion algorithms.
353 citations
"Redundancy Discrete Wavelet Transfo..." refers background or methods in this paper
...In Laplacian Pyramid method each input image is decomposed into a subband of low frequency of original image and a bandpass high frequency subbands [5]....
[...]
...The larger value indicates, the better fusion result is obtained [5]...
[...]
...The minimum value corresponds to good fusion result is obtained [5]....
[...]
...Here the local energy contourlet domain is developed as the measurement, then the selection and averaging modes are used to compute the final coefficients [5]....
[...]
TL;DR: In this letter, a precise relationship between RDWT-domain and original-signal-domain distortion for additive white noise in the RDWT domain is derived.
Abstract: The behavior under additive noise of the redundant discrete wavelet transform (RDWT), which is a frame expansion that is essentially an undecimated discrete wavelet transform, is studied. Known prior results in the form of inequalities bound distortion energy in the original signal domain from additive noise in frame-expansion coefficients. In this letter, a precise relationship between RDWT-domain and original-signal-domain distortion for additive white noise in the RDWT domain is derived.
331 citations