Research On Applications Of Contourlet Transform In Texture Image Retrieval And Medical Image Segmentation

In the image processing domain, sparse representation of images has important significance in both theory and the applications. Usually, the Fourier transform and the wavelet transform can not most sparsely represent an image. In order to resolve this problem, the multi-scale geometric analysis (MGA) methods are proposed. They are rapidly and widely used in the different fields, such as mathematical analysis, image processing, computer vision, pattern recognition and statistical analysis etc. However, the MGA methods, like the ridgelet transform and the curvelet transform which are defined in the continuous domain, have difficulties in discrete implementation. Therefore, a "ture" and optimal image representation method which has pyramid directional filter bank structure is proposed, called contourlet transform.The contourlet transform is defined and implemented by the filter banks in the discrete domain. It has the function of the multi-scale transform and the multi-directional transform. The multi-scale transform is achieved by Laplacian pyramid (LP), and the directional transform of the multi-scale detail subbands is implemented by the directional filter bank (DFB). The contourlet transform has a lot of good properties, which include multi-resolution analysis, localization, nearly critical sampling, multi-directionality and anisotropy of the basis functions. Different from the wavelet transform, the multi-directionality and the property that the support of basis functions has a variety of elongated shaped with different aspect ratios make it effectively capture the geometric structure features of the image information. More importantly, the contourlet transform can flexibly and effectively combine the multi-scale with the multi-directionality for image representation, therefore it can accurately and optimally describe an image. Currently, the theory and applications based on the contourlet transform are the research focus. The recent studies show that it has wide application prospects in the image processing domain.The contourlet transform, including the improved contourlet transform, can most sparsely characterize the features of an image. The good statistical features can represent the contents of an image. Therefore, the statistical modeling has important significance in the contourlet transform domain. The research shows that the margin distribution of the multi-subbands of natural images can sufficiently represent their features, and the modeling of generalized Gaussian distribution (GGD) is considered as the most approximate and successful modeling form of the margin distribution for natural images in the transform domain. At the same time, the simple independent and identically distributed GGD models can be more robust and efficient than subband-dependent GGD models. Consequently, the directional subbands of the contourlet transform can be modeled statistically as the GGD. At present, the GGD modeling in the contourlet transform domain finds wide applications. However, it has the problem that the model parameters can not be accurately estimated. In this thesis, we do further research on this problem, and propose an algorithm about the improved maximum likelihood parameter estimation. Performance comparisons between the new method and the other corresponding one verify its efficiency. At the same time, using this parameter estimation method, we have modeled the directional subband coefficients in the contourlet transform domain as GGD. The experimental results show the accuracy of this modeling method.With the explosive growth in the volume of digital library and multi-media databases, the texture image retrieval has become the research hotspot in content-based image retrieval domain. The research and development of the texture image retrieval revolve around finding good visual features or defining robust similarity measurements. Because of having the hierarchy and rich directional information, the texture image can be analyzed especially well by using multiscale directional filter bank. Thus, the statistical modeling in the contourlet transform domain can be used as an efficient tool of detecting the features of the texture image. On the other hand, defining the similarity measurements which can match the texture features and reflect human perception is an important and challenging task. Usually, the similarity measurements adopt the distance metrics, but the support vector machine (SVM) can effectively reflect the similarity measurements of the human perception. Therefore, in this thesis the GGD parameters of the directional subband coefficients in the contourlet transform domain are used as the features of texture images, and the Kullback-Leibler (K-L) distance and SVM are adopted as the similarity measurements for the texture image retrieval. Except retrieving the texture image directly, the pre-classification of texture images can also be used to retrieve. Therefore, in this thesis, a mixture retrieval scheme based on the structural and random texture image is also proposed in the improved contourlet transform domain. The experimental results show that the retrieval performance has been remarkably improved.The goal of image denoising is to reduce noise sufficiently with image structures, such as edges and textures well preserved. Currently, the research focus lies in combining non-linear diffusion filtering with the multiscale filtering in the computation harmony domain, which is used for reducing the additive noise and speckle noise. The effective combination of thses two methods can interpret the image denoising problems in bran-new angle of view. Considering that the non-linear diffusion filtering can reduce the Gibbs artifacts which are produced by the multiscale shrinkage denoising, and the multiscale and multi-directional shrinkage denoising methods are fast, an additive noise reduction method which combines the contourlet shrinkage with the spatially adaptive total variation is proposed in this thesis. The difference image of the noisy image and the denoised image by the contourlet shrinkage is filtered using the spatially adaptive total variation method, and the detail information of small edges and textures is detected. For the speckle noise, a speckle denoising algorithm based on the improved contourlet transform and non-linear diffusion is proposed in this thesis, and it is used for processing the intravascular ultrasound (IVUS) image. The results of the simulation experiments show their effectiveness.The intima and adventitia edge detection of IVUS images has great significance in the diagnosis and treatment of the coronary artery disease. It is the research hotspot in medical image processing domain. Currently, the effectiveness based on the methods of the IVUS image edge detection is not ideal because of the severe speckle noise and some unadvisable priori hypotheses. Consider that the edge detection method based on active contour model is the most promising techniques. In this thesis, an edge detection algorithm of the simulated IVUS sequential images based on active contour model is firstly proposed. It uses the contrast and Raylaigh distribution characteristics of the IVUS image, and automatically detects the intima and adventitia edge under different energy functions through the dynamic programming and heuristic graph searching. For the real IVUS image containing speckle noise, an automatic edge detection algorithm of IVUS sequential images is proposed based on the contourlet speckle denoising in this thesis. It uses the active contour model and the edge gradient of IVUS image, and automatically detects the intima and adventitia of coronary artery vessel under the different energy functions through the dynamic programming. The experimental results show that these edge detection methods are accurate and robust.Currently, the studies on the contourlet transform have just started in domestic, therefore many applications need to be further researched and explored in image processing fields. In this thesis, the applications of the contourlet transform in statistical image modeling, texture image retrieval, image denoising and the edge detections of medical images are mainly studied. The main contributions and innovations of this thesis are as follows:(1) For the original contourlet transform and the improved contourlet transform which are achieved by simulation, their directional subband coefficients are accurately modeled using generalized Gaussian statistical distribution. For the parameter estimation of the model, an improved iterative algorithm is proposed, and a new initial value of the parameter is used. The simulated results show that the performance of the new parameter estimation method is superior to the currently typical estimation method.(2) Based on the energy and GGD parameter features of the contourlet directional subband coefficients of the texture image, a new retrieval method of two-run SVM is proposed. The experimental results show that the performance of the retrieval is greatly improved.(3) Based on the effective discrimination of the structural and random texture images, a new mixture retrieval method is proposed. The performance of this retrieval method is superior to the recent results of the other methods.(4) A new image denoising method based on combining the hard threshold shrinkage of the improved contourlet transform with the spatially adaptive non-linear diffusion is proposed. It can reduce the additive noise effectively with stronger edges well prserved, and will nearly not lose the weak edges and texture information. At the same time, the Gibbs artifacts are effectively reduced in the denoised image.(5) A new speckle reduction algorithm is proposed based on the improved contourlet transform and non-linear diffusion. It can directly reduce the speckle noise of IVUS image, and do not need the homomorphic pre-processing.(6) A detection method of the inima and adventitia edges of sequential IVUS simulated images is proposed by means of their statistical features. It is based on active contour model, contrast and Raylaigh distribution characteristics of the IVUS image, and optimally detects the image edge by using dynamic programming and heuristic graph searching method.(7) An automatic edge detection algorithm of sequential images is proposed according to the geometric structures of the real IVUS images. It works on the images in which speckle noise is reduced by the contourlet transform and the anisotropic diffusion, uses active contour model and the edge gradient of the image, and automatically detects the intima and adventitia edges of the vessel by using the optimal searching method of dynamic programming under the different energy functions respectively. Accordingly, a new method which can automatically estimate the initial contour of the intima edge of IVUS image is proposed based on different physical properties of the blood and tissue, a priori information of the vessel geometry, and the temporal and spatial information of sequential images. The experimental results show that this method is algorithmically simple, statistically accurate, reproducible and robust for sequential IVUS images.