Image Enhancement Of Digital Radiography

Digital Radiography(DR) is a new medical imaging method in the X-ray radiography field during the last decade.It uses flat panel detector to receive X-ray and transforms X-ray to digital image signals directly with many signific(?)nt advantages such as high image resolution,wide dynamic range,fast imaging speed and low-dose exposure on the human body.Now DR is one of the most advanced medical imaging methods,and used in clinical diagnosis more widely.The noises,especially Gaussian noise,reduce images' quality,X-ray source size and human motion cause the fuzzy edge,and inhomogeneous resp(?)se of the flat panel detector brings about low contrast in detail region.Due to the uncertainty of the reasons above,it's very difficult to enhance DR image quality which will hinder the development of DR technology.Therefore,DR image enhancement has become the research hotspot in the medical imaging field,and any research in this field will play an important role in the development and application of DR technology.Several DR image denoising,enhancement and segmentation algorithms are proposed in this dissertation to improve the quality of DR images and solve the key problem of tissue enhancement based on the in-depth study of DR imaging principle and the analysis of DR image noise and vague detail.Firstly,We propose Laplace-Impact mixture model to denoise DR image and improve noisy image's quality based on Laplace model by analyzing parameter denoising method.Then two kinds of contrast enhancement algorithms are proposed to enhance low-contrast details of the structure,as well as the edges of overlapping bones and small bones,by optimizing some multiscale contrast amplification algorithms such as MUSICA.At last,we make better a segmentation algorithm based on graph cuts and apply it to DR images to improve the images' quality further.Our meaningful and detailed works are organized as follows:1.A novel DR image denoising algorithm based on Laplace-hnpact mixture model in dual-tree complex wavelet domain is proposed to denoise Gaussian noise.It uses local variance to build probability density function of Laplace-Impact model fitted to the distribution of high-frequency subband coefficients well.Within Laplace-Impact framework,we describe a novel method for image denoising based on designing minimum mean squared error(MMSE) estimators,which relies on strong correlation between amplitudes of nearby coefficients.The experimental results show that the MMSE algorithm based on the Laplace-Impact mixture model proposed in this paper outperforms several state-of-art denoising methods such as Bayes least squared Gaussian scale mixture(BLS-GSM) and Laplace prior.2.Two improved DR image contrast enhancement algorithms are proposed.On the basis of analyzing the human vision and the multiscale contrast enhancement algorithms,a new multiscale pyramid image enhancement algorithm is proposed.At first,low frequency and high frequency subbands of different scales are got by multiscale decomposition,then the high frequency subbands are mapped by a nonlinear function,and the low frequency subbands of each level is equalized by histogram equalization during reconstructing process.The experiment shows that both the contrast and visual effect of chest radiography are improved efficiently after the process.The second algorithm proposes a novel multiscale and multi-modality nonlinear enhancement method based on local contrast.Firstly,we use high-frequency subbands' coefficients in different direction to estimate local contrast measurement. Then the image is divided into detail,possible edge and smooth region according to multi-modality selection criteria and we use edge detection to get better edge.Finally, multi-modality nonlinear mapping function in different regions of high-frequency subband is used to enhance the image quality and reconstructed image is got through inverse steerable pyramid transforming.The experimental results show that the algorithm can enhance the low-contrast details better and have achieved very good edge enhancement results in overlapping bone and small bone structure.3.A novel method which is based on the Random-Walk algorithm is proposed for DR image segmentation.Firstly,the original image is decomposed to build the backbone graph by using Mallat's fast wavelet transform.And the edge weight is optimized by using gradient information from high-frequency subband.Then a further division of the ambiguous area is done under the probability threshold criteria.Finally, the label with the greatest probability is assigned to each unlabeled vertex,and image segmentation boundaries are obtained by expanding the labeled backbone graph to the original image.The experimental results on Microsoft GrabCut segmentation database images and real DR images demonstrated that our algorithm is able to segment out the expectable part from DR image effectively and last(?).