| dc.description.abstract | As image acquisition devices are rapidly evolving and extreme amount of medical images are daily produced, the needs for medical image transmission, storage, diagnostic analysis, and therapeutic support are ever-increasing. Among the technologies to achieve the needs mentioned above, medical image compression and segmentation are two of the indispensable technologies. In this dissertation, we propose the medical image compression and segmentation methods based on the principles of statistical inference.
To achieve the compression of medical images, a lossless wavelet-based image compression method with adaptive prediction (WCAP) is proposed. The proposed method consists of three steps: (i) the correlations between wavelet coefficients are analyzed to identify a proper wavelet basis function; (ii) predictor variables are statistically test to determine which relative wavelet coefficients should be included in the prediction model; (iii) prediction differences are encoded by an adaptive arithmetic encoder. Instead of relying on a fixed number of predictors on fixed locations, we propose the adaptive prediction approach to overcome the multicollinearity problem of predictor variables. The proposed approach integrating correlation analysis for selecting wavelet basis function with predictor variable selection is fully achieving high accuracy of prediction. Comparing with several state-of-the-art methods, the proposed approach achieves a higher compression ratio on computed tomography (CT), magnetic resonance (MRI), and ultrasound images.
Moreover, to provide the variety requirements of region of interest (ROI) coding containing progressive transmission, polygon-shaped ROI, and multiple ROIs, we further propose another progressive lossy-to-lossless compression technique. Firstly, split and mergence algorithms were proposed to separate concave ROIs into smaller convex ROIs. Secondly, row-order scan and an adaptive arithmetic coding were used to encode the pixels in ROIs. Thirdly, a lifting integer wavelet transform was used to decompose the original image in which the pixels in the ROIs had been replaced by zeros. Fourthly, the WCAP method was used to obtain predicted coefficients for difference encoding. Finally, the adaptive arithmetic coding was also adopted to encode the differences between the original and corresponding predicted coefficients. The proposed method only needs less shape information to record the shape of ROI and provides a lossy-to-lossless coding function; thus the approach is suitable for achieving the variety of ROI requirements including polygon-shaped ROI and multiple ROIs. Experimental results show that the proposed lossy-to-lossless coding with ROI function reduces bit rate as comparing with the MAXSHIFT method in JPEG2000.
To assist doctors to analyze and explore the medical images, a level set method based on the Bayesian risk is proposed, so that doctors can make better diagnosis and accurately examine disease symptoms. At first, the image segmentation is formulated as a classification of pixels. Then the Bayesian risk is formed by the losses of pixel classification. Through minimizing the risk of misclassification, the level set evolution functional is deduced for finding the boundaries of targets. To prevent the propagating curves from generating excessively irregular shapes and lots of small regions, curvature and gradient of edges in the image are integrated into the functional. Finally, the Euler-Lagrange approach is used to find the iterative level set equation from the derived functional. Comparing with other level-set methods, the proposed approach relies on the optimum decision of pixel classification; thus the approach has more reliability in theory and practice. Experiments show that the proposed approach can accurately extract the complicated shape of targets and is robust for various types of images including high-noisy and low-contrast images. Moreover, the algorithm is extendable for multiphase segmentation.
In this dissertation, we proposed medical image compression and segmentation methods which are mainly derived from the statistical inferences. In the first technique, we adopt correlation analysis to identify a proper wavelet basis function, and statistical F test to adaptively select predictor variable; thus the proposed WCAP approach can achieve high compression ratio on various medical images. In the second technique, we further extended the lossless compression technique to provide the variety of ROI requirements. In the third technique, the statistical decision theories are integrated into the derivation of Bayesian level set method; thus the proposed segmentation method is suitable for complicated medical image segmentation. | en_US |