Wavelet-based Image Processing and Cross-section Model Reconstruction

• Main Authors: Ming-Yu Shih, 石明于
• Other Authors: Din-Chang Tseng
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/65532642424688416435

博士 === 國立中央大學 === 資訊工程研究所 === 93 === Reconstructing 3-D surface models from a series of parallel planar cross sections of objects or bodies is important for medical and industrial applications, such as detection, diagnosis, simulation, and training. In this study, we propose three techniques: image denoising, edge detection, and model reconstruction to achieve the purpose of automatic surface model reconstruction. Firstly, we propose a wavelet-based image denoising method using the proposed contextual hidden Markov tree (CHMT) model to remove noise from images corrupted with Gaussian noise. The hidden Markov tree (HMT) model is a novel statistical model for image processing on wavelet domain. The HMT model captures the persistence property of wavelet coefficients, but lacks the clustering property of wavelet coefficients within a wavelet scale. The proposed CHMT model enhances the clustering property of the HMT model by adding extended coefficients associated with wavelet coefficients without changing the wavelet tree structure. In experiments, the proposed CHMT model produced almost better results than the HMT model produced for image denoising. Furthermore, the CHMT model needs fewer iterations of training than the HMT model needs to get the same denoised results. Secondly, a two-stage edge extraction approach is proposed to extract object contours. A gradient image describes the differences of neighboring pixels in the original image. Traditional edge detectors only depending on the gradient information will result in noised and broken edges. Here we propose the two-stage edge extraction approach with contextual-filtered edge detector and multiscale edge tracker to solve the problems. The edge detector detects most edges and the tracker refines the results as well as reduces the noised or blurred influence; moreover, the extracted results are nearly thinned edges which are suitable for further applications. Based on six wavelet basis functions, qualitative and quantitative comparisons with other methods show that the proposed approach extracts better edges than the other wavelet-based edge detectors and Canny detector extract. Finally, a versatile method for surface model reconstruction from serial planar contours is proposed. Like most similar-purposed methods, the proposed method tiles triangles between contours on every two adjacent slice images, then the surface model is constructed by aggregating all tiled triangles; however, the proposed method uses more criteria and rules than the other methods use to construct reasonable and less-distorted models. The proposed method consists of seven stages: (i) contour-pair candidates are first found between two adjacent slices; (ii) feature points on the contour pairs are extracted to link; (iii) similar contour segments are extracted by a tracking algorithm; (iv) cross lines are found to tile partial triangles; (v) near points are found to link; (vi) cleft polygons are extracted; at last, (vii) the proposed multi-objective dynamic programming algorithm is used to construct the triangulated strips formed by the extracted similar contour segments and cleft polygons. Comparing with other similar-purposed methods, the proposed method has the advantages: (i) more than one (cross) contour are allowed in a slice image; (ii) reasonable data structure describing the relationship among contours on a slice image is proposed for helping the surface reconstruction; (iii) the proposed method is versatile to construct reasonable and less-distorted models; (iv) complex models can be properly reconstructed.