Aiming at the problem of inaccurate matching of weak texture and pure color region in stereo matching and long time consumption of image segmentation algorithms, a stereo matching algorithm fused with image segmentation was proposed. Firstly, the initial image was filtered by Gaussian and smoothed by Sobel to obtain the edge feature map of the image. Secondly, the red, green and blue channel values of the original image were dichotomized by using the Otsu method and then refused to obtain the segmentation template map. Finally, the obtained grayscale map, edge feature map and segmentation template map were applied in the parallax calculation and parallax optimization process in order to calculate the parallax map. The proposed algorithm has the accuracy improved by 14.23 percentage points on average with the time cost per 10 000 pixels increased by 7.16 ms in comparison with Sum of Absolute Differences (SAD) algorithm. The experimental results show that the proposed algorithm can obtain smoother matching results in pure color and weak texture regions and parallax discontinuity regions, and it can automatically calculate the threshold and segment the image faster.

Focused on the topic that the ordinary full-text searching technology could not realize mathematical expression retrieval resulted from the complex two-dimensional structure characteristics of formulae, a method of LaTeX formulae analyzing and indexing was proposed. On the basis of the fully consideration of formulae' characteristics and the structure of LaTeX language, a parsing algorithm was designed for analyzing LaTeX expressions and extracting their retrieval features. Taking it as a foundation, a hierarchical index model was designed which employed the information of operands and operators extracted from mathematical expressions through the parsing algorithm. The index model has two layers, Treap data structure layer and inverted index layer, which laid the foundation of the retrieval and matching to formulae. The experiment was carried out under the mode of browser/server through taking 6234 formulae from mathematical textbooks as data set. The parsing algorithm gets 124960 expression nodes from resource formulae of which the highest baseline level is 11. The average time consumed of the index system is 33.8317 seconds. The experimental results show the proposed parsing algorithm and the index method are helpful for realizing mathematical expression retrieval with high efficiency and correctness.

To deal with the lung segmentation problem under complex conditions, Random Walk algorithm was applied to automatic lung segmentation. Firstly, according to the anatomical and imaging characteristics of the chest Computed Tomography (CT) images, foreground and background seeds were selected respectively. Then, CT image was segmented roughly by using the Random Walk algorithm and the approximate mask of lung area was extracted. Next, through implementing mathematical morphology operations to the mask, foreground and background seeds were further adjusted to adapt to the actually complicated situations. Finally, the fine segmentation of lung parenchyma for chest CT image was implemented by using the Random Walk algorithm again. The experimental results demonstrate that, compared with the gold standard, the Mean Absolute Distance (MAD) is 0.44±0.13 mm, the Dice Coefficient (DC) is 99.21%±0.38%. Compared with the other lung segmentation methods, the proposed method are significantly improved in accuracy of segmentation. The experimental results show that the proposed method can solve the difficult cases of the lung segmentation, and ensure the integrity, accuracy, real-time and robustness of the segmentation. Meanwhile, the results and time of the proposed method can meet the clinical needs.

Aiming at the problem of pulmonary small nodules was difficult to identify, a method using fuzzy C-means clustering algorithm to analyse the lung Region Of Interest (ROI) was presented. An improved Fuzzy C-Means clustering algorithm based on Plurality of Weight (PWFCM) was presented to enhance the accurate rate and speed of small nodules recognition. To improve the convergence, each sample and its features were weighted and a new membership constraint was introduced. The low sensitivity from the uneven ROI data was decreased by using a double clustering strategy. The experimental results tested on the real CT image data show that PWFCM algorithm can detect lung nodules with a higher sensitivity and lower false positive rate.