Research On Computer-aided Diagnosis Of Lung Disease Based On High Resolution CT Images

Lung Cancer is the main reason of high mortality caused by cancer. Early diagnosis and treat of lung cancer is the main means to improve the survival rate of patients. Lung nodule is the symbol of most early stage of lung cancer. Detecting lung nodules correctly, understanding and treating lung diseases in early stage is of great significance to save the lives of lung cancer patients. In recent years, the number of Multi-slice Spiral Computerized Tomography (MSCT) installed in China's major hospital has increased significantly. MSCT can provide High Resolution Computerized Tomography (HRCT) images, which could be used to better describe surface and internal structure of pulmonary nodules. MSCT is a powerful tool for accurate diagnosis of lung diseases. However, while massive CT data provides more detailed and more accurate diagnostic information for radiologists, it brings heavy burden of work to radiologists. In order to improve the efficiency of medical diagnosis and to reduce radiologists' labor intensity, Computer-Aided Diagnosis (CAD) system came into being.To study Computer-Aided Diagnosis of lung diseases, assist radiologists to diagnose early lung cancer, the problem first of all we must face and solve is automatic detection of pulmonary nodules in thoracic CT images on the computer. In order to realize automatic detection of pulmonary nodules, a series of processing and analysis methods in image segmentation must be studied. With a direction of research on Computer-Aided Diagnosis of lung disease, and with the goal of automatic detection of pulmonary diseases on the computer, we have made an extensive survey on the research status at home and aboard in this domain. By using HRCT data as research materials, and by combining knowledge of human tissue anatomy, we have made an in-depth study on pulmonary disease detection and related processing methods of medical images. The contributions of this dissertation are as follows:(1) An automatic segmentation algorithm for lung region abstraction from CT images is proposed. The quality of lung segmentation results will affect the efficiency and effectiveness of follow-up processing. Aim at the characteristic of gray value of lung tissue and others tissues in human body in CT images, optimal threshold is obtained by an iterative process of calculation, which can reduce the impact of threshold selection on segmentation results. The relationship between the locations of tracheal/bronchia in two adjacent CT slices is studied, the location of tracheal/bronchia in anterior slice is used to produce a seed point for automatic region growth of tracheal/bronchia in posterior slice. A border tracking algorithm based on 8-neighborhood searching method is adopted to eliminate background and to abstract the boundary of lung, which avoids many morphological operations, so processing time is saved. According to the smoothness of boundaries of human body and lung region, 8-neighborhood searching method is improved utilizing previous direction to increase the searching efficiency. The proposed algorithm is quite efficient and accurate for automated lung segmentation in CT images.(2) Lung nodule is the symbol of most early stage of lung cancer, enhancement of pulmonary nodules in CT images can improve, the precision of pulmonary nodule detection. Based on the assumption that nodule is spherical and vessel is cylindrical, an enhancement algorithm of pulmonary nodule is proposed. Points that need to be enhanced are selected from those pixels whose gray-value is relatively high, according to that whether the three eigenvalues of each point are all negative, and then correlation matrix is calculated. The relationship between eigenvalues of each point is used to design an enhancement filter for pulmonary nodules. By using the first order partial differential information of images, the pulmonary nodules are enhanced effectively and the sensitivity to noise is reduced.(3) By comprehensively considering the fact that two-dimensional detection is relatively faster and three-dimensional detection is more precise, a pulmonary nodule detection algorithm based on three-dimensional spatial structure of nodule is proposed. Nodule candidates are extracted by a two-dimensional Convergence Index (CI) filter firstly, and then three-dimensional features of each candidate are calculated to eliminate false-positive nodules from candidates. Rounded and elliptic regions can be found quickly by CI filter. This false-positive eliminating method is able to take full advantage of three-dimensional spatial structure information of nodules to improve detection precision. In the process of implementing the algorithm does not require manual intervention, and with high sensitivity and low false positive.(4) According to the disturbing of cross-sections of vessels in the process of nodule detection, a pulmonary nodule detection algorithm based on vessel eliminating is proposed. In the algorithm, a tubular vascular model is supposed, with Gaussian intensity distribution on cross-sections. A vessel detection filter based on tubular model is designed to eliminate false-positive nodules from candidates. This vessel detection filter may also be used in other occasions, where vessel detection is needed.