Research On Medical CT Image Segmentation Methods

Medical image segmentation is the basis of medical image processing. Solving this problem not only directly affect the successful application of computer graphics technology in medicine but also has important theoretical and practical significance. Medical image segmentation is a process to extract the region of interest and segmentation result can provide a reference for subsequent diagnosis, designment of treatment programms and evaluation of treatment regimens. Due to its higher resolution, sharper highlight anatomical structures and lesions, CT has been widely applied to many systems for disease diagnosis. Therefore, the study of image segmentation method in CT images has very important significance. This paper studies segmentation methods in the hip joint, lungs with juxtapleural nodules and liver CT images, resectively. The aim of this study is to build accurate, automatic segmentation methods to assist the doctor’s diagnosis and treatment.A normal hip joint is composed of two parts: femoral head and acetabulum. In hip joint CT images, due to the narrow inter-bone space of the connecting area between femoral head and acetabulum and the uneven bone density caused by diseases, it is difficult to segment the femoral head and the acetabulum accurately. In response to these problems, this paper proposes an automatic segmentation method for the hip joint from three-dimentional volumetric CT images, which combines iterative adaptive threshold classification and Bayes discriminant analysis techniques. Our method uses morphological enhancement techniques to highlight the intensity contrast between the connection area and the bone tissue. In the subsequent process, for the segmented results obtained by thresholding, we use iterative adaptive reclassification scheme that is based on Bayes discriminant analysis to achieve the separation of the femoral head and the acetabulum.Since the above segmentation method repeatly uses mathematical morphology, this makes the segmented results lose bone details caused by overly smoothing. Moreover, morphological operations depend on the selection of structural elements. Choosing a different shapes or sizes of structural elements will take some influence on segmentation results. In response to these problems, a boundary correction algorithm based on gray changes in the vertex normal direction of bone surface is presented. By the correction algorithm, we can not only locate the bone boundary voxels accurately but also can get three-dimensional visualization results of objects to be segmented. Experimental results demonstrate the accuracy and clinical feasibility of the proposed algorithm.As lungs fill with air and have a lower density compared with the surrounding tissue, thresholding is a common method used for lung segmentation. However, for CT images including juxtapleural nodules, due to the variability of the position and size of the juxtapleural nodules and the similarity intensity with surrounding tissue, it is difficult for thresholding to include these juxtapleural nodules accurately. Furthermore, high density pulmonary vessels are also ruled out from the lung area, which bring about indentations and salience in the lung boundary near the mediastinum. Traditionally, mathematical morphology is generally used for smoothing the lung boundary. However, morphology highly relies on the selected structure element. To cope with these problems, we develop an accurate and fully automatic method for segmenting lung boundary in chest CT images and an efficient scheme for smoothing and correcting the segmented lung boundary. The proposed method uses fuzzy c means algorithm to achieve the fast lung segmentation. For the slices containing juxtapleural nodules and pulmonary vessels, a contour correction and smoothing algorithm is proposed which is based on iterative weighted averaging and adaptive curvature threshold. This method can be automatically and accurately detect juxtapleural nodules and pulmonary vessels and smoothly include in the lung segmentation. Experimental results demonstrate the rapidity and effectiveness of the algorithm.In liver CT images, due to the low contrast of adjacent organs, the presence of abnormalities and the highly varying shapes between subjects, it is difficult for traditional segmentation methods only relying on gray intensity information to obtain good segmentation results, which often lead to the leakage of liver. In response to these problems, this paper proposes a three-dimensional liver segmentation method from contrast enhancement CT images. The proposed segmentation method contains a training and test phrase. In the training phase, we use principal component analysis method to get the shape-intensity model of the liver as well as the variabilities of shape and intensity on different modes. For each target image in the test phase, the most likely liver region is first obtained by computing the similarities between the atlases and the target image. Then, the precise liver segmentation result is obtained by a maximum a posteriori classification of probability map followed by applying a shape-intensity prior level set segmentation implemented by narrowband technique inside the most likely liver region. Experimental results demonstrate the accuracy of the algorithm.