The Segmentation Study Of The Chest CT Based On DICOM Document Pattern

Lung has a complicated structure which contains a large number of vessels and tracheas. The disease in lung also is numerous which impacts on human health seriously. With the development of medical image, a variety of medical equipment can be used for the detection of lung disease. Give an exhaustive analysis to chest CT and segment out the lungs. Pulmonary vessels and specific lesions are signality for doctors to give accurate diagnosis and relieve patients’ pain.This thesis first has introduced the knowledges of image segmentation which including the principle of image segmentation and the classical and emerging methods of image segmentation. Then it has introduced the important purpose and meaning of medical image, also including the domestic and foreign research present situation and future development trend. Because the division of the object is DICOM format of medical image, it’s the basis and prerequisite of how to show out the images clearly for the follow-up processing. Therefore, this thesis has introduced the related knowledge of DICOM in detail and how to read DICOM image and its display methods rightly. This research object of this thesis is chest CT images. In the face of such a professional medical image, we must have a profound understanding in the imaging theory of CT image and the scanning method of chest CT. We also must know well about the knowledges before reading CT. It’s a preparetion for later image segmentation and the right image selection. Then this thesis has introduced angiogram simply. The thesis puts forward two kinds of use of image segmentation in CT image: One is to process chest CT, Two is to give a further division of lung parenchyma that has segmented out, that is the segmentation of pulmonary vascular. Two segmentations are based on the division of the different objects of different segmentation algorithms.For how to segment out the essence of the lungs from chest CT, First this thesis has introduced the classic boundary tracking algorithm based on the boundary, the region growing algorithm based on the region and the Ostu threshold value method based on the threshold value. Through using the three algorithms for chest CT segmentation to get lung essence area, We find that although classical algorithm has many advantages, it also hasn’t a good effect in segmenting out lung parenchyma. At last this thesis has introduced the algorithm mainly:Using the mean-shift algorithm and snakes model combined with chest CT segmentation to get lung essence area. The experimental results show that the algorithm is obviously better than the classical three image segmentation method. It has a full effect and clinical use value of the lung parenchyma which has segmented out.Then, this thesis gives a further study for the lung parenchyma which has segmented out, with hoping to segment the pulmonary vascular from the lung parenchyma. Because of the bad contrast ratio, fuzzy boundary and complex shape of the lung vascula, it’s not good to use the traditional segmentation. This thesis combines Gabor wavelet transform with threshold method to segment and extract lung blood vessels. The experiments show that, Gabor wavelet transform can strengthen the lungs blood vessels and extract pulmonary vascular’s direction characteristics well. Then it’s better to extract pulmonary vessels combined with threshold value method.And the selected threshold in this thesis has robustness, so that it’s simple and easy to do with no human-computer interaction. It’s also helpful for the clinical diagnosis and treatment and convenient for doctors.